JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)
type: channel
description: JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Nusa Mandiri is a scientific journal containing research results written by lecturers, researchers, and practitioners who have competencies in the field of computer science and technology. This journal is expected to develop research and provide meaningful contributions to improve research resources in the fields of Information Technology and Computer Science. JITK is published by the University of Nusa Mandiri Research Center in open access and free. Each published article has a digital object identifier (DOI): Prefix: 10.33480. The JITK journal has obtained an accreditation value for the SINTA 2, to send scientific articles to JITK, first read the article shipping instructions at the next link. P-ISSN: 2685-8223 & E-ISSN: 2527-4864
publisher: LPPM Nusa Mandiri
language: en
publication name: JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)
issn: 2685-8223
items: COMPARATIVE ANALYSIS OF BAGGING AND BOOSTING MODELS IN ENSEMBLE LEARNING FOR GRADUATION PREDICTION
items: ADAPTIVE AL-QUR’AN MEMORIZATION RECOMMENDATION SYSTEM BASED ON FUZZY LOGIC COGNITIVE MEMORY AND PROFILE MATCHING
items: OPTIMIZATION OF PREDICTION OF LUNG DISORDERS USING LSTM COMPARISON OF RMSPROP AND ADAM
items: IMPLEMENTATION OF A SMART CONTRACT-BASED E-VOTING SYSTEM FOR COMPETITIONS
items: IMPROVING SENTIMENT ANALYSIS OF WOMEN IN STEM DISCOURSE USING SMOTE-ENHANCED SVM–VADER
items: SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION FOR CONTINUOUS LEARNING: BENEFITS, IMPACTS, AND CHALLENGES
items: IMPLEMENTATION OF RANDOM FOREST FOR ANIMAL PROTEIN CLASSIFICATION THROUGH HYPERPARAMETER OPTIMIZATION
items: SOFTWARE DEFECT PREDICTION TRENDS: A BIBLIOMETRIC ANALYSIS OF MACHINE AND DEEP LEARNING
items: SENTIMENT CLASSIFICATION MODEL BASED ON COMPARATIVE STUDIES USING MACHINE LEARNING TECHNOLOGY
items: SCRUM AND ITIL-BASED SUPPORT SYSTEM DESIGN AND IMPLEMENTATION AT RAPHA THERESIA HOSPITAL
items: CONVERSION OF GRAPHICAL TO NUMERICAL DATA WITH WEB PLOT DIGITIZER IN OIL RESERVE DETERMINATION
items: TOGAF ADM - BASED ENTERPRISE ARCHITECTURE FOR TANTAN DIGITAL VILLAGE
items: SUPPORT VECTOR MACHINE TO CLASSIFY SENTIMENT REVIEWS ON GOOGLE PLAY STORE
items: DATA AUGMENTATION EFFECTS ON PROTONET FEW-SHOT YELLOW DISEASE SEVERITY IN CHILI LEAVES
items: PUBLIC SECTOR INNOVATION IN SMART CITIES: A FRAMEWORK OF AMBIDEXTROUS AI GOVERNANCE
items: TRANSFER LEARNING-BASED CLASSIFICATION OF BELL PEPPER LEAF DISEASES USING VGG16 AND EFFICIENTNETB3 ARCHITECTURES
items: DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING
items: HYBRID TRANSFER LEARNING AND ADVANCED DATA AUGMENTATION FOR MULTICLASS BRAIN TUMOR CLASSIFICATION USING EFFICIENTNET
items: PERFORMANCE EVALUATION OF LIGHTWEIGHT DEEP LEARNING MODELS FOR BORAX-CONTAMINATED MEATBALL IMAGE CLASSIFICATION
items: OPTIMIZATION IN ZAKAT MANAGEMENT THROUGH THE DEVELOPMENT OF A CHATBOT-BASED MOBILE APPLICATION
items: COMPARATIVE STUDY OF GENERATIVE AI TOOLS IN VISUAL COMMUNICATION DESIGN EDUCATION: CREATIVITY AND USABILITY
items: DEVELOPING A MICRO-ENTERPRISE E-READINESS FRAMEWORK: A CASE STUDY FROM INDONESIA
items: OPTIMIZATION OF MLP-NN FOR MANGO LEAF DISEASE PREDICTION USING IMAGE-BASED FEATURE EXTRACTION
items: EVALUATING CLUSTERING METHODS FOR SEMANTIC REPRESENTATION OF DISASTER NEWS USING BERT EMBEDDINGS AND HBDSCAN
items: OPTIMIZING VGG-16 CONVOLUTIONAL NEURAL NETWORK FOR PAP SMEAR IMAGE CLASSIFICATION IN CERVICAL CANCER DETECTION
items: HYBRID PSO K-MEANS AND ROBUST SPARSE K-MEANS FOR EMPLOYEE STUDY DECISIONS
items: MACHINE LEARNING TO IDENTIFY ELIGIBILITY OF STUDENTS RECEIVING SINGLE TUITION RELIEF
items: A HYBRID BERT–GNN FOR DETECTING HOAXES AND NEGATIVE CONTENT IN INDONESIAN SOCIAL MEDIA
items: ASSESSING USER EXPERIENCE OF SITURAWA GEDE TOURISM WEBSITE USING PSSUQ AND HEURISTIC
items: HYBRIDIZATION OF FASTTEXT-BLSTM AND BERT FOR ENHANCED SENTIMENT ANALYSIS ON SOCIAL MEDIA TEXTS

COMPARATIVE ANALYSIS OF BAGGING AND BOOSTING MODELS IN ENSEMBLE LEARNING FOR GRADUATION PREDICTION
type: item
description: Student graduation prediction is an important aspect in supporting academic decision-making in higher education. However, conventional evaluation approaches have not been able to identify the risk of early graduation delays. This study aims to compare the performance of two ensemble learning approaches, namely Bagging using Random Forest and Boosting using XGBoost, in predicting student graduation. The study used the Predict Students' Dropout and Academic Success dataset consisting of 4,424 student data. Both models were trained on the same data and evaluated using the Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The results of the experiment showed that both models had almost equal accuracy, i.e. 82.6% for Random Forest and 82.5% for XGBoost. However, XGBoost showed better performance on Recall (0.878) and F1-Score (0.834), which indicated a higher ability to detect students who actually graduated. Based on these results, this study concludes that XGBoost is more effective than Random Forest in the context of predicting student graduation and is more suitable to be applied to the Academic Early Warning System in universities
subject: Articles
  topic: section
subject: Bagging
  topic: keywords
subject: Boosting
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Student Graduation Prediction
  topic: keywords
creator: Sartika Lina Mulani Sitio
creator: Darmawati
creator: Yuda Samudra
rights: Copyright (c) 2026 Sartika Lina Mulani Sitio, Darmawati, Yuda Samudra http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-28
publication date: 2026-02-28
starting page: 979
ending page: 986
doi: 10.33480/jitk.v11i3.7579

ADAPTIVE AL-QUR’AN MEMORIZATION RECOMMENDATION SYSTEM BASED ON FUZZY LOGIC COGNITIVE MEMORY AND PROFILE MATCHING
type: item
description: Memorizing mutasyabihat verses in the Qur’an is particularly challenging due to similarities in structure, linguistic patterns, and semantic density that place a heavy load on short-term memory. Conventional memorization approaches do not account for individual cognitive differences when dealing with verse complexity. This study proposes an adaptive recommender system based on cognitive modeling to align verse group selection with the user’s memory profile.The system models memory capacity as a multidimensional profile using fuzzy inference derived from three quantitative indicators: continuous memory score, total correct recall, and average response time. This profile is matched with verse group feature vectors through a profile matching approach and a weighted Euclidean distance similarity measure within a Multi-Attribute Decision Making (MADM) framework. Four verse characteristics are considered: thematic (35%), semantic (25%), linguistic (25%), and pattern (15%).An adaptive calibration phase combines 20% of the initial cognitive profile with 80% of actual memorization performance, reflecting the dominance of behavioral evidence over initial assessment. System evaluation employs the Top-N Accuracy method commonly used in recommender systems.Testing with 29 participants resulted in a Top-3 success rate of 66% and an overall Top-N accuracy of 62.07%. These results indicate that cognitive profile–based multidimensional similarity can adaptively match verse complexity to individual memory capacity. This study demonstrates that fuzzy cognitive modeling and profile matching can be effectively implemented in adaptive personalized learning systems to optimize memorization of mutasyabihat verses
subject: Articles
  topic: section
subject: Adaptive Recommender System
  topic: keywords
subject: Cognitive Modeling
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
subject: Multi-Attribute Decision Making (MADM)
  topic: keywords
subject: Mutasyabihat Verses
  topic: keywords
creator: Afifah Fikriyah Dhiya'ulhaq
creator: Muhammad Dzulfikar Fauzi
creator: Pima Hani Safitri
rights: Copyright (c) 2026 Afifah Fikriyah Dhiya'ulhaq, Muhammad Dzulfikar Fauzi , Pima Hani Safitri http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-28
publication date: 2026-02-28
starting page: 937
ending page: 948
doi: 10.33480/jitk.v11i3.8048

OPTIMIZATION OF PREDICTION OF LUNG DISORDERS USING LSTM COMPARISON OF RMSPROP AND ADAM
type: item
description: Accurate prediction of pulmonary disorders is essential to support early diagnosis and clinical decision-making. Medical time-series data are inherently nonlinear and temporally dependent, making conventional statistical approaches insufficient. This study formulates pulmonary disorder prediction as a regression problem and proposes an optimized Long Short-Term Memory (LSTM) model by comparing two widely used optimization algorithms, RMSProp and Adam. The dataset consists of 30,000 clinical records obtained from an open-source Kaggle repository, including demographic, behavioral, and health-related variables relevant to respiratory conditions. Data preprocessing involved categorical encoding and Min–Max normalization, followed by an 80:20 train–test split. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Experimental results demonstrate that the Adam optimizer achieves superior performance with lower prediction errors and more stable convergence compared to RMSProp and the baseline SGD optimizer. These findings highlight the critical role of optimizer selection in LSTM-based medical time-series modeling.
subject: Articles
  topic: section
subject: LSTM
  topic: keywords
subject: Medical Time-Series
  topic: keywords
subject: Pulmonary Disorder Prediction
  topic: keywords
subject: Regression
  topic: keywords
subject: RMSProp
  topic: keywords
creator: Egi Batubara
creator: Solikhun
creator: Agus Perdana Windarto
rights: Copyright (c) 2026 Egi Batubara, Solikhun, Agus Perdana Windarto http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-24
publication date: 2026-02-24
starting page: 928
ending page: 936
doi: 10.33480/jitk.v11i3.7767

IMPLEMENTATION OF A SMART CONTRACT-BASED E-VOTING SYSTEM FOR COMPETITIONS
type: item
description: Traditional voting methods in competitions often face challenges related to transparency and fraud, undermining fairness. This research presents the design and implementation of a hybrid e-voting system built on Ethereum blockchain technology to mitigate these issues. Specifically, this research integrates an off-chain HMAC-SHA256 privacy mechanism with Ethereum’s Proof-of-Stake (PoS) consensus to ensure that voting records remain immutable and publicly auditable, while preserving voter anonymity. A prototype was developed using a decentralized architecture, leveraging smart contracts to automate the entire electoral process from registration to tallying. An evaluation involving 153 participants based on the Technology Acceptance Model (TAM) demonstrated high user acceptance, with scores of 76.6% for Perceived Usefulness, 73.4% for Perceived Ease of Use, and 72.8% for Acceptance of Technology. Although the system demonstrates effectiveness in competitive settings, current testing is limited to small- to medium-scale implementations. This research concludes that the proposed framework provides a secure, transparent, and efficient alternative for competitions, significantly enhancing trust in the election outcomes.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: E-Voting
  topic: keywords
subject: Smart Contract
  topic: keywords
subject: Transparency
  topic: keywords
creator: Tony Tan
creator: Eric Valentino
creator: Fredian Simanjuntak
rights: Copyright (c) 2026 Tony Tan, Eric Valentino, Fredian Simanjuntak http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-28
publication date: 2026-02-28
starting page: 958
ending page: 966
doi: 10.33480/jitk.v11i3.7622

IMPROVING SENTIMENT ANALYSIS OF WOMEN IN STEM DISCOURSE USING SMOTE-ENHANCED SVM–VADER
type: item
description: The participation of women in Science, Technology, Engineering, and Mathematics (STEM) remains shaped by complex social and structural factors. This study investigates public sentiment regarding the role of technology in supporting women’s participation in STEM through a machine learning–based sentiment analysis. Using 1,533 social media comments, sentiment classification was performed by integrating Support Vector Machine (SVM) and VADER-based automatic labeling, with imbalance handling to improve classification reliability. The results indicate a dominance of positive sentiment (98%), suggesting an optimistic tendency within the analyzed dataset, although this may be influenced by dataset characteristics and methodological bias. Among the evaluated models, a linear-kernel SVM achieved the highest accuracy (98.31%). This study contributes methodologically by demonstrating the effectiveness of integrating lexicon-based labeling with supervised learning for public sentiment analysis on gender equality in STEM, offering empirical insights to inform technology-driven policy interventions.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: SVM Kernels
  topic: keywords
subject: Vader Lexicon
  topic: keywords
subject: Women in STEM
  topic: keywords
creator: Dwi Andini Putri
creator: Siti Nurwahyuni
rights: Copyright (c) 2026 Dwi Andini Putri, Siti Nurwahyuni http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-23
publication date: 2026-02-23
starting page: 891
ending page: 900
doi: 10.33480/jitk.v11i3.7353

SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION FOR CONTINUOUS LEARNING: BENEFITS, IMPACTS, AND CHALLENGES
type: item
description: Artificial Intelligence (AI) is believed to be a crucial driver and force for realizing sustainable learning, so there is an urgent need to consolidate existing research to provide a clear and structured understanding of its tangible benefits, broader impacts, and ongoing challenges. This systematic literature review (SLR) aims to fill this gap by offering a concise overview of the role of AI in supporting sustainable learning in terms of benefits, impacts, and challenges in the era of society. Through a periodic review (SLR) of studies published between 2023 to 2025, this paper summarizes evidence on how AI enhances student learning engagement. This synthesis outlines the challenges, impacts, and pitfalls of using AI. The findings reveal that AI-driven tools—including intelligent tutoring systems, chatbots, emotion recognition systems, and adaptive learning platforms—significantly enhance personalized learning experiences and student motivation. This review synthesizes the technological landscape, outlining its benefits, impacts, and persistent challenges. Despite its potential, ethical, technical, and pedagogical hurdles remain. Consequently, this study lays the groundwork for future research and development in AI-based continuous learning. This study has several limitations. The literature review does not cover the specific designs and methodologies for measuring student engagement with AI. It also focuses on explicit outcomes like engagement and motivation, potentially overlooking unintended consequences or long-term impacts of AI integration. Furthermore, the analysis is constrained by the varying methodological quality and reporting transparency of the primary studies included.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Benefit
  topic: keywords
subject: Challenges
  topic: keywords
subject: Impact
  topic: keywords
subject: Systematic Literature Review
  topic: keywords
creator: Winanti Winanti
creator: Yoga Prihastomo
creator: Yulius Denny Prabowo
creator: Achmad Sidik
creator: Penny Hendriyati
creator: Muhamad Luthfian
creator: Rizky Setiawan
creator: Wardiansyah Wardiansyah
creator: Zaki Ma'rufan Chandra
rights: Copyright (c) 2026 Winanti Winanti, Yoga Prihastomo, Yulius Denny Prabowo, Achmad Sidik, Penny Hendriyati, Muhamad Luthfian; Rizky Setiawan, Wardiansyah Wardiansyah; Zaki Ma'rufan Chandra http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-23
publication date: 2026-02-23
starting page: 911
ending page: 919
doi: 10.33480/jitk.v11i3.7583

IMPLEMENTATION OF RANDOM FOREST FOR ANIMAL PROTEIN CLASSIFICATION THROUGH HYPERPARAMETER OPTIMIZATION
type: item
description: Accurate identification of animal protein types is crucial to ensure food authenticity and safety, particularly in the context of compliance with halal principles. This study aims to implement the Random Forest (RF) algorithm to classify four types of animal protein—broiler chicken, free-range chicken, pork, and beef through hyperparameter optimization using GridSearchCV. The dataset was evaluated using 5-fold cross-validation, and feature importance analysis was conducted to identify the variables that contributed most to classification. Results showed that RF with optimized hyperparameters achieved a test accuracy of 92.81%, with macro-average precision, recall, and F1-score of 93%. The model performed best for the broiler chicken and pork classes, while the beef class exhibited a higher misclassification rate, likely due to the similarity of spectral characteristics among classes. ODOR, CO₂, H₂, NH₃, and VOC were identified as the key indicators for distinguishing animal protein types. This study contributes to halal authentication by integrating FTIR spectral data with optimized Random Forest, enabling efficient and accurate classification. Although RF proved reliable and capable of handling high-dimensional data, the study is limited by dataset size and spectral feature complexity. Future research is recommended to explore deep learning architectures, such as Convolutional Neural Networks (CNN), with larger FTIR datasets to improve model generalization and robustness
subject: Articles
  topic: section
subject: Animal Protein
  topic: keywords
subject: Classification
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Accuracy
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Ridho Ikhram
creator: Anton Yudhana
creator: Imam Riadi
rights: Copyright (c) 2026 Ridho Ikhram, Anton Yudhana, Imam Riadi http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 692
ending page: 699
doi: 10.33480/jitk.v11i3.7613

SOFTWARE DEFECT PREDICTION TRENDS: A BIBLIOMETRIC ANALYSIS OF MACHINE AND DEEP LEARNING
type: item
description: This study provides a comprehensive bibliometric mapping of global research trends and emerging frontiers in Software Defect Prediction (SDP), emphasizing the integration of machine learning (ML) and deep learning (DL) approaches. Unlike previous bibliometric surveys that focused narrowly on metric-based or short-term analyses, this work offers a broader and more integrated perspective on the intellectual evolution, collaboration patterns, and thematic directions in SDP research. Using data retrieved from the Scopus database and analyzed through Bibliometrix and VOSviewer, the study systematically applied the PRISMA protocol to ensure transparency and replicability. A total of 1,549 publications were examined, revealing a steady increase in scientific output dominated by China, India, and the United States. Thematic and keyword analyses identified five core clusters that trace the paradigm shift from traditional statistical models to advanced ML- and DL-driven predictive frameworks. Emerging topics such as transfer learning, cross-project prediction, and explainable AI (XAI) were identified as promising frontiers shaping the next phase of software quality prediction research. Beyond mapping academic progress, this study contributes strategic insights for researchers seeking to identify research gaps, industry practitioners developing intelligent defect prediction tools, and policymakers designing AI-driven software quality initiatives
subject: Articles
  topic: section
subject: bibliometrik
  topic: keywords
subject: deep learning
  topic: keywords
subject: machine learning
  topic: keywords
subject: software defect prediction
  topic: keywords
subject: transfer learning
  topic: keywords
creator: Harsih Rianto
creator: Omar Pahlevi
creator: Desmulyati
creator: Amrin
creator: Ade Surya Budiman
creator: Budi Supriyadi
rights: Copyright (c) 2026 Harsih Rianto, Omar Pahlevi, Desmulyati, Amrin, Ade Surya Budiman, Budi Supriyadi http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-28
publication date: 2026-02-28
starting page: 967
ending page: 978
doi: 10.33480/jitk.v11i3.7351

SENTIMENT CLASSIFICATION MODEL BASED ON COMPARATIVE STUDIES USING MACHINE LEARNING TECHNOLOGY
type: item
description: The development of social media has generated large amounts of text data, which is a valuable source for sentiment analysis. This study aims to conduct a comparative study of sentiment classification models on Indonesian-language YouTube comments, specifically comparing lexicon-based approaches, traditional machine learning models (Naive Bayes), and deep learning models (LSTM). Data was collected from YouTube videos themed around the youth generation and demographic bonuses, totaling 9,162 comments that underwent comprehensive text preprocessing. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the LSTM model outperforms Naive Bayes with an accuracy of 78.78% and an average F1-score of 0.79, compared to Naive Bayes, which only achieves an accuracy of 62.08% and an F1-score of 0.54. Although LSTM offers higher performance, the Naive Bayes model remains relevant due to its simplicity and efficiency. This study makes an important contribution to the selection of sentiment classification models for the Indonesian language and suggests the development of hybrid models and the use of contextual features for more optimal results. The LSTM model outperforms Naive Bayes with an accuracy of 82.15% (improved from 78.78% through enhanced regularization) and an average F1-score of 0.84. Comprehensive hyperparameter tuning via grid search and expanded manual annotation (40% of the dataset with κ=0.83) ensures robust model evaluation and reduces labeling bias. The study provides methodologically sound benchmarks for Indonesian sentiment analysis
subject: Articles
  topic: section
subject: Hybrid Model
  topic: keywords
subject: Lexicon Approach
  topic: keywords
subject: LSTM
  topic: keywords
subject: Naive Bayes
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
creator: J PRAYOGA
creator: T. Irfan Fajri
creator: Febri Dristyan
rights: Copyright (c) 2026 J PRAYOGA, T. Irfan Fajri, Febri Dristyan http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 755
ending page: 764
doi: 10.33480/jitk.v11i3.7105

SCRUM AND ITIL-BASED SUPPORT SYSTEM DESIGN AND IMPLEMENTATION AT RAPHA THERESIA HOSPITAL
type: item
description: This research addresses the challenges of manual IT service management at Rapha Theresia Hospital, where existing processes lacked systematic tracking and reporting, leading to operational inefficiencies. The purpose was to design and implement a web-based IT support system for systematic documentation of IT requests and repairs, integrating the Scrum agile development methodology with the ITIL framework, and enabling comprehensive IT performance reporting for management evaluation. The study employed a hybrid methodological approach, combining Scrum for iterative development and ITIL for robust service delivery. Research methods included problem identification, and iterative implementation across four sprints with defined Service Level Agreements (SLAs). Rigorous User Acceptance Testing (UAT) validated the system's functionality. Results show successful implementation of a centralized system managing IT requests, assets, and reports, significantly improving operational efficiency, service reliability, and fostering data-driven decision-making. The system enhanced coordination, transparency, and accelerated service resolution within the IT team.
subject: Articles
  topic: section
subject: Agile
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Itil
  topic: keywords
subject: Scrum
  topic: keywords
subject: Support System Design
  topic: keywords
creator: Kasrizal Kasrizal
creator: Sharipuddin Sharipuddin
creator: Joni Devitra
creator: Gunardi Gunardi
rights: Copyright (c) 2026 Kasrizal, Sharipuddin, Joni Devitra, Gunardi http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 864
ending page: 873
doi: 10.33480/jitk.v11i3.7004

CONVERSION OF GRAPHICAL TO NUMERICAL DATA WITH WEB PLOT DIGITIZER IN OIL RESERVE DETERMINATION
type: item
description: Old oil fields that are to be reactivated have production data only in graphical form, making it difficult to determine remaining reserves. Web Plot Digitizer helps convert graphical data into numerical data for determining oil reserves using the decline curve method. The use of Web Plot Digitizer reduces numerical errors, which impact decline parameters (qi, Di, b) and time efficiency in reserve determination. The purpose of this study is to apply Web Plot Digitizer to convert graphical production data into numerical data and determine oil reserves using decline curve analysis. The novelty of this research lies in the use of digitized graph data as direct input in Decline Curve Analysis (DCA) analysis for oil reserve estimation. The purpose of this research is to apply Web Plot Digitizer in converting production graph data into numerical data, as well as determining oil reserves using decline curve analysis. This research method uses exponential Decline Curve Analysis (DCA), which is applied to old oil fields, production rate data in the form of graphs is converted into numerical data using Web Plot Digitizer. The digitized numerical data is then made into a semilog graph of production rate versus time, then a trend line is taken for the decline in oil production rate and used in determining oil reserves. The analysis results obtained an initial decline rate (Di) value of 0.041 per month and oil reserves are estimated at 5 million barrels of oil (5 MBO), where oil will be exhausted in January 1985 if no workover is carried out. The results of this analysis provide a solution for old oil fields that only have historical graphs without access to numerical data, so that they can still calculate reserves using Decline Curve
subject: Articles
  topic: section
subject: Decline Curve
  topic: keywords
subject: Reserve
  topic: keywords
subject: Web Plot Digitizer
  topic: keywords
creator: Lia Yunita
rights: Copyright (c) 2026 Lia Yunita http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-28
publication date: 2026-02-28
starting page: 1000
ending page: 1008
doi: 10.33480/jitk.v11i3.7226

TOGAF ADM - BASED ENTERPRISE ARCHITECTURE FOR TANTAN DIGITAL VILLAGE
type: item
description: The acceleration of digital transformation in rural governance requires an integrated information system to ensure efficient, transparent, and accountable public services. Yet, many villages including Tantan Village in Muaro Jambi Regency still operate with fragmented applications and redundant data. This study proposes a comprehensive enterprise architecture blueprint for Tantan Village using the TOGAF ADM framework, specifically adapted to the operational and institutional characteristics of rural public administration. Employing a qualitative case study approach, the research develops a four-layered architecture encompassing business, data, application, and technology domains. Theoretically, this study advances the understanding of how enterprise architecture can be localized for small-scale government entities; practically, it provides a replicable model that supports sustainable digital village transformation in Indonesia.
subject: Articles
  topic: section
subject: Enterprise Architecture Blueprint
  topic: keywords
subject: Rural e-Government
  topic: keywords
subject: Smart Village Transformation
  topic: keywords
subject: TOGAF ADM
  topic: keywords
subject: Village Information System
  topic: keywords
creator: Bujangdek
creator: Setiawan Assegaff
creator: Jasmir
rights: Copyright (c) 2026 Bujangdek, Setiawan Assegaff, Jasmir http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-11
publication date: 2026-02-11
starting page: 795
ending page: 807
doi: 10.33480/jitk.v11i3.7318

SUPPORT VECTOR MACHINE TO CLASSIFY SENTIMENT REVIEWS ON GOOGLE PLAY STORE
type: item
description: This research addresses the "rating-content discrepancy" on the Google Play Store, where numerical star ratings often conflict with the actual sentiment of textual reviews. Utilizing the CRISP-DM framework, the study evaluates the effectiveness of machine learning in resolving these inconsistencies by classifying Instagram user reviews into positive and negative categories. Two primary algorithms were compared using a dataset of 600 reviews. The Support Vector Machine (SVM) model demonstrated high efficacy with an accuracy of 0.84. In contrast, the K-Nearest Neighbors (KNN) model performed poorly, achieving an accuracy of only 0.48. This significant performance gap highlights SVM's superior ability to handle high-dimensional text data through optimal hyperplane separation. The research further integrated the Streamlit library to create an interactive web interface for real-time sentiment prediction and result visualization. Ultimately, this study confirms that a structured CRISP-DM approach combined with SVM provides a robust solution for automated opinion mining, offering a reliable methodology for future data science applications in social media analysis
subject: Articles
  topic: section
subject: CRISP-DM
  topic: keywords
subject: Google Play Store
  topic: keywords
subject: KNN
  topic: keywords
subject: Sentiment
  topic: keywords
subject: SVM
  topic: keywords
creator: Agus Nursikuwagus
creator: Suherman
creator: Heri Purwanto
creator: Tono Hartono
rights: Copyright (c) 2026 Agus Nursikuwagus, Suherman, Heri Purwanto, Tono Hartono http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 724
ending page: 732
doi: 10.33480/jitk.v11i3.7282

DATA AUGMENTATION EFFECTS ON PROTONET FEW-SHOT YELLOW DISEASE SEVERITY IN CHILI LEAVES
type: item
description: Yellow curling disease in chili plants is one of the leading causes of declining horticultural productivity because it reduces the quality and quantity of crops. Variations in symptoms at each level of severity make the identification process difficult, especially when labeled data is minimal. This study proposes a Prototypical Network-based Few-Shot Learning (FSL) approach with VGG16 architecture as a feature extractor. Five augmentation techniques, namely horizontal flip, rotation, zoom, brightness, and contrast adjustment, were used to increase data diversity in data-scarce conditions. Experiments were conducted with N-way K-shot configurations (2–5 classes; 1, 5, and 10 examples per class) to evaluate the impact of augmentation on prototype representation stability. Results show that increasing the number of examples per class consistently improves accuracy from 34.6% in 5-way 1-shot to 49.4% in 5-way 10-shot without augmentation. However, the use of augmentation decreases performance in higher N-way scenarios because it increases intra-class variability. The t-SNE visualization reinforces this study, where the healthy and severely diseased classes are clearly separated, while the intermediate class shows overlap. The novelty of this study is that it is the first to evaluate the impact of augmentation strategies on prototype representation stability in the agricultural domain with limited data. The results of this Few-Shot Learning approach are effective for plant disease classification despite the limited dataset.
subject: Articles
  topic: section
subject: Few-Shot Learning
  topic: keywords
subject: Prototypical Network
  topic: keywords
subject: VGG16
  topic: keywords
subject: Yellow Leaf Curl Disease
  topic: keywords
creator: Rizal Amegia Saputra
creator: Rusda Wajhillah
creator: Yusti Farlina
creator: Hani Noviani
creator: Saela Nurusysyifa
rights: Copyright (c) 2026 Rizal Amegia Saputra, Rusda Wajhillah; Yusti Farlina, Hani Noviani, Saela Nurusysyifa http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-23
publication date: 2026-02-23
starting page: 882
ending page: 890
doi: 10.33480/jitk.v11i3.7458

PUBLIC SECTOR INNOVATION IN SMART CITIES: A FRAMEWORK OF AMBIDEXTROUS AI GOVERNANCE
type: item
description: The increasing adoption of artificial intelligence (AI) in smart city public services has intensified governance challenges related to accountability, data security, transparency, and risk management. Existing AI governance studies tend to emphasize either innovation enablement or control mechanisms, offering limited guidance on how public institutions can balance both simultaneously. This study addresses this gap by proposing an Ambidextrous AI Governance Framework grounded in COBIT 2019, which systematically integrates exploration-oriented innovation and exploitation-oriented control within public-sector governance. Using a Design Science Research (DSR) approach, the framework was developed and evaluated through a 2025 case study at the Jakarta Provincial Communication and Information Agency. Validation was conducted via expert interviews, document analysis, and simulation-based governance maturity assessments. The findings indicate that baseline AI governance maturity remains at Levels 2–3, while simulation results suggest potential advancement toward Levels 4–5, particularly in risk management (APO12) and security services (DSS05). The study contributes theoretically by operationalizing organizational ambidexterity within public-sector AI governance and practically by offering structured guidance to support secure, transparent, and sustainable AI adoption in smart city environments.
subject: Articles
  topic: section
subject: AI Governance
  topic: keywords
subject: Ambidextrous Governance
  topic: keywords
subject: COBIT 2019
  topic: keywords
subject: Public Sector Innovation
  topic: keywords
subject: Smart City
  topic: keywords
creator: Agustinus Fritz Wijaya
creator: Merryana Lestari
creator: Mega Kartika Sari
creator: Rio Ferdiandinata
rights: Copyright (c) 2026 Agustinus Fritz Wijaya, Merryana Lestari, Mega Kartika Sari, Rio Ferdiandinata http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 765
ending page: 774
doi: 10.33480/jitk.v11i3.7672

TRANSFER LEARNING-BASED CLASSIFICATION OF BELL PEPPER LEAF DISEASES USING VGG16 AND EFFICIENTNETB3 ARCHITECTURES
type: item
description: Diseases affecting pepper leaves can significantly reduce crop productivity and quality, while manual disease identification remains subjective, time-consuming, and prone to error. Therefore, an accurate automated classification system is required to support early disease detection. This study aims to evaluate and compare the performance of a conventional Convolutional Neural Network (CNN) with two transfer learning–based architectures, VGG16 and EfficientNetB3, for classifying pepper leaf images into healthy and bacterial spot classes, as well as to analyze the impact of applying a soft voting ensemble method on classification performance. The dataset was obtained from Kaggle and divided into training, validation, and test sets. Image preprocessing included resizing all images to 224×224 pixels and applying data augmentation to improve model generalization. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that EfficientNetB3 outperforms the conventional CNN and VGG16 models. Furthermore, the application of the soft voting ensemble enhances prediction stability, achieving an accuracy of 99.68% on the test dataset with balanced precision and recall across both classes. These findings demonstrate that the integration of transfer learning and soft voting ensemble methods is an effective approach for image-based pepper leaf disease classification under the experimental conditions, and provides a basis for further validation using more diverse datasets.
subject: Articles
  topic: section
subject: CNN
  topic: keywords
subject: EfficientNetB3
  topic: keywords
subject: Ensemble Soft Voting
  topic: keywords
subject: Pepper leaf classification
  topic: keywords
subject: Transfer learning
  topic: keywords
creator: Siti Nurhasanah Nugraha
creator: Evita Fitri
creator: Muji Ernawati
rights: Copyright (c) 2026 Siti Nurhasanah Nugraha, Evita Fitri, Muji Ernawati http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-11
publication date: 2026-02-11
starting page: 829
ending page: 837
doi: 10.33480/jitk.v11i3.7913

DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING
type: item
description: The phenomenon of micro-virality on TikTok illustrates how content can rapidly spread on a small scale before reaching broader virality. Understanding its driving factors is essential for supporting digital marketing strategies, managing content creators, and analyzing social media trends. This study aims to detect and predict the potential of micro-virality in TikTok videos by integrating a social listening approach with machine learning techniques. The dataset consists of approximately 4,000 TikTok posts enriched with 20 features across five categories, including user metadata (author popularity, follower ratio), temporal features (posting time and day), network features (hashtags and mentions), content features (text length and keywords), and contextual elements (trending music and video duration). To ensure objective labeling, a quantile-based threshold was applied, categorizing videos in the top 25% of view counts (≥ 26,300,000 views) as viral, resulting in a class distribution of 24.74% viral and 75.26% non-viral. To address this imbalance, the SMOTENC technique was used to oversample the minority class and enhance data representativeness. Three machine learning algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were implemented. Experimental results show that Random Forest improved from 88% to 92%, XGBoost maintained strong performance at 95%, and ANN increased significantly from 92% to 93% after SMOTENC application. These findings indicate that SMOTENC effectively improves model generalization and reduces bias toward majority classes, supporting more reliable early-stage virality prediction. Overall, the study enriches social media analytics research and provides practical insights for optimizing TikTok content strategies and early trend detection.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Micro-Virality
  topic: keywords
subject: Smotenc
  topic: keywords
subject: Social Listening
  topic: keywords
subject: TikTok Content Analytics
  topic: keywords
creator: Ratih Anggraeni
creator: Purwadi
creator: Pungkas Subarkah
rights: Copyright (c) 2026 Ratih Anggraeni, Purwadi, Pungkas Subarkah http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-11
publication date: 2026-02-11
starting page: 775
ending page: 783
doi: 10.33480/jitk.v11i3.7472

HYBRID TRANSFER LEARNING AND ADVANCED DATA AUGMENTATION FOR MULTICLASS BRAIN TUMOR CLASSIFICATION USING EFFICIENTNET
type: item
description: Accurate Accurate brain tumor diagnosis from MRI images remains challenging due to dataset limitations, class imbalance, and high morphological variability across tumor types. Existing deep learning approaches often yield suboptimal results when trained on small or imbalanced datasets. This study proposes a hybrid learning strategy that integrates transfer learning with advanced data augmentation to classify four brain tumor categories: glioma, meningioma, pituitary adenoma, and normal tissue. Using a large-scale dataset of 7,023 MRI images, the proposed framework incorporates Mixup, CutMix, and a comprehensive augmentation pipeline with an optimized EfficientNet-B0 architecture. The model achieves a test accuracy of 99.05% with F1-scores of 0.99, representing a 4.05 percentage point improvement over a baseline InceptionV3 model (95.00%) and outperforming ResNet-based approaches (93.80%) reported in previous studies. This quantitative improvement demonstrates the effectiveness of combining modern CNN architectures with advanced augmentation strategies. The streamlined architecture and high accuracy make the method suitable for deployment in resource-constrained healthcare environments. These results indicate that hybrid augmentation and transfer learning can deliver clinically meaningful performance for early brain tumor identification, offering a scalable and practical solution for computer-aided medical diagnosis
subject: Articles
  topic: section
subject: Brain Tumor Classification
  topic: keywords
subject: Data Augmentation
  topic: keywords
subject: Efficientnet
  topic: keywords
subject: Mixup
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: A M H Pardede
creator: Riki Winanjaya
creator: Juni Ismail
rights: Copyright (c) 2026 A M H Pardede, Riki Winanjaya, Juni Ismail http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 669
ending page: 679
doi: 10.33480/jitk.v11i3.7524

PERFORMANCE EVALUATION OF LIGHTWEIGHT DEEP LEARNING MODELS FOR BORAX-CONTAMINATED MEATBALL IMAGE CLASSIFICATION
type: item
description: Food safety, particularly concerning the use of illegal additives such as borax in processed meat products like meatballs, remains a critical issue in Indonesia. This study analyzes the performance of several lightweight deep learning models based on Convolutional Neural Networks (CNN) and Transformers to classify images of meatballs containing borax, enabling their deployment on resource-constrained devices such as smartphones. Data collection involved capturing 1,429 images of meatballs with and without borax using a smartphone camera under varying lighting conditions and shooting angles. The five main architectures evaluated were ConvNeXt-Nano, Swin-Tiny, ViT-Tiny, MobileViT-XS, and EfficientNet-B0. Hyperparameter optimization was conducted using Optuna, followed by training with a 5-fold cross-validation scheme. Model evaluation metrics included accuracy, precision, recall, F1 score, and inference speed. The results show that MobileViT-XS was the best-performing architecture, achieving 65.93% accuracy, 0.703 precision, 0.706 recall, 0.659 F1 score, and efficient memory consumption (45.94 MB). These findings indicate that a hybrid approach combining the strengths of CNNs and Transformers can achieve an optimal balance between detection accuracy and computational efficiency. Therefore, such models have the potential to be applied as food safety detection systems on devices with limited resources
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Food Safety
  topic: keywords
subject: Image Classification
  topic: keywords
subject: Lightweight Model
  topic: keywords
creator: Aryo Michael
creator: Ireve Devi Damayanti
rights: Copyright (c) 2026 Aryo Michael, Ireve Devi Damayanti http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 743
ending page: 754
doi: 10.33480/jitk.v11i3.7462

OPTIMIZATION IN ZAKAT MANAGEMENT THROUGH THE DEVELOPMENT OF A CHATBOT-BASED MOBILE APPLICATION
type: item
description: The growing awareness of zakat, infaq, and sadaqah (ZIS) in Indonesia calls for intelligent and transparent management systems. This study proposes a chatbot-based mobile application integrated with the SECI knowledge management model to optimize ZIS management and distribution. Using the waterfall software development model, the research includes requirement analysis, system design, chatbot implementation, and validation. The decision-tree-based chatbot enables interactive and personalized guidance for muzakki, while the SECI framework ensures structured knowledge sharing among zakat institutions. Functional and compatibility testing show that the system operates reliably on Android version 10 and above, with intent classification accuracy reaching 92 percent. The findings demonstrate that combining intelligent interaction and structured knowledge management improves transparency, operational efficiency, and institutional learning in digital zakat systems. The proposed framework provides both theoretical and practical contributions to advancing socio-economic management through mobile technology
subject: Articles
  topic: section
subject: Chatbot
  topic: keywords
subject: Infaq
  topic: keywords
subject: Mobile Application
  topic: keywords
subject: Sadaqah (ZIS)
  topic: keywords
subject: SECI Model
  topic: keywords
creator: Fajar Delli Wihatiko
creator: Gustian Rama Putra
rights: Copyright (c) 2026 Fajar Delli Wihatiko, Gustian Rama Putra http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-28
publication date: 2026-02-28
starting page: 987
ending page: 999
doi: 10.33480/jitk.v11i3.7402

COMPARATIVE STUDY OF GENERATIVE AI TOOLS IN VISUAL COMMUNICATION DESIGN EDUCATION: CREATIVITY AND USABILITY
type: item
description: The integration of generative artificial intelligence (AI) into visual communication design education presents opportunities to enhance creativity and usability in learning. This study compares the effectiveness of three generative AI tools (MidJourney, DALL·E, and Adobe Firefly) in supporting students’ creative outcomes and perceived usability, while also examining their broader pedagogical role in design education. A quasi-experimental design was conducted with 30 undergraduate students who each produced two poster designs: one manually and one with AI assistance. Creativity was evaluated using the Consensual Assessment Technique (CAT) by expert judges, and usability was measured using the System Usability Scale (SUS). Results showed that AI-assisted designs achieved significantly higher creativity scores (M = 4.3 vs. 3.2, p < 0.05) and usability ratings (range M = 74–82) compared to manual designs, with MidJourney rated highest in creativity and Adobe Firefly in usability. These findings provide empirical evidence that generative AI can act as a catalyst for creativity and usability in design education, offering theoretical insights into human–AI co-creation and practical implications for curriculum integration. Limitations include the small sample size and the study’s focus on a single academic program, which may affect generalizability.
subject: Articles
  topic: section
subject: CAT-SUS
  topic: keywords
subject: Creativity Assessment
  topic: keywords
subject: Education
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Usability
  topic: keywords
creator: Yana Erlyana
creator: M Garry Saputra
rights: Copyright (c) 2026 Yana Erlyana, M Garry Saputra http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 733
ending page: 742
doi: 10.33480/jitk.v11i3.7087

DEVELOPING A MICRO-ENTERPRISE E-READINESS FRAMEWORK: A CASE STUDY FROM INDONESIA
type: item
description: Micro-enterprises play a vital role in Indonesia’s economy but continue to face persistent barriers in adopting information and communication technology (ICT). Understanding E-Readiness is essential for implementing effective digital interventions, especially for micro-enterprises. To address the lack of validated frameworks for assessing digital readiness at the micro-enterprise level, this study develops and empirically tests a novel E-Readiness Assessment Framework specifically designed for micro-enterprises. The proposed conceptual model consists of four dimensions: Technology, Organisation, External Environment, and Human Resources, which are derived from established e-readiness models. This study also proposed the measurement indicators that have been adapted to the characteristics of micro enterprises. Using quantitative data from 641 food and beverage (F&B) micro-enterprises in Batu City, Indonesia, exploratory factor analysis (EFA) was applied to evaluate construct validity. Despite the elimination of five indicators due to insufficient communality value, the overall model structure remained statistically valid. Subsequently, factor analysis was succeeded by the calculation of E-Readiness index using weighted aggregation and normalisation methods. The resulting E-Readiness Index for Batu City was 46.47, with 57.10% of enterprises classified as “Not Ready,” primarily due to technological and infrastructural limitations. The proposed model in this study efficiently assesses e-readiness at the micro-enterprise level and is adaptable for application in different regions or business sectors. This model also provides valuable insights for policymakers in formulating targeted digital support initiatives. Future research may consider expanding the scope of indicators and validating the model using confirmatory analysis.
subject: Articles
  topic: section
subject: E-Readiness
  topic: keywords
subject: Exploratory Factor Analysis
  topic: keywords
subject: F&B Sector
  topic: keywords
subject: Micro Enterprises
  topic: keywords
subject: Readiness Index
  topic: keywords
creator: Nori Wilantika
creator: Fitri Kartiasih
creator: Ernawati Pasaribu
creator: Aisha Arthamevia
creator: Yunarso Anang
creator: Achmad Nizar Hidayanto
rights: Copyright (c) 2026 Nori Wilantika, Fitri Kartiasih, Ernawati Pasaribu, Aisha Arthamevia, Yunarso Anang, Achmad Nizar Hidayanto http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-11
publication date: 2026-02-11
starting page: 808
ending page: 820
doi: 10.33480/jitk.v11i3.7189

OPTIMIZATION OF MLP-NN FOR MANGO LEAF DISEASE PREDICTION USING IMAGE-BASED FEATURE EXTRACTION
type: item
description: Mango (Mangifera indica Linn.) is a nutrient-rich fruit, yet leaf diseases caused by microorganisms can significantly reduce crop productivity. Early detection is essential to prevent further damage and support effective disease management. This study proposes an optimized mango leaf disease prediction model using a multi-layer perceptron neural network (MLP-NN). Image-based feature extraction is performed using the Inception v3 architecture to obtain high-level color and texture features that improve classification performance. Unlike previous studies that rely solely on manually engineered features or full CNN training, this research introduces a hybrid approach that integrates deep feature extraction with MLP-NN optimization, offering a lightweight yet highly accurate alternative. Several hyperparameter combinations, including activation functions (ReLU, tanh, sigmoid) and optimization algorithms (Adam and SGD), were evaluated using the Orange platform. The optimized MLP-NN model with ReLU and Adam achieved the highest accuracy of 93.5%, demonstrating better stability and training efficiency compared to other configurations. These findings highlight the novelty and advantages of the proposed method, showing improved accuracy with lower computational cost relative to many existing approaches. This study provides an efficient solution for mango leaf disease prediction and supports future development of automated plant disease detection systems
subject: Articles
  topic: section
subject: Adam
  topic: keywords
subject: Inception v3
  topic: keywords
subject: Mango leaf disease
  topic: keywords
subject: Neural network
  topic: keywords
subject: Optimization
  topic: keywords
creator: Budi Triandi
creator: Lili Tanti
rights: Copyright (c) 2026 Budi Triandi, Lili Tanti http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 680
ending page: 691
doi: 10.33480/jitk.v11i3.7031

EVALUATING CLUSTERING METHODS FOR SEMANTIC REPRESENTATION OF DISASTER NEWS USING BERT EMBEDDINGS AND HBDSCAN
type: item
description: Natural disasters that frequently occur in Indonesia demand a fast and accurate information monitoring and analysis system through online news sources. This study aims to identify topic patterns related to natural disasters in Indonesia using news articles from Detik.com through a semantic clustering approach. A total of 1,000 articles were collected, preprocessed, and represented using the Sentence-BERT (SBERT) model to capture contextual relationships between sentences. The vector representations were then clustered using three methods: K-Means, Agglomerative Hierarchical Clustering, and HDBSCAN. The performance of each method was evaluated using the Silhouette Score, Davies–Bouldin (DB) Index, and Calinski–Harabasz (CH) Index. The results show that HDBSCAN achieved the best performance with a Silhouette Score of 0.215, a DB Index of 1.557, and a CH Index of 18.102, outperforming Agglomerative (0.028, 3.945, 29.669) and K-Means (0.055, 3.678, 36.778). Moreover, the HDBSCAN model achieved the highest coherence score of 0.8669, indicating strong semantic consistency within clusters. Five coherent clusters emerged, representing major disaster themes: landslides, earthquakes, tornadoes, flash floods, and volcanic activity. The visualization of word clouds for each cluster reinforced the interpretation of these disaster topics. Overall, the combination of SBERT and HDBSCAN effectively groups news articles based on semantic similarity. These findings highlight the potential of Natural Language Processing (NLP) to enhance data-driven media monitoring, support early warning systems, and strengthen disaster communication and mitigation strategies in Indonesia
subject: Articles
  topic: section
subject: Natural Disasters News
  topic: keywords
subject: Sentence BERT
  topic: keywords
subject: Text Mining
  topic: keywords
subject: Text Clustering
  topic: keywords
creator: Ariska Fitriyana Ningrum
creator: Dannu Purwanto
creator: Abdel Nasser Sharkawy
rights: Copyright (c) 2026 Ariska Fitriyana Ningrum, Dannu Purwanto, Abdel Nasser Sharkawy http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-11
publication date: 2026-02-11
starting page: 784
ending page: 794
doi: 10.33480/jitk.v11i3.7204

OPTIMIZING VGG-16 CONVOLUTIONAL NEURAL NETWORK FOR PAP SMEAR IMAGE CLASSIFICATION IN CERVICAL CANCER DETECTION
type: item
description: Early detection of cervical cancer through Pap smear image analysis plays a crucial role in reducing mortality rates associated with this disease. This study aims to optimize the VGG16 architecture to improve the classification accuracy of Pap smear images. The proposed method employs transfer learning with pre-trained ImageNet weights, customization of fully connected layers, and data augmentation techniques to enhance the diversity of training images. Experimental results demonstrate a significant improvement in training accuracy, reaching 98.50%, while validation accuracy remained stable at 88.24%, indicating potential overfitting. Performance testing on unseen data yielded an accuracy of 80%, with high precision for the negative class but low recall for the positive class, suggesting a bias toward the majority class. These findings highlight the need for additional strategies, such as data balancing and hybrid method integration, to improve sensitivity to positive cases. This research contributes to the development of adaptive deep learning-based classification models that support clinical decision-making in cervical cancer screening and opens opportunities for further research on model optimization and dataset expansion.
subject: Articles
  topic: section
subject: Cervical Cancer
  topic: keywords
subject: Image Classification
  topic: keywords
subject: Pap Smear
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: VGG16
  topic: keywords
creator: Odi Nurdiawan
creator: Heliyanti Susana
creator: Ade Rizki Rinaldi
creator: Ahmad Asyraful Hijrah
creator: Indah Diniarti
rights: Copyright (c) 2026 Odi Nurdiawan, Heliyanti Susana, Ade Rizki Rinaldi, Ahmad Asyraful Hijrah, Indah Diniarti http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-11
publication date: 2026-02-11
starting page: 821
ending page: 828
doi: 10.33480/jitk.v11i3.7131

HYBRID PSO K-MEANS AND ROBUST SPARSE K-MEANS FOR EMPLOYEE STUDY DECISIONS
type: item
description: Human Resources (HR) are a strategic asset in institutional advancement, so employee performance evaluation must be conducted objectively and based on data. This study aims to cluster employee performance data at XYZ University for determining further studies, using the K-Means, PSO K-Means, and Robust Sparse K-Means methods, as well as three types of distance measurements: Euclidean, Manhattan, and Mahalanobis Distance. The dataset consists of 17 attributes. The evaluation was conducted using the Silhouette Score, Davies-Bouldin Index, and visualization using PCA. The results indicate that the combination of PSO K-Means with Euclidean Distance provides the best balance between quantitative performance (Silhouette Score 0.1253 and DBI 2.0521) and a more visually representative distribution of cluster members. The interpretation of the clustering results yielded three clusters: Cluster 0 (no further study) consisting of 8 employees, Cluster 1 (further study) consisting of 97 employees, and Cluster 2 (awaiting study decision) consisting of 58 employees. These findings can be utilized by institutions to design more targeted and data-driven human resource development strategies.
subject: Articles
  topic: section
subject: Clustering
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Distance Metric
  topic: keywords
subject: K-Means
  topic: keywords
subject: Optimization
  topic: keywords
creator: Luh Dwi Ari Sudawati
creator: Roy Rudolf Huizen
creator: Dandy Pramana Hostiadi
rights: Copyright (c) 2026 Luh Dwi Ari Sudawati, Roy Rudolf Huizen, Dandy Pramana Hostiadi http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-18
publication date: 2026-02-18
starting page: 838
ending page: 850
doi: 10.33480/jitk.v11i3.7101

MACHINE LEARNING TO IDENTIFY ELIGIBILITY OF STUDENTS RECEIVING SINGLE TUITION RELIEF
type: item
description: The cost of higher education in Indonesia varies greatly and often becomes a financial burden for students. Socio-economic factors such as parental income, occupation, number of dependents, vehicle ownership, and place of residence influence the determination of single tuition as regulated by the Ministry of Education Regulation No. 55 of 2013. This study aims to classify freshmen eligibility for single tuition relief using five machine learning models: RF, LR, KNN, SVM, and NB. The dataset contains 2000 rows of data with six socio-economic attributes divided into two classes: eligible and ineligible. The data were split into 80% training and 20% testing, and model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Results show that without SMOTE, all models suffer from severe majority-class bias, yielding critically low recall for the minority class SVM = 0.014; NB = 0.004. SMOTE significantly improves minority-class detection, with RF and SVM achieving the highest performance F1-scores of 0.820 and 0.801, and ROC-AUC of 0.966 and 0.990, respectively. SHAP analysis identifies Number of Dependents of Parents as the most influential predictor across all models, highlighting its central role in financial need assessment. These findings demonstrate that combining SMOTE with ensemble or margin-based models enhances classifiying fairness and sensitivity in educational support systems. The future work recommend expanding features to include behavioral, academic, and regional indicators, using multi-institutional data, and exploring deep learning or advanced resampling methods to enhance generalizability and robustness
subject: Articles
  topic: section
subject: Classification
  topic: keywords
subject: Higher Education
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: SHAP
  topic: keywords
subject: Single Tuition
  topic: keywords
creator: M. Ghofar Rohman
creator: Zubaile Abdullah
creator: Shahreen Kasim
creator: M Ulul Albab
rights: Copyright (c) 2026 M. Ghofar Rohman, Zubaile Abdullah, Shahreen Kasim, M Ulul Albab http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 613
ending page: 626
doi: 10.33480/jitk.v11i3.7294

A HYBRID BERT–GNN FOR DETECTING HOAXES AND NEGATIVE CONTENT IN INDONESIAN SOCIAL MEDIA
type: item
description: The rapid spread of hoaxes on social media threatens public trust and information integrity, especially within the Indonesian digital landscape. This study proposes a hybrid deep learning model that integrates transformer-based semantic representation from IndoBERT with Graph Neural Networks (GNNs) to enhance hoax detection performance. A heterogeneous social graph is constructed to model relationships among posts, users, and news sources, where post node features are extracted from the [CLS] embeddings of a fine-tuned IndoBERT. The GNN component consists of two graph convolutional layers with ReLU activation and dropout, followed by a multilayer perceptron classifier for binary classification. Experiments conducted on the Indonesia False News dataset (Kaggle) employ SMOTE resampling to handle class imbalance and 5-fold stratified cross-validation for robust evaluation across three configurations: BERT-only, GNN-only, and the proposed BERT–GNN hybrid model. The hybrid model achieves an average F1-score of 0.89 ± 0.01 and ROC-AUC of 0.92 ± 0.01, outperforming both single-model baselines while maintaining a balanced precision–recall trade-off. These results confirm that combining contextual semantic understanding with relational graph topology substantially enhances accuracy, robustness, and generalization in detecting hoaxes within Indonesian-language social media content
subject: Articles
  topic: section
subject: BERT
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Graph Neural Network
  topic: keywords
subject: Hoax
  topic: keywords
subject: Negative Content
  topic: keywords
creator: Khairunnisa
creator: Khairunnas
creator: Sutriawan
rights: Copyright (c) 2026 Khairunnisa, Khairunnas, Sutriawan http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 627
ending page: 639
doi: 10.33480/jitk.v11i3.7330

ASSESSING USER EXPERIENCE OF SITURAWA GEDE TOURISM WEBSITE USING PSSUQ AND HEURISTIC
type: item
description: The Siturawagede platform is an information site built to support services, promotions, and interactions with the public. Therefore, the quality of the user experience (UX) is a crucial factor in ensuring the site's effective, efficient, and satisfying use. This study aims to analyze the user experience of the Siturawagede website, measure user satisfaction with ease of use, efficiency, and information quality using the Post-Study System Usability Questionnaire (PSSUQ), assess the site's compliance with Nielsen's usability principles through a heuristic evaluation, and provide recommendations for improving the site's user-friendliness. The study involved 100 respondents who completed the PSSUQ questionnaire and three expert evaluators who conducted the heuristic assessment. The results showed that the average PSSUQ score of 2.6 was categorized as "good." This is based on the PSSUQ 1–7 scale, where a score closer to 1 indicates a positive experience (Strongly Agree) and a score closer to 7 indicates a negative experience (Strongly Disagree), indicating that users were quite satisfied with the system. The heuristic evaluation obtained a score of 1.49, identifying several minor navigation issues, but the system was generally good and needed only minor improvements. These findings provide guidance for improving the quality of the Siturawagede website to make it more informative and optimal in supporting tourism management.
subject: Articles
  topic: section
subject: Heuristic Evaluation
  topic: keywords
subject: PSSUQ
  topic: keywords
subject: Siturawagede
  topic: keywords
subject: Usability
  topic: keywords
subject: User Experience
  topic: keywords
creator: Anie Yoraeni
creator: Cep Adiwiharja
creator: Syifa Nur Rakhmah
creator: Sinta Rukiandari
creator: Sari Hartini
rights: Copyright (c) 2026 Anie Yoraeni, Cep Adiwiharja, Syifa Nur Rakhmah, Sinta Rukiandari, Sari Hartini http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-20
publication date: 2026-02-20
starting page: 874
ending page: 881
doi: 10.33480/jitk.v11i3.7332

HYBRIDIZATION OF FASTTEXT-BLSTM AND BERT FOR ENHANCED SENTIMENT ANALYSIS ON SOCIAL MEDIA TEXTS
type: item
description: The development of internet technology and social media has driven the increasing use of sentiment analysis to understand public opinion. This study aims to improve the classification performance of sentiment analysis by proposing a hybrid model that combines FastText-BLSTM and BERT. The dataset used consists of 900 Indonesian-language Netflix app user reviews obtained through crawling using Google Play Scraper. The research stages include text preprocessing, feature extraction using FastText and BERT, and classification using BLSTM, which are then combined in a concatenation layer to produce a richer feature representation. Experimental results show that the FastText-BLSTM-BERT hybrid model provides the best performance with an accuracy of 94.22%, a precision of 95.98%, a recall of 95.68%, and an F1-score of 95.83%. This achievement is superior to the single models of FastText-BLSTM and BERT. The main novelty of this research lies in the integration of contextual embeddings from BERT with subword-level semantic and sequential representations from FastText-BLSTM, which has not been extensively explored in prior studies on Indonesian sentiment analysis. This hybridization demonstrates significant improvement in model generalization and robustness for low-resource language texts
subject: Articles
  topic: section
subject: BERT
  topic: keywords
subject: BLSTM
  topic: keywords
subject: FastText
  topic: keywords
subject: Hybrid Model
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
creator: Jasmir
creator: Maria Rosario
creator: Irawan Irawan
creator: Agus Siswanto
creator: Tiko Nur Annisa
rights: Copyright (c) 2026 Jasmir, Maria Rosario, Irawan Irawan, Agus Siswanto, Tiko Nur Annisa http://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 640
ending page: 650
doi: 10.33480/jitk.v11i3.7488