TECHNO Nusa Mandiri: Journal of Computing and Information Technology is a journal published by LPPM Universitas Nusa Mandiri. The TECHNO Nusa Mandiri: Journal of Computing and Information Technology was originally intended to accommodate scientific papers made by Informatics Engineering lecturers. TECHNO Nusa Mandiri Journal has ISSN: 1978-2136 (Print Media) and 2527-676X (Online Media). The TECHNO Nusa Mandiri: Journal of Computing and Information Technology have the accredited National Journal status is accredited by the Indonesian Ministry of Research and Higher Education at the Sinta S4 level, in accordance with Decree on Strengthening SK Research and Development Number 21 / E / KPT / 2018 which has been in effect since July 9, 2018, for 5 years. Source: Risbang Ristekdikti.go.id. This journal is Rank 4 Accreditation Certificate (S4), Accreditation is valid for 5 years. Starting from Vol. 13, No. 1 the Year 2016 to Vol. 17, No. 1 the Year 2020. Journal of TECHNO Nusa Mandiri, re-accreditation remains at Rank 4 (SINTA 4), starting Vol. 16 No. 2 of 2019 based on the Decree of the Minister of Research and Technology / National Research and Innovation Agency Number 85/M/ KPT/2020, April 1, 2020
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MODEL FOR DETERMINING CANDIDATES FOR VILLAGE SOCIAL ASSISTANCE USING THE AHP–MOORA METHOD
Social assistance programs at the village level are one of the government's efforts to reduce poverty and improve community welfare. However, in practice, the process of determining eligible beneficiaries is often carried out manually, which may lead to subjectivity, inaccurate targeting, and a lack of transparency in decision-making. This study aims to develop a decision support system that assists village administrators in determining priority candidates for social assistance in a more objective and systematic manner. The proposed approach integrates the Analytical Hierarchy Process (AHP) and the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods. The AHP method is used to determine the weight of each criterion through pairwise comparison matrices and consistency testing. Subsequently, the MOORA method is applied to normalize the decision matrix, calculate preference values, and rank the alternatives. The results show that AHP-MOORA can generate a priority ranking of potential social assistance beneficiaries. The calculated weights for the criteria respectively are Kepemilikan Tanah: 0.5579 (highest importance), Kondisi Rumah: 0.2633, Pekerjaan: 0.1219, Penghasilan: 0.0569 (lowest importance) and the value of CR is 0.0654, which is less than 0.1, indicating that the criteria weights are consistent. The ranking based on AHP-MOORA show significant differences than the earlier dataset. Based on sensitivity analysis, the high correlation values in all sensitivity tests show that the ranking results remain consistent. Furthermore, the system improves transparency and consistency in the decision-making process.
Copyright (c) 2026 Hardian Oktavianto, Dodik Aris Setiawan, Guruh Wijaya, Zainul Arifin, Henny Wahyu Sulistyo http://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE ANALYSIS OF K-NN AND SVM IN DIGITAL IMAGE-BASED TEA LEAF DISEASE CLASSIFICATION
Tea is a commodity with high economic value, but it is susceptible to diseases such as Brown Blight, Red Rust, and Red Spider Mite. The manual identification process currently relies on visual observation, which is time-consuming and prone to error. This research aims to analyze the performance of K-NN and SVM algorithms in classifying tea leaf diseases based on digital images. This research utilized a perfectly balanced dataset of 5,000 images. The research methodology involves image preprocessing and classification using 5-Fold, 10-Fold, and 20-Fold Cross-Validation. The results demonstrate that the SVM algorithm consistently outperforms K-NN across all testing scenarios. Specifically, SVM achieved its highest accuracy of 96.6% using 20-Fold Cross-Validation, whereas the highest accuracy for K-NN was 96.1%. The research concludes that SVM provides superior sensitivity and accuracy for identifying tea leaf diseases, offering a viable solution for automated detection systems in the plantation sector
Copyright (c) 2026 P.P.P.A.N.W.Fikrul Ilmi R.H Zer, Abdi Rahim Damanik, P.A.M. Zidane R.W.P.P Zer http://creativecommons.org/licenses/by-nc/4.0
SENTISTRENGTH-BASED SENTIMENT ANALYSIS TO UNDERSTAND THE LOYALTY AND SHOPPING INTERESTS OF DIGITAL BUSINESS MARKETPLACE
In Indonesia's dynamic digital economy, customer reviews on marketplace platforms like TikTok Shop, Shopee, and Tokopedia are strategic assets for understanding consumer loyalty and online shopping interest. However, extracting information from thousands of informal reviews presents a significant challenge for rapid business decision-making. This study aims to implement an automated sentiment analysis system by comparing three major machine learning algorithms: Logistic Regression (LR), Naive Bayes (NB), and K-Nearest Neighbors (KNN), utilizing the sentiment strength feature of the Indonesian SentiStrength method. The research dataset consists of 881 reviews collected through crawling techniques and subjected to text preprocessing stages including case folding, cleaning, tokenization, stemming, and stop word removal. Automatic labeling using SentiStrength resulted in a sentiment distribution consisting of Neutral (41.9%), Positive (40.2%), and Negative (17.9%). The data was then divided into training and test data to evaluate the performance of the three algorithms. Experimental results show that all three models performed very reliably in classifying customer opinions. Based on an evaluation using the Classification Report, K-Nearest Neighbors (KNN) provided the most optimal results with an accuracy rate of 99%, followed by Naive Bayes with 96% accuracy, and Logistic Regression with 94%. The high performance of these three models demonstrates that using SentiStrength sentiment scores as input features is highly effective in minimizing language ambiguity. Managerially, this research contributes to digital business practitioners' ability to monitor public perception in real-time to formulate more responsive marketing strategies and maintain customer retention in the marketplace ecosystem
Copyright (c) 2026 Widi Astuti, Elly Firasari, F. Lia Dwi Cahyani, Fajar Sarasati, Rendi Septian http://creativecommons.org/licenses/by-nc/4.0
K-MEANS CLUSTERING OF INDONESIAN BANKING STOCKS USING FINANCIAL RATIOS
This study aims to classify banking sector issuers listed on the Indonesia Stock Exchange based on financial ratios, namely Return on Assets (ROA), Return on Equity (ROE), and Loan to Deposit Ratio (LDR), to assist investors in analyzing financial performance and making more objective investment decisions. The method used in this study is Knowledge Discovery in Databases (KDD) with the K-Means clustering algorithm. The dataset was obtained from the annual financial reports of 25 banking issuers for the period 2022–2024. The research stages consist of data selection, data cleaning, data transformation, data mining, and interpretation/evaluation. In the transformation stage, the average values of ROA, ROE, and LDR were calculated and normalized using the Min-Max method. The optimal number of clusters was determined using the Elbow method, while cluster quality was evaluated using the Silhouette Coefficient. The results indicate that the optimal number of clusters is three with a Silhouette Coefficient value of 0.5946. The clustering results consist of a dominant cluster containing 22 issuers with relatively stable financial performance, a second cluster consisting of two issuers characterized by higher lending activity and relatively higher risk, and a third cluster containing one issuer with significantly lower financial performance. These findings reveal latent patterns among banking issuers that may not be easily identified through conventional ratio analysis and provide a clearer structural overview of banking sector performance to support investment evaluation. These insights provide a clearer structural overview of the banking sector and may assist investors in identifying banks with comparable financial risk and performance profiles.
Copyright (c) 2026 Fajar Rizki Aditiya, Nina Sulistiyowati, Siska http://creativecommons.org/licenses/by-nc/4.0
SYSTEMATIC LITERATURE REVIEW OF THE DEVELOPMENT AND RESEARCH DIRECTIONS OF TECHNOLOGY INNOVATION-BASED TECHNOPRENEURSHIP
The rapid development of digital technology has positioned technopreneurship as a strategic approach to strengthening innovation-driven economic competitiveness. However, research on technology innovation-based technopreneurship remains fragmented, and a systematic mapping of research trends and success determinants is still limited. This study aims to analyze research themes, identify key success factors, and highlight future research directions in technology innovation-based technopreneurship. The study employs a Systematic Literature Review (SLR) approach following the PRISMA guidelines to ensure transparency and replicability in the article selection process. A structured literature search was systematically conducted using the Scopus database, focusing on publications published between 2022 and 2025. The search, guided by predefined keywords, yielded a total of 200 articles for initial consideration. After applying predefined inclusion and exclusion criteria, a final corpus of five articles was identified and analyzed using a thematic analysis approach. The results reveal several dominant themes, including digital innovation, entrepreneurial capability development, startup ecosystem collaboration, and sustainability-oriented technopreneurship. The findings also indicate that the success of technopreneurship is influenced by the integration of individual competencies, digital leadership, and collaborative innovation ecosystems involving academia, industry, and government. This study contributes by providing a systematic mapping of recent technopreneurship research and identifying thematic patterns that shape technology-driven entrepreneurship in the digital era, offering academic insights and practical implications for strengthening innovation ecosystems.
Copyright (c) 2026 Rifanny Lysara Annastasya, Herbert Siregar http://creativecommons.org/licenses/by-nc/4.0
INTEGRATED DIGITAL LAUNDRY APPLICATION FOR MSMES: END-TO-END OPERATIONS AND REMOTE MONITORING
Micro, Small, and Medium Enterprises (MSMEs), particularly in service sectors such as laundry businesses, often rely on fragmented and manual operational practices, resulting in inefficiencies and limited managerial visibility. Existing digital laundry applications typically support isolated functionalities and lack end-to-end integration. This study proposes and evaluates an integrated digital laundry application that supports end-to-end operations and remote business monitoring for MSMEs. The research adopts a Design Science Research (DSR) approach combined with Agile-based development. A functional prototype was implemented using Flutter-based mobile applications for customers and staff, along with a web-based dashboard for business owners. The system integrates customer booking, operational workflows, digital payments, automated financial recording, and real-time monitoring within a unified platform. Evaluation was conducted through functional testing, System Usability Scale (SUS) assessment, and business impact analysis involving 10 participants. The system achieved a SUS score of 78.5, indicating good usability. In addition, transaction errors were reduced by approximately 70%, and service processing time improved by 47%. These results demonstrate that the proposed system enhances operational efficiency, financial transparency, and managerial control. This study contributes an empirically validated, domain-specific digital artifact that operationalizes digital transformation and technopreneurship concepts in MSMEs
Copyright (c) 2026 Joharini, Kursehi Falgenti, Intan Saesaria, Hanief Fathul Bahri Ahmad, Mohamad Prastya http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING TOMATO STORAGE-TIME USING SUPPORT VECTOR MACHINE ALGORITHM TO IMPROVE QUALITY AND REDUCE WASTE
Tomatoes are an agricultural commodity that is susceptible to spoilage, with a limited shelf life if not stored under optimal conditions. Optimizing tomato storage time is very important for improving product quality and reducing waste in distribution. This study aims to implement the Support Vector Machine (SVM) algorithm in predicting the optimal storage time for tomatoes, taking into account environmental factors such as temperature and humidity, as well as tomato ripeness. The dataset used consists of tomato images taken at various ripeness levels, as well as environmental data during storage. The SVM model was trained to classify tomato ripeness conditions and predict the optimal storage duration before significant quality deterioration occurs. The results of the study show that the SVM model has high accuracy in classifying tomato ripeness and can be used to predict the optimal storage time, which in turn can extend the shelf life of tomatoes and reduce crop waste. This research contributes to more efficient and sustainable tomato post-harvest management.
Copyright (c) 2026 Rahmat Nurdin, Sunardi, Fitriani, Andi Saenong, Muhammad Rusdi Rahman, Herman Heriadi, Hernawati http://creativecommons.org/licenses/by-nc/4.0
AUTONOMOUS AND EXPLAINABLE DETECTION OF SUSPICIOUS BEHAVIORS IN CONNECTED VEHICLE ENVIRONMENTS THROUGH MULTI-SENSOR VISION
The safety of connected and autonomous vehicles requires intelligent systems capable of detecting suspicious behaviors in real time while providing clear explanations to human operators. This paper presents an innovative framework for the autonomous and explainable detection of suspicious activities around connected vehicles, combining multi-sensor vision, multi-agent reinforcement learning (MARL), and explainable artificial intelligence (XAI). The system relies on lightweight deep learning models (YOLO-tiny, MobileNet) for perception, along with spatio-temporal reasoning to identify abnormal events such as prolonged parking, restricted area crossings, or the placement of suspicious objects. Cooperative decision-making between vehicles and roadside units (RSUs) is managed through MARL. In parallel, an XAI module generates visual and textual explanations to enhance transparency and user trust. The framework has been implemented and evaluated in simulation (CARLA, SUMO/Veins) and on embedded platforms (Jetson Nano/Orin). Results demonstrate an F1-score of 0.91, real-time performance at 7.5 FPS, and a 40% reduction in false positives, confirming the robustness of the proposed system for the cyber-physical security of intelligent transportation systems.
Copyright (c) 2026 Senghor Gihonia Abraham, Rostin Mabela Makengo Matendo, Felicien Masakuna, Celeste Muluba Mfumudimbu Lireh, Blaise Muhala Luhepa http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING RANDOM FOREST FOR HEART DISEASE PREDICTION THROUGH GENETIC ALGORITHM FEATURE SELECTION AND SMOTEENN
Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for early prediction methods capable of accurately identifying individuals at risk. This study aims to develop a heart disease prediction model based on Random Forest by integrating a Genetic Algorithm for feature selection and SMOTEENN to address class imbalance. The research stages include data preprocessing, class balancing using SMOTEENN, feature selection using a Genetic Algorithm, Random Forest model development, and hyperparameter optimization using GridSearchCV, with recall as the primary evaluation metric. The initial Random Forest model demonstrated good predictive performance, achieving a recall of 0.94 for the positive class on the test set, while yielding a lower recall of 0.74 for the negative class, indicating an imbalance in classification performance across classes. Following the application of SMOTEENN, Genetic Algorithm-based feature selection, and hyperparameter optimization, the optimized model achieved a mean recall of 0.91 based on cross-validation. These findings indicate that the proposed approach maintains high predictive sensitivity while incorporating mechanisms to address class imbalance and identify relevant features. Therefore, the integration of Random Forest, SMOTEENN, and Genetic Algorithm demonstrates potential as a predictive approach to support the early detection of heart disease. However, as this study employs a non-local dataset, further validation using datasets representative of the target population is required before the model can be generalized or applied to specific populations.
Copyright (c) 2026 Ruhul Amin, Ummu Radiyah http://creativecommons.org/licenses/by-nc/4.0
ANALYSIS OF THE EFFECTIVENESS OF THE SCRUM METHODOLOGY IN AN EMPLOYEE TASK MONITORING APPLICATION
This study evaluates the effectiveness of the Scrum methodology in improving the productivity of IT development teams at PT. Erajaya Swasembada Tbk, focusing on the development of an employee task monitoring application. Using a mixed-methods approach, it combines qualitative insights from interviews and observations with quantitative metrics from Evidence-Based Management (EBM), including Employee Satisfaction, Lead Time, Velocity, and Innovation Rate. The findings show that Scrum significantly enhances team collaboration and transparency, with an average Employee Satisfaction score of 4.19 out of 5 and Velocity exceeding targets in several sprints (e.g., 167.5% in PROD Sprint 7). However, inconsistent Scrum practices and limited inter-team communication remain challenges. This study provides recommendations to optimize Scrum implementation to improve project outcomes.
Copyright (c) 2026 Dama Sukma Kusuma Diwirya, Nissa Almira Mayangky http://creativecommons.org/licenses/by-nc/4.0
ANALYSIS OF THE CANTEEN INFORMATION SYSTEM AT AN-NAWAWI ISLAMIC BOARDING SCHOOL USING PIECES
The canteen at An-Nawawi Modern Islamic Boarding School Bogor plays a crucial role in supporting students' daily activities; however, its current information system is not optimally integrated to handle the growing student population and operational complexity. This study aims to analyze the problems within the canteen's information system to identify priority areas for improvement. The methodology employs a quantitative analysis using the PIECES framework, which evaluates six variables: Performance, Information, Economy, Control, Efficiency, and Service. Data was collected by distributing a questionnaire to respondents via Google Forms. The research findings show that among the six variables, the Control variable obtained the lowest average score (563.4), indicating a significant weakness in the supervision and data security aspects of the current system. Therefore, it is concluded that the main priority for future development should be focused on strengthening the control aspects to create a more secure, reliable, and well-managed canteen information system.
Copyright (c) 2025 Nurajijah Nurajijah, Syarif Hidayatulloh http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZED DEEP AUTOENCODER WITH L1 REGULARIZATION AND DROPOUT FOR ANOMALY DETECTION IN 6G NETWORK SLICING
The increasing complexity of 6G network slicing introduces new challenges in identifying abnormal behavior within highly virtualized and dynamic network infrastructures. This study aims to address the anomaly detection problem in 6G slicing environments by comparing the performance of three models: a supervised random forest classifier, a basic unsupervised autoencoder, and an optimized deep autoencoder enhanced with L1 regularization and dropout techniques. The optimized autoencoder is trained to reconstruct normal data patterns, with anomaly detection performed using a threshold- based reconstruction error approach. Reconstruction errors are evaluated across different percentile thresholds to determine the optimal boundary for classifying abnormal behavior. All models are tested on a publicly available 6G Network Slicing Security dataset. Results show that the optimized autoencoder outperforms both the baseline autoencoder and the random forest in terms of anomaly sensitivity. Specifically, the optimized model achieves an F1- score of 0.1782, a recall of 0.2095, and an accuracy of 0.714. These results indicate that introducing regularization and dropout significantly improves the ability of autoencoders to generalize and isolate anomalies, even in highly imbalanced datasets. This approach provides a lightweight and effective solution for unsupervised anomaly detection in next- generation network environments.
Copyright (c) 2025 Valencia Claudia Jennifer Kaunang, Nur Alamsyah, Titan Parama Yoga, Acep Hendra, Budiman Budiman http://creativecommons.org/licenses/by-nc/4.0
EVALUATION OF USER PERCEPTIONS AND SATISFACTION THROUGH SENTIMENT ANALYSIS NEWS APPLICATIONS WITH NAIVE BAYES
The development of digital technology has driven the transformation of mass media into online news platforms such as Detikcom, Kompas.id, and CNN Indonesia. Competition among these news applications has created the need to evaluate user perceptions of service quality. This study aims to analyze user sentiment toward the three news applications based on reviews from the Google Play Store. The methods employed include web scraping, text pre-processing, labeling using the IndoBERT model, feature extraction with the TF-IDF method, and sentiment classification with the Naive Bayes algorithm. To address class imbalance in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the Naive Bayes model achieved high accuracy, namely 88.5% for Kompas.id, 88.8% for Detikcom, and 90.8% for CNN Indonesia. The analysis also revealed that positive reviews are more dominant, although recurring criticisms were identified regarding advertisements and technical performance of the applications. The use of Generative AI further assisted in automatically summarizing opinions and sentiment patterns. These findings provide valuable insights for developers in enhancing user experience and refining the features of digital news applications
Copyright (c) 2025 Aldiansyah Kusuma Kusuma, Diaz Aditya Yudha, Muhammad Bahril Afwa, Hanafi Eko Darono http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING PHARMACEUTICAL DISTRIBUTION IN PUBLIC HEALTH CENTERS USING FUZZY C-MEANS CLUSTERING
Efficient drug distribution is fundamental to ensuring the quality of public healthcare services. However, health departments often face challenges with imbalances between drug demand and available supply. This study addresses this issue by applying the Fuzzy C-Means (FCM) clustering algorithm to categorize drug demand levels across 16 public health centers (puskesmas) in Langkat Regency, Indonesia, from 2021 to 2023. Using historical data from 2,400 drug records, the analysis identified five distinct demand clusters: Very Low, Low, Medium, High, and Very High. The results revealed a significant disparity in drug needs, with the "Very High" demand cluster dominating (51.29% of data) in centers like Besitang and Tanjung Selamat, driven by high morbidity rates. In contrast, other clusters were less prevalent, such as the "Low" demand cluster, which was primarily concentrated in the Gebang health center. These findings, visualized using t-SNE plots, highlight significant regional variations in pharmaceutical needs. This data-driven clustering provides a robust framework for the Langkat District Health Office to develop more targeted, efficient, and equitable drug distribution strategies, ultimately improving healthcare service delivery.
Copyright (c) 2025 Syahfitri Nurahma, Eva Darnila, Fajriana http://creativecommons.org/licenses/by-nc/4.0
NAVIGATING ETHICAL LONG-TERM PERSONAL DATA STORAGE: PRIVACY, SECURITY, REGULATORY CHALLENGES, AND SOCIETAL IMPACT REVIEW
Digital data storage is now fundamental to modern society, facilitating the collection and utilization of vast information resources. Yet, the exponential growth of stored data has intensified ethical concerns, particularly regarding long-term retention. This multidisciplinary review synthesizes literature on the ethical implications of long-term data storage, focusing on privacy, security, data ownership, accessibility, sustainability, and societal impact. The study analyzes ethical frameworks encompassing deontology, privacy by design, fair information practices, data minimization, and accountability while examining the responsibilities of organizations, individuals, regulators, technology providers, and ethics committees in upholding ethical standards. Key findings reveal that the ethical landscape of long-term data storage is highly complex and demands a collaborative, multi-stakeholder approach. The review concludes by recommending the integration of robust security measures, enhanced user control and consent mechanisms, sustainable storage technologies, and governance frameworks prioritizing fairness, non-discrimination, and the public good. These recommendations aim to guide stakeholders toward responsible and ethical data stewardship, ensuring that digital data storage practices remain trustworthy and sustainable amid ongoing technological and societal change
Copyright (c) 2025 Simeon Ayoade Adedokun, Theophilus Adedayo Adedokun http://creativecommons.org/licenses/by-nc/4.0
LAND COVER CHANGE PREDICTION USING CELLULAR AUTOMATA AND MARKOV CHAIN MODELS
This research examines the impact of land use change on mobility. Spatial problems arise due to increased activity, population, and transportation in the same space, necessitating the development of modeling strategies. This aligns with SDG 11 on cities and settlements, as well as the PRN's focus on transportation innovation. The urgency of this research lies in its adaptive and sustainable spatial prediction efforts aimed at controlling future land use. This study aims to analyze land use change patterns using the Cellular Automata Markov Chain (CA-Markov) model in Kupang City until 2043. CA-Markov simulations efficiently evaluate land cover changes and movement. The quantitative research method was conducted based on spatial predictions and spatial configuration. Quantum GIS (QGIS) and GeoSOS-FLUS were used to obtain results from each stage. There are three research stages. First, identification of land cover (land use in 2018 and 2023), driving factors (distance to settlements, airports, highways, elevation, slope, slope orientation, rainfall, population density), and conservation areas. Second, standardisation of spatial data. Third, land cover prediction using GeoSOS software (five-year prediction) to identify patterns of land use change. These findings emphasize the importance of using CA-Markov-based spatial predictions as a foundation for adaptive spatial planning to control land-use conversion and maintain sustainable spatial connectivity in Kupang City until 2043.
Copyright (c) 2025 Amandus Jong Tallo, Maria Gratiana Yudith Tallo , Antonius Leonardo Antjak, Maria Imanuela Doko, Maria Anita Christanti Lodang http://creativecommons.org/licenses/by-nc/4.0
ASSESSING SMPIT AJIMUTU GLOBAL INSANI WEBSITE QUALITY USING THE WEBQUAL 4.0 METHOD
Digitalization in the world of education encourages schools to have quality websites to provide online information and learning services. This study aims to measure the quality of the SMPIT Ajimutu Global Insani website using the Webqual 4.0 method, which involves three main dimensions: usability quality, information quality, and service interaction quality. This research method involves a survey of 50 respondents consisting of teachers, students, and parents of students. Data were analyzed descriptively using a Likert scale to evaluate the level of user satisfaction. The results showed that the information quality dimension had the highest score (4.2), followed by service interaction quality (4.0), while usability quality scored the lowest (3.8). These findings indicate that the website content is relevant, but navigation and interface design need improvement. Recommendations are given to improve the quality of the website, including optimizing interactive features and adding multimedia content. The implementation of the results of this study is expected to support the digital transformation of schools more effectively.
Copyright (c) 2025 Andi Saryoko, Faruq Aziz, Instianti Eliyana, Elin Panca Saputra, Bagas Eka Saputra http://creativecommons.org/licenses/by-nc/4.0
A LIGHTWEIGHT AND PRACTICAL PIPELINE FOR CROSS-PROJECT DEFECT PREDICTION USING METRIC-BASED LEARNING
Cross-Project Defect Prediction (CPDP) addresses the scarcity of defect data in new software projects by transferring knowledge from existing ones. However, domain shift between projects remains a major challenge. This study introduces a lightweight and practical CPDP pipeline based on traditional metric features, integrating domain adaptation (CORAL, TCA, TCA+), feature selection, and resampling techniques. Through 120 configurations evaluated on multiple PROMISE datasets, we found that combining TCA or TCA+ with Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) consistently improved F1-Score and Recall on imbalanced datasets. LightGBM demonstrated the most stable performance across projects, while Logistic Regression yielded the highest MCC in specific cases. Principal Component Analysis (PCA) visualizations supported the effectiveness of domain alignment. The proposed pipeline offers a reproducible, cost-efficient alternative to deep learning models and provides actionable insights for defect prediction in resource-constrained environments.
Copyright (c) 2025 Novia Heriyani, Agus Subekti http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF MACHINE LEARNING ALGORITHMS IN THE CLASSIFICATION OF VECTOR-BORNE DISEASES
Developing a predictive model is the objective of this study, focusing on vector-borne diseases using various machine learning methods, including Random Forest (RF), Logistic Regression (LR), k-nearest Neighbors (kNN), Tree (DT), and XGBoost. The main goal is to use oversampling techniques like SMOTE and Random Oversampling to correct the dataset's class imbalance. The dataset was obtained from Kaggle and literature references published in Frontiers in Ecology and Evolution (Endo and Amarasekare 2022), consisting of approximately 9,490 entries with environmental, demographic, and clinical attributes. Dengue Fever is one of the diseases that this study focuses on. Aedes aegypti mosquitoes spread it, and it is a significant health risk in tropical areas. The DT and XGBoost models had the highest accuracy, at 99.2%. Logistic Regression and Random Forest also did well, with 99.1% accuracy. KNN did well, too, but with a lower recall, at 99.0%. The ROC curve gave a complete picture of how well each model classified things. These findings indicate that when combined with proper data handling, machine learning models can significantly improve early detection of vector-borne diseases and support more accurate and timely decision-making in public health interventions.
Copyright (c) 2025 Sukrul Ma’mun, Eni Heni Hermaliani http://creativecommons.org/licenses/by-nc/4.0
DESIGN OF WEB-BASED CAR RENTAL INFORMATION SYSTEM USING EXTREME PROGRAMMING AT CV. NUGROHO
This study discusses the development of a web-based car rental information system for CV. Nugroho Trans Surabaya using the Extreme Programming (XP) methodology. The system was designed to address issues in the previous manual rental process, such as transaction recording on paper, which was prone to errors and delays in data management. The developed system includes key features such as car booking, fleet data management, rental confirmation, and payment integration. Testing was conducted through various methods, including performance testing, usability testing, and security testing. Performance testing using PageSpeed Insights in desktop mode showed the following scores: performance 93, accessibility 84, best practices 93, and SEO 82. Meanwhile, testing with GTmetrix yielded a performance score of 96%, a structure score of 72%, a Largest Contentful Paint (LCP) time of 909 ms, and a fully interactive time of 1.3 seconds, indicating excellent speed and interface stability. In terms of security, testing with Pentest Tools indicated an overall medium risk level, with 1 medium risk finding, 5 low-risk findings, and 13 informational findings, and no high-risk vulnerabilities. The application of the XP method enabled adaptive system development tailored to user needs and iterative changes. This system has proven to increase the company’s operational efficiency by up to 40%, based on faster transaction completion times compared to the manual system. However, some limitations remain, such as user interface constraints and suboptimal integration of online payment channels. For future research, it is recommended to improve user experience, optimize the mobile interface, enhance server security protection, and expand system features to support the broader business growth of CV. Nugroho Trans Surabaya
Copyright (c) 2025 Ramadhan Nugroho Satrio Wibowo, Anton Anton http://creativecommons.org/licenses/by-nc/4.0
EARLY DETECTION OF ROT IN THAI PAPAYA (CARICA PAPAYA) USING THE K-NN METHOD
Determining the category of a plant or fruit involves several criteria. One of the easiest methods to use is morphological criteria, which entails studying the external structure that can be directly observed. However, this approach cannot be regarded as a fixed standard since people's interpretations may vary. To address this, a system was developed to assess the ripeness of Thai papaya fruit, utilizing image processing and the K-Nearest Neighbor (KNN) method. This study analyzes a data set to detect rotten papaya fruit, which is expected to help consumers recognize papaya fruit that is purchased in a perfectly ripe condition, not ripe with certain parts that are rotting. The indicator used to determine the category is the color of the skin of Thai Papaya fruit with an ROI of 600 pixels x 300 pixels by finding the mean RGB value and then calculating it using the Euclidean distance formula. From the results of these calculations, it is expected to get a classification using K-Nearest Neighbor (KNN) to get an image pattern of the level of rottenness on the surface of the papaya. Therefore, by improving the RGB image eliminating noise in the papaya image, and using the K-NN classification of the image pattern obtained from the research results from the sampling data, an accuracy level of 80% was obtained with a range of mean R values: 130,671-169,630, mean G: 106,891-131,895, and mean B: 61,119-100,776 which came from 120 data.
Copyright (c) 2025 Agus Prayitno http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF COVID-19 DETECTION THROUGH TRANSFER LEARNING ON CHEST X-RAY IMAGES
The urgent need for rapid and reliable identification of COVID-19 cases has highlighted the importance of auxiliary diagnostic tools. Chest X-ray imaging serves as a key resource in clinical settings, yet manual interpretation remains susceptible to inter-observer variability and diagnostic delays. This study introduces an optimized deep learning framework based on transfer learning to enhance the detection of COVID-19 from chest X-ray images. The aim is to improve classification accuracy and operational efficiency using pre-trained models tailored for radiographic analysis. We applied transfer learning with fine-tuning to convolutional neural networks pre-trained on large-scale image datasets. The models were adapted and evaluated on a curated collection of chest X-rays representing COVID-19 positive and negative cases. The proposed model achieved a test accuracy of 99% with a loss of 0.15, indicating high diagnostic performance and robustness in distinguishing COVID-19 cases from other pulmonary conditions. Transfer learning offers a viable and efficient strategy for COVID-19 screening using chest X-rays. This approach has the potential to support frontline clinical decision-making and scale public health response during outbreaks.
Copyright (c) 2025 LEBEAU YAMBA MUAMBA, Albert Ntumba Nkongolo, Matendo Mabela Esther, Mbuyi Mpumbu Ben http://creativecommons.org/licenses/by-nc/4.0
ANALYSIS OF CUSTOMER SATISFACTION WITH LIVIN BY MANDIRI VIRTUAL CREDIT CARD FEATURES
Credit cards are a payment method that replaces cash, enabling transactions at various locations that accept them. Credit cards can be physical or virtual, with virtual cards facilitating transactions without the need for a physical card. Bank Mandiri offers virtual credit cards to support digital transactions, providing benefits such as discounts and cashback. Although Bank Mandiri experienced a decline in credit card usage during the pandemic, there has been a post-pandemic upward trend influenced by the features of the virtual credit card in Livin' by Mandiri. This is attributed to customer satisfaction, which has become a key factor in this increase. However, as of now, there has been no research conducted on customer satisfaction regarding the virtual credit card feature in Livin' by Mandiri. Therefore, this study aims to analyze customer satisfaction levels with the virtual credit card feature using the PIECES Method (Performance, Information, Economy, Control, Efficiency, and Service). To support the theoretical framework of this issue, relevant journal reviews and data collection through questionnaires were conducted, with the population size based on Bank Mandiri's annual reports for 2022–2023. The analysis of the questionnaire data, employing the PIECES method and the Likert Scale, revealed the highest scores in the categories of Performance and Service, both scoring 4.20. The Efficiency category scored 4.19, while the Information and Control categories each scored 4.15. The lowest score was observed in the Economy category, with a score of 4.13. Therefore, it can be concluded that customers are satisfied.
Copyright (c) 2025 Iman Ary Hartono, Onny Marleen, Meilani Basaria Siregar http://creativecommons.org/licenses/by-nc/4.0
SELECTION OF MUARA ABU BEACH SAFETY SYSTEM IN KUPANG CITY USING ANALYTICAL HIERARCHY PROCESS METHOD
Kupang City has a large coastal area, most of them live so close to the coastline that there is no longer a coastal buffer zone. One of the beaches close to the settlement is Muara Abu beach, which is located in Oesapa Barat Village, Kelapa Lima Sub-district, Kupang City. This research uses the Analytical Hierarchy Process (AHP) method with the following objectives are Establishing a coastal safety system at Muara Abu beach location based on the decision results of the AHP method used optimally and Analyze the coastal safety system using the AHP method. The criteria used in the selection of coastal safety systems are Waves (history, vulnerability, probability, and threat), Erosion (Shoreline change, scouring at the foot of the building, length of eroded beach), Abrasion (Width of abraded beach, length of abraded beach), Sedimentation (Length of closed estuary, percentage of estuary opening, and influence of sedimentation) and Environment (Sea water quality, coral reefs, mangroves). And the alternative system chosen is structural coastal protection, namely Seawall, Groin and Jetty. The results of calculations with the AHP method show the priority scale for securing Muara Abu Beach can be sorted as follows are Jetty is 46.53%, Seawall is 33.37% and Groin is 20.10% The selection of coastal safety systems using the AHP method provides objective results in determining the best alternative. Jetty is the main solution recommended to be implemented in Muara Abu Beach. Further research is recommended to examine the effectiveness of Jetty implementation in the long term.
Copyright (c) 2025 Dian Johannis http://creativecommons.org/licenses/by-nc/4.0
SENTIMENT ANALYSIS OF PUBLIC OPINION ON TRANSPORTATION SERVICES IN INDONESIA USING MACHINE LEARNING
This study analyzes public sentiment towards transportation services in Indonesia through social media using Naïve Bayes and Support Vector Machine (SVM) algorithms. Data was collected from Twitter using an API with transportation-related keywords over a three-month period. The analysis results indicate that 93.5% of the opinions are neutral, 3.5% are positive, and 3% are negative. The dominance of neutral sentiment suggests potential dataset imbalance or user hesitation in expressing strong opinions. SVM achieved a higher accuracy (100%) compared to Naïve Bayes (92%), which may be influenced by dataset limitations or the model's validation method. Data preprocessing involved several steps, including tokenization, stopword removal, stemming, lemmatization, and handling of missing data to ensure cleaner and more structured text input. These findings highlight the potential of sentiment analysis for transportation policy improvements, providing insights for policymakers and transport service providers. Future research should address data balancing and broader dataset usage to enhance the robustness of findings and support better decision-making in the transportation sector.
Copyright (c) 2025 Fina Sifaul Nufus, Windu Gata http://creativecommons.org/licenses/by-nc/4.0
SENTIMENT ANALYSIS OF JAKLINGKO APP REVIEWS USING MACHINE LEARNING AND LSTM
Application-based transportation services have rapidly developed in recent years, with various studies indicating that service quality and user experience play a crucial role in the adoption of this technology. Previous research has analyzed user satisfaction with digital transportation applications, highlighting factors such as ease of use, service reliability, and the effectiveness of fare systems. This study aims to analyze user sentiment toward the JakLingko application to assess satisfaction levels and identify aspects that need improvement. Utilizing a dataset of 200 user reviews, this research applies data preprocessing techniques to clean and organize the information before performing sentiment classification. The machine learning models used include Naïve Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Long Short-Term Memory (LSTM), categorizing sentiment into positive, negative, and neutral. The analysis results indicate a dominance of negative sentiment in user reviews, reflecting a significant level of dissatisfaction with the application. This highlights major challenges in the implementation of transportation applications, potentially affecting public adoption and trust in the service. Therefore, besides providing insights into user perceptions, this study also proposes improvement strategies aimed at enhancing features and the overall user experience. Given the high proportion of negative sentiment, this research emphasizes the importance of improving the accuracy of sentiment analysis models to generate deeper and more precise insights. These findings can serve as a foundation for designing policies and strategies to improve application-based transportation services, ultimately enhancing service quality and expanding user adoption.
Copyright (c) 2025 Maghfiroh Maulani, Windu Gata http://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE OF THE DELTA MODULATION SYSTEM WITH VARIOUS DELTA STEP SIZES
Delta Modulation Systems are widely used in Analog-to-Digital Converter (ADC) systems. This research aims to determine the optimal delta step size that can be achieved in a Delta Modulation system, as the system's performance is highly influenced by the delta step size. The method used involves simulations with MATLAB to identify the optimal delta step value. The performance of a Delta Modulation system is greatly influenced by the Delta step size. The optimal value in this study was achieved at a Delta step size of 0.4 with the smallest error, namely MSE = 0.1186. If the Delta step size is smaller or larger than this optimal value, the MSE increases. When the frequency of the input signal increases, the Delta step size needs to be increased to follow the changes in the input signal. Otherwise, the MSE will also increase, a phenomenon known as Slope-overload Distortion. Granular Noise occurs when the input signal changes very slowly or is almost constant, while the step size is too large, resulting in a high MSE. To overcome this problem, a dynamic Delta step size is needed, adjusted to the frequency changes of the input signal. Such a system with a dynamic Delta step size is known as Adaptive Delta Modulation.
Copyright (c) 2025 Djadjat Sudaradjat, Andi Rosano http://creativecommons.org/licenses/by-nc/4.0
IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS
This article presents an analysis of agricultural yields in the Democratic Republic of Congo (DRC) using machine learning algorithms. The study is based on around 30,000 records covering several years of agricultural production. Each record includes variables such as seed type, climatic conditions (temperature, rainfall and humidity), soil characteristics (pH, nutrients), farming practices (fertilizer use, irrigation) and yields obtained. The data comes from a variety of sources, including METTELSAT, the World Meteorological Organization (WMO) and WorldClim for climate data, and the DRC Ministry of Agriculture and the FAO for soil and agricultural data. The algorithms evaluated include linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The performance of the algorithms is measured using metrics such as MSE, MAE, RMSE, R² Score and MAPE on three separate case studies (Farm A, Farm B and Farm C). The results show that artificial neural networks (ANNs) perform best, with MSE ranging from 600 to 850, MAE from 12 to 17, RMSE from 24.49 to 29.15, R² Score from 0.92 to 0.95, and MAPE from 8.5% to 10.7%. Next came GBM, random forest regression, SVM and finally linear regression. These results highlight the potential of machine learning algorithms to improve agricultural yield forecasts in the DRC.
Fiston Chrisnovic Balanganayi Kabutakapua
Copyright (c) 2025 Rodolphe Nsimba Malumba, Mardochee Longo Kayembe, Fiston Chrisnovic Balanganayi Kabutakapua, Bopatriciat Boluma Mangata, Trésor MAZAMBI KILONGO, Rufin Tabiaki Tandele, Emmanuel Ntanyungu Ndizieye, Parfum Bukanga Christian http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING
MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.
Copyright (c) 2025 Nur Alamsyah, Venia Restreva Danestiara, Budiman Budiman, Reni Nursyanti, Elia Setiana, Acep Hendra http://creativecommons.org/licenses/by-nc/4.0
FEATURE SELECTION COMPARATIVE PERFORMANCE FOR UNSUPERVISED LEARNING ON CATEGORICAL DATASET
In the era of big data, Knowledge Discovery in Databases (KDD) is vital for extracting insights from extensive datasets. This study investigates feature selection for clustering categorical data in an unsupervised learning context. Given that an insufficient number of features can impede the extraction of meaningful patterns, we evaluate two techniques—Chi-Square and Mutual Information—to refine a dataset derived from questionnaires on college library visitor characteristics. The original dataset, containing 24 items, was preprocessed and partitioned into five subsets: one via Chi-Square and four via Mutual Information using different dependency thresholds (a low-mid-high scheme and dynamic quartile thresholds: Q1toMax, Q2toMax, and Q3toMax). K-Means clustering was applied across nine variations of K (ranging from 2 to 10), with clustering performance assessed using the silhouette score and Davies-Bouldin Index (DBI). Results reveal that while the Mutual Information approach with a Q3toMax threshold achieves an optimal silhouette score at K=7, it retains only 4 features—insufficient for comprehensive analysis based on domain requirements. Conversely, the Chi-Square method retains 18 features and yields the best DBI at K=9, better capturing the intrinsic characteristics of the data. These findings underscore the importance of aligning feature selection techniques with both clustering quality and domain knowledge, and highlight the need for further research on optimal dependency threshold determination in Mutual Information.
Copyright (c) 2025 Rachmad Fitriyanto, Mohamad Ardi http://creativecommons.org/licenses/by-nc/4.0