JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)
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
OPTIMIZING CAYENNE PEPPER PRICE FORECASTING USING HYBRID SARIMAX-LSTM MODEL FOR FOOD SECURITY
The price volatility of cayenne pepper in traditional markets significantly impacts household purchasing power and regional inflation. While traditional statistical models can capture seasonal patterns, they often fail to model complex non-linear fluctuations driven by external factors such as weather anomalies and national holidays. To address these limitations, this study proposes a hybrid SARIMAX-LSTM model. The Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX) component is utilized to model linear structures, seasonality, and the influence of exogenous variables (temperature, rainfall, and holidays), whereas the Long Short-Term Memory (LSTM) component specifically models the remaining non-linear patterns within the residuals. Daily data comprising chili prices, weather metrics, and holiday schedules were employed to train and test the model using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) as performance metrics. Experimental results demonstrate that the proposed hybrid model significantly outperforms the single SARIMAX baseline model, reducing the RMSE by 26.7% (from 11.09 to 8.13) and MAPE by 28.6% (from 23.45% to 16.74%). This approach not only provides a more accurate and robust decision-support tool for price stability but also contributes to the advancement of artificial intelligence-based hybrid methods in the domain of food security.
Copyright (c) 2026 Adi Supriyatna, Mari Rahmawati, Burhanudin Rabbani, Asta Wenang, Sulthan Adly http://creativecommons.org/licenses/by-nc/4.0
SIMPLE ARTIFICIAL INTELLIGENCE APPLICATION FOR CLASSIFYING HOUSEHOLD WASTE AT THE NEIGHBORHOOD WASTE BANK
Waste management remains a critical environmental issue globally, including in Indonesia, where increasing household waste generation creates significant environmental and social challenges, particularly at the neighborhood level. In community-based Waste Banks, manual sorting processes are often inconsistent due to limited human resources and varying levels of public understanding of waste categories. This study aims to develop and evaluate a lightweight, web-based real-time waste detection and classification system to support community-level waste management. The proposed system utilizes the YOLOv8 object detection architecture implemented through the Ultralytics framework with PyTorch as the deep learning backend, integrated with OpenCV for real-time video processing and Streamlit for web-based deployment. The dataset consists of approximately 9,200 annotated images across 24 waste categories, divided into training, validation, and testing sets, with data augmentation applied to improve robustness. Model performance was evaluated using precision, recall, and mean Average Precision at IoU 0.5 (mAP@0.5). The results demonstrate high detection performance, achieving 99.5% mAP@0.5, 99.4% precision, and 100.0% recall, while maintaining stable real-time detection under varying lighting conditions. However, these results are obtained under relatively controlled dataset conditions; therefore, further evaluation in more diverse real-world environments is necessary to ensure generalization capability. The system enables multi-object detection without requiring specialized hardware, making it accessible for neighborhood-level Waste Banks and providing a practical solution for community-based waste management.
Copyright (c) 2026 Irmawati Carolina, Mari Rahmawati, Al Ghoni Achmed. J, Arifin Salam, M. Arif Budiman, M. Daffa Ramadhani http://creativecommons.org/licenses/by-nc/4.0
ENHANCED QR CODE BASED DIGITAL SIGNATURES FOR SECURE DOCUMENT MANAGEMENT TOWARD SUSTAINABLE GREEN INVESTMENT
This study proposes a secure QR Code–based digital signature framework to support paperless legal document management and sustainable green investment. The method integrates SHA-256 hashing and enhanced Multi-Factor RSA encryption, combined with a block-based hexadecimal processing approach to improve computational efficiency while maintaining strong cryptographic security. The generated encrypted signature is embedded into a QR Code, enabling real-time document verification through decryption and hash comparison. The main contribution of this study lies in the integration of optimized Multi-Factor RSA with QR-based authentication, providing both high security and practical verification capability within a unified system. Experimental results demonstrate successful authentication across all test cases, with robust performance under image distortions such as rotation, sharpening, Poisson noise, and Gaussian noise. The proposed method achieves an average Avalanche Effect of 85.78%, indicating strong diffusion and resistance to cryptographic attacks. Furthermore, a sustainability assessment involving 100 authorized respondents from education, corporate, and government sectors produced average scores above 4.0 across green computing, legal reliability, technology adoption, and digital security dimensions. These findings confirm that the proposed framework enhances digital document security while supporting environmentally sustainable and legally compliant paperless transformation.
Copyright (c) 2026 Bagas Dwi Yulianto, Wisnu Wendanto http://creativecommons.org/licenses/by-nc/4.0
STUNTING CLASSIFICATION IN CHILDREN USING VIOLA-JONES AND MULTI-FEATURE FUSION WITH PRE-TRAINED MODELS
Stunting remains a critical public health issue, particularly in developing countries, where early detection plays a vital role in prevention and intervention. Previous studies have generally relied on single-feature approaches, either using handcrafted descriptors or convolutional neural networks (CNNs) alone, which often fail to capture subtle craniofacial differences associated with stunting. This study proposes an image-based classification system for detecting stunting in children using facial analysis. The proposed method integrates Viola–Jones face detection with facial landmarks, Gray Level Co-occurrence Matrix (GLCM), Color Co-occurrence Matrix (CCM), and local descriptors such as SIFT–FAST/ORB, combined with deep features extracted from a pre-trained EfficientNet model. Feature fusion was performed by concatenating handcrafted and deep features before classification using a fully connected layer with Softmax activation. Experimental results demonstrated that the proposed fusion model achieved superior performance compared to single-feature baselines, reaching 98% accuracy, 0.98 precision, 0.97 recall, and an F1-score of 0.98. These findings indicate that the integration of geometric, texture, color, and deep semantic cues effectively enhances sensitivity toward the stunting class and improves model interpretability. The novelty of this study lies in the combination of classical computer vision and deep learning techniques for robust, interpretable, and clinically relevant stunting detection. This approach offers strong potential for developing digital health tools that enable early, non-invasive stunting screening in children.
Copyright (c) 2026 Maylani Kusuma Wardhani, Garin Muhammad Akbar, Christian Sri Kusuma Aditya http://creativecommons.org/licenses/by-nc/4.0
EXPERT SYSTEM FOR CLASSIFYING AUTISM CHILDREN’S INDEPENDENCE LEVEL FROM DAILY ACTIVITY USING FORWARD CHAINING
Children with Autism Spectrum Disorder (ASD) require early intervention during their developmental stages. Currently, the availability of experts capable of accurately classifying the independence levels of children with ASD remains limited. Determining these independence levels is crucial, as it serves as the basis for establishing appropriate early interventions. The system aims to assist specialists in conducting more consistent and efficient assessments. This study contributes a novel application of a forward chaining–based expert system for classifying ASD children’s independence levels, integrating rule-based reasoning with user-centered evaluation, which distinguishes it from previous studies that primarily focus on diagnosis rather than functional independence assessment. Data were collected from three institutions: two Public Special Need Schools and a Regional Technical Implementation Unit of Children with Special Needs in East Java. The dataset consists of 400 records encompassing five daily activities: eating, drinking, brushing teeth, dressing, and taking off clothes. The independence levels are classified into three categories: independent, partially independent, and dependent. This research consists of seven stages, namely data collection, rule based system using forward chaining, database design using CDM and PDM, user interface development, implementation of the Next.js framework system and PostgreSQL database, system testing, and system evaluation. The results of the study showed that the accuracy was 98.5% and the user satisfaction score was 80.85%. These results indicate that the proposed method is effective in supporting therapists in determining the level of independence of children with ASD based on rules established by experts.
Copyright (c) 2026 Indah Werdiningsih, Fachrizal Fikri, Nania Nuzulita, Barry Nuqoba, Sigit Dani Perkasa http://creativecommons.org/licenses/by-nc/4.0
HYBRID RESAMPLING METHOD AND HYPERPARAMETER OPTIMIZATION FOR HIV/AIDS PREDICTION: EVIDENCE FROM EIGHT MACHINE-LEARNING MODELS
HIV/AIDS remains a global health challenge with continuously increasing infection rates, highlighting the importance of accurate prediction models to support prevention and early detection. However, the development of such models is often constrained by class imbalance and irrelevant features. This study aims to improve HIV/AIDS infection prediction by integrating feature selection, data balancing techniques, and eight machine learning algorithms. Feature selection was performed using Mutual Information and Chi-Square to identify the most relevant features. The dataset used was the HIV/AIDS Infection Prediction Dataset from Kaggle, consisting of 2,139 instances and 23 features, with an imbalanced distribution of 1,618 non-infected and 521 infected cases. The dataset was divided into 80% training data and 20% testing data, with resampling applied only to the training set to prevent data leakage. Three resampling scenarios were evaluated: no sampling, SMOTE, and SMOTE-ENN. Hyperparameter tuning was conducted using Bayesian Optimization integrated with 5-fold Cross-Validation to improve model robustness and reliability. Eight machine learning algorithms were evaluated, including Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, LightGBM, K-Nearest Neighbors, and Logistic Regression. The results show that SMOTE-ENN combined with hyperparameter optimization significantly improved model performance. The best model, Gradient Boosting + SMOTE-ENN, achieved 96.1% accuracy, 94.8% precision, 98.4% recall, and 96.5% F1-score. These findings indicate that the proposed integrated framework is highly effective for predicting HIV/AIDS infection and has strong potential to support early diagnosis and data-driven decision-making in healthcare.
Copyright (c) 2026 Lydia Nur Sa'adah, Fatkhurokhman Fauzi, Prizka Rismawati Arum, M Al Haris, Yan Nazala Bisoumi http://creativecommons.org/licenses/by-nc/4.0
CNN MODEL OPTIMIZATION USING MULTI-STAGE DATA AUGMENTATION FOR LOCAL PLANT LEAF DISEASE CLASSIFICATION
Plant leaf diseases are a major factor in reducing agricultural productivity, particularly for local commodities that often lack adequate artificial intelligence-based disease detection systems. This study aims to optimize the performance of a Convolutional Neural Network (CNN) model using the Inception V3 architecture through the application of multi-stage data augmentation to improve the classification accuracy of local plant leaf diseases. The dataset used is PlantifyDR from Kaggle, which has limited data volume and visual variation, requiring an effective augmentation strategy to improve the model's generalization ability. The proposed multi-stage augmentation approach consists of three stages—geometric, photometric, and texture-noise augmentation—that systematically enrich the diversity of training images. Evaluation results show that the proposed model provides significant performance improvements compared to the baseline model. The Inception V3 model with multi-stage augmentation achieved an accuracy of 0.762, an F1-score of 0.727, and a perfect AUC (1.00) across all classes, while the baseline model only achieved an accuracy of 0.595 and an average AUC of 0.877. Accuracy, loss, ROC curve, and confusion matrix analyses confirmed that multi-stage augmentation reduced overfitting and enhanced the model's ability to differentiate disease symptoms across leaf types. Therefore, this study concludes that multi-stage data augmentation is an effective approach for optimizing deep learning models on small and complex datasets, while also providing a significant contribution to the development of more accurate and reliable AI-based plant disease detection systems.
Copyright (c) 2026 Verdi Yasin, Timbo Faritcan P. Siallagan http://creativecommons.org/licenses/by-nc/4.0
UTILIZING TEXT MINING FOR ENCRYPTION ALGORITHM RECOMMENDATION USING CONTENT-BASED FILTERING
The selection of an appropriate encryption algorithm is crucial in ensuring data security, as each algorithm has distinct advantages and disadvantages in terms of speed, efficiency, and security level. Many users struggle to determine the most suitable algorithm due to limited technical knowledge and the vast amount of literature that must be reviewed. Therefore, this study proposes a recommendation system based on Content-Based Filtering (CBF) integrated with text mining to facilitate faster, more accurate, and data-driven algorithm selection. The objective of this research is to develop a recommendation system capable of analyzing the technical characteristics of encryption algorithms from scientific literature and providing relevant suggestions according to user needs. The methodology includes collecting 300 articles from the Garuda Kemdikbud portal using web scraping, performing data preprocessing such as tokenization, stop word removal, and case folding, representing text with TF-IDF, and calculating similarity using Cosine Similarity. The results indicate that the most frequently discussed algorithms are RSA (52 articles), AES (40 articles), and RC4 (25 articles), reflecting research trends focusing on modern public-key and symmetric cryptography. The evaluation results show that the system achieved Precision@3 of 1.0000 and Average Precision (AP) of 0.0583, indicating that the top recommendations generated are highly relevant to user needs. The developed system successfully generated recommendations tailored to specific needs, such as suggesting AES as the primary choice for “fast encryption of sensitive data.” This study demonstrates that combining text mining and CBF is effective in assisting the selection of encryption algorithms through literature-based analysis.
Copyright (c) 2026 Mukti Qamal, Muhammad Iqbal, Yesy afrillia http://creativecommons.org/licenses/by-nc/4.0
DEEP LEARNING APPROACH FOR RECOGNIZING SUBSIDIZED GAS RECIPIENTS USING CONVOLUTIONAL NEURAL NETWORKS
Inaccurate targeting in subsidized LPG distribution remains a persistent policy challenge in Indonesia, where manual verification processes are vulnerable to misuse and administrative error. Addressing this gap, the present study develops and evaluates a biometric identity verification system based on Convolutional Neural Networks (CNNs) to improve the accuracy and accountability of subsidy allocation at the point of distribution. Following the CRISP-DM framework, two CNN architectures with fundamentally different design philosophies were compared: ResNet-IR, optimized for representational depth and recognition accuracy, and MobileFaceNet, designed for computational efficiency on resource-constrained hardware. Both models were sourced from the InsightFace framework as pre-trained models and evaluated on a locally acquired dataset of 111 registered subsidy recipients from Pajang Village, Tangerang City. Evaluation across face identification (1:N) and face verification (1:1) tasks reveals that ResNet-IR consistently outperforms MobileFaceNet, achieving an accuracy of 94.7% with a precision, recall, and F1-score of 0.9043, compared to MobileFaceNet’s accuracy of 93.7% and F1-score of 0.8862. The primary contribution of this work is to demonstrate, for the first time in the Indonesian subsidy distribution context, that deep learning-based facial recognition can serve as a viable, deployable mechanism for biometric identity verification in public service programs offering a technically grounded pathway toward more transparent and equitable subsidy targeting.
Copyright (c) 2026 Achmad Sidik, M. Bucci Ryando, M. Ramaddan Julianti, Agus Rifaldi http://creativecommons.org/licenses/by-nc/4.0
EVALUATION OF REAL-TIME SPEECH RECOGNITION ACCURACY IN INTERACTIVE VIDEO MEDIA FOR DEAF STUDENTS
Deafness is a type of disability characterized by partial or complete hearing loss in one or both ears. Deaf students in higher education face several critical challenges: (1) dependence on oral communication they cannot directly access, (2) limited sign language interpreters in regular classrooms, (3) the absence of media that converts speech into real-time text while displaying the speaker's facial expressions. These conditions cause deaf students to struggle with following explanations, engaging in discussions, and participating actively in the learning process. However, individuals with hearing impairment tend to rely on visual learning, whereas the majority of instructional information is delivered through oral communication. This research aims to develop interactive media based on speech recognition and real-time video as a solution to improve communication in the learning process of deaf students. The novelty of this research lies in the integration of web-based speech recognition with a multi-actor interface (instructor, student, and general user) specifically designed for inclusive education in higher education settings, distinguishing it from conventional solutions. The method used is Research and Development (R&D) with the stages of needs analysis, system design, implementation, and functional testing and performance testing using Word Error Rate (WER). The overall average WER was 19.70%, with the range of WER being 14.05% (from the minimum of 13.22% to the maximum of 27.27%). The results showed that all system features performed as required, and an average WER indicated a good level of accuracy for interactive educational contexts.
Copyright (c) 2026 Hilman Nuril Hadi, Adnan Zulkarnain http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE ANALYSIS OF BAGGING AND BOOSTING MODELS IN ENSEMBLE LEARNING FOR GRADUATION PREDICTION
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
Copyright (c) 2026 Sartika Lina Mulani Sitio, Darmawati, Yuda Samudra http://creativecommons.org/licenses/by-nc/4.0
DIABETIC RETINOPATHY SEVERITY CLASSIFICATION USING GAMMA CORRECTION-BASED IMAGE ENHANCEMENT AND BN-VGG ARCHITECTURE
Diabetic retinopathy (DR) is a diabetes-related condition that can cause vision impairment or vision loss. Accurately identifying the level of DR from retinal fundus images is crucial for early detection. However, poor image quality often degrades classification performance. This study proposes an approach that integrates gamma correction-based image enhancement with a Batch Normalization–Visual Geometry Group (BN-VGG) architecture for multiclass DR severity classification. Gamma correction is applied to improve image contrast, while BN-VGG enhances training stability and feature representation. The proposed method categorizes DR into five classifications: normal, mild, moderate, severe, and proliferative. The enhanced images achieved PSNR of 30.85 and SSIM above 0.86, indicating improved visual quality. The model achieved accuracy at 0.97, sensitivity at 0.92, specificity at 0.98, F1-score at 0.92, Cohen's Kappa at 0.90, and G-Mean at 0.97. The innovative aspect of this study is the incorporation of gamma correction with BN-VGG architecture, demonstrating that image enhancement can significantly improve multiclass DR classification performance without increasing model complexity. The study's results indicate the proposed method's effectiveness for accurate & reliable DR severity classification
Copyright (c) 2026 Indri Ramayanti, Karnadi, Septiani Nadra Indawaty, Muhammad Umar Abdussalam, Malika Zilda, Anita Desiani http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE EVALUATION OF YOLOV5–YOLOV11 MODELS FOR DETECTING NUTRIENT DEFICIENCY IN CHILI SEEDLINGS
Nutrient deficiencies during the seedling stage of chili plants can reduce crop productivity, while conventional identification methods remain subjective and costly. This study compares YOLOv5 to YOLOv11 object detection models for detecting nutrient deficiency symptoms in Bonita chili seedling leaves, including complete nutrition, nitrogen deficiency, phosphorus deficiency, potassium deficiency, and NPK deficiency. The final dataset comprised 4,173 images derived from 1,739 original annotated leaf images through controlled dataset preparation, including split-before-augmentation, laboratory validation of nutrient conditions, and expert-reviewed labeling. All YOLO models were trained and evaluated using the same dataset partition and comparable experimental settings. Performance was assessed using mAP@0.5, computational complexity (FLOPs), inference speed, and model size. The results show that all evaluated models achieved high detection performance, with differences mainly appearing in computational efficiency and the balance between accuracy and speed. YOLOv10s and YOLOv11s obtained the highest mAP@0.5 in this experiment, whereas YOLOv8s showed a competitive balance between accuracy, inference speed, and model compactness. These findings indicate that recent YOLO developments are promising for fine-grained nutrient deficiency detection in computer vision–based precision agriculture.
Copyright (c) 2026 Rangga Pebrianto Rangga, Agus Buono, Heru Sukoco, Aziz Kustiyo, Muhamad Syukur http://creativecommons.org/licenses/by-nc/4.0
EARLY DETECTION OF STUNTING IN CHILDREN USING HSV AND GLCM FEATURES WITH CNN-BASED CLASSIFICATION
Stunting is a chronic nutritional issue in children that can have long-term impacts on physical growth and cognitive development. Early detection is therefore important to support timely intervention. This study develops an image-based stunting detection approach that integrates HSV color descriptors, Gray Level Co-occurrence Matrix (GLCM) texture descriptors, and CNN features extracted with MobileNetV2. Images are preprocessed through resizing and Region of Interest (ROI) cropping based on bounding-box annotations. The handcrafted HSV and GLCM features are fused with CNN features at the feature level through vector concatenation before classification into stunting and non-stunting categories. This design was selected to preserve complementary low-level color-texture information and high-level semantic representations in a single classifier input. The hybrid model achieved a test accuracy of 84.39% with a stunting recall of 83%. Although the results indicate that multimodal visual descriptors can improve classification performance compared with single-feature approaches, the model still shows mild overfitting and was evaluated on a relatively limited dataset. In addition, inference efficiency and robustness to variations in imaging conditions were not yet quantitatively measured. Therefore, the present system should be interpreted as a proof of concept for objective, early, image-based stunting screening by healthcare personnel.
Copyright (c) 2026 Ade Cristian Silalahi, Bonatio Vincent E Hutagalung, Sandy Walfredo Ritonga, Marlince NK Nababan http://creativecommons.org/licenses/by-nc/4.0
ENHANCING ACCURACY OF WEATHER CLASSIFICATION USING DEEP FEATURES AND SUPPORT VECTOR MACHINE
Weather is a determinant of farmers' planting calendar. Farmers usually start planting rice in the rainy season because rice requires sufficient water to produce optimal harvests. The weather is almost unpredictable in certain months, so farmers now look at cloud conditions to predict the season. Seasonal predictions based on cloud imagery can be assisted using Artificial Intelligence methods. Previous research used deep learning via transfer learning, but the results were not optimal. This research dataset is sourced from Kaggle and consists of five classes, namely cloudy, foggy, rainy, shine, and sunrise with a total data of 1500 images. This research proposes that a hybrid deep features and machine learning approach be used to increase the accuracy of the results. The MobileNet deep learning method is used at the feature extraction stage, then for classification using the Support Vector Machine (SVM) method. Experimental results with the Radial Basis Function (RBF) kernel on SVM produced an accuracy of 0.9500 for training data. The evaluation results using testing data produced an accuracy of 0.9667. This result also saw an increase of 4.2% in training data compared to previous research. Through these results, MobileNet-SVM is proven to be able to improve classification accuracy when using a small dataset with 1500 images.
Copyright (c) 2026 Raden Sumiharto, Faisal Dharma Adhinata http://creativecommons.org/licenses/by-nc/4.0
ADAPTIVE AL-QUR’AN MEMORIZATION RECOMMENDATION SYSTEM BASED ON FUZZY LOGIC COGNITIVE MEMORY AND PROFILE MATCHING
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
Copyright (c) 2026 Afifah Fikriyah Dhiya'ulhaq, Muhammad Dzulfikar Fauzi , Pima Hani Safitri http://creativecommons.org/licenses/by-nc/4.0
ABSTRACTIVE SUMMARIZATION FOR INDONESIAN HUMAN TRAFFICKING COURT DECISIONS USING TRANSFORMER MODELS
Indonesian Supreme Court decisions on Human Trafficking (TPPO) are lengthy and structurally complex, rendering manual review inefficient for legal practitioners. Existing abstractive summarization research for Indonesian text concentrates on news and social media, while no publicly benchmarked XSum-style dataset exists for the Indonesian legal domain. This study has two explicit objectives: (i) an XSum-structured legal summarization dataset is constructed from 404 TPPO decisions, and (ii) four fine-tuned Transformer models (T5 Base Indonesia, mT5 Small, DistilBART CNN, BART Large XSum) are benchmarked against extractive and classical abstractive baselines. The method couples an n8n-based PDF extraction pipeline with CSV-sourced verdict statements as reference summaries, followed by fine-tuning and evaluation using ROUGE-1/2/L and BERTScore F1, complemented by paired bootstrap significance testing (n=10,000). Results show T5 Base Indonesia attains the highest ROUGE-L of 39.49 and BERTScore F1 of 74.82, while mT5 Small achieves the highest ROUGE-1 of 44.97, all significantly outperforming Seq2Seq+Attention (ROUGE-1 27.31) and First-2-Sentences (ROUGE-1 10.86) with p<0.001. The contributions are: an XSum-formatted TPPO dataset, an automated extraction pipeline, and a comprehensive benchmark spanning extractive, classical abstractive, and Transformer-based methods. These findings offer practical benefits for legal document analysis and judicial information retrieval in Indonesia.
Copyright (c) 2026 Faradhita Eka Septiana, Febri Bagus Triwibowo, Galih Wasis Wicaksono http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE STUDY OF RESAMPLING TECHNIQUES FOR STUDENT PERFORMANCE PREDICTION USING SMOTE-ENN AND ENSEMBLE LEARNING
This study analyzes the effectiveness of resampling techniques and ensemble learning in addressing class imbalance problems in student performance prediction using the xAPI-Edu-Data dataset from the Kalboard 360 LMS. The class imbalance ratio of 1:1.66 leads to bias in traditional classification models toward the majority class. The study evaluates six resampling methods, including hybrid SMOTE-ENN, combined with nine individual classifiers and three ensemble models (bagging, voting, and stacking). Evaluation was conducted using accuracy, precision, recall, and F1-score with stratified 5-fold cross-validation and hyperparameter optimization through GridSearchCV. The results indicate that the combination of SMOTE-ENN with voting and stacking achieved the best performance of 98.18% across all evaluation metrics and significantly improved minority-class recall, demonstrating its effectiveness for developing early warning systems to identify at-risk students.
Copyright (c) 2026 Eni Heni Hermaliani, Ahmad Zainul Fanani, Heru Agus Santoso, Affandy http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF PREDICTION OF LUNG DISORDERS USING LSTM COMPARISON OF RMSPROP AND ADAM
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.
Copyright (c) 2026 Egi Batubara, Solikhun, Agus Perdana Windarto http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTATION OF A SMART CONTRACT-BASED E-VOTING SYSTEM FOR COMPETITIONS
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.
Copyright (c) 2026 Tony Tan, Eric Valentino, Fredian Simanjuntak http://creativecommons.org/licenses/by-nc/4.0
IMPROVING SENTIMENT ANALYSIS OF WOMEN IN STEM DISCOURSE USING SMOTE-ENHANCED SVM–VADER
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.
Copyright (c) 2026 Dwi Andini Putri, Siti Nurwahyuni http://creativecommons.org/licenses/by-nc/4.0
SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION FOR CONTINUOUS LEARNING: BENEFITS, IMPACTS, AND CHALLENGES
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.
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
NIANTIC LIGHTSHIP BASED AUGMENTED REALITY FOR ENHANCING IPAS LEARNING IN ELEMENTARY EDUCATION
Abstract concepts in Natural and Social Sciences (IPAS), particularly the complete metamorphosis of butterflies, often present visualization challenges for elementary school students. This study addresses this issue by developing an interactive markerless Augmented Reality (AR) application using Niantic Lightship ARDK. The application employs Meshing and Occlusion features to enhance spatial immersion. Following the Multimedia Development Life Cycle (MDLC) method, a one-shot case study design (N=22) was used to evaluate the software’s functional feasibility and the students’ basic cognitive mastery. Functional testing across various mid-range Android devices indicated technical stability, confirming that advanced AR features can operate on standard hardware. Expert validation rated the media as “Highly Feasible” with a maximum score. Preliminary testing on student learning outcomes yielded an average post-test score of 94.09% with a 90.9% classical mastery level. Although limited by a small sample size and the absence of a control group, these findings suggest that the developed AR application is functional and contributes to student comprehension in IPAS learning.
Copyright (c) 2026 Ria Andriani, Muhammad Jaelani Ramadhan Ash Shidiq http://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE EVALUATION OF TRANSFER LEARNING MODELS BASED ON OPTIMIZATION IN AGRICULTURAL PEST CLASSIFICATION
Pests in agriculture lower crop yields and jeopardize the world’s food security. Thus, quick and precise pest identification is crucial for successful pest management. Convolutional Neural Networks (CNN) and other deep learning techniques have made it possible to automatically classify pests thanks to developments in digital image processing and artificial intelligence (AI). Using three optimization algorithms, Adam, RMSprop, and SGD, this study assesses three transfer learning architectures, ResNet50V2, Xception, and EfficientNetB0. This study’s primary contribution is a comparative analysis of CNN architectures and optimization techniques to determine the best configuration for classifying agricultural pests. The dataset, which includes 5494 pest photos from 12 classes, was acquired via Kaggle. A ratio of 80%, 10%, and 10% was used to separate the data into training, validation, and testing sets. The performance of feature extraction and classification was enhanced by applying transfer learning with fine-tuning. According to findings, Xception with Adam and RMSprop has the highest accuracy of 94%. Adam and EfficientNetB0 both achieved competitive results with the same precision. These results suggest that the performance of agricultural pest classification models is influenced by both optimizer and architecture choices.
Copyright (c) 2026 Attarik Mohammad, Sugiyarto Surono, Aris Thobirin http://creativecommons.org/licenses/by-nc/4.0
LEVERAGING CONTINUAL FINE-TUNING FOR EMOTION CLASSIFICATION IN PRODUCT REVIEWS ON MSME SUSTAINABILITY SUPPORT
Automatic analysis of consumer product reviews is essential for understanding granular customer perceptions beyond basic sentiment. While transformer-based models are prevalent in Indonesian sentiment analysis, their adaptation for multi-emotion classification shifting from broad polarities to specific affective states remains underexplored. This study addresses this gap by proposing a Continual Fine-Tuning (CFT) approach to adapt a pre-trained IndoBERTweet model from three sentiment categories into five distinct emotion classes: Happiness, Sadness, Fear, Love, and Anger. The novelty lies in the strategic repurposing of sentiment-oriented weights to capture nuanced emotional representations in Indonesian e-commerce discourse. Experimental results on the PRDECT-ID dataset demonstrate that the proposed CFT model achieves an accuracy of 0.8157 and a weighted F1-score of 0.8118, significantly outperforming traditional neural networks and multilingual baselines. The CFT model demonstrates a 2.13% improvement in accuracy compared to the base IndoBERTweet without continual tuning and a substantial 59.54% lead over the multilingual BERT (mBERT) baseline. Despite limitations concerning the dataset scale (5,400 samples) and inherent subjectivity in emotion labeling, this research provides a robust conceptual framework for model adaptation in the Indonesian NLP ecosystem. These findings suggest that CFT is an efficient strategy for enhancing the emotional intelligence of transformer models, especially in domain-specific tasks where high-quality labeled data is constrained.
Copyright (c) 2026 Galih Setiawan Nurohim, Heribertus Ary Setyadi, Pudji Widodo, Yusuf Sutanto http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE ANALYSIS OF COGNITIVE DIAGNOSTIC MODELS IN POMDP-BASED ADAPTIVE TESTING
Recent progress in learning analytics and educational data mining has accelerated the development of adaptive learning systems, where Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) has emerged as a significant approach. CD-CAT employs Cognitive Diagnosis Models (CDMs) to generate detailed evaluations of student competencies. Nevertheless, increasing numbers of attributes and test items introduce challenges related to the complexity and uncertainty of adaptive policies. This research investigates and compares four cognitive diagnosis models, namely GD-DINA, MIRT, MCD, and KaNCD, within an adaptive testing framework based on a Partially Observable Markov Decision Process (POMDP). The evaluation was conducted using the ASSISTments dataset with Accuracy, AUC, and expected reward as performance metrics. The findings indicate that KaNCD achieved the best overall performance, obtaining the highest diagnostic accuracy (0.7503) and AUC score (0.7410), while also maintaining stable results in POMDP-based adaptive testing (expected reward = 0.792). Although GD-DINA produced the highest expected reward (0.901), its accuracy was comparatively lower. Meanwhile, MCD demonstrated a balanced performance, with high accuracy (0.7464) and strong adaptability of policy (reward = 0.873). Overall, these results suggest that KaNCD offers the most effective balance among accuracy, interpretability, and efficiency in POMDP-based adaptive testing systems.
Copyright (c) 2026 Mohamad Ridho Mubarok, Fandy Setyo Utomo http://creativecommons.org/licenses/by-nc/4.0
DEVELOPMENT OF THE SWARA AUGMENTED REALITY APPLICATION FOR WASTE SORTING EDUCATION IN ELEMENTARY SCHOOLS
The prevailing issue of suboptimal waste management significantly contributes to environmental pollution. While early environmental education is crucial for instilling the habit of waste sorting, conventional methods often fail to engage elementary school students. To address this, this study developed an innovative learning medium integrating Augmented Reality (AR) to provide a more interactive and enjoyable learning experience. This research employed using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Initial Analysis identified user needs, leading to the Design of an application incorporating AR for interactive visualization for waste classification and marker creation. This development resulted in the "Smart Waste Sorting Augmented Reality" (SWARA) learning medium. Implementation involved trials in elementary schools in Bekasi Regency, with sampling determined by simple random sampling and the Slovin formula. The developed SWARA application features three waste marker categories: organic, Inorganic, and hazardous waste. Trial results demonstrated that the application successfully improved students' waste sorting accuracy and enhanced their knowledge of recycling. Evaluation using the User Experience Questionnaire (UEQ) across six main scales (Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, Novelty) showed an overall average result in the "Good" category. The highest scores were for Perspicuity (1.78) and Efficiency (1.74), while the lowest was Novelty (1.15), though still within the good range. The findings demonstrate that AR based learning significantly enhances students’ conceptual understanding, engagement, and practical skills in waste sorting compared to conventional learning methods.
Copyright (c) 2026 Syahbaniar Rofiah, Endang Retnoningsih http://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE EVALUATION OF RECENT YOLO VERSIONS FOR CLASSROOM STUDENT BEHAVIOR DETECTION
The increasing adoption of smart classroom systems underscores the need for automated, objective, and real-time monitoring of student behavior to support effective teaching and learning. Computer vision–based object detection, particularly the You Only Look Once (YOLO) family, has shown strong potential for this task. However, existing studies predominantly evaluate YOLO models in isolation or across different frameworks, resulting in biased comparisons. To address this gap, this study presents a controlled intra-family comparative evaluation of four recent YOLO generations YOLOv8, YOLOv10, YOLOv11, and YOLOv12 across three weight variants (nano, small, and medium), yielding 12 model configurations. All experiments were conducted under a uniform training pipeline and computing environment using an NVIDIA T4 GPU to ensure fair benchmarking. Model performance was assessed using Precision, Recall, F1-Score, mean Average Precision (mAP), inference speed (FPS), and computational complexity. The results reveal a consistent trade-off between detection accuracy and inference speed: YOLOv12m achieves the highest detection accuracy but the lowest FPS due to increased architectural complexity. At the same time, YOLOv10n offers the fastest inference at the cost of reduced reliability for subtle behaviors. Within the scope of the evaluated dataset and controlled classroom setting, YOLOv8s and YOLOv11s demonstrate the most balanced accuracy–speed performance, making them suitable candidates for real-time classroom monitoring under similar conditions. This study provides practical insights for researchers and developers by offering an objective benchmark and model-selection guidance tailored to smart classroom applications, while accounting for dataset and environmental constraints.
Copyright (c) 2026 Mahendra Adiastoro, Febry Putra Rochim, Syahroni Hidayat http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTATION OF RANDOM FOREST FOR ANIMAL PROTEIN CLASSIFICATION THROUGH HYPERPARAMETER OPTIMIZATION
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
Copyright (c) 2026 Ridho Ikhram, Anton Yudhana, Imam Riadi http://creativecommons.org/licenses/by-nc/4.0
IMPACT OF ROI SEGMENTATION METHODS ON ANTHRACNOSE DETECTION IN PAPAYA LEAVES USING RESNET-50
Early detection of anthracnose disease on papaya leaves is important for mitigating crop yield losses, but manual methods are inefficient and prone to subjectivity; this study evaluates the effect of region of interest (ROI) extraction strategies on deep learning-based classification performance. The objective of this study is to compare four classification pipelines: ResNet-50 without segmentation, (M1) ExG+Otsu + ResNet-50, (M2) U-Net + ResNet-50, and (M3) RCNN + ResNet-50, in detecting anthracnose on papaya leaf images. The methods included the use of the public BDPapayaLeaf dataset, pre-processing and augmentation, and evaluation using stratified K-fold cross-validation with evaluation metrics including precision, recall, and F1. The results show that the semantic segmentation-based pipeline, U-Net + ResNet-50 (M2), provides the best performance with an F1-score (macro/weighted) ≈ 0.961 and precision–recall balance in both classes; M0 showed the highest recall for the anthracnose class (≈0.99) while M1 based on color thresholding provided the lowest performance due to sensitivity to lighting variations; M3 (RCNN) was in the middle. The findings recommend the use of segmentation and classification pipelines for field applications, noting the need for dataset expansion and inference optimization for deployment.
Copyright (c) 2026 Shinta Siti Sundari, Ruuhwan, Evi Dewi Sri Mulyani http://creativecommons.org/licenses/by-nc/4.0