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
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
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
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
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
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
SOFTWARE DEFECT PREDICTION TRENDS: A BIBLIOMETRIC ANALYSIS OF MACHINE AND DEEP LEARNING
This study provides a comprehensive bibliometric mapping of global research trends and emerging frontiers in Software Defect Prediction (SDP), emphasizing the integration of machine learning (ML) and deep learning (DL) approaches. Unlike previous bibliometric surveys that focused narrowly on metric-based or short-term analyses, this work offers a broader and more integrated perspective on the intellectual evolution, collaboration patterns, and thematic directions in SDP research. Using data retrieved from the Scopus database and analyzed through Bibliometrix and VOSviewer, the study systematically applied the PRISMA protocol to ensure transparency and replicability. A total of 1,549 publications were examined, revealing a steady increase in scientific output dominated by China, India, and the United States. Thematic and keyword analyses identified five core clusters that trace the paradigm shift from traditional statistical models to advanced ML- and DL-driven predictive frameworks. Emerging topics such as transfer learning, cross-project prediction, and explainable AI (XAI) were identified as promising frontiers shaping the next phase of software quality prediction research. Beyond mapping academic progress, this study contributes strategic insights for researchers seeking to identify research gaps, industry practitioners developing intelligent defect prediction tools, and policymakers designing AI-driven software quality initiatives
Copyright (c) 2026 Harsih Rianto, Omar Pahlevi, Desmulyati, Amrin, Ade Surya Budiman, Budi Supriyadi http://creativecommons.org/licenses/by-nc/4.0
SENTIMENT CLASSIFICATION MODEL BASED ON COMPARATIVE STUDIES USING MACHINE LEARNING TECHNOLOGY
The development of social media has generated large amounts of text data, which is a valuable source for sentiment analysis. This study aims to conduct a comparative study of sentiment classification models on Indonesian-language YouTube comments, specifically comparing lexicon-based approaches, traditional machine learning models (Naive Bayes), and deep learning models (LSTM). Data was collected from YouTube videos themed around the youth generation and demographic bonuses, totaling 9,162 comments that underwent comprehensive text preprocessing. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the LSTM model outperforms Naive Bayes with an accuracy of 78.78% and an average F1-score of 0.79, compared to Naive Bayes, which only achieves an accuracy of 62.08% and an F1-score of 0.54. Although LSTM offers higher performance, the Naive Bayes model remains relevant due to its simplicity and efficiency. This study makes an important contribution to the selection of sentiment classification models for the Indonesian language and suggests the development of hybrid models and the use of contextual features for more optimal results. The LSTM model outperforms Naive Bayes with an accuracy of 82.15% (improved from 78.78% through enhanced regularization) and an average F1-score of 0.84. Comprehensive hyperparameter tuning via grid search and expanded manual annotation (40% of the dataset with κ=0.83) ensures robust model evaluation and reduces labeling bias. The study provides methodologically sound benchmarks for Indonesian sentiment analysis
Copyright (c) 2026 J PRAYOGA, T. Irfan Fajri, Febri Dristyan http://creativecommons.org/licenses/by-nc/4.0
SCRUM AND ITIL-BASED SUPPORT SYSTEM DESIGN AND IMPLEMENTATION AT RAPHA THERESIA HOSPITAL
This research addresses the challenges of manual IT service management at Rapha Theresia Hospital, where existing processes lacked systematic tracking and reporting, leading to operational inefficiencies. The purpose was to design and implement a web-based IT support system for systematic documentation of IT requests and repairs, integrating the Scrum agile development methodology with the ITIL framework, and enabling comprehensive IT performance reporting for management evaluation. The study employed a hybrid methodological approach, combining Scrum for iterative development and ITIL for robust service delivery. Research methods included problem identification, and iterative implementation across four sprints with defined Service Level Agreements (SLAs). Rigorous User Acceptance Testing (UAT) validated the system's functionality. Results show successful implementation of a centralized system managing IT requests, assets, and reports, significantly improving operational efficiency, service reliability, and fostering data-driven decision-making. The system enhanced coordination, transparency, and accelerated service resolution within the IT team.
Copyright (c) 2026 Kasrizal, Sharipuddin, Joni Devitra, Gunardi http://creativecommons.org/licenses/by-nc/4.0
CONVERSION OF GRAPHICAL TO NUMERICAL DATA WITH WEB PLOT DIGITIZER IN OIL RESERVE DETERMINATION
Old oil fields that are to be reactivated have production data only in graphical form, making it difficult to determine remaining reserves. Web Plot Digitizer helps convert graphical data into numerical data for determining oil reserves using the decline curve method. The use of Web Plot Digitizer reduces numerical errors, which impact decline parameters (qi, Di, b) and time efficiency in reserve determination. The purpose of this study is to apply Web Plot Digitizer to convert graphical production data into numerical data and determine oil reserves using decline curve analysis. The novelty of this research lies in the use of digitized graph data as direct input in Decline Curve Analysis (DCA) analysis for oil reserve estimation. The purpose of this research is to apply Web Plot Digitizer in converting production graph data into numerical data, as well as determining oil reserves using decline curve analysis. This research method uses exponential Decline Curve Analysis (DCA), which is applied to old oil fields, production rate data in the form of graphs is converted into numerical data using Web Plot Digitizer. The digitized numerical data is then made into a semilog graph of production rate versus time, then a trend line is taken for the decline in oil production rate and used in determining oil reserves. The analysis results obtained an initial decline rate (Di) value of 0.041 per month and oil reserves are estimated at 5 million barrels of oil (5 MBO), where oil will be exhausted in January 1985 if no workover is carried out. The results of this analysis provide a solution for old oil fields that only have historical graphs without access to numerical data, so that they can still calculate reserves using Decline Curve
Copyright (c) 2026 Lia Yunita http://creativecommons.org/licenses/by-nc/4.0
TOGAF ADM - BASED ENTERPRISE ARCHITECTURE FOR TANTAN DIGITAL VILLAGE
The acceleration of digital transformation in rural governance requires an integrated information system to ensure efficient, transparent, and accountable public services. Yet, many villages including Tantan Village in Muaro Jambi Regency still operate with fragmented applications and redundant data. This study proposes a comprehensive enterprise architecture blueprint for Tantan Village using the TOGAF ADM framework, specifically adapted to the operational and institutional characteristics of rural public administration. Employing a qualitative case study approach, the research develops a four-layered architecture encompassing business, data, application, and technology domains. Theoretically, this study advances the understanding of how enterprise architecture can be localized for small-scale government entities; practically, it provides a replicable model that supports sustainable digital village transformation in Indonesia.
Copyright (c) 2026 Bujangdek, Setiawan Assegaff, Jasmir http://creativecommons.org/licenses/by-nc/4.0
SUPPORT VECTOR MACHINE TO CLASSIFY SENTIMENT REVIEWS ON GOOGLE PLAY STORE
This research addresses the "rating-content discrepancy" on the Google Play Store, where numerical star ratings often conflict with the actual sentiment of textual reviews. Utilizing the CRISP-DM framework, the study evaluates the effectiveness of machine learning in resolving these inconsistencies by classifying Instagram user reviews into positive and negative categories. Two primary algorithms were compared using a dataset of 600 reviews. The Support Vector Machine (SVM) model demonstrated high efficacy with an accuracy of 0.84. In contrast, the K-Nearest Neighbors (KNN) model performed poorly, achieving an accuracy of only 0.48. This significant performance gap highlights SVM's superior ability to handle high-dimensional text data through optimal hyperplane separation. The research further integrated the Streamlit library to create an interactive web interface for real-time sentiment prediction and result visualization. Ultimately, this study confirms that a structured CRISP-DM approach combined with SVM provides a robust solution for automated opinion mining, offering a reliable methodology for future data science applications in social media analysis
Copyright (c) 2026 Agus Nursikuwagus, Suherman, Heri Purwanto, Tono Hartono http://creativecommons.org/licenses/by-nc/4.0
MACHINE LEARNING WITH LIGHTWEIGHT CNN (RESNET-18) FOR EARLY DETECTION OF RICE LEAF DISEASES
Rice leaf diseases such as blast and brown spot significantly threaten rice productivity, especially in agrarian countries like Indonesia. Manual diagnosis methods remain subjective, slow, and inconsistent across field conditions, highlighting the need for an automated and reliable detection system. This study presents a lightweight deep learning framework for the automatic classification of rice leaf diseases from image data. To assess its effectiveness, four Convolutional Neural Network (CNN) architectures ResNet-18, VGG-16, Inception V3, and MobileNetV2 were evaluated. The dataset, obtained from Kaggle, consists of three classes healthy, blast-infected, and brown spot with all images preprocessed through normalization and augmentation before being split into training and validation sets. Experimental results show that ResNet-18 achieves the best overall performance, with 96.94% accuracy, 100% precision, 95.45% recall, an F1-score of 96.18%, and an AUC of 1.0000. Compared to the other architectures, ResNet-18 demonstrates higher stability, stronger generalization, and lower overfitting tendencies while maintaining computational efficiency. The findings indicate that ResNet-18 is a promising lightweight model for practical deployment in mobile or IoT-based agricultural monitoring systems, supporting early disease detection and enhancing local food security efforts
Copyright (c) 2026 Poningsih, Suhendra, Ahmad Zamsuri http://creativecommons.org/licenses/by-nc/4.0
DATA AUGMENTATION EFFECTS ON PROTONET FEW-SHOT YELLOW DISEASE SEVERITY IN CHILI LEAVES
Yellow curling disease in chili plants is one of the leading causes of declining horticultural productivity because it reduces the quality and quantity of crops. Variations in symptoms at each level of severity make the identification process difficult, especially when labeled data is minimal. This study proposes a Prototypical Network-based Few-Shot Learning (FSL) approach with VGG16 architecture as a feature extractor. Five augmentation techniques, namely horizontal flip, rotation, zoom, brightness, and contrast adjustment, were used to increase data diversity in data-scarce conditions. Experiments were conducted with N-way K-shot configurations (2–5 classes; 1, 5, and 10 examples per class) to evaluate the impact of augmentation on prototype representation stability. Results show that increasing the number of examples per class consistently improves accuracy from 34.6% in 5-way 1-shot to 49.4% in 5-way 10-shot without augmentation. However, the use of augmentation decreases performance in higher N-way scenarios because it increases intra-class variability. The t-SNE visualization reinforces this study, where the healthy and severely diseased classes are clearly separated, while the intermediate class shows overlap. The novelty of this study is that it is the first to evaluate the impact of augmentation strategies on prototype representation stability in the agricultural domain with limited data. The results of this Few-Shot Learning approach are effective for plant disease classification despite the limited dataset.
Copyright (c) 2026 Rizal Amegia Saputra, Rusda Wajhillah; Yusti Farlina, Hani Noviani, Saela Nurusysyifa http://creativecommons.org/licenses/by-nc/4.0
PUBLIC SECTOR INNOVATION IN SMART CITIES: A FRAMEWORK OF AMBIDEXTROUS AI GOVERNANCE
The increasing adoption of artificial intelligence (AI) in smart city public services has intensified governance challenges related to accountability, data security, transparency, and risk management. Existing AI governance studies tend to emphasize either innovation enablement or control mechanisms, offering limited guidance on how public institutions can balance both simultaneously. This study addresses this gap by proposing an Ambidextrous AI Governance Framework grounded in COBIT 2019, which systematically integrates exploration-oriented innovation and exploitation-oriented control within public-sector governance. Using a Design Science Research (DSR) approach, the framework was developed and evaluated through a 2025 case study at the Jakarta Provincial Communication and Information Agency. Validation was conducted via expert interviews, document analysis, and simulation-based governance maturity assessments. The findings indicate that baseline AI governance maturity remains at Levels 2–3, while simulation results suggest potential advancement toward Levels 4–5, particularly in risk management (APO12) and security services (DSS05). The study contributes theoretically by operationalizing organizational ambidexterity within public-sector AI governance and practically by offering structured guidance to support secure, transparent, and sustainable AI adoption in smart city environments.
Copyright (c) 2026 Agustinus Fritz Wijaya, Merryana Lestari, Mega Kartika Sari, Rio Ferdiandinata http://creativecommons.org/licenses/by-nc/4.0
TRANSFER LEARNING-BASED CLASSIFICATION OF BELL PEPPER LEAF DISEASES USING VGG16 AND EFFICIENTNETB3 ARCHITECTURES
Diseases affecting pepper leaves can significantly reduce crop productivity and quality, while manual disease identification remains subjective, time-consuming, and prone to error. Therefore, an accurate automated classification system is required to support early disease detection. This study aims to evaluate and compare the performance of a conventional Convolutional Neural Network (CNN) with two transfer learning–based architectures, VGG16 and EfficientNetB3, for classifying pepper leaf images into healthy and bacterial spot classes, as well as to analyze the impact of applying a soft voting ensemble method on classification performance. The dataset was obtained from Kaggle and divided into training, validation, and test sets. Image preprocessing included resizing all images to 224×224 pixels and applying data augmentation to improve model generalization. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that EfficientNetB3 outperforms the conventional CNN and VGG16 models. Furthermore, the application of the soft voting ensemble enhances prediction stability, achieving an accuracy of 99.68% on the test dataset with balanced precision and recall across both classes. These findings demonstrate that the integration of transfer learning and soft voting ensemble methods is an effective approach for image-based pepper leaf disease classification under the experimental conditions, and provides a basis for further validation using more diverse datasets.
Copyright (c) 2026 Siti Nurhasanah Nugraha, Evita Fitri, Muji Ernawati http://creativecommons.org/licenses/by-nc/4.0
DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING
The phenomenon of micro-virality on TikTok illustrates how content can rapidly spread on a small scale before reaching broader virality. Understanding its driving factors is essential for supporting digital marketing strategies, managing content creators, and analyzing social media trends. This study aims to detect and predict the potential of micro-virality in TikTok videos by integrating a social listening approach with machine learning techniques. The dataset consists of approximately 4,000 TikTok posts enriched with 20 features across five categories, including user metadata (author popularity, follower ratio), temporal features (posting time and day), network features (hashtags and mentions), content features (text length and keywords), and contextual elements (trending music and video duration). To ensure objective labeling, a quantile-based threshold was applied, categorizing videos in the top 25% of view counts (≥ 26,300,000 views) as viral, resulting in a class distribution of 24.74% viral and 75.26% non-viral. To address this imbalance, the SMOTENC technique was used to oversample the minority class and enhance data representativeness. Three machine learning algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were implemented. Experimental results show that Random Forest improved from 88% to 92%, XGBoost maintained strong performance at 95%, and ANN increased significantly from 92% to 93% after SMOTENC application. These findings indicate that SMOTENC effectively improves model generalization and reduces bias toward majority classes, supporting more reliable early-stage virality prediction. Overall, the study enriches social media analytics research and provides practical insights for optimizing TikTok content strategies and early trend detection.
Copyright (c) 2026 Ratih Anggraeni, Purwadi, Pungkas Subarkah http://creativecommons.org/licenses/by-nc/4.0
HYBRID TRANSFER LEARNING AND ADVANCED DATA AUGMENTATION FOR MULTICLASS BRAIN TUMOR CLASSIFICATION USING EFFICIENTNET
Accurate Accurate brain tumor diagnosis from MRI images remains challenging due to dataset limitations, class imbalance, and high morphological variability across tumor types. Existing deep learning approaches often yield suboptimal results when trained on small or imbalanced datasets. This study proposes a hybrid learning strategy that integrates transfer learning with advanced data augmentation to classify four brain tumor categories: glioma, meningioma, pituitary adenoma, and normal tissue. Using a large-scale dataset of 7,023 MRI images, the proposed framework incorporates Mixup, CutMix, and a comprehensive augmentation pipeline with an optimized EfficientNet-B0 architecture. The model achieves a test accuracy of 99.05% with F1-scores of 0.99, representing a 4.05 percentage point improvement over a baseline InceptionV3 model (95.00%) and outperforming ResNet-based approaches (93.80%) reported in previous studies. This quantitative improvement demonstrates the effectiveness of combining modern CNN architectures with advanced augmentation strategies. The streamlined architecture and high accuracy make the method suitable for deployment in resource-constrained healthcare environments. These results indicate that hybrid augmentation and transfer learning can deliver clinically meaningful performance for early brain tumor identification, offering a scalable and practical solution for computer-aided medical diagnosis
Copyright (c) 2026 A M H Pardede, Riki Winanjaya, Juni Ismail http://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE EVALUATION OF LIGHTWEIGHT DEEP LEARNING MODELS FOR BORAX-CONTAMINATED MEATBALL IMAGE CLASSIFICATION
Food safety, particularly concerning the use of illegal additives such as borax in processed meat products like meatballs, remains a critical issue in Indonesia. This study analyzes the performance of several lightweight deep learning models based on Convolutional Neural Networks (CNN) and Transformers to classify images of meatballs containing borax, enabling their deployment on resource-constrained devices such as smartphones. Data collection involved capturing 1,429 images of meatballs with and without borax using a smartphone camera under varying lighting conditions and shooting angles. The five main architectures evaluated were ConvNeXt-Nano, Swin-Tiny, ViT-Tiny, MobileViT-XS, and EfficientNet-B0. Hyperparameter optimization was conducted using Optuna, followed by training with a 5-fold cross-validation scheme. Model evaluation metrics included accuracy, precision, recall, F1 score, and inference speed. The results show that MobileViT-XS was the best-performing architecture, achieving 65.93% accuracy, 0.703 precision, 0.706 recall, 0.659 F1 score, and efficient memory consumption (45.94 MB). These findings indicate that a hybrid approach combining the strengths of CNNs and Transformers can achieve an optimal balance between detection accuracy and computational efficiency. Therefore, such models have the potential to be applied as food safety detection systems on devices with limited resources
Copyright (c) 2026 Aryo Michael, Ireve Devi Damayanti http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION IN ZAKAT MANAGEMENT THROUGH THE DEVELOPMENT OF A CHATBOT-BASED MOBILE APPLICATION
The growing awareness of zakat, infaq, and sadaqah (ZIS) in Indonesia calls for intelligent and transparent management systems. This study proposes a chatbot-based mobile application integrated with the SECI knowledge management model to optimize ZIS management and distribution. Using the waterfall software development model, the research includes requirement analysis, system design, chatbot implementation, and validation. The decision-tree-based chatbot enables interactive and personalized guidance for muzakki, while the SECI framework ensures structured knowledge sharing among zakat institutions. Functional and compatibility testing show that the system operates reliably on Android version 10 and above, with intent classification accuracy reaching 92 percent. The findings demonstrate that combining intelligent interaction and structured knowledge management improves transparency, operational efficiency, and institutional learning in digital zakat systems. The proposed framework provides both theoretical and practical contributions to advancing socio-economic management through mobile technology
Copyright (c) 2026 Fajar Delli Wihatiko, Gustian Rama Putra http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE STUDY OF GENERATIVE AI TOOLS IN VISUAL COMMUNICATION DESIGN EDUCATION: CREATIVITY AND USABILITY
The integration of generative artificial intelligence (AI) into visual communication design education presents opportunities to enhance creativity and usability in learning. This study compares the effectiveness of three generative AI tools (MidJourney, DALL·E, and Adobe Firefly) in supporting students’ creative outcomes and perceived usability, while also examining their broader pedagogical role in design education. A quasi-experimental design was conducted with 30 undergraduate students who each produced two poster designs: one manually and one with AI assistance. Creativity was evaluated using the Consensual Assessment Technique (CAT) by expert judges, and usability was measured using the System Usability Scale (SUS). Results showed that AI-assisted designs achieved significantly higher creativity scores (M = 4.3 vs. 3.2, p < 0.05) and usability ratings (range M = 74–82) compared to manual designs, with MidJourney rated highest in creativity and Adobe Firefly in usability. These findings provide empirical evidence that generative AI can act as a catalyst for creativity and usability in design education, offering theoretical insights into human–AI co-creation and practical implications for curriculum integration. Limitations include the small sample size and the study’s focus on a single academic program, which may affect generalizability.
Copyright (c) 2026 Yana Erlyana, M Garry Saputra http://creativecommons.org/licenses/by-nc/4.0
DEVELOPING A MICRO-ENTERPRISE E-READINESS FRAMEWORK: A CASE STUDY FROM INDONESIA
Micro-enterprises play a vital role in Indonesia’s economy but continue to face persistent barriers in adopting information and communication technology (ICT). Understanding E-Readiness is essential for implementing effective digital interventions, especially for micro-enterprises. To address the lack of validated frameworks for assessing digital readiness at the micro-enterprise level, this study develops and empirically tests a novel E-Readiness Assessment Framework specifically designed for micro-enterprises. The proposed conceptual model consists of four dimensions: Technology, Organisation, External Environment, and Human Resources, which are derived from established e-readiness models. This study also proposed the measurement indicators that have been adapted to the characteristics of micro enterprises. Using quantitative data from 641 food and beverage (F&B) micro-enterprises in Batu City, Indonesia, exploratory factor analysis (EFA) was applied to evaluate construct validity. Despite the elimination of five indicators due to insufficient communality value, the overall model structure remained statistically valid. Subsequently, factor analysis was succeeded by the calculation of E-Readiness index using weighted aggregation and normalisation methods. The resulting E-Readiness Index for Batu City was 46.47, with 57.10% of enterprises classified as “Not Ready,” primarily due to technological and infrastructural limitations. The proposed model in this study efficiently assesses e-readiness at the micro-enterprise level and is adaptable for application in different regions or business sectors. This model also provides valuable insights for policymakers in formulating targeted digital support initiatives. Future research may consider expanding the scope of indicators and validating the model using confirmatory analysis.
Copyright (c) 2026 Nori Wilantika, Fitri Kartiasih, Ernawati Pasaribu, Aisha Arthamevia, Yunarso Anang, Achmad Nizar Hidayanto http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF MLP-NN FOR MANGO LEAF DISEASE PREDICTION USING IMAGE-BASED FEATURE EXTRACTION
Mango (Mangifera indica Linn.) is a nutrient-rich fruit, yet leaf diseases caused by microorganisms can significantly reduce crop productivity. Early detection is essential to prevent further damage and support effective disease management. This study proposes an optimized mango leaf disease prediction model using a multi-layer perceptron neural network (MLP-NN). Image-based feature extraction is performed using the Inception v3 architecture to obtain high-level color and texture features that improve classification performance. Unlike previous studies that rely solely on manually engineered features or full CNN training, this research introduces a hybrid approach that integrates deep feature extraction with MLP-NN optimization, offering a lightweight yet highly accurate alternative. Several hyperparameter combinations, including activation functions (ReLU, tanh, sigmoid) and optimization algorithms (Adam and SGD), were evaluated using the Orange platform. The optimized MLP-NN model with ReLU and Adam achieved the highest accuracy of 93.5%, demonstrating better stability and training efficiency compared to other configurations. These findings highlight the novelty and advantages of the proposed method, showing improved accuracy with lower computational cost relative to many existing approaches. This study provides an efficient solution for mango leaf disease prediction and supports future development of automated plant disease detection systems
Copyright (c) 2026 Budi Triandi, Lili Tanti http://creativecommons.org/licenses/by-nc/4.0