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All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Sistem Informasi dan Teknologi Jaringan International Journal of Basic and Applied Science Jurnal Media Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) DISTANCE: Journal of Data Science, Technology, and Computer Science Jurnal Mandiri IT Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Sistem Informasi Galuh Jurnal Ilmu Pendidikan dan Humaniora Jurnal Pengabdian Masyarakat Nauli Journal of Defense Technology and Engineering
Manurung, Jonson
Universitas Pertahanan Republik Indonesia, Bogor, Indonesia
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation

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Optimization of quicksort algorithm for real-time data processing in IoT systems with random pivot division and tail recursion Laia, Firdaus; Wau, Ferdinand Tharorogo; Manurung, Jonson
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.401

Abstract

Real-time data processing in Internet of Things (IoT) systems requires efficient sorting algorithms to handle large and ever-increasing volumes of data. The QuickSort algorithm is often used due to its speed and efficiency, but on large pre-sorted datasets, this algorithm can experience performance degradation due to poor pivot selection and the use of regular recursion. This study aims to optimize the QuickSort algorithm through random pivot selection and the application of tail recursion to improve sorting efficiency on IoT datasets. Experiments were conducted by comparing the standard QuickSort version and the optimized version, using synthetic and real-time IoT datasets from temperature and humidity sensors. Performance evaluation was based on execution time and memory usage metrics. The results show that QuickSort with random pivot and tail recursion can reduce execution time by up to 27% and memory usage by up to 18% compared to the standard QuickSort implementation. These findings indicate that the proposed algorithm is more efficient for IoT applications that require real-time data processing, and has the potential to be applied in distributed data systems and parallel processing for large-scale scenarios.
Optimizing the performance of the K-Nearest Neighbors algorithm using grid search and feature scaling to improve data classification accuracy Manurung, Jonson; Saragih, Hondor; Prabukusumo, Muhammad Azhar; Firdaus, Eryan Ahmad
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.466

Abstract

The performance of distance-based classification algorithms such as K-Nearest Neighbors (KNN) is highly dependent on proper feature scaling and optimal parameter selection. Without systematic optimization, KNN may experience decreased accuracy due to feature scale disparities and suboptimal k-values. This study aims to enhance the performance of the KNN algorithm through the integration of Feature Scaling and Grid Search Cross-Validation as a parameter optimization strategy. The research employs the Breast Cancer Wisconsin Dataset, divided into 80% training and 20% testing data. Feature normalization was performed using StandardScaler, while Grid Search was applied to determine the optimal combination of parameters, including the number of neighbors (k), weighting function (weights), and distance metric (metric). The optimized KNN configuration with k = 9, weights = distance, and metric = manhattan achieved an average accuracy of 97.19%, outperforming the baseline accuracy of 93.86%. A paired t-test confirmed that the improvement was statistically significant (p < 0.05). These findings demonstrate that the synergy between feature scaling and parameter tuning can substantially improve both the accuracy and stability of KNN models. The scientific novelty of this study lies in the systematic integration of normalization and parameter optimization through Grid Search, providing an empirical framework that enhances KNN’s robustness across datasets with heterogeneous feature distributions. The proposed approach is recommended for medical data classification and can be adapted to other domains with heterogeneous numerical feature distributions.
Optimization of XGBoost hyperparameters using grid search and random search for credit card default prediction Firdaus, Eryan Ahmad; Manurung, Jonson; Saragih, Hondor; Prabukusumo, Muhammad Azhar
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.468

Abstract

This study explores the optimization of the Extreme Gradient Boosting (XGBoost) algorithm for credit card default prediction through systematic hyperparameter tuning using Grid Search and Random Search methodologies. Utilizing the publicly available Default of Credit Card Clients dataset from the UCI Machine Learning Repository, the research focuses on enhancing model performance by fine-tuning critical parameters such as learning rate, maximum tree depth, number of estimators, subsample ratio, and column sampling rate. The baseline XGBoost model achieved an accuracy of 0.8118, while the tuned models using Grid Search and Random Search improved the accuracy to 0.8183 and 0.8188, respectively. Although the improvement appears modest, the optimized models exhibited enhanced balance between precision and recall, particularly in identifying defaulters within an imbalanced dataset—an essential aspect in credit risk assessment. The results demonstrate that systematic hyperparameter optimization not only improves predictive performance but also contributes to model stability and generalization. Moreover, Random Search proved to be more computationally efficient, achieving near-optimal performance with fewer evaluations than Grid Search, thereby emphasizing its practicality for large-scale financial risk modeling applications. The novelty of this study lies in the comparative evaluation of two optimization techniques within the context of financial risk prediction, providing practical insights into how efficient hyperparameter tuning can enhance the reliability and scalability of machine learning models used in real-world credit risk management systems.
Hyperparameter optimization of graph neural networks for predicting complex network dynamics using bayesian meta-learning Saragih, Hondor; Manurung, Jonson; Prabukusumo, Muhammad Azhar; Firdaus, Eryan Ahmad
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.469

Abstract

The rapid growth of graph-structured data in domains such as transportation, social networks, and biological systems has increased the demand for more adaptive and efficient Graph Neural Network (GNN) architectures. However, GNN performance remains highly sensitive to hyperparameter configurations, which are often tuned through computationally expensive manual or heuristic methods. This study proposes a novel Bayesian Meta-Learning (BML)-based framework for hyperparameter optimization of GNNs aimed at improving the prediction accuracy of complex network dynamics. The framework integrates Bayesian optimization with a meta-learning prior adaptation mechanism, enabling the model to learn optimal hyperparameter distributions across multiple graph tasks. Experimental evaluations conducted on three benchmark datasets—Cora, Citeseer, and PubMed—comprising up to 20,000 nodes with diverse structural complexities, demonstrate that the proposed BML-GNN framework achieves faster convergence, lower validation loss, and higher predictive accuracy than both baseline GNN and traditional Bayesian Optimization approaches. Quantitatively, the BML-GNN model attains an R² score exceeding 0.97 with a significant reduction in RMSE, confirming its strong generalization capability. Although the method shows notable performance improvements, its computational overhead during meta-training and reliance on well-defined prior distributions represent potential limitations. Overall, the integration of Bayesian Meta-Learning provides a robust, scalable, and uncertainty-aware optimization strategy that advances the development of reliable GNN models for complex network modeling and intelligent system design.
Swarm driven automatic feature selection and classification framework for parkinson voice data Prabukusumo, Muhammad Azhar; Saragih, Hondor; Manurung, Jonson
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.470

Abstract

Parkinson’s disease (PD) severely impairs motor and vocal functions, and early detection is crucial for effective intervention. Conventional diagnostic procedures remain subjective and time-consuming, highlighting the need for automated, data-driven approaches. This study aims to develop an intelligent and fully automated framework integrating Particle Swarm Optimization (PSO)–based feature selection with ensemble machine learning classifiers for PD detection using voice data. The proposed Swarm-Driven Automatic Feature Selection and Classification Framework (SAFSCF) automates data preprocessing, adaptive feature optimization, and classification within a unified pipeline. The framework was evaluated on the Parkinson’s Speech Dataset comprising 743 numerical features. Baseline models achieved accuracies of 0.7738 (Logistic Regression), 0.8651 (Random Forest), and 0.8690 (Gradient Boosting). After PSO optimization, the feature set was reduced by nearly 50% to 382 attributes, achieving a test accuracy of 0.8421 slightly higher than the full-feature model (0.8355). Convergence plots confirmed that PSO effectively minimized the fitness function while maintaining high classification stability. Feature importance analysis revealed that the most discriminative attributes were derived from log energy, Teager Kaiser energy operators (TKEO), MFCCs, Shimmer, and entropy-based features biomarkers known to reflect Parkinsonian speech degradation. These findings demonstrate that the proposed framework enhances computational efficiency and interpretability, offering a reproducible and scalable solution for non-invasive, voice-based PD diagnosis.
Implementation of gaming In the cloud through construct engine applications on heroku infrastructure Phatoni, Khaerul Imam; Adha, Rochedi Idul; Manurung, Jonson; Prabukusumo, M Azhar; Piliang, Rizqullah Aryaputra; Nasyira, Muhammad Sulthan
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.478

Abstract

This study presents a performance analysis of real-time multiplayer gaming through web-based game engines deployed on cloud Platform-as-a-Service infrastructure, specifically examining Construct 3 integration with Heroku's managed deployment platform. A multiplayer Pong game was developed to evaluate the viability of browser-based gaming architectures for real-time applications, utilizing WebSocket communication protocols, room-based session management, and hybrid client-server prediction models. The implementation demonstrates five architectural components: secure WebSocket connection establishment, 60 frames-per-second server-side game state synchronization, minimal cloud deployment configuration, scalable room management supporting multiple concurrent sessions, and responsive input handling with client-side prediction. Performance evaluation with ten concurrent game instances revealed exceptional resource efficiency, consuming maximum 34 megabytes memory with dyno load averages of 0.01, validating JavaScript-based server implementations for real-time gaming applications. The results indicate that web-based game engines can achieve performance characteristics traditionally associated with dedicated server architectures while maintaining significant advantages in development velocity, deployment simplicity, and operational efficiency. The study contributes evidence supporting the democratization of multiplayer game development through accessible web technologies, demonstrating that traditional barriers between browser-based and native gaming applications are diminishing as platform capabilities mature. These findings establish benchmarks for web-based multiplayer gaming performance and provide foundation for future research in cloud-based game development paradigms.
Energy consumption prediction and optimization for Ki Hajar Dewantara student dormitory Using Extreme Gradient Boosting (XGBoost) algorithm Sinaga, Jeremia; Manurung, Jonson; Prabukusumo, M. Azhar
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.482

Abstract

Energy consumption optimization in student dormitories requires accurate prediction and strategic intervention strategies. This study presents a comprehensive prediction and optimization system for energy usage at Ki Hajar Dewantara Student Dormitory, Indonesia Defense University, utilizing Extreme Gradient Boosting (XGBoost) algorithm integrated with temporal operational scheduling features a novel approach for institutional dormitory energy forecasting. The system analyzes over 3,900 electrical devices across three dormitory buildings, incorporating temporal features and operational schedules to predict hourly energy consumption. The XGBoost model demonstrates excellent prediction performance with R² = 0.9482 and MAPE = 10.24%, significantly exceeding established benchmarks for building energy forecasting. Feature importance analysis reveals working hours as the dominant factor (>85%) influencing consumption patterns, followed by occupancy rate and temperature. The analysis identifies air conditioning systems as the primary energy consumer, accounting for over 80% of total consumption. The optimization framework identifies potential energy savings of approximately 28% through strategic device replacement and schedule modifications, translating to annual cost savings of over Rp 600 million with economically viable return on investment periods. This machine learning-based approach demonstrates practical applicability for student dormitory energy management and provides a replicable methodology adaptable to diverse residential institutional buildings in tropical climates.
Decision-making model for cadet selection using the AHP TOPSIS method Tsany, Tazky; Manurung, Jonson; Prabukusumo, M. Azhar
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.486

Abstract

Cadet selection in defense institutions requires a comprehensive assessment process because it must cover the academic, psychological, health, physical, and ideological integrity aspects of prospective participants. This multidimensional complexity poses challenges in producing decisions that are objective, consistent, and free from assessor bias. Therefore, a quantitative approach-based evaluation model is needed that can integrate all assessment components in a measurable manner. This study developed a cadet selection decision-making model using a combination of the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. AHP is used to determine the weight of importance of the seven main criteria: Academic Potential Test, Academic Interview, Psychological Test, Ideological Mental Test, Ideological Mental Interview, Health Test, and Physical Test, while TOPSIS is used to determine the ranking of candidates based on their proximity to the ideal profile of a cadet. The results of the study show that the integration of AHP–TOPSIS is able to provide evaluation results that are more objective, transparent, and accountable than conventional assessments. In addition to formulating a selection model, this study also discusses alternative methods in multi-criteria decision making as material for developing a selection system in the future. Overall, this model is expected to become a scientific basis for defense institutions in improving the quality and accuracy of the cadet selection process.
Mixed integer linear programming for cadet dormitory placement at Indonesia Defense University Pradhana Putra, I Made Aditya; Manurung, Jonson; Saragih, Hondor
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.487

Abstract

Cadet dormitory placement at Indonesian Defense University was currently performed manually by administrative staff, resulting in potential inefficiencies in room assignments regarding walking distance, study program cohesion, and cadet preferences. This research developed a Mixed Integer Linear Programming (MILP) optimization model to automate and improve the dormitory assignment process for military education institutions. The general framework addresses 1,550 cadets distributed across four cohorts and 13 study programs in   dormitory buildings with standardized configurations (3 floors, 25 rooms per floor, 2 cadets per room). The MILP model incorporated three objectives: minimizing total walking distance to academic facilities, maximizing study program cohesion by concentrating programs within specific floors, and maximizing cadet floor preference satisfaction. The model was formulated with configurable weight parameters (w₁, w₂, w₃) enabling administrators to balance competing objectives according to institutional priorities. A validation case study with 38 male cadets from two study programs demonstrated computational feasibility, with the CBC solver achieving optimal solutions in 0.34 seconds (strict constraint approach) and 0.11 seconds (maximum occupancy approach) on standard desktop hardware, both with 0.00% MIP gap confirming proven optimality. The validation study compared two policy approaches: strict constraint enforcement achieving 95% room occupancy with 20 rooms, and maximum space utilization achieving 100% occupancy with 19 rooms. This research contributed the first application of MILP optimization to military education dormitory management in Indonesia, providing a scalable framework with empirical validation for computational tractability and a replicable methodology for resource allocation optimization in defense institutions.
Mapping monthly consumer purchasing patterns at the UNHAN RI Cooperative using time series analysis and LSTM Sigalingging, Miranda Bintang Maharani; Prabukusumo, M. Azhar Prabukusumo; Manurung, Jonson
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.488

Abstract

This study investigated the monthly purchasing patterns of consumers at Koperasi Unhan RI and developed forecasting models to support data-driven inventory and procurement planning. Historical cooperative sales data from 2020–2024 were analyzed using time series decomposition, autocorrelation analysis, ARIMA modeling, and a Long Short-Term Memory (LSTM) neural network. The analysis revealed a clear upward trend and strong annual seasonality, with consistent demand peaks occurring in December. The ARIMA model achieved significantly lower prediction errors than the LSTM model and successfully captured both trend and seasonal components. A 12-month forecast for 2025 was then generated to support operational decision-making. The forecasting results provide practical managerial insights for cooperative management, particularly in optimizing inventory levels, scheduling procurement, and anticipating seasonal demand fluctuations. The novelty of this study lies in the comparative application of classical time-series and deep learning approaches within a cooperative context using limited historical data, demonstrating that ARIMA remains a robust and interpretable solution for small to medium-sized cooperative environments. This research concludes that time series analysis combined with ARIMA forecasting effectively mapped consumer purchasing patterns and produced actionable demand predictions for the subsequent year.
Co-Authors Adam Mardamsyah Adha, Rochedi Idul Agus Firmansyah Agustina Simangunsong Al Hashim, Safa Ayoub Amran Sitohang Andri Budiman, Mohammad anindito anindito Bagus Hendra Saputra Bagus Hendra Saputra Barus, Nadela Bosker Sinaga Bosker Sinaga Bosker Sinaga, Bosker Sinaga Br Sitepu, Siska Feronika Br Tarigan, Nera Mayana Dhaifullah, Rendi Hanif Erika Novianti Eryan Ahmad Firdaus Febrian Wahyu Christanto Ferdinand Tharorogo Wau Firdaus Laia Firdaus Situmorang Hanan, Rohman Ali Hardy Priyatno Ambarita Harpingka Sibarani Hasugian , Paska Marto Hengki Tamando Sihotang Hidayati, Ajeng Hondor Saragih Hondor Saragih I Made Aditya Pradhana Putra Jeremia Paskah Sinaga Johanes Perdamenta Sembiring Kadin Darlianto Tinambunan Kanur L. P. Situmorang Logaraj Logaraj Logaraj, Logaraj M Azhar Prabukusumo Maria Siahaan Maya Theresia Br. Barus Maya Theresia Br. Barus Merlin Helentina Napitupulu Mina Kumari Muhammad Azhar Prabukusumo Muthmainnah, Ihmatull Nasyira, Muhammad Sulthan Nick Holson M. Silalahi Nuriansyah, Agam Pandiangan, Boyner Phatoni, Khaerul Imam Piliang, Rizqullah Aryaputra Poltak Sihombing Prabukusumo, M Azhar Prabukusumo, M. Azhar Prabukusumo Prabukusumo, Muhammad Azhar Pradhana Putra, I Made Aditya Putra, Muhammad Ridho Alghifari Ramen, Sethu Rinaldy Chaniago Sawaluddin Sawaluddin, Sawaluddin Sethu Ramen Sethu Ramen, Sethu Ramen Sidiq, Maulana Sigalingging, Miranda Bintang Maharani Sihombing, Agus Putra Emas Sihotang, Amran Silalahi, Monalisa Hotmauli Simangunsong, Humala Sinaga, Jeremia Sinaga, Ryan Fahlepy Sri Kumala Sari Tsany, Tazky Uzitha Ram Vernando, Deden