JCEIT: Journal of Computer Engineering and Information Technology
The Journal of Computer Engineering and Information Technology (JCEIT) is a peer-reviewed scientific journal published by Karya Techno Solusindo, with its first issue released in 2024. The primary goal of this journal is to provide a reliable and high-quality platform for publishing articles that reflect the latest and most impactful advancements in computer science. Through its rigorous Double Blind Review process, JCEIT ensures that only the most significant and relevant contributions are selected, thereby promoting cutting-edge research that helps advance the field. The journal serves as an essential resource for researchers, practitioners, and academics who seek to stay informed about new developments and groundbreaking ideas within computer science. JCEIT is published consistently three times a year, with issues released in November, March, and July. This regular publication schedule is designed to provide timely and continuous access to new research and developments, allowing readers to keep pace with the rapidly evolving landscape of computer science. The journal’s triannual frequency also ensures that it can accommodate a diverse range of high-quality submissions, giving authors a dependable and well-regarded venue to share their work with the academic and professional communities. JCEIT is officially registered with ISSN 3089-106X (online) based on the decree of the Direktur Pusat Nasional ISSN Indonesia No. 3089106X/II.7.4/SK.ISSN/02/2025, ensuring its credibility and recognition as an authoritative scientific publication.
Analysis of Accuracy and Computational Efficiency of Android-Based Palm Maturity Classification System Using K-Nearest Neighbor Method
Accurately determining the ripeness of oil palm Fresh Fruit Bunches (FFB) is crucial to maximizing the quality of Crude Palm Oil (CPO). Conventional methods rely on visual assessment or laboratory tests that are destructive, expensive, and inefficient at the field scale. This study proposes an android-based, non-destructive FFB ripeness classification system that uses color feature extraction and the K-Nearest Neighbors (K-NN) algorithm. A total of 65 FFB images directly from the tree are divided into training data (50 images) and test data (15 images) with three ripeness classes: raw, ripe, and overripe. Features are extracted through multilevel color thresholding segmentation, then calculated using RGB color averages, RGB normalization, and four Vegetation Indices (NDVI, SAVI, EVI, VARI). The test results show that the combination of Vegetation Indices with K-NN achieves the highest accuracy, 98% on the training data and 93.33% on the test data, with only one classification error. The system runs on-device with an average computation time of 3.3 seconds per image, demonstrating sufficient efficiency for real-time applications in plantations. This study concludes that the mobile approach based on K-NN and the Vegetation Index is worthy of adoption as a fast, accurate, and non-destructive harvest decision-support tool. However, further lighting optimization and dataset expansion are still needed for broader generalization. REFERENCES Alfatni, M. S. M., Mohamed Shariff, A. R., Ben Saaed, O. M., Albhbah, A. M., & Mustapha, A. (2020). Colour Feature Extraction Techniques for Real Time System of Oil Palm Fresh Fruit Bunch Maturity Grading. IOP Conference Series: Earth and Environmental Science, 540(1). https://doi.org/10.1088/1755-1315/540/1/012092 Bannari, A., Asalhi, H., & Teillet, P. M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. IEEE International Geoscience and Remote Sensing Symposium, 5, 3053–3055 vol.5. https://doi.org/10.1109/IGARSS.2002.1026867 Barrera, K., Rodellar, J., Alférez, S., & Merino, A. (2023). Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks. Computer Methods and Programs in Biomedicine, 240. https://doi.org/10.1016/j.cmpb.2023.107629 Boucetta, C., Hussenet, L., & Herbin, M. (2023). Improved Euclidean Distance in the K Nearest Neighbors Method. In U. R. Krieger, G. Eichler, C. Erfurth, & G. Fahrnberger (Eds.), the 23rd International Conference on Innovations for Community Services (pp. 315–324). Springer Nature Switzerland. Burge, M. J. (2022). Digital Image Processing: An Algorithmic Introduction. Springer Nature. Cherie, D., Herodian, S., Ahmad, U., Mandang, T., & Makky, M. (2015). Optical characteristics of oil palm fresh fruits bunch (FFB) under three spectrum regions influence for harvest decision. International Journal on Advanced Science, Engineering and Information Technology, 5(3), 255–263. https://doi.org/10.18517/ijaseit.5.3.534 Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/https://doi.org/10.1016/0034-4257(88)90106-X Khan, A. I., & Al-Habsi, S. (2020). Machine Learning in Computer Vision. Procedia Computer Science, 167(2019), 1444–1451. https://doi.org/10.1016/j.procs.2020.03.355 Makky, M. (2016). Trend in non-destructive quality inspections for oil palm fresh fruits bunch in Indonesia. International Food Research Journal, 23(1), 81–90. https://doi.org/10.4149/neo_2010_01_079 Makky, M., Soni, P., & Salokhe, V. M. (2014). Automatic non-destructive quality inspection system for oil palm fruits. International Agrophysics, 28(3), 319–329. https://doi.org/10.2478/intag-2014-0022 Naji, S., Jalab, H. A., & Kareem, S. A. (2019). A survey on skin detection in colored images. Artificial Intelligence Review, 52(2), 1041–1087. https://doi.org/10.1007/s10462-018-9664-9 Pertanian RI, K. (2023). Statistik Perkebunan Kelapa Sawit Indonesia 2022. In Direktorat Jenderal Perkebunan. https://ditjenbun.pertanian.go.id Purbolingga, Y., Ridwan, A., & Putri, D. M. (2025). A Machine Learning-Based Ambiguous Alphabet Recognition for Indonesian Sign Language System (SIBI). CogITo Smart Journal, 11(1), 1–14. https://doi.org/10.31154/cogito.v11i1.816.1-14 Resta, F. S. A., Setiawan, R., Rivai, M., Arif, R. El, Natawijaya, A., & Hadad, A. G. Al. (2026). Multimodal Radar-Vision for Oil Palm Fresh Fruit Bunch Ripeness Classification. IEEE Access, 14, 42975–42991. https://doi.org/10.1109/ACCESS.2026.3675310 Ridwan, A., Purbolingga, Y., & Hanisah, H. (2024). Utilizing Convolutional Neural Network for Learning Web-Based Braille Letter Classification System. Journal of Computer Networks, Architecture and High Performance Computing, 6(1). https://doi.org/10.47709/cnahpc.v6i1.3386 Saad, B., Ling, C. W., Jab, M. S., Lim, B. P., Mohamad Ali, A. S., Wai, W. T., & Saleh, M. I. (2007). Determination of free fatty acids in palm oil samples using non-aqueous flow injection titrimetric method. Food Chemistry, 102(4), 1407–1414. https://doi.org/https://doi.org/10.1016/j.foodchem.2006.05.051 Srivastava, S., & Sadistap, S. (2018). Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review. Journal of Food Measurement and Characterization, 12(4), 2758–2794. https://doi.org/10.1007/s11694-018-9893-2 Suharjito, Elwirehardja, G. N., & Prayoga, J. S. (2021). Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches. Computers and Electronics in Agriculture, 188, 106359. https://doi.org/https://doi.org/10.1016/j.compag.2021.106359 Wang, A. X., Chukova, S. S., & Nguyen, B. P. (2023). Ensemble k-nearest neighbors based on centroid displacement. Information Sciences, 629, 313–323. https://doi.org/https://doi.org/10.1016/j.ins.2023.02.004 Yan, K., Gao, S., Yan, G., Ma, X., Chen, X., Zhu, P., Li, J., Gao, S., Gastellu-Etchegorry, J. P., Myneni, R. B., & Wang, Q. (2025). A global systematic review of the remote sensing vegetation indices. International Journal of Applied Earth Observation and Geoinformation, 139(November 2024), 104560. https://doi.org/10.1016/j.jag.2025.104560
Copyright (c) 2026 Ahmad Ridwan https://creativecommons.org/licenses/by-nc/4.0
Early Warning System for Student Academic Risk Prediction Using Gradient Boosting with SMOTE-Based Class Balancing
This study proposes a machine learning-based Early Warning Academic System for predicting student academic risk levels using historical academic performance data from ITB Indonesia. The aim of this research is to identify at-risk students at an early stage using routinely collected academic indicators, including Quiz Score, Attendance, Assignment Score, and Midterm Score. The proposed framework integrates Gradient Boosting as the classification model, Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, and Stratified 5-Fold Cross Validation to ensure robust performance evaluation. The model classifies students into three categories, namely Safe, Warning, and Risk. Experimental results show that the proposed model achieves an accuracy of 80.00%, macro F1-score of 78.24%, Cohen’s Kappa of 0.6306, and Matthews Correlation Coefficient of 0.6311, indicating strong and reliable predictive performance under imbalanced data conditions. ROC-AUC analysis further confirms strong discriminative capability, particularly for identifying Risk students. Feature importance analysis reveals that Midterm Score is the most influential predictor (43.49%), followed by Quiz Score (25.60%) and Assignment Score (21.99%), while Attendance contributes 8.93%. These findings indicate that academic performance indicators, especially midterm assessments, play a critical role in early risk detection. The results demonstrate that effective early warning prediction can be achieved using a limited set of routinely available academic variables, providing a practical and scalable approach to support early intervention strategies in higher education institutions. REFERENCES Akello, E. F., Ijiga, O. M., & Idoko, I. P. (2026). Sequence-Aware Learning Analytics for Early Identification of At-Risk Academic Trajectories in Higher Education Using Transformer Models. International Journal of Innovative Science and Research Technology, 818. https://doi.org/10.38124/ijisrt/26jan563 Albreiki, B., Habuza, T., & Zaki, N. (2023). Extracting topological features to identify at-risk students using machine learning and graph convolutional network models. International Journal of Educational Technology in Higher Education, 20(1), 23. https://doi.org/10.1186/s41239-023-00389-3 Almalawi, A., Soh, B., Li, A., & Samra, H. (2024). Predictive Models for Educational Purposes: A Systematic Review. Big Data and Cognitive Computing, 8(12), 187. https://doi.org/10.3390/bdcc8120187 Alomari, S. A., Aldiabat, K., Hashim, F. A., Zitar, R. A., Liu, Z., Migdady, H., Hu, G., Smerat, A., & Abualigah, L. (2025). Supervised Learning: Teaching Machines with Labeled Data. In L. Abualigah, Mastering the Minds of Machines (1st ed., pp. 26–33). CRC Press. https://doi.org/10.1201/9781003516385-4 Aluso, L., & Enyejo, J. O. (2025). Using XGBoost and Time-Series Forecasting to Predict Student Academic Trajectories in Educational Analytics Platforms. International Journal of Innovative Science and Research Technology, 143. https://doi.org/10.38124/ijisrt/25dec159 Angeioplastis, A., Aliprantis, J., Konstantakis, M., & Tsimpiris, A. (2025). Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques. Computers, 14(3), 83. https://doi.org/10.3390/computers14030083 Aslam, Z., Missen, M. M. S., Ghaffar, A. A., Mehmood, A., Villar, M. G., Alvarado, E. S., & Ashraf, I. (2025). Advancing fake news combating using machine learning: A hybrid model approach. Knowledge and Information Systems, 67(12), 12137–12177. https://doi.org/10.1007/s10115-025-02588-y Bagawade, R. P., & Thirupurasundari. (2025). Ensemble Machine Learning Techniques. International Journal of Emerging Technology and Advanced Engineering, 15(4), 15–23. https://doi.org/10.46338/ijetae0425_02 Călin, S. (2025). Handling Imbalanced Data: The SMOTE Technique. 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–5. https://doi.org/10.1109/ECAI65401.2025.11095450 Charaya & Sonia. (2026). Towards Accurate Student Performance Prediction: An Assessment of Machine Learning Models and Metrics. International Journal of Latest Technology in Engineering Management & Applied Science, 15(1), 600–610. https://doi.org/10.51583/IJLTEMAS.2026.150100053 Chukwura. (2023). A comparative study of several classification metrics and their performances on data. World Journal of Advanced Engineering Technology and Sciences, 8(1), 308–314. https://doi.org/10.30574/wjaets.2023.8.1.0054 Elreedy, D., Atiya, A. F., & Kamalov, F. (2024). A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning. Machine Learning, 113(7), 4903–4923. https://doi.org/10.1007/s10994-022-06296-4 Ersozlu, Z., Taheri, S., & Koch, I. (2024). A review of machine learning methods used for educational data. Education and Information Technologies, 29(16), 22125–22145. https://doi.org/10.1007/s10639-024-12704-0 Ganage, S. S. (2026). Machine Learning Techniques for Student Performance Prediction and Academic Analytics. International Journal of Engineering Research and Science & Technology, 22(1(S)), 311–314. https://doi.org/10.62643/ijerst.2026.v22.i1(S).2064 Ge, X., Fang, C., Li, X., Sun, W., Wu, D., Zhai, J., Lin, S.-W., Zhao, Z., Liu, Y., & Chen, Z. (2025). Machine Learning for Actionable Warning Identification: A Comprehensive Survey. ACM Computing Surveys, 57(2), 1–35. https://doi.org/10.1145/3696352 Giap, N., Nghiem, T. L., Ngo, T. H., Nguyen, M. T. L., & Phung, H. Q. (2024). Increment of Academic Performance Prediction of At‐Risk Student by Dealing With Data Imbalance Problem. Applied Computational Intelligence and Soft Computing, 2024(1), 4795606. https://doi.org/10.1155/2024/4795606 Idhmad, A., Kaicer, M., Nejjar, C., & Benjouad, A. (2024). Intelligent Bankruptcy Prediction Models Involving Corporate Governance Indicators, Financial Ratios and SMOTE. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 12(1), 233–244. https://doi.org/10.52549/ijeei.v12i1.5241 Kaban, R., Rachman, A. I., Sari, I. P., Zulherry, A., Basri, M., Fadilah, N., Maulidiyah, N., Sembiring, D. J., Pratama, G. U., Ronal, R., & others. (2026). Pengantar Artificial Intelligence, Machine Learning, dan Big Data. Yayasan Kita Menulis. Kabumba, R. K., Mbayandjambe, A. M., Tashev, T., Slavov, V., Kambale, W. V., Ngoie, R.-B. M., Kyamakya, K., & Kasereka, S. K. (2025). Predictive Modeling of Academic Success Using Machine Learning: A Comparative Analysis with SMOTE-Based Class Balancing. Procedia Computer Science, 272, 242–250. https://doi.org/10.1016/j.procs.2025.10.202 Li, G., & Sheng, H. (2025). A hybrid machine learning framework for predicting aircraft scaled sound pressure levels: A comparative study. Journal of Engineering and Applied Science, 72(1), 163. https://doi.org/10.1186/s44147-025-00714-9 Ling, Q. (2023). Machine learning algorithms review. Applied and Computational Engineering, 4(1), 91–98. https://doi.org/10.54254/2755-2721/4/20230355 Liu, H. (2025). Machine Learning Approaches for Predicting and Intervening in Students’ Academic Status. Applied and Computational Engineering, 183(1), 102–109. https://doi.org/10.54254/2755-2721/2025.BJ27254 Lokanan, M. (2024). Exploring Resampling Techniques in Credit Card Default Prediction. In Review. https://doi.org/10.21203/rs.3.rs-4087259/v1 Lu, X. (2024). Modern Education: Advanced Prediction Techniques for Student Achievement Data. International Journal of Advanced Computer Science and Applications, 15(1). https://doi.org/10.14569/IJACSA.2024.01501126 Lünich, M., Keller, B., & Marcinkowski, F. (2024). Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms. Technology, Knowledge and Learning, 29(2), 1079–1107. https://doi.org/10.1007/s10758-023-09698-y Mujahid, M., Kına, E., Rustam, F., Villar, M. G., Alvarado, E. S., De La Torre Diez, I., & Ashraf, I. (2024). Data oversampling and imbalanced datasets: An investigation of performance for machine learning and feature engineering. Journal of Big Data, 11(1), 87. https://doi.org/10.1186/s40537-024-00943-4 Nafea, A. A., Mishlish, M., Haban Shaban, A. M., AL-Ani, M. M., Alheeti, K. M. A., & Mohammed, H. J. (2023). Enhancing Student’sPerformance Classification Using Ensemble Modeling. Iraqi Journal for Computer Science and Mathematics, 4(4). https://doi.org/10.52866/ijcsm.2023.04.04.016 Nguyen, C., Thi Tran, T., Thanh Thi Nguyen, T., Ha Thi Nguyen, T., Astarkhanova, T. S., Van Vu, L., Tai Dau, K., Nguyen, H. N., Pham, G. H., Nguyen, D. D., Prakash, I., & Pham, B. (2024). Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam. Journal of Hydroinformatics, 26(1), 72–87. https://doi.org/10.2166/hydro.2023.327 Osborne, J. B., & Lang, A. S. I. D. (2023). Predictive Identification of At-Risk Students: Using Learning Management System Data. Journal of Postsecondary Student Success, 2(4), 108–126. https://doi.org/10.33009/fsop_jpss132082 Ozyurt, O., Ozyurt, H., & Mishra, D. (2023). Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis. IEEE Access, 11, 120192–120208. https://doi.org/10.1109/ACCESS.2023.3327624 Pan, J., Zhao, Z., & Han, D. (2025). Academic Performance Prediction Using Machine Learning Approaches: A Survey. IEEE Transactions on Learning Technologies, 18, 351–368. https://doi.org/10.1109/TLT.2025.3554174 Paul, C., & Devi, A. (2025). AI-Driven Early Warning Systems for Dropout Risk and Passed-Out Prediction in Higher Education. In Dr. Mohd. A. Gandhi, Dr. A. Dutt, Dr. V. Saravanan, & K. T. Umare (Eds.), AI, Machine Learning, and IoT Applications for Academic Performance Prediction, Faculty Well-Being, and Educational Outcomes in Higher Education (2025th ed., pp. 89–110). RADemics Research Institute. https://doi.org/10.71443/9789349552258-04 Rahmi, N. A., Defit, S., & Okfalisa. (2024). Enhancing Classification Performance: A Study on SMOTE and Ensemble Learning Techniques. 2024 International Conference on Future Technologies for Smart Society (ICFTSS), 63–68. https://doi.org/10.1109/ICFTSS61109.2024.10691339 Rajasekhar, C., & Fernandes, A. S. H. D. (2026). Student Performance Prediction Using Machine Learning and Deep Learning. In R. Shanthi, Artificial Intelligence–Driven Intelligent Learning Systems for Language Proficiency, Student Success, and Well-Being in Higher Education (2026th ed., pp. 275–297). RADemics Research Institute. https://doi.org/10.71443/9789349552401-12 Rakitin, I. V., Rakitina-Qureshi, E. V., Arabadzhieva-Kalcheva, N. A., Todorova, M. P., Penev, I. P., & Petrova, D. I. (2025). Predicting Student Placement Status Using Tree-Based Ensemble Classifiers: A Comparative Analysis of Random Forest and Gradient Boosting Models. 2025 International Conference Automatics and Informatics (ICAI), 53–58. https://doi.org/10.1109/ICAI67591.2025.11325082 Rawat, B., & Pant, H. (2025). Boosting vs. Traditional Algorithms for Student Performance Prediction. 2025 International Conference on Sustainable Communication Networks and Application (ICSCN), 1998–2003. https://doi.org/10.1109/ICSCN67106.2025.11308228 Rico-Juan, J. R., Cachero, C., & Macià, H. (2024). Study regarding the influence of a student’s personality and an LMS usage profile on learning performance using machine learning techniques. Applied Intelligence, 54(8), 6175–6197. https://doi.org/10.1007/s10489-024-05483-1 Rochmadi, T. (2025). Addressing Class Imbalance in Predicting Student Academic Outcomes: A Comparative Study of Resampling Techniques with Machine Learning Classifiers in Higher Education. Artificial Intelligence in Learning, 1(3), 211–227. https://doi.org/10.63913/ail.v1i3.32 Sathvik & Nagaveni Biradar. (2025). Student Performance Detection Using Machine Learning Techniques. International Research Journal on Advanced Engineering Hub (IRJAEH), 3(09), 3513–3516. https://doi.org/10.47392/IRJAEH.2025.0516 Sharanya, B. (2024). An Efficient Ensemble Machine Learning Model for Cardiovascular Disease Prediction Using Digital Health Records. International Journal for Research in Applied Science and Engineering Technology, 12(3), 2598–2609. https://doi.org/10.22214/ijraset.2024.58888 Sharma, Y. P., & Sasikala, K. (2026). Early Warning Systems for Student Dropout Risk Prediction. In R. Shanthi, Artificial Intelligence–Driven Intelligent Learning Systems for Language Proficiency, Student Success, and Well-Being in Higher Education (2026th ed., pp. 298–321). RADemics Research Institute. https://doi.org/10.71443/9789349552401-13 Sujon, K. M., Hassan, R., Khairudin, A. R., Moi, S. H., Mohd Shafie, M. L., Saringat, Z., & Erianda, A. (2024). The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance. JOIV : International Journal on Informatics Visualization, 8(3–2), 1599. https://doi.org/10.62527/joiv.8.3-2.2449 Teresa, & et. al. (2025). Predictive Modelling of Student Outcomes Using Ensemble Regression and Classification Methods. https://doi.org/10.5281/ZENODO.17599070 Tiwari, M., & Jain, N. (2024). STUDENT PERFORMANCE PREDICTION USING MACHINE LEARNING ALGORITHMS. ShodhKosh: Journal of Visual and Performing Arts, 5(6). https://doi.org/10.29121/shodhkosh.v5.i6.2024.4552 Villar, A., & Andrade, C. R. V. (2024). Supervised machine learning algorithms for predicting student dropout and academic success: A comparative study. Discover Artificial Intelligence, 4(1), 2. https://doi.org/10.1007/s44163-023-00079-z Yu, L., Zhao, X., Huang, J., Hu, H., & Liu, B. (2023). Research on Machine Learning with Algorithms and Development. Journal of Theory and Practice of Engineering Science, 3(12), 7–14. https://doi.org/10.53469/jtpes.2023.03(12).02 Yuningsih, L., Pradipta, G. A., Hermawan, D., Ayu, P. D. W., Hostiadi, D. P., & Huizen, R. R. (2023). IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification. Emerging Science Journal, 7(5), 1501–1516. https://doi.org/10.28991/ESJ-2023-07-05-04
Copyright (c) 2026 Roberto Kaban, Jamaludin Bin Sallim, Susmanto Susmanto, Rozlina Binti Mohamed https://creativecommons.org/licenses/by-nc/4.0
Modeling and Performance Analysis of a Series-Parallel AC Circuit under Supply Voltage Variations Using Simulation
Series-parallel Alternating Current (AC) circuits are widely used in electrical power distribution and electronic systems because their operating characteristics are strongly influenced by supply voltage variations. Although simulation-based circuit analysis using Multisim has been widely applied in engineering education, studies systematically investigating the operating characteristics of series-parallel AC circuits over a broad range of supply voltages remain limited. This study aims to analyze the effect of gradual supply voltage variation on the operating characteristics of a series-parallel AC circuit using Multisim simulation. A quantitative experimental approach based on computer simulation was employed. The simulated circuit consisted of two resistors (220 Ω and 230 Ω) and two lamps connected as electrical loads. The supply voltage was gradually varied from 100 V to 320 V to observe changes in the operating conditions of the loads. The simulation showed that increasing the supply voltage progressively changed the lamp operating conditions, ranging from no illumination at 100 V, low brightness at 130 V, gradual increases in brightness at intermediate voltage levels, maximum brightness at 300 V, and lamp failure at 320 V due to excessive applied voltage. These observations are consistent with the theoretical relationships described by Ohm's Law, where increasing supply voltage results in greater electrical power delivered to resistive loads. The findings suggest that Multisim provides a practical environment for the preliminary evaluation of circuit behavior under different supply voltage conditions. However, the conclusions of this study are limited to simulation-based observations and require further validation through quantitative electrical measurements and hardware experiments. REFERENCES Anindya et al. (2025). Analisis Arus Dan Tegangan Pada Rangkaian Seri Dan Paralel Berdasarkan Hukum Ohm. Phydagogi : Jurnal Fisika Dan Pembelajarannya, 7(2), 1–8. Bhowmic, A. (2024). Verification of Electrical Circuit Simulation Results Using Multisim. Dewi Tiyas Saputri, Ayu Widiana Putri, & Aisyiyah Marfa Berliana Buanasari. (2025). Pengaruh Tegangan Terhadap Besar Kuat Arus Listrik Pada Pengukuran Hukum OHM Berbasis Simulasi Phet HTML5. Jurnal Teknik Mesin, Industri, Elektro Dan Informatika, 4(1), 321–331. https://doi.org/10.55606/jtmei.v4i1.4843 Dewy, M. S., & Isnaini, M. (2022). Pengembangan Modul Pembelajaran Berbasis Software Simulasi Ni Multisim Pada Mata Kuliah Rangkaian Listrik Dc. 9(1). Fan, C., Yang, M., Zhao, Y., Geng, X., & An, J. (2026). Research and Practice of a Virtual Simulation Circuit Experiment Platform based on LabVIEW and Multisim. In Academic Journal of Science and Technology (Vol. 19, Number 1). Halim, A., Alinda, M., Mahzum, E., & Wahyuni, A. (2024). Impact of Using PhET and NI Multisim Simulation on Understanding Electrical Circuit Concepts. https://doi.org/10.21009/1 Hilma, & Malik, A. (2024). Analisis Pengaruh Tegangan dan Hambatan Terhadap Kuat Arus dengan Menggunkan Phet Simulation. JFT: Jurnal Fisika Dan Terapannya, 10(2), 76–85. https://doi.org/10.24252/jft.v10i2.39275 Mabruroh, F., Talakua, P., Suhendi, H. Y., & Maipauw, M. M. (2025). The Utilization of Virtual Reality in Ohm’s Law Experiment Simulation. Jurnal Pendidikan Dan Ilmu Fisika, 5(1), 25–39. https://doi.org/10.52434/jpif.v5i1.42510 Malik, A., Asyidik, D., Nursamsika, K. H., Khotimah, R. N., & Fitriyani, R. (2020). Learning Ohm’s Law through Electric Puzzle Media. https://doi.org/10.21009/1 Nasab, M. R., Cometa, R., Bruno, S., Giannoccaro, G., & Scala, M. La. (2024). Power Systems Simulation and Analysis: A Review on Current Applications and Future Trends in DRTS of Grid-Connected Technologies. In IEEE Access (Vol. 12, pp. 121320–121345). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2024.3452468 Nuryanto. (2017). Analisa Rangkaian Elektronika Dengan Menggunakan Program Simulasi Spice. http://www.orcad.com/pspicead.aspx. Putri et al. (2025). Analisis Hubungan Tegangan dan Arus dalam Rangkaian Seri dan Paralel Berdasarkan Hukum Ohm. Jurnal Fisika Dan Pembelajarannya, 7(2), 201–206. Rangkuti, N. A. (2026). Analisis Topologi Rangkaian Seri-Paralel Berbasis Simulasi Multisim Untuk Evaluasi Distribusi Arus Dan Tegangan. Hadron Jurnal Fisikadan Terapan, 8, 1–6. Ridwan, R., & Kembuan, D. R. E. (2021). Efektivitas Penggunaan Simulasi dengan Multisim Berbantuan Virtual Laboratory untuk Meningkatkan Kemampuan Berpikir Kritis Mahasiswa Jurusan Pendidikan Teknik Elektro. Jurnal Kiprah, 9(1), 39–47. https://doi.org/10.31629/kiprah.v9i1.3235 Rupawanti, N. (2018). J E : Electronic Control, Telecomunication, Computer Information and Power Systems Studi Karakteristik Transformator Daya Listrik dengan Multisim 12.0. 3, 5–8. Saha, A. K. (2022). A Real-Time Simulation-Based Practical on Overcurrent Protection for Undergraduate Electrical Engineering Students. IEEE Access, 10, 52537–52550. https://doi.org/10.1109/ACCESS.2022.3175813 Sidiq & Sukandar. (2023). Analisis Perbandingan Karakteristik Arus Dan Tegangan Pada Rangkaian Listrik Seri Dan Paralel. 1–4. Surahmat, A., & Dedy Fu’ady, T. (2020). Simulasi Rangkaian Seven Segment Menggunakan Multisim Pada Pembelajaran Rangkaian Elektronika Analog Dan Digital Di Smks Informatika Sukma Mandiri. Sya’ban, A. N., Faiza, D., Thamrin, & Jasril, I. R. (2024). “METRIKA”: Electronics Trainer Media for Supporting Learning in the Application of Ohm’s Law and Kirchhoff’s Law. Journal of Hypermedia & Technology-Enhanced Learning, 2(1), 63–79. https://doi.org/10.58536/j-hytel.v2i1.112 Zhang, M., & Pan, S. (2023). Flipped Classroom of Electrical and Electronic Technology based on Multisim Simulation. In Academic Journal of Science and Technology (Vol. 5, Number 1).
Copyright (c) 2026 Eka Feby Ronauli Lubis, Nurhafiz Ahmad Rangkuti, Nirwan Sinuhaji, Indah Mawati Giawa https://creativecommons.org/licenses/by-nc/4.0
Design of Augmented Reality-Based Learning Media Using the TPACK Framework for Visualizing Graph Concepts in Discrete Mathematics
The advancement of educational technology has created opportunities to develop innovative learning media that can enhance students’ understanding of complex and abstract concepts. In the Discrete Mathematics course, graph theory is considered one of the most challenging topics because it involves abstract relationships among vertices and edges, making concepts such as paths, cycles, trees, and spanning trees difficult for students to comprehend through conventional two-dimensional learning media. Therefore, this study aims to design an Augmented Reality (AR)-based learning media using the Technological Pedagogical Content Knowledge (TPACK) framework to support graph concept visualization in higher education. This study employed the Design and Development Research (DDR) method, consisting of problem identification, literature review, needs analysis, TPACK analysis, learning media design, system architecture design, user interface design, prototype development, and expert validation. The results produced a conceptual model of AR-based learning media that integrates technological, pedagogical, and content knowledge within a unified framework. The proposed design includes learning materials, AR-based three-dimensional graph visualization, practice exercises, evaluations, and user guidance features. The practical implication of this study is the provision of a systematic design framework that can serve as a reference for the development of AR-based educational applications in mathematics and computer science education. In conclusion, the integration of AR technology and the TPACK framework has the potential to facilitate the visualization of abstract graph concepts and support more interactive learning experiences. Further research is recommended to implement and evaluate the effectiveness of the proposed design in actual learning environments. REFERENCES Adipat, S. (2021). Developing Technological Pedagogical Content Knowledge (TPACK) through Technology-Enhanced Content and Language-Integrated Learning (T-CLIL) Instruction. Education and Information Technologies, 26(5), 6461–6477. https://doi.org/10.1007/s10639-021-10648-3 Belda-Medina, J., & Calvo-Ferrer, J. R. (2022). Integrating augmented reality in language learning: Pre-service teachers’ digital competence and attitudes through the TPACK framework. Education and Information Technologies, 27(9), 12123–12146. https://doi.org/10.1007/s10639-022-11123-3 Bödding, R., & Maier, G. W. (2026). Investigating the Modality and Signalling Principles in Immersive Augmented Reality Learning Environments. Journal of Computer Assisted Learning, 42(1), e70183. https://doi.org/10.1002/jcal.70183 Chen, H.-C., & Wong, L.-H. (2026). The MTA-TPACK Dynamic Collaboration Spiral: Making Pedagogical Thinking Visible in Human–AI Scientific Visualization for Sustainable Teacher Innovation. Sustainability, 18(6), 2718. https://doi.org/10.3390/su18062718 Choi, B., & Young, M. F. (2021). TPACK-L: Teachers’ pedagogical design thinking for the wise integration of technology. Technology, Pedagogy and Education, 30(2), 217–234. https://doi.org/10.1080/1475939X.2021.1906312 Irwanto, I. (2021). Research Trends in Technological Pedagogical Content Knowledge (TPACK): A Systematic Literature Review from 2010 to 2021. European Journal of Educational Research, volume–10–2021(volume–10–issue–4–october–2021), 2045–2054. https://doi.org/10.12973/eu-jer.10.4.2045 Kim, W., & Ok, M. W. (2021). A Comparison of Special and General Educators’Usage, Perceptions, and TPACK Levels Regarding the Use of Augmented and Virtual Reality. Journal of Special Education : Theory and Practice, 22(2), 15–44. https://doi.org/10.19049/JSPED.2021.22.2.02 Lachner, A., Fabian, A., Franke, U., Preiß, J., Jacob, L., Führer, C., Küchler, U., Paravicini, W., Randler, C., & Thomas, P. (2021). Fostering pre-service teachers’ technological pedagogical content knowledge (TPACK): A quasi-experimental field study. Computers & Education, 174, 104304. https://doi.org/10.1016/j.compedu.2021.104304 Nilsson, P. (2024). From PCK to TPACK - Supporting student teachers’ reflections and use of digital technologies in science teaching. Research in Science & Technological Education, 42(3), 553–577. https://doi.org/10.1080/02635143.2022.2131759 Nusroh, H., Khalif, M. A., & Saputri, A. A. (2022). Developing Physics Learning Media Based on Augmented Reality to Improve Students’ Critical Thinking Skills. Physics Education Research Journal, 4(1), 23–28. https://doi.org/10.21580/perj.2022.4.1.10912 Prihantini, P., Rukmini, A., Mahardhani, A. J., & Mustika, A. (2026). Mapping the Progress of Technological Pedagogical and Content Knowledge Research: Bibliometric Analysis. Jurnal Pembelajaran, Bimbingan, Dan Pengelolaan Pendidikan, 6(2), 12. https://doi.org/10.17977/um065.v6.i2.2026.12 Putri, N. P. D. M., Suharta, I. G. P., & Astawa, I. W. P. (2022). DEVELOPMENT OF AUGMENTED REALITY BASED GEOMETRY LEARNING MEDIA ORIENTED TO BALINESE ARCHITECTURE TO IMPROVE ABILITY STUDENT MATHEMATICS SPATIAL. International Journal of Engineering Technologies and Management Research, 9(10), 26–42. https://doi.org/10.29121/ijetmr.v9.i10.2022.1183 Santos, J. M., & Castro, R. D. R. (2021). Technological Pedagogical content knowledge (TPACK) in action: Application of learning in the classroom by pre-service teachers (PST). Social Sciences & Humanities Open, 3(1), 100110. https://doi.org/10.1016/j.ssaho.2021.100110 Schwajda, D., Friedl, J., Pointecker, F., Jetter, H.-C., & Anthes, C. (2023). Transforming graph data visualisations from 2D displays into augmented reality 3D space: A quantitative study. Frontiers in Virtual Reality, 4, 1155628. https://doi.org/10.3389/frvir.2023.1155628 Sriadhi, S., Hamid, A., Sitompul, H., & Restu, R. (2022). Effectiveness of Augmented Reality-Based Learning Media for Engineering-Physics Teaching. International Journal of Emerging Technologies in Learning (iJET), 17(05), 281–293. https://doi.org/10.3991/ijet.v17i05.28613 Tarigan, I. M. B. (2026). Development of a Student-Centered Digital Mathematics Learning Prototype Using the ADDIE Model to Improve Conceptual Understanding. Journal of Computer Engineering and Information Technology, 2(1). Tarigan, I. M., Simanjorang, M. M., & Siagian, P. (2022). Analisis Kemampuan Pemecahan Masalah Matematis Siswa Ditinjau dari Perbedaan Gender di SMP N 1 Kuta Buluh. Jurnal Cendekia : Jurnal Pendidikan Matematika, 6(3), 2984–2998. https://doi.org/10.31004/cendekia.v6i3.1791 Yang, L., Susanti, W., Hajjah, A., Marlim, Y. N., & Tendra, G. (2022). Perancangan Media Pembelajaran Matematika Menggunakan Teknologi Augmented Reality. Edukasi: Jurnal Pendidikan, 20(1), 122–136. https://doi.org/10.31571/edukasi.v20i1.3830 Yanuarto, W. N., & Iqbal, A. M. (2022). The Augmented Reality Learning Media to Improve Mathematical Spatial Ability in Geometry Concept. Edumatica : Jurnal Pendidikan Matematika, 12(01), 30–40. https://doi.org/10.22437/edumatica.v12i01.17615 Yeh, Y.-F., Chan, K. K. H., & Hsu, Y.-S. (2021). Toward a framework that connects individual TPACK and collective TPACK: A systematic review of TPACK studies investigating teacher collaborative discourse in the learning by design process. Computers & Education, 171, 104238. https://doi.org/10.1016/j.compedu.2021.104238
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Implementation and Optimization of a Voucher-Based Internet Access System Using MikroTik RB941 for RT/RW Net Networks
The increasing demand for stable, secure, and affordable internet access in RT/RW Net environments requires more effective network management strategies. Although MikroTik-based hotspot systems have been widely implemented, many existing studies primarily focus on basic hotspot deployment without integrating bandwidth optimization and voucher-based user authentication into a unified network management framework. Consequently, network administrators continue to encounter challenges related to unequal bandwidth allocation, inefficient user management, and limited access control. This study aims to implement and optimize a voucher-based internet access system using the MikroTik RB941 router to improve bandwidth management, user authentication, and network administration in RT/RW Net networks. The research employed an experimental approach involving network design, router configuration, hotspot implementation, voucher generation, bandwidth management using the Per Connection Queue (PCQ) method, and system testing. The implementation utilized MikroTik RouterOS, Winbox, and Mikhmon to configure and manage the network services. The proposed system successfully established centralized user authentication, automated voucher management, and more organized bandwidth allocation under the implemented testing scenario. The novelty of this study lies in the integration of Time-Based and Volume-Based voucher management with PCQ bandwidth allocation within a single MikroTik RB941 framework, providing a more comprehensive network management approach than conventional hotspot implementations. The findings indicate that the proposed configuration provides a structured and practical solution for managing internet access in RT/RW Net environments and establishes a foundation for future evaluations of network performance using Quality of Service (QoS) parameters. REFERENCES Anwar, I. D., & Akbar, Y. (2024). Network Bandwidth Management with Per Connection Queue (PCQ) Method on Mikrotik at Masterpiece Family Karaoke Tebet. Indonesian Journal: Informatics and Communication Management, 5(3), 3125–3137. Aulia, B. W., Rizki, M., Prindiyana, P., & Surgana, S. (2023). The crucial role of computer networks and databases in the digital era. Journal of Information Systems and Information Technology, 1(1), 9–20. Darmawan, M. D., & Rudianto, R. (2025). Design and build internet billing based on using Mikhmon-based wifi vouchers. Jati (Journal of Informatics Engineering Students), 9(6), 9373–9381. Ernawati, D., & Setiawan, A. (2025). Implementation of a web-based Mikrotik hotspot voucher sales management information system to improve the efficiency of isk-net ISP services. Journal of Bhinneka Community Service, 4(2), 2406–2418. Faiha, H. (2024). Network Security Analysis Using Mikrotik Router OS to Optimize Computer Network Security in Improving the Quality of Public Services at the Lubuk Sikaping Religious Court Office with Port Knocking and ACL (Access Control List) methods. Fajaruddin, H., & Kurniawan, A. (2025). The use of wireless fidelity (Wi-Fi) technology in the development of RT-RW Net to grow the digital economy. National Journal of Informatics (June), 4(2), 42–49. Fauzi, R., Zainy, A., Lubis, I. S., Haqi, A. B., Akhir, A. Z., Kumana, B., Simamora, N., & Juliana, R. (2023). Installation of Mikrotik on Virtualbox and connection between Mikrotik in Virtualbox and Winbox at Smk S Teruna Padang Sidempuan. ADAM Journal: Journal of Community Service, 2(1), 106–118. Gunawan, G. G. I., & Alijoyo, F. A. (2024). Design and build a wifi voucher purchase application with a payment gateway and radius. Journal of Business Information Technology and Systems, 6(2), 310–321. Haqi, A. B., Siregar, D. A., Mutiara, M., Lubis, N. F., & Akhir, A. Z. (2023). Introduction to Basic Computer Networks at SMK Negeri 1 Batang Onang. ADAM Journal: Journal of Community Service, 2(2), 293–303. Ikhwandi, M. I., & Azinar, A. W. (2025). Wireguard implementation as a connection using mikrotik routing. Smatics Journal, 15(02), 292–301. Ismawati, I., & Softianto, S. (2025). Implementation of Mikrotik RouterOS-Based Voucher Hotspot System for Internet Access Management on LAN Networks. Karapan Network Journal: Journal of Computer Technology and Mobile Ad Hoc Network, 1(01). Lestyaningrum, I. K. M., Trisiana, A., Safitri, D. A., Pratama, A. Y., & Wahana, T. P. (2022). Global education based on digital technology in the millennial era. Unisri Press. Lukito, K. D., Liliana, L., & Palit, H. N. (n.d.). Creation of crowdsource applications for Android-based household services. Manuaba, I. B. K., Agus, F., Ramayu, I. M. S., Saputro, V. A., Pakpahan, A. V., Skawanti, J. R., & Judijanto, L. (2025). Computer Network. PT. Sonpedia Publishing Indonesia. Masruha, M. (2022). Islamic Law Perspective on the Voucher System at KPRI Nusa Indah. JURISY: Sharia Scientific Journal, 2(1). Mojasa, B. W. (2024). Criminal liability for misuse of RT/RW Net by using resold broadband internet. Journal of Legal Compilation, 9(2), 223–234. Nugroho, P. A. (2022). The design of the RT/RW Net computer network uses a Power Line (PLC) communication line in Taman Berdikari Sentosa housing. JEIS: Swadharma Journal of Electrical and Informatics, 2(1), 9–14. Prameswari, I. A., Noviyanti, Y., & Susilowati, T. (2024). Introduction to Information Systems. NEM Publishers. Putri, D. L., Pratama, A. R., & Nurjanah, L. (2025). Optimizing Digital Access through Internet Network Planning in Dawuhan Village. JUPAMU: Journal of Multidisciplinary Community Service, 1(1), 25–36. Rahman, R. (2024). Implementation of Mikrotik-Based RT RW Net Network Network and Hotspot Networks with Mikhmon Feature in Fast. Net. Rusyunizal, D., & Karim, H. A. (2025). Transparency of Web-Based Islamic Education Management System in the Digital Era. Journal of Educational Management, 10(4), 3319–3331. Sanjaya, R. (2020). 21 Reflections on Online Learning in Emergency Times. SCU Knowledge Media. Sari, A. I., Syaifuddin, M., & Andriani, T. (2023). Optimizing the strategic management of educational infrastructure. Journal of Multidisciplinary Sciences, 1(4), 814–822. Ulfa, H., Basuki, A. I., Suranegara, G. M., & Fauzi, A. (2024). DDoS Protection System for SDN Network Based on Multi Controller and Load Balancer. Systematic: Journal of Information Systems, 13(2), 555–571. Yusman, Y., Putra, R. R., & Sinaga, I. (2024). The application of information systems to improve governance and public services in the digital era. Compatible with Media Technology.
Copyright (c) 2026 Adelia Amanda, Alfridom Wau, Dede Priyandi Bawamenewi, Siti Rahmayani Sitorus, Romulo P Aritonang, Meiliyani Br Ginting https://creativecommons.org/licenses/by-nc/4.0
Performance Evaluation of HTB-Based Bandwidth Management for Campus Networks Using MikroTik Routerboard
The rapid growth of internet usage in campus environments, driven by academic, administrative, and digital learning activities, has led to significant increases in data traffic that can impact network performance and stability. At ITB Indonesia, the computer network relies on MikroTik RouterBoard as a core device for traffic management. However, during peak usage periods, network congestion may occur, necessitating a comprehensive performance analysis. This study aims to evaluate the performance of the MikroTik RouterBoard network in handling surges in data traffic within the ITB Indonesia campus. The research employs a quantitative experimental approach by simulating different traffic conditions, including normal and peak loads. Data were collected using monitoring tools integrated within the MikroTik system, focusing on key performance indicators such as throughput, latency, packet loss, and CPU utilization. The results indicate that the network performs efficiently under normal conditions, maintaining stable throughput and low latency. However, under high traffic loads, performance degradation is observed, including increased latency, higher CPU usage, and slight packet loss. The application of traffic management techniques, such as bandwidth allocation and load balancing, significantly enhances network performance. Therefore, MikroTik RouterBoard remains an effective solution when supported by proper optimization strategies. REFERENCES Adisetiawan, R. A. R. (2025). Transformation of Education in the Era of Digitalization Efforts Towards Technology-Based Education. Karapan Network Journal: Journal Computer Technology And Mobile Ad Hoc Network, 1(01). Aprelyani, S. (2025). Factors that affect network performance: signal quality and bandwidth. Greenation Journal of Engineering Sciences, 3(2), 85–92. Aritonang, M. A. S., & Simanullang, M. J. (2025). Application of a Network Monitoring System based on SNMP for early detection of network disturbances. Journal of Artificial Intelligence Technology Studies, 5(3), 735–742. Aulia, B. W., Rizki, M., Prindiyana, P., & Surgana, S. (2023). The crucial role of computer networks and databases in the digital era. Journal of Information Systems and Information Technology, 1(1), 9–20. Dachi, A. C., & Noprisson, H. (2025). Mikrotik Firewall Implementation Model In Traffic Management And Network Security. Jsai (Journal of Scientific And Applied Informatics), 8(3), 788–793. Dora Sandova, & Cahyo Prihantoro. (2021). Traffic Analysis on the LAN Network. Jsai: Journal of Scientific And Applied Informatics, 4(3), 329–337. Fitrian, H. P., Difa, D. H., Melianti, V. A., & Ramdhani, F. M. (2025). Analysis of factors that affect the performance of computer networks in a multi-user environment. Tamika: Journal of Informatics Management and Computerized Accounting, 5(2), 410–415. Kuncoro, W. A., & Santoso, L. (2025). Network Congestion Due to Social Media: Analyzing Its Impact on the Efficiency of Wireless Data Transmission. Scientific Journal of Information Systems, 4(2), 130–141. Nabawi, M., Permana, D. S., & Subekti, R. (2025). Design and implementation of internet connection management with load balancing and failover methods at Trans Coffee headquarters. National Journal of Informatics (Junif), 4(2), 1–13. Nasrullah, M. G., Heryana, N., & Solehudin, A. (2024). Bandwidth management uses the Token Bucket Hierarchical method on internet speed limiting. Jati (Student Journal of Informatics Engineering), 8(2), 2291–2296. Ndun, Y. J. (2025). The implementation of VLAN (Virtual Local Area Network) to improve the security and efficiency of the Dapodik application network at Oesusu Elementary School, Kupang Regency. Journal of Informatics and Applied Electrical Engineering, 13(3). Octavian, A. (2024). The redundancy network design uses the concept of etherchannel and hsrp with intervlan routing at Pln Uid Jakarta Raya. Journal of Informatics and Applied Electrical Engineering, 12(2). Pragasta, Y. (2025). Application of Load Balancing Technology on Mikrotik Routers with the Peer Connection Classifier Method. Digital Transformation Technology, 5(2), 131–140. Pramudito, T. D., Pranoto, W. J., & Hallim, A. (2025). Analysis of the quality of 4g LTE network services using the Walk Test method and Qos (Quality of Service) measurement at Samarinda Central Plaza. Pendas: Scientific Journal of Basic Education, 10(01), 341–360. Prasetio, D., Yuniar, R. O., Fahlewi, F., Agustina, D., Citra, E. J., & Rahman, A. (2025). Analysis of students' habits of using the internet to study at Baturaja University. Journal of Computer Innovation (Inokom), 1(3), 125–134. Prasetyo, F. H. P., Infitharina, E., & Febriyansyah, M. (2025). Application of the Network Development Life Cycle (NDLC) method in the development of computer networks. Journal Of Informatics And Communication Technology (JCT), 7(1), 80–87. Pratama, N. M. W. P., Fatchurrohman, F., & Susanto, I. B. (2024). Optimization of the internet network by using simple queue and microtik firewall at Malang City State Junior High School. Jati (Student Journal of Informatics Engineering), 8(6), 12828–12835. Rahman, T., Rafi, A. P., & Rifai, B. (2026). Implementation of VLAN on WLAN for optimization of enterprise network management. Technology, 20(1), 207–219. Rivaldi, K., & Purnama, G. (2025). Design and implementation of monitoring of computer network device infrastructure in data centers and informatics facilities through the application of Zabbik Network Engineering. Journal of Adijaya Multidisciplinary, 3(04), 589–611. Rizky, M. A. H., Solehudin, A., & Nurkifli, E. H. (2024). Bandwidth optimization on the internet network uses the simple queue and peer connection queue methods. Jati (Student Journal of Informatics Engineering), 8(4), 7856–7863. Subektiningsih, S., Renaldi, R., & Ferdiansyah, P. (2022). Comparative analysis of Tiphon Standard Qos parameters on wireless networks in the application of the PCQ method. Explore, 12(1), 57–63. Sufa, A. M., & Nasution, M. I. P. (2025). Perceptions and Barriers to the Use of Web-Based Academic Information Systems at the State Islamic University of North Sumatra. Scientific Journal of Economics, Management, Business and Accounting, 2(3), 347–357. Utomo, A. A. S., Supandi, S., & Rozzaqi, A. R. (2025). Analysis of wireless network performance based on Qos (throughput, delay, packet loss) parameters on variations in traffic during operational hours in users in the school environment at SMP Negeri 1 Ngaringan. Sibatik Journal: Scientific Journal of Social, Economic, Cultural, Technological, and Educational, 4(9), 2691–2970.
Copyright (c) 2026 Sumarlin, Iscar Meriah Zai, Candra Irawan Batee, Naomi Rut M. Siboro, Tuboy Martin Gea, Hotma Mentalita https://creativecommons.org/licenses/by-nc/4.0