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.
Product Segmentation for Apparel MSMEs Using K-Means and CRISP-DM Approach
Apparel Micro, Small, and Medium Enterprises (MSMEs) frequently provide significant sales data that is inadequately leveraged for strategic business advancement. This study intends to examine sales data utilising the K-Means Clustering technique within the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The dataset consists of 360 sales records during the period 2021–2023, including factors such as amount sold, unit price, and total turnover. The analysis adheres to the systematic CRISP-DM phases: business understanding, data understanding, data preparation, modelling, assessment, and deployment. The assessment outcomes, quantified by the Silhouette Score, determined two ideal clusters for the dataset. Cluster 1 denotes well-performing products distinguished by elevated sales volume, comparatively high unit prices, and substantial turnover. Conversely, Cluster 2 comprises underperforming products characterised by diminished sales volume, reduced unit pricing, and negligible turnover. These findings offer a data-driven basis for MSMEs to develop more efficient marketing strategies and inventory management procedures. By categorising products according to performance, business owners can prioritise high-value commodities and optimise inventory for less productive categories. This study indicates that employing K-Means clustering inside the CRISP-DM framework effectively converts raw sales data into meaningful business intelligence for the garment sector. Subsequent study may enhance this technique by integrating external variables, including seasonal trends or customer demographics, to improve clustering precision. REFERENCES Abdul-Azeez, O., Ihechere, A. O., & Idemudia, C. (2024). Enhancing business performance: The role of data-driven analytics in strategic decision-making. 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Mapping Research Trends of Query Expansion in Information Retrieval: A Bibliometric Analysis
This study aims to analyze the development of research on query expansion in the field of information retrieval using a bibliometric approach to understand research trends, distribution, and current research focus. The data were obtained from 676 publications indexed in Scopus during the period from 2020 to February 2026. The research method involves quantitative analysis of annual publication trends, distribution of subject areas, document types, and keyword analysis using VOSviewer to map keyword relationships through co-occurrence analysis, overlay visualization to identify keyword trends, and density visualization to observe the concentration of research topics. The results show fluctuations in the number of publications with a peak occurring in 2025 with 141 publications. The research is dominated by the Computer Science field with 596 publications, and the majority of documents are conference papers with 369 publications. Keyword analysis identifies core topics such as information retrieval with 483 occurrences, query expansion with 354 occurrences, and search engines with 221 occurrences. Recent research trends include large language models, word embedding, and retrieval-augmented generation. The keyword network visualization indicates a shift from traditional methods such as relevance feedback toward modern approaches based on artificial intelligence and machine learning, which are increasingly relevant for improving the effectiveness of information retrieval systems. These findings provide both quantitative and qualitative insights into the evolution of query expansion research. The results also highlight the integration of modern technologies in retrieval practices and provide a foundation for new researchers to identify trends, research gaps, and opportunities for future innovation. REFERENCES Ahmed, M. (2024). Bibliometrix: An Easy Yet Powerful Approach for Quantitative and Qualitative Analyses of Scholarly Literature. Information Research Communications, 1(1), 43–45. https://doi.org/10.5530/irc.1.1.7 Al-Lahham, Y. (2024). Improved Arabic Query Expansion using Word Embedding. https://doi.org/10.21203/rs.3.rs-4065010/v1 Allahim, A., Cherif, A., & Imine, A. (2025). Semantic approaches for query expansion: Taxonomy, challenges, and future research directions. PeerJ Computer Science, 11, e2664. https://doi.org/10.7717/peerj-cs.2664 Baumann, O., & Schoenfeld, M. (2024). PSQE: Personalized Semantic Query Expansion for user-centric query disambiguation. https://doi.org/10.21203/rs.3.rs-4178030/v1 Bernard, N., & Balog, K. (2025). A Systematic Review of Fairness, Accountability, Transparency, and Ethics in Information Retrieval. ACM Computing Surveys, 57(6), 1–29. https://doi.org/10.1145/3637211 Breuer, T., Frihat, S., Fuhr, N., Lewandowski, D., Schaer, P., & Schenkel, R. (2025). Large Language Models for Information Retrieval: Challenges and Chances. Datenbank-Spektrum, 25(2), 71–81. https://doi.org/10.1007/s13222-025-00503-x Ganti, L., Persaud, N. A., & Stead, T. S. (2025). Bibliometric analysis methods for the medical literature. Academic Medicine & Surgery. https://doi.org/10.62186/001c.129134 Hambarde, K. A., & Proença, H. (2023). Information Retrieval: Recent Advances and Beyond. IEEE Access, 11, 76581–76604. https://doi.org/10.1109/ACCESS.2023.3295776 Hidri, M. (2024). Learning-Based Models for Building User Profiles for Personalized Information Access. Interdisciplinary Journal of Information, Knowledge, and Management, 19, 010. https://doi.org/10.28945/5275 Kaban, R., Sihombing, P., Efendi, S., & Lydia, M. S. (2025a). Enhancing Retrieval Performance in Social Media Using Corpus-Based Query Expansion. 2025 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 1–6. https://doi.org/10.1109/AIMS66189.2025.11229497 Kaban, R., Sihombing, P., Efendi, S., & Lydia, M. S. (2025b). Enhancing retrieval performance in social media with corpus-based query expansion using bidirectional encoder representations from transformers. Eastern-European Journal of Enterprise Technologies, 5(2 (137)), 70–83. https://doi.org/10.15587/1729-4061.2025.340258 Kumar, R. (2025). Bibliometric Analysis: Comprehensive Insights into Tools, Techniques, Applications, and Solutions for Research Excellence. Spectrum of Engineering and Management Sciences, 3(1), 45–62. https://doi.org/10.31181/sems31202535k Meliukh, V., Potapova, E., Nalyvaichuk, M., & Dychka, A. (2025). Query expansion based on context-dependent sentiment analysis in databases with domain-specific filtering. Eastern-European Journal of Enterprise Technologies, 1(2 (133)), 6–17. https://doi.org/10.15587/1729-4061.2025.322120 Naamha, E. Q., & Abdulmunim, M. E. (2024). Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12(1), 29–40. https://doi.org/10.14500/aro.11397 Pan, M., Liu, Y., Chen, J., Huang, E. A., & Huang, J. X. (2024). A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval. Scientific Reports, 14(1), 31806. https://doi.org/10.1038/s41598-024-82871-0 Pan, M., Xiong, W., Zhou, S., Gao, M., & Chen, J. (2025). LLM-Based Query Expansion with Gaussian Kernel Semantic Enhancement for Dense Retrieval. Electronics, 14(9), 1744. https://doi.org/10.3390/electronics14091744 Patel, V., Hiran, D., & Dangarwala, K. (2024). Recent Trends of Information Retrieval System: Review Based on IR Models and Applications. In V. K. Gunjan & J. M. Zurada (Eds.), Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (Vol. 873, pp. 619–629). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-9442-7_51 Peikos, G., & Pasi, G. (2024). A systematic review of multidimensional relevance estimation in information retrieval. WIREs Data Mining and Knowledge Discovery, 14(5), e1541. https://doi.org/10.1002/widm.1541 Raj, G. D., Mukherjee, S., Robin, C. R. R., & Jasmine, R. L. (2025). An Intelligent Feature Concatenation Process-Based Effective Query Expansion for Patent Retrieval Approach Using Optimal Bi-clustering and Enhanced Social Engineering Optimizer. International Journal of Computational Intelligence Systems, 18(1), 259. https://doi.org/10.1007/s44196-025-00963-9 Roberts, K. (2024). Information Retrieval. In H. Xu & D. Demner Fushman (Eds.), Natural Language Processing in Biomedicine (pp. 195–230). Springer International Publishing. https://doi.org/10.1007/978-3-031-55865-8_8 Stathopoulos, E. A., Karageorgiadis, A. I., Kokkalas, A., Diplaris, S., Vrochidis, S., & Kompatsiaris, I. (2023). A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics? Computers, 12(6), 119. https://doi.org/10.3390/computers12060119 Venkatachalam, C., & Venkatachalam, S. (2023). Optimal Intelligent Information Retrieval and Reliable Storage Scheme for Cloud Environment And E-Learning Big Data Analytics. In Review. https://doi.org/10.21203/rs.3.rs-2545685/v1 Vishwakarma, D., & Kumar, S. (2025). Fine-Tuned BERT Algorithm-Based Automatic Query Expansion for Enhancing Document Retrieval System. Cognitive Computation, 17(1), 23. https://doi.org/10.1007/s12559-024-10354-5 Vladlenov, D. (2023). MODERN METHODS OF APPLYING SCIENTIFIC THEORIES. Proceedings of the X International Scientific and Practical Conference, 1–481. https://doi.org/10.46299/ISG.P.2023.1.10 Wang, L., Yang, N., & Wei, F. (2023). Query2doc: Query Expansion with Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 9414–9423. https://doi.org/10.18653/v1/2023.emnlp-main.585 Wang, Z., & Pei, Q. (2024). Dense Retrieval Systems with LLM-Based Query Expansion. 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 682–686. https://doi.org/10.1109/WI-IAT62293.2024.00110 Ye, F., Fang, M., Li, S., & Yilmaz, E. (2023). Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting. Findings of the Association for Computational Linguistics: EMNLP 2023, 5985–6006. https://doi.org/10.18653/v1/2023.findings-emnlp.398 Yıldız, M., & Karakuş, T. (2024). Bibliometric Analysis in Scientific Research Using R: A Review of Scopus and Web of Science Databases. Journal of Data Applications, 0(2), 31–46. https://doi.org/10.26650/JODA.1462396 Zahhar, S., Mellouli, N., & Rodrigues, C. (2025). Leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval. In M. Barhamgi, H. Wang, X. Wang, E. Aïmeur, M. Mrissa, B. Chikhaoui, K. Boukadi, R. Grati, & Z. Maamar (Eds.), Web Information Systems Engineering – WISE 2024 PhD Symposium, Demos and Workshops (Vol. 15463, pp. 101–110). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-1483-7_8 Zhang, L., Wu, Y., Yang, Q., & Nie, J.-Y. (2024). Exploring the Best Practices of Query Expansion with Large Language Models (arXiv:2401.06311). arXiv. https://doi.org/10.48550/arXiv.2401.06311
Real-Time Air Quality Prediction Using Metrologically Calibrated Gas Sensors and Random Forest Algorithm
The increasing level of urban air pollution requires monitoring system that are capable not only of measurement but also real time prediction. Low coast gas sensor such as MQ-135 are widely used due to their affordability and ease of integration. However, these sensors exhibit limitations in terms of accuracy, signal stability, and drift characteristics. This research proposes a real time air quality prediction model based on gas sensor data using a machine learning approach integrated with metrological calibration. The system consists of a microcontroller base data acquisition module, aserver for data storage, and a predictive model deployed for real time computation. Data were collected over a controlled observation period with fixed sampling intervals. Preprocessing steps included regression based calibration, min max normalization, and noise reduction using a movig avarage filter. Three algorithms were evaluated Linear Regression, Random Forest, and Long Short-Term Memory. Model performance was assessed using Root Mean Square Error, Mean Absolute Error, and coefficient of determination. The results indicate that the Random Forest model achieved the lowest RMSE and demonstrated stable prediction performance under sensor signal fluctuations. The integration of calibration prior to model training significantly improved prediction accuracy compared to models without metrological correction. The proposed system provides reliable real-time air quality prediction and can support intelligent environmental monitoring and local decision-making processes. REFERENCES Alahi, M. E. E., Sukkuea, A., Tina, F. W., & Mukhopadhyay, S. C. (2020). Integration of IoT-enabled technologies for air quality monitoring and prediction. IEEE Internet of Things Journal, 7(10), 9871–9882. https://doi.org/10.1109/JIOT.2020.2994523 Chen, J., Li, X., Wang, Y., & Zhang, H. (2022). Comparative evaluation of machine learning models for air pollution forecasting. Atmospheric Environment, 268, 118804. https://doi.org/10.1016/j.atmosenv.2021.118804 Esposito, E., De Vito, S., Salvato, M., & Bright, V. (2021). Dynamic calibration of low-cost air quality sensors using machine learning techniques. Sensors, 21(12), 3989. https://doi.org/10.3390/s21123989 Gao, L., Zhang, D., & Li, J. (2020). Calibration and drift compensation of gas sensors using data-driven models. Sensors and Actuators B: Chemical, 305, 127451. https://doi.org/10.1016/j.snb.2019.127451 Hernandez, W., & Garcia, R. (2021). Data preprocessing strategies for improving air quality prediction accuracy. Environmental Monitoring and Assessment, 193, 512. https://doi.org/10.1007/s10661-021-09234-5 Khan, M. A., Kumar, R., & Gupta, S. (2023). IoT-based smart air quality monitoring systems: A review of recent developments. Sustainable Computing: Informatics and Systems, 38, 100871. https://doi.org/10.1016/j.suscom.2023.100871 Kim, J., Park, Y., & Lee, K. (2022). Impact of sensor uncertainty on machine learning-based environmental prediction systems. IEEE Transactions on Instrumentation and Measurement, 71, 1–10. https://doi.org/10.1109/TIM.2022.3145678 Kumar, P., Morawska, L., Martani, C., & Biskos, G. (2022). The rise of low-cost sensing for managing air pollution in cities. Environment International, 164, 107253. https://doi.org/10.1016/j.envint.2022.107253 Li, Z., Zhao, Y., Sun, W., & Chen, Q. (2023). Time-series prediction of air quality using LSTM and ensemble learning methods. Environmental Modelling & Software, 162, 105634. https://doi.org/10.1016/j.envsoft.2023.105634 Liu, H., Wei, X., & Zhang, Q. (2023). Hybrid deep learning architecture for spatiotemporal air quality forecasting. Applied Soft Computing, 134, 110029. https://doi.org/10.1016/j.asoc.2023.110029 Maag, B., Zhou, Z., & Thiele, L. (2021). A survey on sensor calibration in air quality monitoring deployments. ACM Computing Surveys, 54(3), 1–36. https://doi.org/10.1145/3448304 Park, S., Kim, D., & Lee, H. (2021). Noise reduction techniques for low-cost environmental sensor data. IEEE Sensors Journal, 21(14), 15947–15956. https://doi.org/10.1109/JSEN.2021.3071123 Rahman, M. M., Islam, M. R., & Hossain, M. S. (2021). Edge-based real-time environmental monitoring using machine learning. Future Generation Computer Systems, 121, 87–97. https://doi.org/10.1016/j.future.2021.03.021 Singh, A., Gupta, R., & Sharma, N. (2022). Ensemble learning models for urban air quality prediction. Environmental Science and Pollution Research, 29, 52312–52325. https://doi.org/10.1007/s11356-022-19654-3 Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., & Bonavitacola, F. (2022). Evaluation of low-cost gas sensors for air quality monitoring applications. Atmospheric Measurement Techniques, 15(2), 475–489. https://doi.org/10.5194/amt-15-475-2022 Torres, J., Martinez, A., & Ruiz, D. (2021). Real-time environmental monitoring framework integrating IoT and AI. Computer Networks, 191, 107977. https://doi.org/10.1016/j.comnet.2021.107977 Wang, T., Li, M., & Chen, L. (2023). Performance comparison of regression algorithms for PM2.5 prediction. Atmospheric Pollution Research, 14(1), 101601. https://doi.org/10.1016/j.apr.2022.101601 World Health Organization. (2023). Global air quality guidelines update 2023. WHO Press. Zhang, Y., Ding, A., Mao, H., & Fu, C. (2021). Machine learning approaches for air pollution prediction: A systematic review. Atmospheric Research, 250, 105348. https://doi.org/10.1016/j.atmosres.2020.105348 Zhou, X., Wang, S., & Liu, J. (2022). Real-time air quality prediction based on hybrid machine learning framework. IEEE Access, 10, 44321–44333. https://doi.org/10.1109/ACCESS.2022.3167890
Development of a Student-Centered Digital Mathematics Learning Prototype Using the ADDIE Model to Improve Conceptual Understanding
Mathematics learning in higher education still faces challenges in the form of low student conceptual understanding due to the dominance of procedural approaches and the suboptimal use of digital technology. Furthermore, there are still gaps in the development of structured, student-centered, and integrated learning designs in the form of prototype-based digital learning systems. This research aims to develop a student-centered digital mathematics learning prototype using the ADDIE model to improve conceptual understanding. This research employed a Research and Development (R&D) method with Analysis and Design stages. The results are a learning prototype design that includes learning objectives, material structure, student-centered learning strategies, digital media, student activities, and an evaluation system. The prototype is designed to support concept visualization, learning interactions, and exploration and problem-solving activities. The results indicate that the developed prototype has the potential to systematically and interactively improve the effectiveness of mathematics learning. In conclusion, this prototype can serve as the basis for developing innovative digital mathematics learning. The novelty of this research lies in the integration of the ADDIE model with a student-centered approach in the design of a comprehensive digital learning prototype. REFERENCES Anisa Ulva Wahyuni & Hasanuddin. (2025). Teknologi Digital dalam pembelajaran Matematika: Tinjauan Bibliometrik terhadap Dampaknya pada Pemahaman Konsep Matematis Siswa. Buana Matematika : Jurnal Ilmiah Matematika Dan Pendidikan Matematika, 15(1), 41–56. https://doi.org/10.36456/buanamatematika.v15i1.10341 Cevikbas, M., Greefrath, G., & Siller, H.-S. (2023). Advantages and challenges of using digital technologies in mathematical modelling education – a descriptive systematic literature review. Frontiers in Education, 8, 1142556. https://doi.org/10.3389/feduc.2023.1142556 Fawns, T., Ross, J., Carbonel, H., Noteboom, J., Finnegan-Dehn, S., & Raver, M. (2023). Mapping and Tracing the Postdigital: Approaches and Parameters of Postdigital Research. Postdigital Science and Education, 5(3), 623–642. https://doi.org/10.1007/s42438-023-00391-y Gourlay, L., Rodríguez-Illera, J. L., Barberà, E., Bali, M., Gachago, D., Pallitt, N., Jones, C., Bayne, S., Hansen, S. B., Hrastinski, S., Jaldemark, J., Themelis, C., Pischetola, M., Dirckinck-Holmfeld, L., Matthews, A., Gulson, K. N., Lee, K., Bligh, B., Thibaut, P., … Knox, J. (2021). Networked Learning in 2021: A Community Definition. Postdigital Science and Education, 3(2), 326–369. https://doi.org/10.1007/s42438-021-00222-y Hafidz, M. A., Herlambang Cahya Pratama, Y., & Maulidya Effendi, P. (2024). Design Thinking: Pengembangan UI/UX Aplikasi Evaluasi Pembelajaran Mata Kuliah Berbasis Web. Jurnal Informatika Polinema, 10(3), 413–420. https://doi.org/10.33795/jip.v10i3.5176 I Gusti Agung Trisna Jayantika & Gaudensia Namur. (2022). PERAN TEKNOLOGI PEMBELAJARAN DALAM MENINGKATKAN LITERASI DIGITAL MATEMATIKA. https://doi.org/10.5281/ZENODO.7033331 Kaban, R., Sembiring, D. J., & Tarigan, I. M. B. (2023). Monitoring System for Student Internships Using the Rapid Application Development (RAD) Method. 15(02). Neef, T., Müller, S., & Mechtcherine, V. (2024). Integrating continuous mineral-impregnated carbon fibers into digital fabrication with concrete. Materials & Design, 239, 112794. https://doi.org/10.1016/j.matdes.2024.112794 Perez, A. S., Nieto-Jalil, J. M., Chim, A. I. T., Huerta, J. M. M., & Cavazos, L. L. (2025). Scientific Research Model Applied to Mathematical Modeling and Prototype Construction. 2025 IEEE Global Engineering Education Conference (EDUCON), 1–7. https://doi.org/10.1109/EDUCON62633.2025.11016324 Sa’dijah, C., Anwar, L., Hidayah, I. R., Abdullah, A. H., & Cahyowati, E. T. D. (2024). Mathematics learning models based on local wisdom of Malang to support critical and creative thinking of secondary school students. 030025. https://doi.org/10.1063/5.0234944 Salinas, P., González-Mendívil, E., Quintero, E., Ríos, H., Ramírez, H., & Morales, S. (2013). The Development of a Didactic Prototype for the Learning of Mathematics through Augmented Reality. Procedia Computer Science, 25, 62–70. https://doi.org/10.1016/j.procs.2013.11.008 Soomro, S. A., Casakin, H., & Georgiev, G. V. (2021). Sustainable Design and Prototyping Using Digital Fabrication Tools for Education. Sustainability, 13(3), 1196. https://doi.org/10.3390/su13031196 Swist, T., Gulson, K. N., & Thompson, G. (2024). Education Prototyping: A Methodological Device for Technical Democracy. Postdigital Science and Education, 6(1), 342–359. https://doi.org/10.1007/s42438-023-00426-4 Syafril, S., Asril, Z., Engkizar, E., Zafirah, A., Agusti, F. A., & Sugiharta, I. (2021). Designing prototype model of virtual geometry in mathematics learning using augmented reality. Journal of Physics: Conference Series, 1796(1), 012035. https://doi.org/10.1088/1742-6596/1796/1/012035 Tarigan, I. M. B., Tarigan, S. J. B., & Ginting, R. B. (2024). Optimalisasi Efektivitas Program MBKM: Sistem Monitoring Berbasis Lokasi dan Analisis aktivitas dengan TF-IDF. 6(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 Taufikurrahman, Budiyono, & Slamet, I. (2021). Development of mathematics module based on meaningful learning. 040032. https://doi.org/10.1063/5.0043239
Performance Analysis of Mesh Network Implementation Using Mikrotik RouterBoard 941 in Radio Link-Based Secure Networks
The increasingly rapid development of computer network technology demands a network system that is reliable, flexible, and able to adapt to dynamic environmental conditions. One of the network technologies that is currently developing is mesh networking. Mesh networking is a network topology where each node can be connected to each other directly or indirectly through other nodes. This research aims to analyze the application of the mesh networking method using the Mikrotik RouterBoard 941 device. The research method used is experimental by configuring, implementing, and testing mesh networking on the Mikrotik RouterBoard 941. The results of the research show that mesh networking can be applied to the Mikrotik RouterBoard 941 by utilizing available features, such as OLSR (Optimized Link State Routing) and WDS (Wireless Distribution System). Mesh networking is able to increase redundancy and network availability, and can adapt to changes in network topology. However, mesh networking also has several disadvantages, such as configuration complexity, routing overhead, and the possibility of bottlenecks at certain nodes. REFERENCES Adiguna, M. A. (2022). WPA2-PSK network security analysis using penetration testing method (Case study: TP-Link Mercusys MW302R router). Anhar. (2020). Jaringan komputer dan internet. Informatika. Athailah. (2020). Bandwidth management in optimizing the use of Mikrotik routers for network connection services. Journal of Informatics, 7(2), 70–78. Brown, S. (2021). Computer network development system. McGraw-Hill. Cisco Networking Academy. (2020a). Introduction to networks: OSI model. Cisco Press. Cisco Networking Academy. (2020b). The application of virtual local area network. Journal of Electrical Engineering Innovation, 1(1), 70–80. Danniel. (2021). Comparison of IPv4 and IPv6 in building networks. International Journal of Computer Networks, 3(2), 30–40. Efendi, M. Y. (2021). Comparative analysis of 128-bit WEP and WPA wireless security methods to improve wireless security. Journal of Information Security, 5(1), 10–18. Goldsmith, A. (2020). Wireless communications. Cambridge University Press. Hamacher, V. C. (2020). Decision-making model under complex picture fuzzy Hamacher aggregation operators. Journal of Intelligent Systems, 9(8), 47–68. Harefa, M. F. (2021). User data security applications and wireless security systems using two-factor authentication and MAC address filtering. Journal of Computer Security, 4(1), 20–28. Harmayani, H., Abdilah, D., Mapilindo, M., Oktopanda, O., & Hutahaean, J. (2021). Aplikasi komputer. Yayasan Drestanta Pelita Indonesia. Heart, A. (2020). Analysis and implementation of Mikrotik router security system from Winbox exploitation, brute-force, and DoS attacks. Jurnal Media Informatika Budidarma, 4(1), 57–59. Jackson, M. (2020). Hierarchical network design-based network security system design. International Journal of Network Security, 5(8), 1–9. John, A. (2021). Analysis and application of Mikrotik RB890. Journal of Network Engineering, 2(6), 50–60. Kamila, M. (2024). Design and construction of practicum module simulation of microwave link installation on mobile systems based on virtual reality technology. Kozierok, C. M. (2021). The TCP/IP guide: A comprehensive, illustrated internet protocols reference. No Starch Press. Madcoms. (2020). Network security implementation using port blocking and port knocking methods on Mikrotik RB941. Journal of ICT: Information Communication & Technology, 5(1), 16–17. Michael, J. (2021). OSI model fundamentals. Computer Networks Journal, 1(5), 1–8. Molisch, A. F. (2021). Wireless communications (2nd ed.). Wiley. Prayoga, J. (2021). Comparison of WPA2 EAP-PSK authentication system on wireless networks using penetration testing method with fluxion tools. Journal of Cyber Security, 3(1), 1–10. Permatasari, U. S., & Widiasari, I. R. (2020). Analisis routing protokol optimized link state routing (OLSR) pada Raspberry Pi. Jurnal Teknologi Informasi (AITI), 16(2), 151–164. Primaned, A., Widyawan, W., & Kusumawardani, S. S. (2014). Unjuk kerja routing protocol OLSR pada wireless mesh network berbasis IEEE 802.11b/g. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 1(1), 8–12. Defri, E. O., Kusmaryanto, S., & Mustofa, A. (2019). Implementasi jaringan access point dengan wireless distribution system (WDS) berbasis Mikrotik. Jurnal Mahasiswa Teknik Elektro Universitas Brawijaya, 7(7). Politeknik Sawunggalih Aji. (2021). Rancang bangun wireless mesh network menggunakan routing OLSR. Jurnal Nasional Teknik Informatika, 9(2). Sofana, I. (2021). Jaringan komputer berbasis TCP/IP. Informatika. Stevens, W. R. (2020). TCP/IP illustrated (Vol. 1). Addison-Wesley. Supendar, H., & Handrianto, Y. (2017). Simple queue in resolving bandwidth management issues on the Mikrotik bridge. Bina Insani ICT Journal, 4(1), 21–30. Sutiono. (2020). Sistem keamanan jaringan. Deepublish. Tanenbaum, A. S., & Wetherall, D. J. (2020). Computer networks (5th ed.). Pearson. Wheeb, A. H., Nordin, R., Samah, A. A., & Kanellopoulos, D. (2023). Performance evaluation of standard and modified OLSR protocols. Electronics, 12(6), 1334. Widiyawati, S. (2020). Network security system firewall using port blocking and firewall filtering methods. Journal of Information Security, 5(2), 78–96. Wirano, B. (2020). Policy analysis from formulation to implementation. Deepublish.
Performance Analysis of Mesh Networking Implementation on Mikrotik Router Board 941
The increasingly rapid development of computer network technology demands a network system that is reliable, flexible, and able to adapt to dynamic environmental conditions. One of the network technologies that is currently developing is mesh networking. Mesh networking is a network topology where each node can be connected to each other directly or indirectly through other nodes. This research aims to analyze the application of the mesh networking method using the Mikrotik RouterBoard 941 device. The research method used is experimental by configuring, implementing, and testing mesh networking on the Mikrotik RouterBoard 941. The results of the research show that mesh networking can be applied to the Mikrotik RouterBoard 941 by utilizing available features, such as OLSR (Optimized Link State Routing) and WDS (Wireless Distribution System). Mesh networking is able to increase redundancy and network availability, and can adapt to changes in network topology. However, mesh networking also has several disadvantages, such as configuration complexity, routing overhead, and the possibility of bottlenecks at certain nodes. REFERENCES Aisyah, A. (2022). Mesh Network Model On Internet Of Things (IoT) Systems For Environmental Monitoring. Ardhitya, A. I. (2021). Definition and Explanation of Microtics. Available at Http://Ilmukomputer. org/2013/01/04/Definition-and-Explanation-Mikrotik/. Accessed, 20. Arman, M., & Kasran, K. (2023). Wireless Network Analysis on IoT-Based ATM Machines at PT. Bank Negara Indonesia (Persero) Tbk KCP Watansoppeng. Scientific Journal of Information Systems and Informatics Engineering (JISTI), 6(1), 77–84. https://doi.org/10.57093/jisti.v6i1.151 Bahtiar, D., Febrianto, W. J., Maulana, A., Saputra, S., Darmawan, W., Tafonao, R. P., Julianto, R., Zai, R., & Djutalov, R. (2021). Basic Introduction to Computer Network InstallationUsing Mikrotik. Informatics Student Creativity, 2, 507–518. Fahmi Faizar, F. (2020). The effect of Bluetooth 5.0 interference on 802.11b network performance. 2(10), 1390–1399. Fahriani, N. (2024). From Wired to Wireless:(Evolution and Innovation of Modern Networks). Hariyanto, T., & Rahayu, M. (2021). The WiFi bandwidth system of ad-hoc networks uses the class-based queue method. JITEL (Scientific Journal of Telecommunications, Electronics, and Power Electricity), 1(1), 17–24. https://doi.org/10.35313/jitel.v1.i1.2021.17-24 Iqbal, M., & Tambunan, L. (2021). Designing samba servers using ubuntu servers and network configuration using mikrotik routerboards (case study of pt. Mesitechmitra purnabangun). JSR: Robotic Information Systems Network, 5(1), 1–8. Juniarti, T. S. J. (2025). Wireless Mesh Network Implementation Strategy for Wireless Network Improvement and Reliability. Journal of Software Engineering and Information Systems (SEIS), 98–107. Kurniasih, D., & Rusfiana, Y. (2021). Analytical Techniques. Nugroho, H. A. S. A., Hartati, S., & Sonhaji, S. (2023). Comparative analysis of OSPF and static routing protocols for the optimization of xyz high school computer networks. Transformation, 18(2), 1–11. https://doi.org/10.56357/jt.v18i2.310 Oktafiandi, H. (2021). Design and build a wireless mesh network using ad-hoc Optimized Link State Routing (OLSR). Journal of Economics and Informatics Engineering, 9(2), 70–75. Putra, F. P. E., Arissandi, D. E., Rofiqi, A., & Hidayat, M. F. (2025). The Utilization of Mikrotik in Bandwidth Management in School Networks. Journal of Informatics and Computer Technology, 5. Rahman, A., & Nurwarsito, H. (2020). Performance analysis of is-is routing protocol and eigrp routing protocol on mesh topology network. Journal of Information Technology and Computer Science Development, 4(11), 4139–4147. Siddik, M., Lubis, A. P., & Sahren, S. (2023). Optimizing Internet Network Speed in Mts Daarussalam Using the Simple Queue Method. Journal of Science and Social Research, 6(1), 117. https://doi.org/10.54314/jssr.v6i1.1179 Simanjuntak, E. (2021). Analysis of Students' Learning Difficulties in Mixed Calculation Operation Material in Grade IV of Sd Negeri 067246 Medan Academic Year 2020/2021. Siswanto, D. (2021). Implementation of Wireless Mesh Network on Local Area Network (LAN) Network. Journal of Science and Social Research, 4307(1), 20–27. Tarigan, I. S. B. (2020). Analysis of Students' Difficulties in Learning to Listen in Class V of Sdn 048232 Kabanjahe Academic Year 2019/2020. Toyib, R., Wijaya, A., & Apridiansyah, Y. (2024). The implementation of the Point to Point method uses Mikrotik Router Board Type RB411AH for internet network access. Decode: Journal of Information Technology Education, 4(1), 225–238. Yastianto, S. (2021). Design and build a VLAN network using the Routing Information Protocol (RIP) method using a Cisco router in the Department of Computer Engineering of the Police.
Development of an Arduino-Based Truck Load Detection System for Bridge Safety Monitoring
The safety of bridge structures is very important to consider, especially in withstanding the weight of passing trucks. Overloading can cause structural damage that can potentially jeopardize the safety of bridge users. Therefore, a truck load weight detection system is needed that is able to monitor the load in real-time and provide early warning in the event of overloading. This research aims to design and implement an Arduino-based truck load weight detection system installed on the bridge. This system uses a load sensor (load cell) to measure the weight of passing vehicles, where the weight data is then processed by an Arduino arduiuno. The system is equipped with a wireless communication module that allows weight data to be sent directly to the control center or bridge operator. The results of system testing show that this system is able to detect the weight of truck loads with a good level of accuracy. With this system, it is expected to improve bridge safety and provide accurate information related to the distribution of loads passing over the bridge. REFERENCES Arsyad, O. R., & Kartika, K. P. (2021). Design and build safe safety devices using Arduino-based fingerprint sensors. JATI (Student Journal of Informatics Engineering), 5(1), 1–6. Gunawan, G., & Ardiyansyah, M. R. (2023). Design and build an Arduino-based photovoltaic panel performance tester. State Polytechnic at the End of the Line. Handiko, Y. T. (2022). Design and Build a Digital Scale Model Using Load Cell Sensors and IoT-Based Scale Recording. Kartika Riyanti, K. P., Kakaravada, I., & Ahmed, A. A. (2022). An Automatic Load Detector Design to Determine the Strength of Pedestrian Bridges Using Load Cell Sensor Based on Arduino. Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, 4(1), 15–22. https://doi.org/10.35882/ijeeemi.v4i1.3 Kazuya, A. S., Ariyadi, T., Dasmen, R. N., & Fitriani, E. (2024). Design of Digital-Based Scales Equipped with Metal Detectors as Metal Sensors. Journal of Tambusai Education, 8(1), 14261–14277. Khristianto, W. & et al. (2022). Management Information System: The purpose of the Management Information System. In CV. Pena Persada (April Issue). Kurnia, R., Firdaus, R., Lufti, L., & Anshor, M. H. (2019). Load cell sensor automation to overcome vehicle overload. National Journal of Electrical Engineering, 8(2), 81. https://doi.org/10.25077/jnte.v8n2.666.2019 Laili, D. T., & Bahri, S. (2022). Prototype of Car Parking System Using Load Cell Sensor with Android-Based Arduino Mega 2560. Coding Journal of Computers and Applications, 8(1). Nurlaila, N., Paembonan, S., & Suppa, R. (2024). Design Arduino-based vehicle speed detection. Journal of Informatics and Applied Electrical Engineering, 12(3). Pranita, E. (2023). Automatic bridge control uses Arduino-based ultrasonic sensors. ICTEE Journal, 4(2), 13. https://doi.org/10.33365/jictee.v4i2.3143 Rachmawati, P. (n.d.). Digital Scale Simulation Design Using HX711 Sensor with Additional ESP32-Based Buzzer. Ridwan, M., Widiastiwi, Y., Zaidiah, A., Purabaya, R. H., Isnainiyah, I. N., Ardilla, Y., & Rahayu, T. (2021). Management Information Systems. Widina Publisher. Rizal-Alfariski, M., Dhandi, M., & Kiswantono, A. (2022). Automatic Transfer Switch (ATS) Using Arduino Uno, IoT-Based Relay and Monitoring. Journal of Telecommunication Systems, Electronics, Control Systems, Power Systems and Computers, 2(1), 1–8. Rustandi, A. (2020). Monitoring Current and Electrical Power with Notification System from Smartphones in Internet of Things (IoT)-Based Household Electrical Installations. Indonesian Computer University. Sibuea, S., & Saftaji, B. (2020). The design of the vehicle load monitoring system uses load cell sensor technology. Journal of Informatics and Computer Technology, 6(2), 144–156. https://doi.org/10.37012/jtik.v6i2.309 Simanjuntak, R. S. (2023). Design and build "Arduino Nano-based Earthquake Alarm Automatic Switch. Sunardi, R. A., Wijaya, S. H., Hidayat, I., & Noerdyah, P. S. (2024). Design and Build Automatic Door Locks Based on Arduino Arduiunos Using RFID and SIM900 as Security Systems. Journal of Industrial Engineering, Information Systems and Informatics Engineering, 3(1). Widharma, I. G. (2021). Arduiuno Textbook (Chapter Six). Wiguna, A. R. (2020). Analysis of how ultrasonic sensors and servo motors work using Arduino Uno arduiunos for pest control in rice fields. OSF PREPR.
Naive Bayes Classification for Construction Workforce Requirement Prediction
The determination of construction worker requirements at the Langkat Regency Public Works and Spatial Planning Office (PUPR) is still conducted manually, often resulting in inaccurate workforce allocation and inefficiencies in project implementation. Therefore, a data-driven approach is needed to improve prediction accuracy. This study aims to apply the Naive Bayes algorithm to predict construction worker needs based on historical project data. This research uses a quantitative approach with a classification method. The dataset includes variables such as project type, budget, duration, and previous labor usage. Data processing involves preprocessing, training, and testing, with a data split of 80% for training and 20% for testing. The Naive Bayes model is then evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the Naive Bayes algorithm achieves an accuracy of 86.7%, precision of 84.5%, recall of 87.2%, and F1-score of 85.8%. The model is capable of classifying workforce needs into low, medium, and high categories effectively. In conclusion, the Naive Bayes algorithm provides a reliable and efficient method for predicting construction worker requirements, supporting better decision-making and workforce planning at the Langkat Regency PUPR Office. REFERENCES Agustina, F. D., Arif, M., & Ahmad, S. (2025). Systematic Literature Review atas Kinerja Algoritma KNN, Naïve Bayes, dan Decision Tree pada Berbagai Studi Prediksi dan Klasifikasi. Jurnal Jawara Sistem Informasi, 3(1). Andika, A., Syarli, S., & Sari, C. R. (2022). Data Mining Klasifikasi Kelulusan Mahasiswa Menggunakan Metode Naïve Bayes. 4(1), 423–428. Anggreni, A., Hakim, A., Rahman, A., & Dinata, M. I. (2026). Klasifikasi Partisipasi Pemilih pada Pemilihan Walikota Bima Tahun 2024 Menggunakan Metode Naive Bayes Classifier. Jurnal Sains Natural, 4(1), 1–13. Azhar, S., Informatika, J. T., & Teknik, F. (2019). Decision Tree Dalam Memprediksi Kelulusan. 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