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JEECS (Journal of Electrical Engineering and Computer Sciences)
Published by Universitas Bhayangkara Surabaya
ISSN : 25280260     EISSN : 25795392     DOI : https://doi.org/10.54732/jeecs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
We aims to promote high-quality Electrical Engineering and Computer Sciences research among academics and practitioners alike, including power system, electrical engineering, industry automation, mechatronics, computer sciences, informatics, and information system. This journal is dedicated for the author or researcher who has focused in the field of technology and intending on publication and sharing knowledge the novel technology include, but are not limited to, the following topics: Data Mining, Informatics algorithm methodology, Mobile Computing, Automation, Power, Green Technology, Advanced Computer Networks, Image Processing, Computer Vision, Robotics Technology, Decision Support System, Big Data, Data Sciences, Internet of Things, Network Security, Virtual Reality, etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 424 Documents
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Correlation-Driven Root Cause Analysis from Multi-Service Logs via Template-Aware Graph Inference: An Empirical Study on LogHub Hadoop and RCAEval Sock Shop Qi Xin
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 2 (2026): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i2.3

Abstract

Root cause analysis (RCA) has become urgent in AIOps because modern incidents rarely stay inside one component: failures propagate across services and operators must identify the initiating service, not only the loudest symptom. Prior log analytics studies have mainly emphasized parsing and anomaly detection, while recent microservice RCA benchmarks have focused on service localization with multimodal telemetry. This paper studies a narrower and practical question: how far can correlation-driven RCA go when only multi-service logs and their event templates are used? We present a template-aware correlation-graph framework that converts raw logs into service-template events, learns directed lagged edges, and ranks root-cause candidates using direct anomaly evidence, one-step edge attribution, and reverse personalized PageRank. Experiments are conducted on the complete LogHub Hadoop logs and the RCAEval RE2-SS Sock Shop data. The Hadoop corpus contains 394,310 raw log lines, 55 applications, and 44 labeled abnormal applications. RE2-SS contains 90 fault cases, five root services, six fault types, and 7,566,220 log rows. On RE2-SS, the proposed HybridRCA reaches 0.267 Top-1, 0.456 Top-3, and 0.435 MRR for service-level root localization, improving Top-1 and MRR over frequency-only ranking. A metric-only reference reaches 0.578 Top-1, showing that log-only RCA remains limited for resource faults whose strongest symptoms appear in metrics rather than logs. The results support correlation graphs as useful diagnostic evidence while clarifying the unresolved weakness of log-only RCA in multimodal microservice incidents.
Data-Driven Sentiment Analysis of Public Opinion on National Football Team Coaching Using Naïve Bayes Luki Ardiantoro; Nisa Ayunda; Muhammad Irfan
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 2 (2026): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i2.4

Abstract

The appointment of a national football team coach often generates intense public discourse, particularly on social media, where opinions are expressed rapidly and at scale. However, such evaluations are typically subjective and lack a systematic, data-driven foundation. This study proposes a quantitative approach to assess public sentiment toward the Indonesian national team coach using sentiment analysis based on the Naïve Bayes classifier within the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. A dataset of 2,000 Indonesian-language tweets was collected from social media X and processed through data cleansing, normalization, tokenization, and sentiment labeling. The Naïve Bayes model was applied to classify sentiment polarity, and its performance was evaluated using accuracy, precision, recall, and ROC–AUC metrics. The results show that the model achieves an accuracy of 94.17%, with precision of 92.86% and recall of 94.55%, indicating strong classification performance. However, the AUC value of 0.686 suggests moderate discriminative ability. The findings reveal that public sentiment toward the coach is predominantly positive, reflecting increased supporter confidence. This study demonstrates the effectiveness of data-driven approaches for objective evaluation in sports analytics and highlights opportunities for improvement using advanced machine learning techniques.
Body Mass Index Estimation Based on Height and Weight Using Image Processing Muhammad Muchlis Abimanyu; Anindita Septiarini; Masna Wati; Andi Tejawati
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 2 (2026): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i2.5

Abstract

Body Mass Index (BMI) is a widely used indicator for evaluating body proportions and individual health status. Traditional measurement methods require direct physical contact and are time-consuming. Previous studies have utilized image-based BMI measurement using controlled backgrounds. However, this limitation restricts their application in real-world settings. Moreover, no studies have combined background-independent segmentation with a camera-parameter-based conversion from pixels to physical units, leaving accurate, background-agnostic BMI estimation an open problem. This research addresses this gap using digital image processing on front-view and side-view human body images. The proposed methods include body segmentation using the U²-Net deep learning model, height estimation using pinhole camera projection approach that accounts for camera parameters (focal length, sensor size, camera distance), and weight estimation based on body volume modeling using an elliptical tube slice geometry. The body is treated as a stack of elliptical cross-sections whose width and depth are read from the front- and side-view silhouettes and summed into a total volume. Calibration and correction factors were determined through numerical optimization to obtain optimal global values. The dataset consists of 120 body images collected from 20 subjects. Experimental results show that the system achieves an accuracy of 96.2% (MAE 6.0 cm, RMSE 7.6 cm, MAPE 3.8%) for height estimation and 82.3% (MAE 9.7 kg, RMSE 13.2 kg, MAPE 17.7%) for weight estimation. BMI category classification accuracy reaches 60%. The use of U²-Net enables body segmentation without requiring a controlled background, making the system more flexible and applicable for real-world environments.
Rainfall Classification in Palembang Using Machine Learning Methods M Faris Al Amin; Keviano Alessandro Delpiero; Rendra Gustriansyah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 2 (2026): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i2.6

Abstract

Rainfall is an important weather parameter that affects various sectors, including agriculture, transportation, and disaster mitigation. As a tropical region, Palembang experiences varying rainfall patterns, making effective rainfall classification essential. However, previous studies have generally focused on specific machine learning methods, used a limited number of weather variables, or classified rainfall into only two categories. These differences make it difficult to understand how various machine learning methods perform when applied to multiclass rainfall classification using more complete meteorological data. Therefore, this study aims to classify rainfall in Palembang using machine learning methods, namely Random Forest, Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The dataset used in this study consists of meteorological data obtained from the Sultan Mahmud Badaruddin II Meteorological Station, Palembang, with an initial total of 365 records. The research stages included data preprocessing, rainfall labeling into three categories (No Rain, Rain, and Extreme Rain), data splitting into training and testing sets, and classification using five machine learning algorithms. The results indicate that the Support Vector Machine (SVM) achieved the best performance, with an accuracy of 72,72%, precision of 74.58%, recall of 48.96%, and F1-score of 72.61%. Based on these findings, SVM is considered more effective than the other methods for classifying rainfall using meteorological data in Palembang.

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