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Electrical Engineering Department Faculty of Engineering State University of Gorontalo Jenderal Sudirman Street No.6, Gorontalo City, Gorontalo Province, Indonesia
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Jambura Journal of Electrical and Electronics Engineering
Published by Universitas Negeri Gorontalo
ISSN : 26547813     EISSN : 27150887     DOI : 10.37905/jjeee
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Jambura Journal of Electrical and Electronics Engineering (JJEEE) is a peer-reviewed journal published by Electrical Engineering Department Faculty of Engineering, State University of Gorontalo. JJEEE provides open access to the principle that research published in this journal is freely available to the public to support the exchange of knowledge globally. JJEEE published two issue articles per year namely January and July. JJEEE provides a place for academics, researchers, and practitioners to publish scientific articles. Each text sent to the JJEEE editor is reviewed by peer review. Starting from Vol. 1 No. 1 (January 2019), all manuscripts sent to the JJEEE editor are accepted in Bahasa Indonesia or English. The scope of the articles listed in this journal relates to various topics, including: Control System, Optimization, Information System, Decision Support System, Computer Science, Artificial Intelligence, Power System, High Voltage, Informatics Engineering, Electronics, Renewable Energy. This journal is available in online and highly respects the ethics of publication and avoids all types of plagiarism.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 228 Documents
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Classification of Pneumonia Severity in Children Using the Fuzzy K-Nearest Neighbor Method Based on Patient Clinical Data Rahmat Thaib; Betrisandi Betrisandi
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.34624

Abstract

Pneumonia is one of the most deadly acute respiratory infections in children, especially in the toddler age group. Indonesia ranks eighth among 15 countries with the highest pneumonia mortality rate, namely 22,000 toddler deaths per year. Pneumonia can be caused by various microorganisms such as viruses, fungi, and bacteria. The occurrence of pneumonia is characterized by symptoms of cough and/or difficulty breathing such as rapid breathing and lower chest wall indrawing. The diagnosis of pneumonia is generally based on a combination of clinical symptoms such as fever, cough, rapid breathing, and physical examination results such as physical or radiological, however, the diagnostic process often encounters obstacles, such as limited trained medical personnel, limited diagnostic tools and subjectivity in assessing symptoms, especially in children who are not yet able to communicate their complaints clearly. This study aims to classify pneumonia based on symptoms and severity, namely severe pneumonia and mild pneumonia in children to assist medical personnel in making more accurate and efficient decisions. The results of this study indicate that the Fuzzy K-Nearest Neighbor method with k=3 and m=2 produces an accuracy of 62.67%, precision of 65.91%, recall of 69.05%, F1-Score of 67.44%, and a deviation of ±8.00% in classifying pneumonia in children.
Application Of The Silver Meal Heuristic Method For Optimizing Tempe Raw Material Inventory Costs On The Web Aplication Mohamad Isro' Eddy; Rahmat Deddy Rianto Dako; Idham Halid Lahay; Wahab Musa; Ifan Wiranto; Amirudin Yunus Dako
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.34572

Abstract

The tempeh industry requires an accurate raw material inventory management system to reduce costs and maintain production continuity. At the HB Azaki Tempeh Home Industry, stock management is still carried out conventionally, which often results in inaccuracies in determining the quantity and timing of orders. This study aims to develop a web inventory planning application that implements the Silver Meal heuristic method to calculate the optimal order quantity based on actual demand data. The application development follows the Rapid Application Development (RAD) method and system testing is carried out using black box testing. The results show that the Silver Meal method is able to reduce total inventory costs from Rp6,480,000 to Rp5,690,404, or an efficiency of 12.23% with a decrease in ordering frequency from 18 to 9 times. The scientific contribution of this study lies in the integration of the Silver Meal method into a web application that allows calculations to be carried out automatically, quickly, and with minimal manual errors. This research provides practical benefits for the small-scale tempeh industry as a reference in making more efficient and measurable raw material ordering decisions.
Design of Axial Flux Generator for Vertical Wind Turbine Power Plant Adi kurniawan saputro; Riza Alfita; Oryza Sativa Nugroho; Achmad Fiqhi Ibadillah; Kunto Aji Wibisono
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.34141

Abstract

This research discusses the design and testing of an axial flux generator using neodymium iron boron (NdFeB) permanent magnets for use in vertical wind turbines at low wind speeds. The main objective of the research is to create a generator design that is compact, efficient, and can work well in low wind speed conditions. The development method begins with calculating the number of poles, coils, windings, and the maximum magnetic field. Next, 2D and 3D designs of the rotor and stator are made with a configuration of 6 pairs of poles or 12 magnets and 9 three-phase coils with a design capacity of 100 watts. After the design is completed, tests are carried out using resistor loads and various lamps to measure the generator's performance. The test results show that the generator is able to produce different efficiencies depending on the load and rotational speed. In the test using a resistor, the highest efficiency was achieved at 89.5% at a speed of 402.8 RPM, while in the test with a lamp load, the electrical efficiency reached 98.46% at a load of 1.5 watts at a speed of 1501 RPM. However, efficiency tends to decrease at higher loads due to increased copper losses in the windings. In conclusion, the designed axial flux generator is effective for use in vertical wind turbines at low speeds, although it still requires design improvements to maintain stable performance at high loads.
Control and Monitoring System for a Three-Phase Induction Motor Based on Arduino Mega and VSD Integrated with LabVIEW Fahrul Marcello Rombon; Kevind Lefinro Rompas; Natanael Hendriko Lombok; Sukandar Sawidin; Yoice R. Putung; Anthoinete P.Y. Waroh
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39876

Abstract

Three-phase induction motors are widely utilized in industrial applications due to their high efficiency and reliability. This study aims to design and implement a real-time control and operational parameter monitoring system for a three-phase induction motor. The system integrates an Arduino Mega as the main data processor, a Variable Speed Drive (VSD) for speed control, and Solid State Relays (SSR) along with contactors as actuators. Monitoring of electrical and mechanical parameters is performed using ACS712 sensors (current), ZMPT101B sensors (voltage), and Hall Effect sensors (speed/RPM), with the results displayed on a LabVIEW interface. The system evaluation and validation method was carried out by comparing sensor readings against standard measuring instruments (digital multimeter and digital tachometer) across various operating frequency variations. The test results show that the system is capable of stable operation with an average sensor measurement error (mean error) of 0.35%. The novelty of this research lies in the integration of a responsive, cost-effective, multi-parameter control and monitoring platform equipped with automatic data logging within a single integrated HMI interface, which is ready to be applied for research as well as industrial automation laboratory practices.
Aspect-Based Sentiment Analysis (ABSA) of Ventela Shoe Reviews on TikTok Shop Using Fine-Tuned IndoBERT Fitrawansyah Butas; Amiruddin Bengnga; Maryam Hasan; Rezqiwati Ishak; Rofiq Harun; Andi Kamaruddin
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39805

Abstract

The massive volume of consumer reviews on the social commerce platform TikTok Shop makes it difficult for local shoe brands such as Ventela to understand consumer perception in a structured manner, while Indonesian-language Aspect-Based Sentiment Analysis (ABSA) studies on this platform remain very limited. This study aims to apply fine-tuned IndoBERT for aspect-based sentiment classification and to measure consumer perception of four product aspects, namely Comfort, Design, Durability, and Price. Using a computational experiment approach, 1,000 reviews were collected, automatically annotated using a lexicon-based method with negation handling, restructured into 706 review-aspect pairs and divided using an 80:20 stratified split, and used to train and compare three models: TF-IDF with Logistic Regression, TF-IDF with Linear SVM, and fine-tuned IndoBERT. Testing on 142 test samples shows that fine-tuned IndoBERT is superior, achieving an Accuracy of 0.8521 and an F1-Macro of 0.7813 and surpassing both baselines on four of five primary metrics. Analysis of 706 review-aspect pairs identifies Design (75.6% positive) and Price (71.8% positive) as the main strengths, while Comfort (32.7% negative) and Durability (30.8% negative) emerge as improvement areas related to sizing and the quality of adhesive and stitching. This study enriches Indonesian ABSA literature in the social commerce domain and delivers a ready-to-use web-based simulator built with Gradio to facilitate periodic consumer-perception monitoring for data-driven decision-making processes.
Development of a Real-Time Multiplayer Web-Based Mathematical Puzzle Game for Geometric Reflection Learning Using WebSocket Fathan Mohamad; Rahmat Deddy Riyanto Dako; Ulfatun Nadifa; Wahab Musa; Syahrir Abdussamad; Bambang Panji Asmara
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.40303

Abstract

Mathematics learning at the senior secondary level often faces challenges related to student engagement, heterogeneous comprehension levels, and the absence of interactive feedback mechanisms. This study develops and evaluates a web-based mathematical puzzle game named "ISOMETRIA" that integrates WebSocket technology to support real-time multiplayer features for learning the reflection topic among Grade XI students at MAN 1 Gorontalo City. The research follows the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model. The system was built using Phaser.js on the front end and Node.js, Express.js, and Socket.io on the back end, deployed via Vercel and Railway. WebSocket communication is structured into four phases: matchmaking, character selection, map exploration, and battle. A server-authoritative approach with a 200 millisecond race window mechanism was implemented to ensure fair and consistent gameplay under varying network latency conditions. Functionality testing across 25 scenarios confirmed that all features performed as expected. Effectiveness was evaluated with 38 students using a pre-test and post-test design. Mean scores improved from 57.1 to 69.2, and Wilcoxon Signed-Rank Test results yielded p 0.001, indicating a statistically significant improvement. Student response questionnaires returned an average score of 3.58 (sufficient agreement), covering usability, engagement, and perceived benefit. The research results indicate a significant improvement in learning outcomes following the use of the media
Comparison of Filter and SHAP Feature Selection for ECG-based Atrial Fibrillation Classification Novie Theresia Pasaribu; Elizabeth Fabiola Wijaya; Che Wei Lin
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39633

Abstract

Atrial Fibrillation (AF) is a type of arrhythmia whose prevalence continues to rise globally and can lead to serious complications such as stroke and heart attack. Early detection based on ECG signals is therefore crucial. This study compares three feature selection methods, namely Pearson Correlation (PC), Mutual Information (MI), and Shapley Additive Explanations (SHAP), for classifying AF from 2-lead ECG signals using XGBoost. The dataset used was the MIT-BIH Atrial Fibrillation Database, comprising 23 ECG recordings, yielding 34,312 10-second data segments. Preprocessing used Stationary Wavelet Transform (SWT) and Min-Max normalisation. A total of 50 features were extracted. Each method was tested on 4 feature subset sizes (5, 10, 15, and 20 features). The model was optimised using GridSearchCV with 5-fold Stratified Cross-Validation. Results showed that SHAP outperformed PC and MI in subsets of 10, 15, and 20 features. SHAP with 20 features achieved the highest performance (97.87% accuracy; F1 score 96.78%; ROC-AUC 0.9971), while the 15-feature SHAP offered the best performance–dimensionality trade-offs: 97.84% accuracy, 97.85% precision, 95.62% recall, 96.72% F1-score, and 0.9965 ROC-AUC, with a 70% dimensional reduction (0.03% below SHAP-20 on accuracy, with higher precision). SHAP's superiority over PC and MI was statistically significant (McNemar and DeLong tests, p 0.05) in subsets of 15 and 20 features; SHAP with 20 features achieved a ROC-AUC that did not differ significantly from the baseline of 50 features (DeLong test, p = 0.4997).
Comparative Analysis of Data-Level and Cost-Sensitive Learning in IndoBERT-Based Sentiment Analysis of Ruangguru App Reviews Ade Toti Febrian; Purwadi Purwadi; Adam Prayogo Kuncoro
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39885

Abstract

User reviews of online learning applications such as Ruangguru provide valuable information for evaluating service quality, user experience, and digital learning effectiveness. Although IndoBERT has demonstrated strong performance in Indonesian sentiment analysis, previous studies generally compared imbalance handling techniques using different datasets, model architectures, and experimental protocols, making the relative effectiveness of data-level and cost-sensitive learning approaches difficult to evaluate objectively under the same Transformer backbone. This study compares a Data-Level Approach using Latent-SMOTE with a Cost-Sensitive Learning Approach using Class-Weighted Loss on an identical IndoBERT architecture. The dataset consists of 3,767 Ruangguru user reviews collected from Google Play Store and processed through text preprocessing, IndoBERT tokenization, stratified train-validation-test splitting, and evaluation using Accuracy, Precision, Recall, Macro F1-score, confusion matrix, Cochran's Q Test, and McNemar Test. Experimental results show that the Baseline model achieved the highest Accuracy (90.05%), while the Cost-Sensitive Learning approach obtained the highest Macro F1-score (0.6275), outperforming both the Baseline (0.5658) and the Data-Level approach. These findings indicate that class-weighted optimization improves minority-class recognition without modifying the original training distribution, whereas Latent-SMOTE enhances minority representation but does not outperform Class-Weighted Loss. McNemar testing further confirms that the improvements over the Baseline are statistically significant. The main contribution of this work is an objective comparison of Data-Level and Cost-Sensitive Learning approaches using the same IndoBERT backbone, dataset, preprocessing pipeline, hyperparameters, and evaluation protocol. In addition, the study applies Latent-SMOTE in the latent feature space and complements performance evaluation with statistical significance testing, providing stronger empirical evidence for handling imbalanced Indonesian sentiment datasets

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