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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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FEM-based analysis of the relationship between track insulation conductivity and stray current in DC traction systems Aussawamaykin, Apiwat; Pao-la-or, Padej
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1212-1220

Abstract

This research investigates the influence of track insulation conductivity on stray current in direct current (DC) traction systems, which is a significant issue in railway operations due to its potential to cause electrochemical corrosion. Utilizing the finite element method (FEM), a simplified geometric model of a DC tram traction system was analyzed under varying conditions of track insulation conductivity. The study examined three levels of insulation conductivity, represented by fastener resistances of 1,000 Ω, 3,000 Ω, and 6,000 Ω, to understand their impact on stray current density. Results revealed that increased insulation resistance leads to reduced stray current density, demonstrating the critical role of track insulation in mitigating stray currents. The study further highlights that the depth of soil beneath the track also significantly affects stray current distribution. These findings provide insights into improving track design and maintenance for better protection against the negative effects of stray current in DC traction systems.
Remaining useful life estimation of turbofan engine: a sliding time window approach using deep learning Alqushaibi, Alawi; Hasan, Mohd Hilmi; Abdulkadir, Said Jadid; Taib, Shakirah Mohd; Al-Selwi, Safwan Mahmood; Sumiea, Ebrahim Hamid; Ragab, Mohammed Gamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp283-299

Abstract

System degradation is a common and unavoidable process that frequently oc curs in aerospace sector. Thus, prognostics is employed to avoid unforeseen breakdowns in intricate industrial systems. In prognostics, the system health status, and its remaining useful life (RUL) are evaluated using numerous sen sors. Numerous researchers have utilized deep-learning techniques to estimate RUL based on sensor data. Most of the studies proposed solving this problem with a single deep neural network (DNN) model. This paper developed a novel turbofan engine RUL predictor based on several DNN models. The method includes a time window technique for sample preparation, enhancing DNN’s ability to extract features and learn the pattern of turbofan engine degradation. Furthermore, the effectiveness of the proposed approach was confirmed using well-known model evaluation metrics. The experimental results demonstrated that among four different DNNs, the long short-term memory (LSTM)-based predictor achieved the better scores on an independent testing dataset with a root mean-square error of 15.30, mean absolute error score of 2.03, and R-squared score of 0.4354, which outperformed the previously reported results of turbofan RULestimation methods.
Toward nuanced sentiment analysis through multi-sense emoji embedding Amalia, Junita; Veronika Sihombing, Agnes; Christi Sihombing, Hanna Dhea; Dioranta Tambunan, Nadya
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1598-1606

Abstract

This research investigates the role of emojis in sentiment analysis using a more comprehensive multi-sense skip-gram approach. Emojis, which can convey facial expressions, body movements, and intonations often challenging to express in text, enhance digital communication by enriching the meaning of messages. Previous studies have shown that emojis improve sentiment analysis, yet most focused solely on their positive and negative connotations. This study broadens the scope by incorporating positive, negative, and neutral sentiment contexts. In the experiments, emojis were embedded in text and converted into vector representations for further analysis. The classification of sentiment texts was performed using a bidirectional long short-term memory (Bi-LSTM) method enhanced with an attention layer. The experiments resulted in accuracy of 0.83, recall of 0.83, precision of 0.82, and F1-score of 0.82. Statistical tests confirmed the significance of these findings, indicating that the approach improves the accuracy of sentiment analysis involving emojis. Overall, the study demonstrates that the integration of text and emojis leads to a more nuanced and precise understanding of sentiment in sentences, confirming the effectiveness of this method.
Cyber hygiene awareness among Malaysian youth Fikry, Amily; Abdul, Azreen Joanna; Kamaruzaman, Khairul Nazlin; Asnawati, Asnawati
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp210-219

Abstract

The study examined cyber hygiene awareness among Malaysian youth by analyzing the roles played by individual knowledge, awareness, attitudes, gender differences, and educational level. An online survey was conducted with 414 respondents in Peninsular Malaysia. The results showed no significant differences in cyber hygiene awareness based on gender and educational level. This suggests equal access to cybersecurity information and training across genders and education levels in Malaysia. This study also found significant relationships between individual characteristics (knowledge, rationality, and attitude) and cyber hygiene awareness. These findings indicate that individuals who are more knowledgeable, have positive attitudes, and make rational decisions tend to have higher cyber hygiene awareness. The results highlight the importance of fostering rationality and consistency in approaches to cybersecurity practices. The study contributes to the thoughtfully reflective decision-making (TRDM) theory, providing insights for developing targeted cybersecurity training programs and policies. Future research could explore additional factors influencing cyber hygiene awareness and examine how these findings translate to actual cybersecurity behaviors in professional settings.
Predicting staple crop yields under climate variability using multiple regression techniques D. Hortizuela, Richard; D. Palaoag, Thelma
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1531-1538

Abstract

Global food systems rely on staple crops—rice, wheat, maize, potato, soybean, and sugarcane, which are vital in Asia, where production is high. However, climate change threatens crop yields, potentially increasing hunger and malnutrition. Yield variability due to climate factors like rainfall and temperature underscores the need for accurate crop yield predictions. This paper analyzed the relationships between staple crop yields, climate variables, and pesticide usage. It aimed to develop a predictive model for crop yields in Asia using multiple regression techniques in Google Colab. The model was evaluated using a hybrid set of metrics like mean absolute error (MAE), root mean squared error (RMSE), and R² score. Findings revealed that reliable yield predictions are achievable despite weak linear relationships among variables. The extreme gradient boosting (XGBoost) achieves the highest R² score of 0.958367, which indicates superior predictive performance for staple crop yield forecasting due to its lower overall error rates and greater consistency in performance. This highlights the effectiveness of ensemble methods like XGBoost in capturing complex crop yield patterns. Despite newer machine learning (ML) techniques, these models remain recommended for similar tasks due to their robust performance.
Forecasting industrial electricity demand using hybrid optimization methods Phatai, Gawalee; Luangrungruang, Tidarat
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1689-1697

Abstract

This study presents a hybrid machine learning framework for forecasting industrial electricity consumption by comparing backpropagation neural networks (BPNN) with models enhanced through metaheuristic optimization algorithms. Using 32 years of annual data from APEC economies, the research addresses rising electricity demand driven by economic and infrastructural development. A key limitation in traditional models— underfitting due to complex data patterns—is addressed via feature selection, which identifies the most relevant variables and reduces model complexity. Five metaheuristic algorithms—cuckoo search (CS), differential evolution (DE), harmony search (HS), particle swarm optimization (PSO), and teaching–learning-based optimization (TLBO)—are applied to optimize both feature selection and BPNN training. The proposed approach improves forecasting accuracy by handling noisy inputs and capturing the nonlinear relationships common in energy datasets. Among the tested methods, TLBO consistently delivers superior accuracy and robustness across most evaluated countries. The findings contribute an effective and adaptable forecasting model with significant implications for long-term energy planning and policy development.
Optimization of a hybrid forward chaining and certainty factor model for malaria diagnosis based on clinical and laboratory data Hasan, Patmawati; Kiswanto, Rahmat H.; Lestari, Susi
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp419-429

Abstract

Malaria remains a serious public health problem in Indonesia, particularly in Papua Province, which accounts for 89% of national malaria cases. The similarity of malaria symptoms with other infectious diseases and limited laboratory facilities often lead to delays and inaccuracies in diagnosis. The study proposes an optimized hybrid model that combines forward chaining and certainty factor (CF) by integrating clinical and laboratory data to improve the accuracy of malaria diagnosis. The research design includes acquiring knowledge from medical experts, developing a rule-based system using forward chaining, and applying CFs to overcome uncertainty in symptom interpretation. The system is implemented using Python with support from libraries such as NumPy and PyKnow. The test results showed that the integration of laboratory data significantly improved diagnostic performance, with accuracy increasing from 81% malaria-positive using clinical data alone to 98% malaria-positive after combining with laboratory data. Expert testing to validate the accuracy of clinical and laboratory data results compared to expert validation results in an accuracy score of 98%. These findings show that the optimization of the hybrid forward chaining model and CF for malaria diagnosis based on clinical and laboratory data as a recommendation tool for early diagnosis of malaria in endemic areas.
Formalization of materialized view problem in ontology-based databases Leouro Mbaiossoum, Bery; Batouma, Narkoy; Doutoum Mahamat, Atteib; Cherif Ali, Ouchar; Dionlar, Lang; Bellatreche, Ladjel
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1430-1438

Abstract

Materialized views are essential for optimizing the performance of traditional databases and data warehouses by accelerating query responses. However, their substantial storage requirements and the impracticality of materializing all possible views raise the problem of selecting which views to persist, a fundamental physical design challenge. This article presents a rigorous formalization of this problem within the context of semantic databases. The methodology employed includes a comprehensive literature review aimed at identifying the variety of se-mantic database representations. This analysis revealed a significant diversity in data models and query languages used. Based on this analysis, a generic formalization framework is pro-posed. This framework enables the expression of various resolution approaches to the materialized view selection problem, taking into account the specificities of semantic databases. It offers broad applicability to any database management system, providing a common language to describe and compare view selection methods.
Comparative evaluation of PVGIS, PVsyst, and SAM models for predicting solar power output in equatorial tropical climates Lara Vargas, Fabian Alonso; Ortiz Padilla, Miguel Angel; Torres Amaya, Alvaro; Vargas Salgado, Carlos
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1221-1231

Abstract

Accurate evaluation of energy production in photovoltaic (PV) systems is critical for renewable projects, especially in tropical climates where environmental factors such as temperature significantly affect performance. Although commercial simulation tools exist (photovoltaic geographic information system (PVGIS), PVsyst, and system advisor model (SAM)), previous studies have identified notable deviations between their predictions and actual data, particularly in tropical climates. Moreover, these investigations are usually limited to short periods (one year) and do not systematically compare multiple tools under interannual conditions. This study evaluates the accuracy of PVGIS, PVsyst, and SAM in predicting the energy production of a PV installation in a tropical equatorial climate for 24 months to identify the most suitable tool for this context. Monthly energy production data were collected from a PV plant in Monteria, Colombia, equipped with 240 modules and two 36 kW inverters. Simulations were performed using the most recent PVGIS, PVsyst, and SAM versions. Accuracy was evaluated using metrics such as root mean square error (RMSE) and mean absolute error (MAE). SAM showed the highest accuracy, with an overall RMSE of 1,993.71 kWh and MAE of 1,615.87 kWh, followed by PVGIS (RMSE: 2,076.65 kWh, MAE: 1,830.84 kWh) and PVsyst (RMSE: 3,546.18 kWh, MAE: 3,250.17 kWh). The results highlight that SAM provides estimates closer to the real data and less dispersion than other tools. This study contributes to the renewable energy field by systematically comparing simulation tools in an understudied tropical context. The findings emphasize the importance of selecting appropriate software according to the specific environmental conditions of the project, thus optimizing the design and efficiency of PV systems in tropical regions.
Deep-fuzzy personalisation framework for robot-assisted learning for children with autism Gyening, Rose-Mary Owusuaa Mensah; Hayfron-Acquah, James Ben; Asante, Michael; Takyi, Kate; Appiahene, Peter
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp320-330

Abstract

Research exploring the efficacy of robots in autism therapy has predominantly relied on the Wizard-of-Oz method, where robots execute predetermined behaviours. However, this approach is constrained by its heavy reliance on human intervention. To address this limitation, we introduce a novel deep-fuzzy personalization framework for social robots to enhance adaptability in interactions with autistic children. This framework incorporates a deep learning model called singleshot emotion detector (SED) with a mean average precision of 93% and a fuzzy-based engagement prediction engine, utilizing factors such as scores, IQ levels, and task complexity to estimate the engagement of autistic children during robot interactions. Implemented on the humanoid robot RoCA, our study assesses the impact of this personalization approach on learning outcomes in interactions with Ghanaian autistic children. Statistical analysis, specifically Mann Whitney tests (U=3.0, P=0.012), demonstrates the significant improvement in learning gains associated with RoCA's adoption of the deep fuzzy approach.

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