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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 49 Documents
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Search results for , issue "Vol 40, No 3: December 2025" : 49 Documents clear
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.
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.
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.
Innovative automation and optimization of solar-powered water purification using siemens programmable logic controller and human-machine interface Bouraiou, Ahmed; Dekhane, Azzeddine; Benghanem, Mohamed; Rahli, Chouaib
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.pp1285-1297

Abstract

This study presents a novel approach to optimizing water purification systems at the Zaouiet Kounta solar power plant through the integration of advanced automation and supervision technologies. By utilizing a siemens programmable logic controller (PLC) and human-machine interface (HMI) programmed via the totally integrated automation (TIA) Portal software, the project aimed to significantly enhance the performance of water production and distribution systems. The objectives included improving operational efficiency, reducing manual intervention, and increasing system reliability and precision. The results presented herein show significant improvements in operational efficiency, system reliability, and automation in a challenging environmental context. This research provides a comprehensive case study that not only highlights the feasibility of using Siemens PLC and HMI systems in solar-powered water purification systems but also proposes scalable solutions for similar industrial applications.
Optimization of photovoltaic pumping system using neuro fuzzy inference system ANFIS control technique Abdelhaq, Laoufi; Moulay-Idriss, Chergui; Chekroun, Soufyane
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.pp1270-1284

Abstract

In recent years, artificial intelligence has become increasingly used due to the development of microcontrollers. In this paper, we propose an intelligent technique that employs the adaptive neuro-fuzzy inference system (ANFIS). We use this approach to improve the conventional direct torque control (DTC), which relies on a PI controller for the induction machine, and to enhance the conventional MPPT control based on the Perturb and Observe algorithm. The overall goal is to improve the performance of the photovoltaic pumping system. In this work, we apply ANFIS control to maximum power point tracking (MPPT-ANFIS). Additionally, we simultaneously optimize the efficiency of the DTC by applying ANFIS control (DTC-ANFIS). We present the results by comparing the photovoltaic pumping system using ANFIS control with the conventional photovoltaic pumping system, using MATLAB/Simulink. The results show that ANFIS control significantly improves the photovoltaic system compared to the conventional control, offering excellent dynamic performance of the induction motor and better utilization of photovoltaic solar energy. However, the ANFIS has some drawbacks, such as high computational time consumption and challenges in implementing a database.
The role of artificial intelligence in advancing the performance of information retrieval Alrabea, Adnan; Ahmad Alhaj, Abdullah; Senthil Kumar, A. V.
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.pp1478-1485

Abstract

The motivation behind applying artificial intelligence (AI) in information retrieval (IR) is that the current methodologies include algorithms designed by researchers, leaving space for the applicability of genetic AI algorithms in IR. While different algorithms designed by developers rely on the originality or performance of the algorithm, precise results are achieved through integrating AI algorithms with traditional algorithms. The proposed methodology introduces document structure weighting with optimized performance. It is enabled by employing genetic algorithm and genetic programming for learning optimal weights in ranking document components. The Croft probabilistic ranking, vector space inner product models, and the BM25 standard were compared with each other after AI integration. Genetic algorithm and genetic programming were applied in the stemming and thesaurus forming processes of these models. Inducing genetic algorithm and genetic programming into the specified models increased the mean average precision of the Croft model and the vector space method by approximately 5% while there were no observable result improvements in BM25. It was found that applying genetic algorithm and genetic programming in learning synonyms and stemming rules, respectively, increased the overall performance of IR models, emphasizing the need for AI in IR.
Quality of services LoRaWAN satellite communication Purnama, Iwan; Dwi Putra, Muhammad Taufik; Samsinar, Samsinar; Aulia, Masyitah; Shina, Ibnu; Yuliyus Maulana, Yudi; Benny Louhenapessy, Bendjamin; Dominggus Lekalette, Johanis; Parulian Sitompul, Peberlin; Manik, Timbul; Nendra Wibawa, Lasinta Ari; Prasetyo Adi, Puput Dani; Sinaini, La; Lestari, Pratiwi; Sulaeman, Yaya; Rohman Setiawan, Iwan; Nugraha, Budi; Yati, Emi; Sadiyah, Lilis; Jatmiko, Irwan; Sacipto, Rian; Sariningrum, Ros
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.pp1401-1416

Abstract

This research discusses research that focuses on the capabilities of satellitebased LoRa, for satellite positions orbiting in low earth orbit (LEO). The expectation of low power wide area network (LPWAN) satellite can find the quality of transmitting data using LoRaWAN which is part of LPWAN and can provide quality of service (QoS) with high-quality real-time sensor data, low latency, long-range, low-power, no attenuation signal, no problem with obstacles in terrestrial areas, and other benefits that can be widely optimized. This article uses a comprehensive analysis of mathematical calculations as well as precise and accurate simulations for the actual development of satellite-based LPWAN. The satellite-based IoT is unlimited in terms of distance, to provide good services to all IoT users in the world. The comparison with terrestrial measurements is analyzed in detail, especially the signal attenuation factor that causes a lot of signal loss and data is not well received. Several methods are used to help reduce collision data, such as adaptive data rate (ADR) which can reduce collisions by 30%.

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