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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 65 Documents
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Search results for , issue "Vol 39, No 1: July 2025" : 65 Documents clear
Influences of the Sm3+ -Eu3+ codoped Ba2Gd(BO3)2Cl phosphors on the commercial white light emitting diodes Quan, Luu Hong; Nguyen Thi, My Hanh
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp62-69

Abstract

The color quality of current commercial white light emitting diodes (wLEDs) suffers low performance owing to the lack of the red-emission component. Developing quality and stable red-emission phosphors is feasible among various approaches to obtain the red spectral supplement for the w-LEDs in the pursuit of color quality improvement. In this paper, the Sm3+-Eu3+ codoped Ba2Gd(BO¬3)2Cl (BGBC:Sm-Eu) red phosphor was proposed for using in commercial w-LEDs. Its luminescence and influences on w-LED properties were simulated and presented. The solid-phase method was utilized for the fabrication of the phosphor. The results indicated that the phosphor emitted the strong emission in orange-red region with a peak centering at 593 nm. It can be caused by the proficient power shift between Sm3+ and Eu3+. In the w-LED package, the presence of BGBC:Sm-Eu phosphor stimulated the scattering efficiency to promote the blue-light conversion and extraction. The orange emission spectrum of the w-LED increased with the higher BGBC:Sm-Eu doping amount. The luminous strength of the w-LED was enhanced and so was the color temperature uniformity. The color rendering properties declined with high BGBC:Sm-Eu phosphor concentration owing to the red-light dominance over the light spectrum. The BGBC:Sm-Eu phosphor is a promising red phosphor for improving commercial w-LED color-temperature stability and luminosity. It also helps to obtain full-spectrum w-LED with high color rendition when combined with other blue-to-green luminescent materials.
Enhancing vocational computer engineering education with a GPT-driven speech recognition tool Eka Sakti, Putra Utama; Muhammad, Alva Hendi; Nasiri, Asro
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp564-574

Abstract

This research investigates the effectiveness of an AI-driven speech recognition and GPT-powered learning tool in enhancing vocational students’ proficiency in computer networks. The study involved 100 students from vocational hig school, who used the prototype as part of their learning process. A pre-test/post-test design was employed to assess changes in proficiency, and students also provided feedback on the tool’s usability and impact. The results showed a consistent improvement in proficiency across all classes. A strong positive correlation was found between students’ feedback and their proficiency improvement, suggesting that students who rated the prototype as Very Helpful were more likely to see significant learning gains. However, the correlation between time spent using the tool and proficiency improvement was minimal, indicating that the quality of engagement with the tool was more important than the duration of usage. These findings highlight the prototype’s potential to improve vocational learning outcomes and underscore the importance of user satisfaction in driving success, with future refinements necessary to ensure the tool’s broader effectiveness across different learning contexts.
Geographic information system for marine ecotourism and rural lifestyle in Prachuap Khiri Khan Puengsom, Sompond; Polpong, Jakkapong; Pornpongtechavanich, Phisit
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp485-496

Abstract

According to the Prachuap Khiri Khan Province tourism statistics report for 2023, there were 11,143,079 Thai and foreign tourists from January to December 2023, which increased by 1,395,195 people or 14.31 percent compared to 2022. Simultaneously, tourist attractions accumulated tourism income in 2023 totaling 44,241 million baht, marking an increase of 11,402.63 million baht or 34.72 percent from 2022. Despite this growth, tourist attractions that are popular with tourists remain centered in Hua Hin District due to a lack of publicity and insufficient information provided to tourists. Consequently, the researcher intended to develop a geographic information system (GIS) for marine ecotourism and rural lifestyles in Prachuap Khiri Khan Province to promote rural tourist attractions and distribute tourism income to the community. The system utilized the classification (precision and recall) model and was developed using ArcGIS and the web app builder ArcGIS. Findings from 8 experts in computers, information technology (IT), and GIS indicated that the overall system efficiency had an average of 4.54 and a standard deviation of 0.50. Additionally, results from the study on retrieval efficiency using the classification (precision and recall) model revealed a precision value of 0.90 and a recall value of 0.95.
Internet of things based smart agriculture using K-nearest neighbor for enhancing the crop yield Dasari, Kalyankumar; Kharde, Mukund Ramdas; Maddileti, Kuruva; Pasupuleti, Venkat Rao; Ram, Mylavarapu Kalyan; Sujana, Challapalli; Komali, Govindu; Fariddin, Shaik Baba
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp436-445

Abstract

Agriculture is one of the major occupations in India and is one of the significant contributors to the economy of India. The agriculture plays a vital role in country gross domestic product (GDP) and is also part of civilization. The production of crop influences the economies of countries. However, still the agriculture filed stands technologically backward. In addition, the lack of favourable weather conditions might result loss of crops yields. The farmers need awareness about their soils, timely weather updates and techniques to improve their soil for growing healthy crops. Hence it is essential to develop a system which can technologically support the farmers for suggesting the crop and improving crop yields. With the development of electronics, researchers have been developed many applications and micro controllerbased systems to do agricultural operations. The internet of things (IoT) has opened many opportunities to design and implements a smart agriculture system and machine learning (ML) algorithm can help to obtain accurate performance. Hence, in this analysis, IoT based smart agriculture using K-nearest neighbor (KNN) for enhancing the crop yields is presented. With the combination of IoT and ML algorithm this system is designed which integrates primary agriculture operations such as recommendation of crops, automated watering and fertilizers recommendation.
Context dependent bidirectional deep learning and Bayesian gaussian auto-encoder for prediction of kidney disease M, Jayashree; N, Anitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp387-398

Abstract

Chronic kidney disease (CKD) has emerged as a significant global health issue, leading to millions of premature deaths annually. Early prediction of CKD is crucial for timely diagnosis and preventive measures. While various deep learning (DL) methods have been introduced for CKD prediction, achieving robust quantification results remains challenging. To address this, we propose the context-dependent bi-directional DL and Bayesian gaussian autoencoder (CDBDP-BGA) method for CKD prediction. This approach utilizes clinical parameters and symptoms from a structured dataset. By incorporating context dependence into the bi-directional long short-term memory (Bi-LSTM) model, CDBDP-BGA efficiently redistributes the representation of information, enhancing its modeling capabilities. Feature selection is optimized using a BGA-based algorithm, which employs the Bayesian gaussian function. The SoftMax activation function classifies CKD into five distinct stages based on estimated-glomerular filtration-rate (eGFR), considering both symptoms (texture and numerical features) and clinical parameters (age, sex, and creatinine). Simulation results using two datasets demonstrate that CDBDP-BGA outperforms conventional methods, achieving 97.4% accuracy without eGFR and 98.7% with eGFR.
Non-contact breathing rate monitoring using infrared thermography and machine learning Salsabila, Anadya Ghina; Setiawan, Rachmad; Hikmah, Nada Fitrieyatul; Syulthoni, Zain Budi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp669-680

Abstract

Monitoring vital physiological parameters such as breathing rate (BR) is crucial for assessing patient health. However, current contact-based measurement methods often cause discomfort, particularly in infants or burn patients. This study aims to develop a non-contact system for monitoring BR using infrared thermography (IRT). This approach permits to detects and tracks the nose from thermal video, extracts temperature variations into a breathing signal, and processes this signal to estimate BR. The estimated BR is then classified into three health categories (bradypnea/normal/tachypnea) using k-nearest neighbors (k-NN). To evaluate system accuracy and robustness, experiments were conducted under three conditions: (i) stationary breathing, (ii) breathing with head movements, and (iii) specific breathing patterns. Results demonstrated high consistency with contact-based photoplethysmography (PPG) measurements, achieving complement of the absolute normalized difference (CAND) index values of 94.57%, 93.71%, and 96.06% across the three conditions and mean absolute BR errors of 1.045 bpm, 1.259 bpm, and 0.607 bpm. The k-NN classifier demonstrated high performance with training, validation, and testing accuracies of 100%, 100%, and 99.2%, respectively. Sensitivity, specificity, precision, and F-measure results confirm system reliability for non-contact BR monitoring in clinical and practical settings.
Using ResNet architecture with MRI for classification of brain images Dhanalakshmi, Subramanian; Arulselvi, Subramanian
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp148-158

Abstract

A strong classification model that can correctly detect abnormalities and neurological disorders in brain images is the main goal. The focus of this research is on improving the accuracy of MRI brain image categorization using residual networks (ResNet) methods. Improving the model's capacity to extract complex characteristics from MRI images and achieving more accurate classification results is the aim of using ResNet architectures. By conducting extensive experiments and validating our results, our project aims to attain top-notch performance in brain image classification tasks. The goal is to help improve medical diagnosis and treatment planning. A secondary goal of the research is to determine if deep learning approaches have any use in radiology, with the hope that this will lead to better medical image analysis pipelines. The main objective is to make it easier to identify neurological problems early on, which will enhance patient outcomes and allow for more calculated treatment decisions. Results proved that the proposed ResNet system achieves 98.8% overall accuracy with 98.6% sensitivity and 99% specificity.
Word embedding and imbalanced learning impact on Indonesian Quran ontology population Utomo, Fandy Setyo; Purwati, Yuli; Azmi, Mohd Sanusi; Shafira, Lulu; Trinarsih, Nikmah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp603-613

Abstract

This research addresses limitations in Quranic instance classification, exceptionally high dimensionality, lack of semantic relationships in the term frequency-inverse document frequency (TF-IDF) technique, and imbalanced data distribution, which reduce prediction accuracy for minority classes. This study investigates the impact of word embedding and imbalance learning techniques on instance classification frameworks using Indonesian Quran translation and Tafsir datasets to handle previous research limitations. Four classification frameworks were built and evaluated using accuracy and hamming loss metrics. The results show that the synthetic minority oversampling technique (SMOTE) technique, TF-IDF model, and logistic regression classifier provide the best accuracy results of 62.74% and a hamming loss score of 0.3726 on the Quraish Shihab Tafsir dataset. This is better than the performance of previous classifiers backpropagation neural network (BPNN) and support vector machine (SVM) used in the previous framework, with accuracies of 59.91% and 62.26%, respectively. Logistic regression can also provide the best classification results with an accuracy of 67.92% and a hamming loss of 0.3208 using the previous framework. These results are better than the performance of the previous classifiers BPNN and SVM used in the previous framework, with accuracies of 62.26% and 66.98%, respectively. TF-IDF feature extraction outperforms word2vec in instance classification results due to its superior support under limited dataset conditions.
An efficient DVHOP localization algorithm based on simulated annealing for wireless sensor network Arroub, Omar; Darif, Anouar; Saadane, Rachid; Rahmani, My Driss; Aarab, Zineb
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp720-736

Abstract

In the last decade, the research community has devoted significant attention to wireless sensor networks (WSNs) because they contribute positively to some critical issues encountered in nature and even in industry. On the other hand, localization is one of the most important parts of WSN. Hence, the conception of an efficient method of localization has become a hot research topic. Lastly, it has been invented, a set of optimal positioning methods that make locate a node with low cost and give precise results. In our contribution, we investigate the source of imprecision in the distance vectorhop (DVHOP) localization algorithm. However, we found the last step of DVHOP caused an imprecision in the calculation. Consequently, our work was to replace this step, aiming to reach satisfactory precision. For that purpose, we created three improved versions of this algorithm by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and Fmincon solver dedicated to optimization in the field of WSN node localization. The experimental results obtained in this work prove the efficiency of simulated annealing (SA)-DVHOP in terms of accuracy. Furthermore, the enhanced algorithm outperforms its opponents by varying the percentage of anchors and the number of nodes.
Enhanced performance and efficiency of robotic autonomous procedures through path planning algorithm Latha, Raman; Sriram, Saravanan; Bharathi, Balu; Fernandes, John Bennilo; Raju, Ayalapogu Ratna; Boopathy, Kannan; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp214-224

Abstract

To optimize surgical routes for better patient outcomes and more efficient operations, we want to test how well these algorithms work. Finding the best algorithms for different types of surgeries and seeing how they affect things like time spent in surgery, precision, and patient safety is the goal of this exhaustive study. By shedding light on the effectiveness of route planning algorithms, this work aspires to aid in the development of autonomous robotic surgery. To find out how well various algorithms work in actual surgical settings; this study compares them. The results of this work have the potential to enhance robotic surgery efficiency and improve surgical outcomes by informing the creation of more efficient route planning algorithms. The overarching goal of this study is to provide evidence that autonomous robotic surgery can benefit from using sophisticated route planning algorithms, which might lead to more accurate, faster, and safer procedures. The surgical patient dataset exhibits a wide variety of medical variables, including ages 38–62, weight 65–85 kg, height 160–180 cm, blood pressure 110–140/90 mm Hg, heart rate 70–85 bpm, hemoglobin 12–14 g/DL, and body mass index (BMI) 25.4–29.4.

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