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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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Performance evaluation of path planning algorithms for blind people Mosquera-Ortega, Paula; Díaz-Toro, Andrés; Villamizar-Carrillo, Anyela; Campaña-Bastidas, Sixto
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.pp1638-1649

Abstract

Blind people face difficulties in identifying objects of interest and moving to them safely and efficiently in unfamiliar environments. Thanks to highperformance computers, high-quality sensors and artificial intelligence algorithms, it is possible to perform real-time tasks such as locating the user, generating occupancy grids that represent the environment and identifying objects of interest. From this information, paths can be generated that allow the user to reach a point of interest in an optimal way. This paper presents the performance evaluation of four path planning algorithms that were implemented in MATLAB and tested with synthetically generated occupancy grids, varying their size and occupancy percentage. The evaluation criteria include time to reach the goal, number of expanded cells and number of cells in the path. In addition, a single indicator that integrates all performance criteria is proposed to identify the best algorithm. The results show that the A* algorithm presents the best performance in static environments, under certain hardware requirements for data processing and restrictions on grid size for real-time applications. These findings expand the fields of application of path planning algorithms, quantify their performance under different conditions of the environment, and make them attractive for implementation in embedded systems.
Relationship between voltage and resistance in hybrid nanoconductive ink on different substrates in wet and dry conditions Shari, Norashikin; Hamid, Nurfaizey Abd; Photong, Chonlatee; Watson, Alan J.; Salim, Mohd Azli
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.pp18-32

Abstract

Hybrid graphene nanoplatelet/silver (GNP/Ag/SA) conductive inks are increasingly used in flexible electronics, yet there is limited understanding of how substrate type, solvent composition, and moisture exposure jointly control the electrical performance on metal and polymer substrates. This work aims to clarify how terpinol content (5T, 10T, 15T) and substrate properties of copper (Cu), polyethylene terephthalate (PET), and thermoplastic polyurethane (TPU) influence voltage, resistance, and resistivity of screen-printed GNP/Ag/SA tracks under dry and postimmersion wet conditions. GNP/Ag/SA inks were formulated with fixed butanol and varied terpinol contents, printed on Cu, PET, and TPU, and characterized using electrical measurements, adhesion evaluation, and microstructural observations to relate resistivity trends to morphology, surface energy, and hygroscopic behavior. The Cu substrate showed the best performance, with Cu 10T achieving the lowest dry resistivity of approximately 1.2×10-5 Ω.m and Cu 15T the lowest wet resistivity of approximately 2.0×10-5 Ω.m, supported by dense, well-adhered microstructures. The PET exhibited higher resistivity values up to about 10-3 Ω.m and clear degradation after water immersion, while TPU showed very high or unmeasurable resistivity in wet conditions caused by severe ink loss and hygroscopic swelling, highlighting the important role of substrate surface energy and moisture response in determining the reliability of GNP/Ag/SA inks for applications in humid or wet environments.
Comparing machine learning and binary regression approach for motor insurance prediction Sefina Samosir, Ridha; Bazán Guzmán, Jorge Luis; Halim, Giselle
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.pp1576-1585

Abstract

This study compares the performance of binary regression with the power cauchit (PC) link function and random forest in predicting motor insurance policyholder behavior using an imbalanced dataset. The dataset comprises 4,000 policyholders, with the response variable indicating whether a client purchased a full coverage plan (1) or not (0). Predictors include characteristics such as men, urban, private, age, and seniority. Binary regression was implemented using PyStan, while random forest was created with scikit-learn without additional hyperparameter tuning. Results demonstrate that random forest outperformed binary regression in a range of performance metrics, as well as specialized metrics suitable for imbalanced data. Findings point to the effectiveness of machine learning (ML) algorithms, exemplified by random forest, offer more robust performance in handling complex, imbalanced datasets compared to traditional statistical models. This highlights the potential of random forest to improve predictive accuracy in applications such as motor insurance policyholder behavior analysis.
A novel approach for detecting diabetic retinopathy using two-stream CNNs model Viet Huong, Pham Thi; Thinh, Le Duc; Oanh, Tran Thi; Bach, Tran Xuan; Huy, Hoang Quang; Vu, Tran Anh
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.pp200-209

Abstract

Major causes of visual impairment, particularly diabetic retinopathy (DR) and aged-related macular degeneration (AMD), has posed significant challenges for clinical diagnosis and treatment. Early detection and prompt intervention can help prevent severe consequences for patients. The study presents a novel approach for detecting eye diseases using a two-stream convolutional neural network (CNN) model. The first stream processes preprocessed fundus images, while the second stream analyzes high-pass filtered fundus images in the spatial frequency domain. To assess the model’s performance, we use the APTOS 2019 dataset, which was originally compiled for the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection competition and is publicly available on Kaggle. Our method shows promise as an early screening tool for DR detection with an accuracy of 0.986.
Development of unified college admission system for Philippine state universities and colleges: a data-driven approach to equity and access Bordios, Abegail G.; Cananua-Labid, Sherrie Ann; Mabansag, Ariel B.; Cañal, Mae V.; D. Carboquillo, Jake Boy; Del Rosario, Ma. Andrea C.
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.pp61-72

Abstract

This paper presents the development and pilot evaluation of the unified college admission system (UCAS), a centralized and equity-oriented digital platform designed to streamline admissions across Philippine state universities and colleges (SUCs). Anchored on Republic Act No. 10931, UCAS functions as a unified application repository that standardizes admissions data, consolidates applicant records, and enables real-time monitoring of equity target students (ETS) to support fair and transparent access to higher education. The system integrates student-facing and administrative portals that facilitate application submission, institutional coordination, and equity-focused analytics. A pilot evaluation involving student applicants and administrators assessed usability, efficiency, and reliability, yielding consistently positive results across user groups. Findings indicate that UCAS is technically robust, user-centered, and suitable for multi-level admissions governance. Overall, the study demonstrates the potential of a centralized, data-driven admissions platform to complement tuition-free education policies by addressing inequities at the admissions stage.
Sentiment analysis in Arabic and dialects: a review utilizing a corpus-based approach Hussein Ali, Abbas; Barişçi, Necaattin
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.pp1500-1516

Abstract

Arabic is one of the most morphologically complex languages, and its numerous dialects render identifying sentiment in digital communication a challenging task. In this study, we conduct a systematic literature review (SLR) to investigate the sentiment analysis (SA) techniques used on modern standard Arabic (MSA) and several Arabic dialects (AD) between 2020 and 2024. A corpus-based analysis of 71 articles indicated that machine learning (ML) and deep learning (DL) algorithms were the dominant methods used. Overall, the most frequently studied dialects are those from Saudi Arabia, Morocco, and to a lesser extent, Algeria, among various algorithms used for text classification, including support vector machines (SVM) and convolutional neural networks (CNN). These techniques emerged as some of the most effective strategies employed for sentiment classification. While new contemporary word embeddings, such as Word2Vec, are gaining traction in the field, traditional feature extraction methods, like term frequency-inverse document frequency (TF-IDF), continue to outperform them. The study highlights the importance of additional labeled datasets and tailored models in navigating the linguistically rich world of AD. Additionally, the results highlight the need for dialect-specific adaptations to improve SA outcomes, and further investigation is needed by leveraging advanced DL methodologies, as well as improved data resources, to address these issues.
Potential field-based approaches for nanobotics in drug delivery Kamajaya, Leonardo; Siradjuddin, Indrazno; Al Azhar, Gillang; Fitri, Fitri; Fahmi Fahanani, Agwin
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.pp1298-1307

Abstract

Nanorobotics has transformed targeted drug delivery by enhancing therapeutic efficacy, minimizing off-target effects, and increasing precision. However, navigating complex biological environments is challenging. In the field of macroscopic robotics, potential field (PF)-based approaches that utilize attractive and repulsive virtual forces provide a promising framework that can be applied to path planning for nanorobots. This study modifies PF algorithms for nanorobotic navigation to address challenges such as avoiding dynamic obstacles, escaping local minima, and optimizing trajectories in real time. We evaluated the movement of the nanorobot through simulations under static and dynamic conditions for the targets and obstacles. The results demonstrate that nanorobotics with hybrid PF methodologies enhance navigation performance, enabling nanorobots to successfully navigate through biological barriers and efficiently reach their target locations. This work is a significant step towards intelligent and autonomous nanorobotic drug delivery systems and contributes to practical biomedical applications.
Cryptojacking detection using model-agnostic explainability Mutombo, Elodie Ngoie; Nkongolo, Mike Wa; Tokmak, Mahmut
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.pp394-408

Abstract

Cryptojacking is the illicit use of computing resources for cryptocurrency mining. It has emerged as a serious cybersecurity threat that degrades critical system performance and increases operational costs. This paper proposes an advanced machine learning (ML) framework that integrates transformer-based language models with post hoc explainable artificial intelligence (XAI) to detect cryptojacking using complementary network traffic and process memory (PM) data. Numerical and categorical features are discretized and tokenized to enable semantic modelling and contextual learning. Experimental results show that transformer models effectively capture cryptojacking-related behavioral patterns, with decoding-enhanced BERT with disentangled attention (DeBERTa) achieving high detection performance and recall exceeding 80%. Bidirectional encoder representations from transformers (BERT) attains comparable recall with lower computational overhead, making it well suited for real-time environments, while robustly optimized BERT approach (RoBERTa) and DeBERTa are more appropriate for offline or batch-based analysis. Model performance is evaluated using standard classification metrics, and XAI techniques provide interpretable insights into feature relevance, supporting transparent and reliable detection. In general, the proposed framework delivers an effective and deployment-ready solution for cryptojacking detection.
Miniaturized reconfigurable metamaterial based bandstop filter for wireless applications Chavda, Khyati; K. Sarvaiya, Ashish; K. Vala, Mehul
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.pp1337-1344

Abstract

The design of compact size and high efficiency metamaterial based reconfigurable microstrip bandstop filter for IEEE 802.11 WLAN applications is developed. This paper presents a switchable dual-mode filter, it resonant at 2.4 GHz and 3.6 GHz. The hexagonal metamaterial resonator inserted switch as PIN diode which form reconfigurable filter. By changing the DC bias of the diode, the filter can be reconfigured with a controlled precision, resulting in the frequency reconfigurable. The CST simulator used to simulate filter design, measuring a return loss over -29.12 dB and a low insertion loss less than -0.2 dB, which is a great performance. The filter is compact at the size of 8 mm×12 mm×1.6 mm design using Rogers RT Duroid 5880 substrate.
Artificial intelligence in diagnostic medicine: a case study of kidney disease applications Douache, Malika; Benbakreti, Samir; Benbakreti, Soumia; Nawal Benmoussat, Badra
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.pp1232-1240

Abstract

The rapid evolution of artificial intelligence (AI), particularly in convolutional neural networks (CNNs) and deep learning, has revolutionized numerous domains, ranging from medical imaging to creative arts and legal analytics. This research emphasizes the role of pre-trained CNN architectures in identifying kidney conditions, leveraging a dataset comprising images of healthy kidneys as well as those affected by cysts, tumors, and stones. The pretrained models known for their outstanding image recognition capabilities, were adapted for this classification task through transfer learning (TL) techniques. By refining these models and carefully calibrating key parameters like learning rate, batch size, and network depth, they demonstrated superior performance compared to traditional machine learning approaches. The findings underscore the transformative potential of pre-trained CNNs in advancing the precision of kidney disease diagnostics, with implications for broader medical applications.

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