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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 9,338 Documents
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GRAND-stream: A galois-ring-based lightweight stream cipher for battery-limited internet of things (IoT) devices Nahom Gebeyehu Zinabu; Yihenew Wondie Marye; Kula Kekeba Tune; Samuel Asferaw Demilew
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp542-551

Abstract

GRAND-Stream is a new lightweight stream encryption framework based on arithmetic over Galois rings that targets battery-constrained internet of things (IoT) devices. Unlike traditional LFSR/NLFSR-based designs, GRAND-stream uses ring-squaring-induced nonlinearity and inter component polynomial coupling to improve algebraic complexity while keeping compact implementation qualities. We give an explicit parameterized construction for Z2 n[x]/(f(x)), specify its state updating and output functions, and investigate algebraic degree growth and diffusion behavior. The security arguments are heuristic, based on explicitly stated assumptions about the difficulty of solving quadratic systems over Galois rings. Energy per bit, cycle count, and gate complexity are estimated using an analytical performance model. Although initial findings show potential compactness, more research is needed for thorough cryptanalysis and empirical validation on embedded devices. Therefore, GRAND-stream should be considered a structured algebraic design concept that needs more assessment.
A computational framework for detection, classification, and visualization of magnetic nulls in multi-spacecraft observations Sri Ekawati; Dongsheng Cai; Hiroyuki Kudo
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp865-874

Abstract

Magnetic nulls, defined as locations where the magnetic field magnitude be comes zero, are theoretically well defined however practically difficult to lo cate, validate, and interpret. To address these challenges, this paper introduces a novel, modular, and fully reproducible automated framework for magnetic null detection, classification, and visualization based on multi-spacecraft observations. The proposed framework consists of two open-source modules: an automated data ingestion and null detection module, and a topological classification and three-dimensional visualization module. Magnetic nulls are detected by combining eigenvalue analysis of the magnetic field gradient tensor with tetrahedron-based geometric validation, enabling both numerical stability assessment and physical consistency checks. Meanwhile, detected nulls are classified into radial (Type A, B) and spiral (Type As, Bs) topologies, and their lo cal magnetic structures are visualized through reconstructed three-dimensional magnetic field lines. The main contribution of this work is the tight integration of detection, numerical validation, classification, and visualization within a single end-to-end pipeline, ensuring consistency between computational output and physical interpretation. The framework is validated using four previously reported electron diffusion region (EDR) events and one storm-time substorm event. The detected null times closely agree with the reported EDR intervals, with several events showing sub-second differences. Among all detected can didates, three nulls satisfy strict numerical validity criteria, including a Type A null, a Type As null, and a Type Bs null.
Cost-effective sentiment analysis with chain-of-thought: a cross-lingual evaluation Shen Haijie; Madhavi Devaraj
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp454-468

Abstract

Sentiment analysis is a core task in natural language processing with broad ap plications in social media monitoring, customer feedback mining, and market research. Although pre-trained language models (e.g., BERT) achieve strong performance, they typically rely on task-specific fine-tuning and substantial la beled data. Recent large language models (LLMs) enable a different paradigm via in-context learning. This paper presents a systematic empirical study investi gating chain-of-thought sentiment (CoT-Sent), a prompting framework that uses structured CoT reasoning to improve classification accuracy. We evaluate CoT Sent on four benchmark datasets in English and Chinese, comparing multiple representative LLMs (GPT-4, Claude-3, Gemini, Qwen-2.5) under zero-shot set tings. Across datasets, CoT-Sent improves average accuracy by 2.5% over zero shot baselines. Crucially, unlike prior work which provides a broad performance overview without analyzing deployment costs or multi-language generalization, we focus on the cost-latency-accuracy trade-offs, and demonstrate CoT-Sent’s superior cross-lingual transfer (English-to-Chinese) with detailed cost analysis. We provide a comprehensive three-dimensional analysis of accuracy, cost, and latency, offering actionable deployment strategies for resource-constrained environments.
Design procedure and digital control of a DCM flyback converter: component sizing and experimental validation Nabil Abouchabana; Mohammed Benmiloud; Khaled Ameur; Aboubakeur Hadjaissa
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp688-698

Abstract

Flyback converters are widely used in low-power switch-mode power supplies (SMPS) due to their simple structure, galvanic isolation capability, and cost effectiveness. This paper presents a systematic design methodology and digital Proportional–Integral (PI) control implementation for a discontinuous conduc tion mode (DCM) flyback converter using a dSPACE 1104 control platform. The proposed approach integrates magnetic component sizing, semiconductor stress evaluation, and RCD snubber design into a unified workflow. A 30 W prototype operating at 30 kHz with an input range of 20–30 V and a regulated 12 V output was developed and experimentally validated. The digital PI con troller was tuned using Takahashi’s method to ensure stable voltage regulation. Experimental results demonstrate proper DCM operation and stable output regulation under input voltage variation (20–30 V), load variation (48 Ω–12 Ω), and reference changes (10–14 V). The measured efficiency exceeded 90% at nominal operating conditions. The results confirm the effectiveness of the proposed design methodology for low-power isolated DC–DC applications.
A transfer learning approach for real-time detection and classification of Indonesian coins Nur Hadisukmana; R. B. Wahyu; Stewart Qiu
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp856-864

Abstract

Automated currency recognition plays an important role in banking automation, retail systems, and assistive technologies. While banknote recognition has been extensively studied, coin recognition remains challenging due to small object size, metallic reflectance, visual similarity across denominations, and circulation-induced wear. This study proposes a real-time system for detecting and classifying Indonesian coins using a transfer learning–based deep learning approach. A curated dataset was developed to address the lack of publicly available training data for this domain. The model was initialized with pretrained weights and fine-tuned to adapt to the specific coin classification task. Experimental evaluation on an unseen test set demonstrates high detection accuracy while maintaining real time inference performance. Qualitative analysis under challenging conditions—including glare, low illumination, occlusion, and coin wear— reveals operational limitations and defines robustness boundaries. The findings confirm that frozen-backbone transfer learning provides an effective and computationally efficient strategy for adapting state-of-the-art object detectors to low-resource, domain-specific currency recognition tasks.
Pomelo maturity classification from field-acquired images using oil-gland morphology and a rule-based image-processing pipeline Sopapun Suwansawang; Harutai Dinsakul; Wirot Buangam; Jiraroj Tosasukul
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp572-583

Abstract

Pomelo maturity assessment in commercial orchards relies predominantly on vi sual inspection and harvest age records, which introduce inconsistency in post harvest grading. Non-destructive alternatives such as near-infrared spectroscopy and acoustic sensing have been reported, but typically require specialised instruments and controlled acquisition conditions. This study investigates the feasibility of oil-gland morphology as an interpretable maturity indicator, implemented as a rule-based image-processing pipeline executable on standard CPU hardware without model training. A hierarchical rule-based framework was developed to classify pomelo maturity from gland count features extracted under natural outdoor illumination. Thirty-three Citrus maxima samples (Khao Yai cultivar) representing three maturity stages were analysed in this proof-of-concept study (n = 11 per stage). The pipeline integrates adaptive thresholding, subregion segmentation, multi-scale morphological detection, and threshold-based classification. Detection reliability was verified on synthetic dot-pattern images prior to real-sample evaluation. On the collected dataset, the framework achieved an overall accuracy of 78.8% with a macro-averaged F1-score of 0.784. No mis classification occurred between the immature and mature groups; errors arose exclusively between adjacent stages. Mean processing time was 57 seconds per image on a consumer-grade laptop. Given the limited sample size and single cultivar scope, these results represent methodological feasibility rather than validated generalisation, and establish a baseline for morphology-based maturity assessment in pomelo.
Resilient artificial intelligence, secure digital ecosystems, and intelligent computing for a connected future Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp631-636

Abstract

This editorial introduces the articles published in Volume 42, Number 3, June 2026 of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), highlighting recent advances and emerging research directions in artificial intelligence, cybersecurity, intelligent computing, and digital transformation. The published studies span a broad range of topics, including machine learning, intelligent analytics, healthcare technologies, computer vision, cryptography, privacy-preserving systems, Internet of Things (IoT) security, cloud computing, distributed optimization, blockchain applications, and decentralized digital platforms. Emerging trends highlighted throughout this issue include foundation and multimodal AI models, AI-enabled cybersecurity, privacy-preserving machine learning, Zero-Trust architectures, edge intelligence, decentralized computing, and digital trust ecosystems. Collectively, these contributions underscore the growing importance of resilient artificial intelligence and secure digital infrastructures in enabling adaptive, efficient, and trustworthy connected environments. The increasing integration of intelligence, security, resilience, and human-centered design principles reflects the evolving requirements of next-generation technologies that support sustainable innovation, economic development, and societal well-being. The research presented in this issue provides valuable perspectives on the opportunities and challenges associated with building secure, resilient, and intelligent digital ecosystems for an increasingly interconnected future.
Assessing cybersecurity awareness and security practices among university students in Jordan Khader Musbah Ismail Titi
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp619-630

Abstract

This paper sets out to evaluate the degree to which Jordanian university students genuinely comprehend and apply cybersecurity principles in their everyday digital lives. The rationale for undertaking this investigation is compelling: as cyber threats continue to intensify and diversify, remarkably little granular evidence exists to identify which sub-populations within the Jordanian student body are most vulnerable due to knowledge gaps. A structured survey reaching 150 students recruited from a range of academic departments served as the empirical foundation, with all quantitative analyses conducted using SPSS and MS-Excel. The analysis examined three demographic dimensions: gender, academic discipline, and geographic origin (urban versus rural). Students enrolled in computing and information technology programmes consistently demonstrated superior preparedness relative to their peers, and urban-based students exhibited more robust awareness profiles than those from rural areas. Building on these findings, the paper makes a case for systematic awareness programmes and for embedding cybersecurity content into curricula across all disciplines rather than restricting it to technical fields.
Fine-tuned IndoBERT for stock market sentiment analysis: evidence from CNBC Indonesia news Tri Agung Jiwandono; MS Hendriyawan Achmad; Suhirman Suhirman
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp774-785

Abstract

Financial sentiment analysis in Indonesian markets faces significant accuracy challenges, with existing models achieving only 78-81% accuracy. We present a fine-tuned IndoBERT-Large model for classifying sentiment in Indonesian stock market news headlines, trained on 9,819 CNBC Indonesia headlines (January 2024-March 2025). Through systematic hyperparameter optimization and stratified vocabulary-balanced splitting, our model achieved 94.20% accuracy, surpassing previous baselines by 4-16 percentage points. These results demonstrate IndoBERT's effectiveness for Indonesian financial NLP and its potential for real-time market monitoring and investment decision support systems.
Emulation-based evaluation of dust-aware automated cleaning system for aggregated solar panels on electric vehicles Mohamed Abubakr Mahgoub Hassan; Belal Ahmed Hamida; El-Sayed Soliman A. Said; Muhammed Zaharadeen Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp637-648

Abstract

The integration of photovoltaic (PV) panels into electric vehicles (EVs) provides a complementary energy source capable of extending driving range and reducing reliance on grid-based charging. However, the practical contribution of vehicle-mounted PV systems is significantly constrained by dust accumulation, which can induce power losses exceeding 20% under prolonged urban and roadside exposure. This study presents a low-power; sensor-driven, automated dust detection and cleaning system specifically designed for aggregated EV-mounted solar panels. Hybrid series–parallel panel aggregation architecture is employed to mitigate mismatch and partial shading effects associated with non-uniform dust deposition. A MATLAB/Simulink-based emulation framework is developed to model dust-induced attenuation, capacitive sensor response, cleaning subsystem energy consumption, and net energy recovery under static parking, urban driving, and mixed-use operating conditions. Results demonstrate that the proposed system maintains panel performance within 95%–98% of clean baseline output and recovers approximately 12%–15% of the dust-induced lost energy per cleaning cycle, while sustaining a positive net energy balance with minimal operational overhead. The main contributions of this work include the development of a quantitative energy trade-off model linking dust density, sensor response, and cleaning cost, the design of an EV specific hybrid aggregation strategy for dust-resilient power extraction, and a reproducible emulation framework for evaluating autonomous cleaning systems under realistic vehicular conditions. These findings confirm the technical feasibility and energy efficiency of intelligent dust mitigation as an enabling mechanism for solar-assisted electric mobility.

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