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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Published by Universitas Ahmad Dahlan
ISSN : 23383070     EISSN : 23383062     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
JITEKI (Jurnal Ilmiah Teknik Elektro Komputer dan Informatika) is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD) in collaboration with Institute of Advanced Engineering and Science (IAES). The aim of this journal scope is 1) Control and Automation, 2) Electrical (power), 3) Signal Processing, 4) Computing and Informatics, generally or on specific issues, etc.
Arjuna Subject : -
Articles 4 Documents
 1 
Search results for , issue "vol. 12 no. 1 (2026): march" : 4 Documents clear
Success Factors in the Implementation of IoT-Enabled Predictive Maintenance Technology in Industrial Electrical Applications: A Systematic Literature Review Daniel Ngolu Jiledo Maringga; Muhamad Ali; Ridho Azahar
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.31651

Abstract

The development of the Internet of Things (IoT) in predictive maintenance (IoT-enabled Predictive Maintenance) for industrial electrical equipment offers significant potential to enhance system efficiency and reliability; however, its implementation is constrained by challenges related to sensor data integration, communication infrastructure quality, and security issues. This study addresses a gap in the literature by describing patterns of successful IoT-enabled Predictive Maintenance implementation in industrial electrical applications. The contribution of this research lies in providing a systematic synthesis of leading technologies and key success factors in the adoption of IoT-enabled Predictive Maintenance.  The method employed is a Systematic Literature Review (SLR) using the PRISMA approach, which resulted in 16 relevant articles. The findings indicate that the combination of IoT technologies, sensors, wireless networks, and edge-cloud architecture represents an appropriate technological configuration for building an effective Predictive Maintenance chain. These implementations are predominantly found in the manufacturing, energy, and transportation sectors, with the main success factors determined by data quality and network sustainability. These findings offer practical solutions for industry practitioners in improving the efficiency and sustainability of their systems. In conclusion, the successful implementation of IoT-enabled Predictive Maintenance in industrial electrical systems is highly dependent on the suitability of technological infrastructure, data governance, and service-based business models, while also opening opportunities for further research and the expansion of applications into other sectors.
Reproducible Biomedical NER and Proxy Relation Extraction for Drug–Adverse Event Analysis in Breast Cancer Deny Nugroho Triwibowo; Hadi Jayusman; Rachman Hidayat; Anisya; Annastasya Nabila Elsa Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.31594

Abstract

Pharmacovigilance requires automated systems to extract biomedical entities and their relationships from text, as manual processes are inefficient and prone to error. This study develops a reproducible pipeline for Named Entity Recognition (NER) and pattern-based proxy relation formation, focusing on drug side effects related to breast cancer. The research contribution is twofold: a domain-specific annotated dataset for pharmacovigilance NER, and a reproducible pipeline for proxy-based relation analysis. The experimental setup combines MobileBERT, DistilBERT, TinyBERT, and ALBERT. Evaluation is conducted using accuracy, precision, recall, F1-score, ROC AUC, and computational efficiency metrics. The results show that ALBERT achieves the highest NER performance (F1-score = 0.9261), while DistilBERT attains the best ROC AUC (0.9037). TinyBERT is the most efficient model, with 4.57 million parameters, 4.68 G FLOPs, and an average training time of 45.8 seconds per scenario. The proposed pipeline demonstrates a trade-off between accuracy and computational efficiency under the evaluated setting. The generated relations act as sentence-level proxy indicators of potential drug–adverse event associations and serve as a preliminary triage layer requiring expert validation rather than a high-precision system. However, the approach does not account for negation, uncertainty, or cross-sentence context, which may introduce false positive associations. Despite these limitations, the pipeline provides a reproducible baseline for exploratory pharmacovigilance analysis.
Development of an Automated Jar Testing System Based on the Internet of Things (IoT) with 3D Web Visualization Muhaimin Toh Arlim; Sritrusta Sukaridhoto; M. Udin Harun Al Rasyid; Evianita Dewi Fajrianti; Faris Saifullah; Wahyu Nur Hidayat
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.32077

Abstract

Manual Jar Testing for coagulant dosage determination in water treatment is labor-intensive, time-consuming, and susceptible to operator bias, limiting the ability of Indonesian Regional Water Utilities (PDAM) to respond in real-time to dynamic raw water quality changes. The research contribution of this study is: (1) an ESP32-based automated Jar Testing platform with closed-loop DC motor control and a multi-parameter sensor array (turbidity, TDS, pH) for objective, repeatable coagulation-flocculation evaluation; and (2) a real-time 3D cloud visualization framework using MQTT and Three.js that provides remote monitoring with sub-1.5-second latency. The system integrates a SEN0189 turbidity sensor, a TDS conductivity sensor, and a pH-4502C sensor, each calibrated against laboratory-grade reference instruments using polynomial calibration equations derived from experimental data. Encoder-based closed-loop feedback regulates DC motor speed across a 0–100 RPM range, while all sensor telemetry is transmitted via the MQTT publish-subscribe protocol to a cloud database and rendered by a Three.js-based 3D digital visualization interface. Sensor validation yielded Mean Absolute Errors (MAE) of 0.15 NTU for turbidity, 5.33 ppm for TDS, and 0.04 pH units, all within the respective sensor tolerance bounds. DC motor control achieved MAE of 0.05–0.30 RPM across the 10–100 RPM setpoint range. Six discrete alum dosage trials on raw water with initial turbidity of 6.6 NTU identified 70 mg/L as the optimal concentration, achieving 90.15% turbidity removal with a residual turbidity of 0.65 NTU, below the 1 NTU threshold for potable water quality. The 3D visualization layer-maintained data synchronization latency below 1.5 seconds under laboratory network conditions. The proposed system substantially reduces operator workload and eliminates visual observation bias compared to conventional manual Jar Testing, offering a scalable and low-cost platform for data-driven coagulant dosing optimization in modern water treatment facilities.
Neutral Current Mitigation and Phase Balancing in Asymmetric Feeders via Optimal DG Placement Trieu Ngoc Ton
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.32245

Abstract

The increasing penetration of single-phase distributed generation (DG), particularly rooftop photovoltaic systems, has intensified phase imbalance issues in low-voltage distribution networks (DNs). Although numerous DG allocation methods have been developed to reduce power losses and improve voltage profiles, direct mitigation of feeder asymmetry remains insufficiently addressed. In particular, neutral current circulation and localized inter-phase voltage disparities are often overlooked despite their significant impacts on power quality, equipment loading, and system reliability. To address this limitation, this study proposes a symmetry-oriented multi-objective framework for optimal allocation of single-phase DG units in asymmetric distribution networks. The proposed formulation simultaneously minimizes neutral current magnitude and Differential Phase Voltage Drop (DPVD), enabling direct enhancement of feeder operating balance in both the current and voltage domains. The resulting nonlinear mixed-integer optimization problem is solved using a Multi-Objective Coot Optimization Algorithm (MOCOA), which determines the optimal DG locations, capacities, and phase assignments while satisfying network operating constraints. The effectiveness of the proposed framework is validated using 33-bus and 69-bus unbalanced DNs and compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and NSGA-II. The results demonstrate that MOCOA consistently achieves superior performance in terms of neutral current mitigation, voltage balancing, minimum voltage enhancement, and power-loss reduction. For the 69-bus system, the proposed method reduces neutral current and DPVD by 60.06% and 70.75%, respectively, relative to the base case, while also providing the lowest phase imbalance index among all compared methods. The obtained findings indicate that incorporating feeder symmetry objectives into DG planning can significantly improve the operational performance of asymmetric distribution networks. The proposed framework therefore provides a practical and effective solution for DG integration in future DNs with high penetrations of single-phase renewable generation.

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