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Journal of Electrical Engineering and Computer (JEECOM)
Published by Universitas Nurul Jadid
ISSN : 27150410     EISSN : 27156427     DOI : -
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy
Journal of Electrical Engineering and Computer (JEECOM) is published by Engineering Faculty of Nurul Jadid University, Probolinggo, East Java, Indonesia. This journal encompasses research articles, original research report, : 1) Power Systems, 2) Signal, System, and Electronics, 3) Communication Systems, 4) Information Technology, etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 265 Documents
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Enhancing User Experience in a Land Transportation Information Application: A Design Thinking Approach to the GOBIS Application Lisana Lisana; Melissa Angga; Eugenia Caitlyn Cristabella
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.16994

Abstract

GOBIS is a digital transportation application developed by the Surabaya City Government to facilitate public access to public transportation services. As a land transportation information system, GOBIS plays an important role in providing route, bus stop, and fleet information for urban mobility. However, user reviews and usability test results indicate that the current version of the application still has several shortcomings, particularly in interface design and user experience. The main issues include a difficult-to-interpret integrated map, the absence of an automatic route-search feature, and unclear information about bus stops and fleets. This study developed a redesigned GOBIS prototype through the five-stage Design Thinking process and evaluated the existing application and the redesigned prototype using the same task scenarios. Effectiveness was measured by task completion rate, efficiency by time-based efficiency, and perceived user experience by the User Experience Questionnaire (UEQ). The redesigned prototype increased task completion from 52% to 92%, representing a 40-percentage-point improvement, and increased time-based efficiency from 0.008 to 0.104 goals/sec. User satisfaction also improved after the redesign. Furthermore, assessment with the User Experience Questionnaire (UEQ) showed that all dimensions fell within the “excellent” category. These findings indicate that the redesigned interface successfully improves the overall quality of user interaction and enhances the user experience of GOBIS as a land transportation information system.
Explainable Entropy-TOPSIS for Digital Infrastructure Prioritization Across Indonesian Island Groups Elta Sonalitha; Bondhan Rio Prambanan; Rahman Arifuddin; Fajar Maulid Zidane
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.16803

Abstract

Indonesia’s national internet penetration has continued to increase, reaching 80.66% in 2025, equivalent to 229,428,417 connected people from a total population of 284,438,900. However, this national achievement does not fully represent regional equality in digital infrastructure readiness. This study proposes an Explainable Entropy-TOPSIS framework to develop a Regional Digital Infrastructure Priority Index (RDIPI) across six Indonesian island groups using APJII 2025 indicators. The model transforms internet penetration, regional contribution, and ISP distribution into three priority-oriented criteria: access gap, ISP supply gap, and demand-supply shortage. The entropy weighting results show that demand-supply shortage is the dominant criterion with a weight of 0.8192, followed by ISP supply gap = 0.1292 and access gap = 0.0517. The TOPSIS results indicate that Sumatra has the highest infrastructure priority with an RDIPI score of 0.9840, followed by Maluku-Papua = 0.5226, Sulawesi = 0.3699, Kalimantan = 0.2422, Bali-Nusa Tenggara = 0.2013, and Java = 0.0000. These findings suggest that digital infrastructure priority should not be determined only by low penetration, but also by the imbalance between internet-user contribution and ISP representation. The proposed framework provides an explainable decision-support approach for APJII, ISPs, and policymakers in designing targeted broadband expansion and digital inclusion strategies.
Optimization of Machine Learning Algorithms in Breast Cancer Classification: A Performance Based Analysis Agus Wantoro; Arie Setya Putra; Ochi Marshella Febriani
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.17160

Abstract

Timely identification of breast cancer recurrence is closely associated with patient survival and the effectiveness of treatment. Inaccurate detection can contribute to greater disease severity, higher treatment costs, longer recovery, and reduced quality of care. For Machine Learning (ML)-based decision-support systems, two important challenges are the unequal distribution of medical-data classes and the large number of features, both of which may affect model accuracy and computational efficiency. This study evaluates an approach that combines feature selection with class-imbalance handling to improve breast cancer detection performance. Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Relief-F are used to rank features according to their weights, while the Synthetic Minority Over-Sampling Technique (SMOTE) is applied to improve representation of the underrepresented class. Seven ML classifiers, namely k-Nearest Neighbor (k-NN), Tree, Support Vector Machine (SVM), Naive Bayes, AdaBoost, Random Forest (RF), and Neural Network (NN), are tested and assessed using confusion-matrix-based accuracy, precision, recall, and computational time. The experimental results indicate that incorporating class-imbalance handling improves the predictive performance of the ML algorithms. Among the evaluated combinations, Information Gain with Random Forest (IG+RF) provides the optimal result in this case. These findings highlight the value of integrating class-balancing and feature-selection procedures when developing machine-learning systems for breast cancer detection.
Development and Field Evaluation of a Solar-Powered LoRa-Based Air Quality Monitoring and Early-Warning System for a Sand-Mining Area Alamsyah -; Aidynal Mustari; Moh. Ikro Fajar Rahman; Mohammed Ikhlayel
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17080

Abstract

Air pollution generated by sand-mining activities can degrade environmental quality and increase public-health risks, particularly in locations lacking continuous air-quality monitoring infrastructure. This study presents the development and field evaluation of a solar-powered LoRa-based air-quality monitoring and early-warning system for the Watusampu sand-mining area in Palu City, Indonesia. The main contribution of the proposed system is the integration of autonomous solar power, long-range wireless communication, multi-parameter sensing, real-time visualization, local backup storage, and ISPU-based warning functions into a single deployable platform. The transmitter node combines ZH03B, MiCS-5524, and DHT22 sensors with an ESP32 and RFM95W LoRa module to measure PM₁, PM₂.₅, PM₁₀, carbon monoxide, temperature, and relative humidity. At the receiver node, the acquired data are processed, stored on a microSD card, uploaded to a web database, and displayed through an LCD and real-time dashboard. Air-quality status is classified using Indonesia’s Air Pollutant Standard Index (ISPU). Sensor performance was evaluated by comparison with a reference air-quality detector, while LoRa communication was tested under Line-of-Sight (LoS) and Non-Line-of-Sight (Non-LoS) conditions. The ZH03B sensor produced average errors of 6.61%, 10.58%, and 8.67% for PM₁, PM₂.₅, and PM₁₀, respectively, while the average errors for CO, temperature, and relative humidity were 18.48%, 0.87%, and 0.61%. LoRa communication achieved 0% packet loss up to 1.2 km under LoS conditions and up to 20 m under Non-LoS conditions. These findings indicate that the proposed platform is feasible for autonomous, real-time air-quality monitoring and early warning in sand-mining areas with limited infrastructure.
Sesame Oil with Butylated Hydroxyanisole as a Candidate Natural Ester for Transformer Insulation Yenni Afrida; Carla Maretha; Fitriono Fitriono
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17036

Abstract

This study evaluates sesame oil modified with butylated hydroxyanisole (BHA) as a candidate eco-friendly transformer insulating liquid. Ten formulations were prepared using 500 mL sesame oil with 1-10 g BHA. Viscosity was measured using a rotational viscotester, and the laboratory-reported kinematic values were treated as estimates derived from the direct viscosity readings. Alternating-current breakdown voltage (BDV) was measured five times for each formulation, and differences among BHA loadings were evaluated using one-way analysis of variance. The estimated kinematic viscosity ranged from 33.33 to 42.59 cSt and generally decreased as BHA loading increased. The highest mean BDV was obtained at 3 g BHA/500 mL (30.24 +/- 7.94 kV), whereas the 10 g formulation showed the lowest estimated viscosity (33.33 cSt) and the second-highest BDV (25.58 +/- 3.38 kV). BDV exhibited a non-monotonic response to BHA loading. One-way ANOVA indicated significant differences among formulations (F(9,40)=7.98, p<0.001). The results identify 3 g BHA/500 mL as the most promising formulation in the current screening because it combined the highest dielectric performance with moderate viscosity. Further validation with BHA-free sesame oil, mineral-oil controls, independent formulation batches, measured density, moisture, acidity, and direct kinematic-viscosity testing is required before transformer application.
Deep Learning Driven Ransomware Detection: A Bibliometric Analysis of Research Trends, Knowledge Structure, and Future Directions Guntoro Guntoro; Lisnawita Lisnawita; Loneli Costaner; Wenni Syafitri
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.17163

Abstract

Ransomware has become a major cybersecurity threat because it encrypts data, disrupts services, and evolves rapidly beyond conventional signature-based detection. This study presents a bibliometric analysis of deep learning-driven ransomware detection research, with emphasis on behavioral and dynamic analysis, API-call sequences, system calls, and future research directions. A total of 481 records were retrieved from the Web of Science Core Collection; after screening one retracted publication and two editorial materials, 478 eligible records remained. The eligible corpus was analyzed using Biblioshiny and Bibliometrix. Results show rapid growth from 2022 to 2025, with IEEE Access, Computers & Security, Sensors, Scientific Reports, and International Journal of Information Security among the prominent sources. Keyword and thematic analyses indicate a shift from static detection toward behavior-aware, sequence-based, explainable, and real-time ransomware detection. The findings highlight the need for robust datasets, cross-dataset validation, low-latency inference, explainable deep learning, and integrated detection systems for practical cybersecurity deployment.
Multi-Source Data Integration for Photovoltaic Power Forecasting in Tropical Indonesia Rahma Fitria Ariani; Machmud Effendy; Yepy Komaril Sofi&#039;i
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17149

Abstract

The variability of photovoltaic (PV) power under tropical weather conditions complicates one-hour-ahead forecasting and operational energy planning. This study evaluates deep learning-based PV power forecasting by integrating historical PV output from a university in Malang, Indonesia with NASA POWER, Solcast, BMKG, and time features. Five data scenarios were assessed using Persistence, LSTM, and GRU, while hybrid architectures and source- and architecture-level ablation tests were evaluated on the complete multi-source configuration. Timestamp continuity was explicitly audited, and 178 holdout sequences that crossed temporal gaps were removed. GRU with the complete configuration achieved the lowest baseline RMSE of 0.218839 kW, with MAE of 0.110686 kW, nRMSE of 6.230669%, and R² of 0.955550. Relative to Persistence, RMSE and MAE decreased by 49.60% and 54.36%, respectively. LSTM with the Solcast scenario produced a nearly identical RMSE of 0.218992 kW, indicating that the benefit of additional data sources was architecture-dependent and non-monotonic. CNN-LSTM was the best hybrid model but remained 3.26% worse in RMSE than GRU, while the tested attention mechanism increased RMSE by 10.02%. Four-fold limited walk-forward validation supported GRU with the lowest mean RMSE of 0.232213 ± 0.019500 kW. The final forecasts were also translated into conceptual Energy Management System decision-support recommendations. Because same-day BMKG daily summaries were used in the main configuration, the findings are primarily interpreted as a retrospective evaluation.
Energy Efficiency and Performance of PMSM Drives in Electric Vehicles under Variable Loads I Ketut Wiryajati; I Made Mara; I Chatur Adhi Wirya Aryadi; Ida Ayu Sri Adnyani
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.16859

Abstract

This study evaluates the performance and energy efficiency of an electric vehicle powertrain based on a Permanent Magnet Synchronous Motor under varying speed and load conditions using a real-time monitoring system. Experiments were conducted at speeds of 10–40 km/h and loads of 50–200 kg, measuring voltage, current, power, efficiency, and specific energy consumption. Results show that efficiency increases significantly from approximately 66% at low speeds to over 90% at higher speeds, while specific energy consumption decreases from 118 Wh/km to around 60 Wh/km, indicating improved energy utilization. Load variation has a relatively minor effect on efficiency but contributes to higher current and power demand. Efficiency gradient analysis reveals a reduced rate of improvement at higher speeds, suggesting saturation effects and the presence of an optimal operating region. Measurement validation confirms low error levels (<5%), ensuring data reliability. Overall, the system demonstrates optimal performance at medium to high speeds with significantly enhanced energy efficiency.
From Active to Zombie: A Literature Review on Bus Factor's Role in Open Source Project Lifecycle Decline Beni Nurfauzi; Kusrini Kusrini
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.15699

Abstract

Open Source Software (OSS) has become critical global digital infrastructure, yet its sustainability remains profoundly threatened by the concentration of knowledge within small cohorts of core developers a vulnerability formally quantified as the Bus Factor. This systematic literature review examines the role of Bus Factor dynamics in driving OSS projects toward the zombie phase, a deceptive intermediate state wherein repositories remain administratively active while genuine human-driven development has ceased. Following a PRISMA-based selection process across Google Scholar, Semantic Scholar, and the ACM Digital Library, 14 peer-reviewed studies were identified, quality-assessed, and thematically categorized to answer four research questions concerning Bus Factor measurement, lifecycle decline, computational prediction methods, and contributor behavioral dynamics. The review traces the evolution of Bus Factor measurement across four methodological generations and constructs a formal causal framework linking knowledge concentration to project death via sleeping developer behavior and zombie phase onset. Empirical evidence confirms that 89.65% of OSS projects experience total core developer loss, with only 27.07% recovering successfully. Critically, dynamic Bus Factor fluctuation remains entirely absent from existing abandonment prediction models. This review identifies this absence as the central research gap and identifies several candidate methodological directions for future empirical investigation. These findings establish a cohesive theoretical framework to guide proactive OSS sustainability research.
Neuro-Fuzzy Green-Time Allocation for Oversaturated Four-Way Signalized Intersections Ifan Wiranto; Yuliyanti Kadir
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17057

Abstract

Fixed-time traffic signals allocate a constant green duration regardless of how vehicle demand fluctuates across approaches. That rigidity becomes critical once an approach enters oversaturation, that is, once its degree of saturation x, the ratio of the arrival rate per cycle to the capacity the signal makes available per cycle, reaches or exceeds unity, so that a residual queue is carried over from one cycle to the next and successive residuals accumulate. This study designs and simulates an adaptive green-time allocation controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) with two inputs, aggregate phase queue length (Q) and maximum approach waiting time (W), for a four-way intersection operated from undersaturated to oversaturated conditions. The training data are not arbitrary: they are generated from a queue-actuated green-time formula whose three terms, queue discharge time, startup lost time and an anti-starvation priority proportional to normalized waiting time, are individually grounded in signal-timing theory, and whose parameters are reported in full. The model, using three Gaussian membership functions per input, nine first-order Sugeno rules and hybrid learning, was trained and validated on an 80:20 split of 180 data pairs and reached a testing RMSE of 2.39 s and an MAE of 1.87 s, that is, 2.1% of the 115-second output range. Performance was then evaluated over a 100-cycle discrete-event simulation with Poisson arrivals against fixed-time control at a constant 30 s green, using an identical queue-update mechanism. Under the baseline plan the effective capacity is 7 vehicles per approach per cycle, and the three scenarios in which at least one approach exceeds it (x = 1.14) are exactly the scenarios in which the fixed-time queue grows linearly with a strictly positive drift and in which the proposed controller yields improvements of 68.6% to 87.1%. In the four scenarios in which no approach exceeds capacity the improvement is 0.8% to 10.0%. The benefit of neuro-fuzzy green-time allocation is therefore concentrated in, and structurally explained by, the oversaturated regime.

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