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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 30 Documents
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Search results for , issue "Vol 7, No 2 (2025)" : 30 Documents clear
Implementation of The Behaviorally Anchored Rating Scale Method for Employee Performance Evaluation of MSMEs in Surabaya City Based on Information Technology Darmanto, Darmanto; Dhaniswara, Erwin -; Widianto, Yonatan
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12587

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Surabaya encounter persistent challenges in conducting objective evaluations of employee performance. To address this issue, the present study introduces the application of the Behaviorally Anchored Rating Scale (BARS), employing both qualitative and quantitative approaches supported by Information Technology (IT). BARS enables performance appraisal based on specific and observable work behaviors, thereby mitigating subjectivity and enhancing the fairness of evaluations. The integration of IT further streamlines data collection processes while reducing administrative complexity. In this study, an open-source web-based application was developed and customized to align with the operational requirements of MSMEs. The primary objective is to design and implement a performance evaluation system grounded in BARS and IT, supporting MSMEs in the formulation of realistic performance benchmarks and more effective human resource management practices. The research methodology adopts the ADDIE Research and Development model, encompassing the phases of analysis, design, development, implementation, and evaluation. System reliability was assessed through black box testing, with results indicating validity and consistency across all scenarios. The findings confirm the successful deployment of a multi-user web application capability in facilitating efficient, accurate, and systematic employee performance evaluations within MSMEs
Analysis Of The Performance Of Solar Power Plants (Plts) On The Electrical Energy Needs Of Mosques hafit, ilhan; Wibowo, Pristisal; Siagian, Parlin
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.11357

Abstract

The need for electrical energy continues to increase, including in public facilities such as mosques. Solar energy is an environmentally friendly and sustainable solution because it is a source of renewable energy. This study aims to analyze the feasibility of implementing a rooftop Solar Power Plant (PLTS) system at Al-Hakim Mosque, Mabar Hilir Village, which has high sunlight intensity and minimal vegetation obstructions. The methods used include direct observation and interviews. The data collected included location identification, energy needs, and calculations of the solar power system. The results show that the mosque requires a daily power of 7,860 Wh, which can be met with 16 100 Wp solar panels and 9 12V 100Ah LiFePO₄ batteries. The research concluded that the location of the Al-Hakim Mosque is suitable for the implementation of the solar power system, while supporting the renewable energy transition and government policies towards energy sustainability in Indonesia
Implementation of Blockchain for Integrated Civil Service Statistical Data (Case Study: Civil Service and Human Resource Development Agency of Madiun Regency, East Java Province) Huda, Syaiful; Kusrini, Kusrini; Kusnawi, Kusnawi
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12170

Abstract

Digital transformation in personnel data management demands a transparent, secure, and integrated system to support data-driven decision-making and enhance accountability in personnel services. An integrated information system and personnel statistical data are necessary to assist leaders in analyzing staffing needs and making more accurate and efficient data-based policies, while also strengthening the principles of good governance through improved transparency and accountability. Therefore, the Personnel and Human Resource Development Agency of the Government of Madiun Regency, East Java, requires technology capable of effectively managing personnel information by offering security, transparency, and data integrity through a decentralized mechanism. Blockchain, as a distributed ledger technology, provides an innovative solution for maintaining data integrity and increasing public trust through permanent, encrypted, and validated transaction records within a decentralized network. The implementation of blockchain in the management of personnel statistical data remains limited, despite the technology’s ability to support real-time audit trails and reliable interactive data visualization. This study proposes a framework for integrating a relational database with smart contracts on the Ethereum network, by recording the hash of statistical data in the smart contract as proof of data authenticity. Data is retrieved from the database, hashed, and the hash is stored in the smart contract to ensure its integrity, with the results visualized in interactive charts. This framework is expected to improve transparency, accountability, and trust in personnel statistical data to support more accurate and efficient strategic decision-making.
Improving Tomato Ripeness Classification Using Knowledge Distillation and Hyperparameter Optimization with Optuna Sholihin, Iasya; Sunyoto, Andi
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.11266

Abstract

Automatic classification of tomato ripeness plays a crucial role in ensuring post-harvest quality and efficiency in the horticultural industry. This study proposes a combined strategy of Knowledge Distillation (KD) and hyperparameter optimization using Optuna to improve the accuracy of the ResNet50 student model by leveraging the performance of a MobileNetV2 teacher model.We used a publicly available Kaggle dataset containing 8,540 images, categorized into four ripeness levels (green, red, ripe, and rotten), comprising 7,157 training images and 1,383 validation images.Each image was resized to 224×224 pixels; light augmentation techniques (random rotation, brightness–contrast adjustment, flipping, and Gaussian blur) were applied only to the training set to prevent overfitting while maintaining consistency during evaluation.The MobileNetV2 teacher model was initially fine-tuned on the last 20 layers using manual hyperparameters (freeze_until = 20, dropout = 0.6), achieving an accuracy of 85.8%.Subsequent tuning via Optuna identified the optimal configuration (freeze_until = 91, dropout_rate = 0.5055), which improved the teacher’s performance to 89.6%.The resulting teacher model was then used to distill knowledge into the ResNet50 student: under manual settings, the student’s accuracy improved from 55.24% to 73.25%; when the student model was also optimized using Optuna, its accuracy surged to 85.54% nearly matching the teacher.Further evaluation using a confusion matrix and ROC curves revealed an increase in per-class AUC to the range of 0.91–0.99 in the KD + Optuna student model, confirming that this method effectively closes the performance gap between student and teacher.These findings demonstrate that combining KD with Optuna-based hyperparameter optimization is an effective approach for producing a lightweight, fast, and highly accurate tomato ripeness classification model ready for deployment in field applications to support post-harvest decision-making.
Study of Potential Scalability Development of Palm Oil Mill Effluent-based Gas-fired Power Plant Widanarko, Ishananta Alfian; Pramono, Wahyudi Budi
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.10813

Abstract

Indonesia, as the world's largest producer of palm oil, generates a large amount of liquid waste that has the potential to be a renewable energy source. The main problem of liquid waste originated from palm oil industry, apart from its nature, is its potential long-term usability, especially when improvements in biogas-based power generating technologies are put into consideration. Based on these problems, the main purpose of this research is to compare the economical, technical, and environmental scalability between palm oil mill effluent (POME)-based biogas power generation and older, existing diesel-based power generation and analyzing their scalability potential in long term, in aim to increase the utilization of renewable energy portion in Indonesia. This research is conducted by observing an actual palm oil mill facility and its power generation system. Data are analyzed by qualitative and quantitative method by means of computer simulation before being compared with diesel-based power generation system. Based on simulations and comparative analysis, POME-based biogas can be utilized as a fuel source for power generation using gas-fired power generating technology, due to its methane content. With these characteristics, it is possible to analyze the scalability potential of POME-based biogas power plants in the diversification of new and renewable energy sources. Studies on potential development show that biogas power plants are more efficient and have a linear correlation with the volume of liquid waste, and can be adapted in lieu of power generating-related technology advancements. As a result, energy derived from POME can support energy independence and contribute to the growth of clean energy.
Currency Exchange Rate Prediction Using Gated Recurrent Unit (GRU) with Historical Data and Economic Factor Adhani, Muhammad Azmi; Kusrini, Kusrini
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12385

Abstract

This study presents a currency exchange rate prediction model using a Gated Recurrent Unit (GRU) with historical price data and selected economic factors. Historical data, including Open, High, Low, and Close (OHLC) prices, were obtained from Yahoo Finance. Economic factor data, including Non-Farm Payrolls (NFP), Gross Domestic Product (GDP), Purchasing Managers Index (PMI), Retail Sales, and Durable Goods Orders, were collected from Trading View. Data preprocessing involved chronological sorting, missing value handling, feature scaling, and sequence generation. Multiple experiment cases were evaluated: historical data alone, historical data combined with all economic factors, and historical data combined with each individual factor. The GRU model achieved its best performance when incorporating historical data with Durable Goods Orders, indicating that this economic indicator provides significant predictive value, as reflected by the lowest RMSE (0.0076) and MAPE (0.0054), and the highest R² (0.9764) indicating that this economic factor provides significant predictive value. These findings highlight the importance of integrating selected economic factors into exchange rate prediction models to enhance forecasting accuracy.
Public Street Lighting Planning (PJU) Using New and Renewable Energy in Tanjung Gusta Village, Hamlet III, Sunggal District ramadhan, ilyasa; Rahmaniar, Rahmaniar; Dalimunthe, Erpandi
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.11334

Abstract

Indonesia has great potential in renewable energy, especially solar energy, due to its geographical location in the tropics. This research aims to plan an efficient and sustainable Public Street Lighting (PJU) system in Tanjung Gusta Village, Sunggal District, by utilizing new and renewable energy (NRE). Tanjung Gusta Village has a need for adequate street lighting to improve the safety, mobility, and quality of life of the community. The research method includes field surveys to analyze conditions, calculation of lighting needs based on applicable standards, and PJU system design using NRE. The results of the study show that the PJU system with solar power is the most suitable solution for Tanjung Gusta Village, taking into account the availability of resources. The planning of this solar PJU system includes the selection of the type of lamp to be used, determining the capacity of the solar panel and battery, and calculating the optimal layout and height of the light pole.
Deep Learning Models For Youtube Sentiment Analysis: A Comparative Study Of Bert And Gru In Danantara Indonesia Rizy, M. Alfa; Utami, Ema
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12064

Abstract

This study aims to analyse public sentiment toward Danantara Indonesia through YouTube comments and compare the performance of two deep learning models, BERT and GRU, in sentiment classification. The dataset consists of 1,065 comments collected through scraping techniques, which were pre-processed and classified into three sentiment categories: positive, neutral, and negative. The results indicate that the IndoBERT model achieved 100% accuracy, with perfect precision, recall, and F1-score for all sentiment classes. In contrast, the GRU model only achieved 60% accuracy, showing a tendency to classify almost all comments as negative sentiment. Sentiment distribution analysis reveals that the majority of comments (60%) express negative sentiment, followed by positive sentiment (25%) and neutral sentiment (15%). The dominance of negative sentiment suggests public distrust and criticism regarding the policies and transparency of Danantara Indonesia. These findings demonstrate that BERT is a more accurate model for sentiment analysis of Indonesian-language texts. Furthermore, this study recommends further evaluation to address data imbalance and improve the model’s generalization across various social contexts in digital media.
Health Risk Level Prediction for Hajj Pilgrims Using Random Forest and Bayesian Optimization (Case Study: Hajj Pilgrims of Balikpapan Embarkation) Husag, Luthfi Bhaktiawan
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12391

Abstract

The Hajj pilgrimage is one of the largest religious rituals in the world, involving millions of pilgrims from various countries. The physical condition and health of pilgrims are crucial factors in ensuring the smooth execution of the Hajj. Data from the Ministry of Health indicates that the mortality risk among Hajj pilgrims tends to increase annually, particularly in the elderly age group and among pilgrims with a history of certain diseases, such as hypertension, diabetes, and heart disease. This study aims to compare the performance of a Random Forest model optimized with Bayesian optimization against a Random Forest model without any optimization in predicting the health risk level of Hajj pilgrims at the Balikpapan Embarkation. The research findings show that the Random Forest model optimized with Bayesian Optimization provides superior performance compared to the non-optimized model, using K-Fold Cross-Validation for data splitting to avoid imbalance. The optimized model achieved an average Accuracy of 88.25% and an F1 Score of 88.19%, higher than the standard model which recorded 87.99% and 87.95% on the same metrics. Although their AUC scores were nearly identical (95.46% vs. 95.47%), the improvement in accuracy and F1 Score indicates that Bayesian Optimization can produce a more balanced and accurate classification model. In conclusion, the application of Bayesian Optimization to Random Forest is proven effective for enhancing the predictive accuracy of Hajj pilgrims' health risks, potentially supporting more proactive Hajj healthcare services.
Analysis of Cybersecurity Awareness Among Social Media Users Among Teenagers Using Exploratory Factor Analysis (EFA) & Confirmatory Factor Analysis (CFA) Methods Angela, Sofia; Huda, Muhamat Maariful; Rusdiyan, Rizqi Darma
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.11407

Abstract

Nowadays, security awareness is very important for social media users, especially teenagers. Many users become victims of cybercrime due to the lack of education about cybersecurity awareness. This case began with many complaints submitted by teenagers from SMP Negeri 2 Ngoro, one of whom was a victim of cybercrime on Instagram social media whose account was hijacked by unknown individuals. This also happens on WhatsApp social media where there are criminals who steal someone's identity to be used as a victim and commit fraudulent money transfers, credit and other fraud. The purpose of this study was to assess and ensure the level of cybersecurity awareness among the three most significant social media platforms in Indonesia, namely WhatsApp, Instagram, and TikTok. The Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) methods were used to analyze the online survey data set. The EFA results obtained were 6.67% insignificant in one of the factors contained in the knowledge variable and 93.33% EFA results stated significant. Meanwhile, the CFA calculation results obtained 80% results which were stated as Fit, indicating that the model accurately represented the data, 20% of the CFA calculation results stated Moderate Fit because they reflected values that were almost close to the fit value.

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