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Eko Prasetyo
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JEECS (Journal of Electrical Engineering and Computer Sciences)
Published by Universitas Bhayangkara Surabaya
ISSN : 25280260     EISSN : 25795392     DOI : https://doi.org/10.54732/jeecs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
We aims to promote high-quality Electrical Engineering and Computer Sciences research among academics and practitioners alike, including power system, electrical engineering, industry automation, mechatronics, computer sciences, informatics, and information system. This journal is dedicated for the author or researcher who has focused in the field of technology and intending on publication and sharing knowledge the novel technology include, but are not limited to, the following topics: Data Mining, Informatics algorithm methodology, Mobile Computing, Automation, Power, Green Technology, Advanced Computer Networks, Image Processing, Computer Vision, Robotics Technology, Decision Support System, Big Data, Data Sciences, Internet of Things, Network Security, Virtual Reality, etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 420 Documents
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Design of IoT-Based Smart Hydroponic Farming with Solar Energy for Sustainable and Precision Crop Production Fahmi, Monika Faswia; Laksono, Deni Tri; Laksono, Dedi Tri
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i2.10

Abstract

Conventional hydroponic farming systems frequently encounter limitations related to unstable environmental control, suboptimal nutrient management, and strong dependence on grid-based electricity, which collectively hinder their sustainability and scalability, particularly in remote or energy-constrained regions. Recent studies have explored smart hydroponic technologies. However, many remain reliant on external power sources or lack integrated, autonomous control of multiple critical growth parameters. Therefore, this problem reveals a research gap in the development of fully self-powered and intelligent hydroponic systems. This study proposes the design and implementation of a solar-powered, IoT-based smart hydroponic farming system that enables real-time monitoring and closed-loop environmental control. The system integrates multi-sensor measurements, including pH, DS18B20 temperature, total dissolved solids (TDS), and light-dependent resistor (LDR) sensors, coupled with an on–off control strategy to regulate light intensity (115 ADC), water temperature (28 °C), pH (5.5-6.5), and nutrient concentration (840 ppm). A standalone photovoltaic energy subsystem, consisting of a 100 Wp solar panel and a 65 Ah battery, was designed based on a daily energy demand of 378.85 Wh to ensure continuous autonomous operation. Experimental results demonstrate high sensor accuracy, with measurement errors of 0.75% for pH, 0.095% for TDS, and 0.24% for temperature. Moreover, the proposed system effectively stabilizes environmental parameters within predefined setpoints, outperforming uncontrolled conditions. These findings confirm the system’s reliability and potential as a sustainable precision agriculture solution for off-grid hydroponic applications.
A Microcontroller-based Fish Drying System for Enhanced Drying Time with Real-Time Environmental Monitoring Guerrero, Rainiell; Calapit, Armiel; Ortega, John Mark; Maaño, Ronaldo C.; P. Santonil, Hannah Shamira; B. Canela, Dhon Niño; A. Maaño, Roselyn; P. Cortez, Emelex
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.1

Abstract

The preservation of fish through drying is a vital practice in coastal regions like the Philippines. Traditional sun-drying methods often suffer from inefficiencies, environmental inconsistencies, and long drying times ranging from 8 to 20 hours. However, existing automated solutions often lack the specific real-time precision required for small-scale costal processing, leading to inconsistent quality. To address these limitations, this study presents the design and implementation of a microcontroller-based fish drying system to enhance drying efficiency through real-time environmental monitoring. The system utilizes an Arduino UNO R3, a DHT22 sensor, infrared heating lamps, and an AC blower fan to maintain a regulated environment between 40°C and 50°C. The microcontroller is the central processing unit, communicating with the sensor to collect real-time temperature and humidity data. This data is used to dynamically control heating elements and ventilation, ensuring optimal drying conditions, reducing drying time, and improving product quality. Experimental results demonstrate that the microcontroller-based system significantly enhances the efficiency and consistency of the drying process to just 3.51 hours compared to conventional methods which recorded 4.48 hours. Technical evaluation through unit and system testing confirmed the system’s reliability in maintaining a 45° indicator. Stakeholders evaluated the prototype using a 5-point scale, resulting in an overall scientific and functional rating of 4.32 (Strongly Agree). This innovation offers a scalable solution for small-scale processors to improve productivity, quality, and production of dried fish products.
Review-Grounded Explainable Recommendation with Faithfulness Evaluation on Amazon Reviews Chang, Xiaohan; Lu, Yifei; Zhong, Ziliang Samuel
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.2

Abstract

Review text can support explainable recommendations, but many recommender systems still optimize ranking accuracy without providing verifiable textual evidence, or they attach post-hoc explanations whose faithfulness to the model is unclear. This study addresses the lack of a reproducible evaluation setting that jointly measures recommendation quality and whether extracted review evidence actually supports model scoring. We propose Review-Grounded eXplainable Recommender (RGXRec), a lightweight hybrid method that combines interaction signals and TF-IDF review similarity, and we evaluate it on the Luxury Beauty and Video Games subsets of the Amazon Review Data. The pipeline includes rating thresholding, iterative 5-core pruning, chronological leave-one-out splitting, ranked recommendation, extractive evidence generation, and faithfulness evaluation. We compare RGXRec with popularity, metadata-graph KNN, SVD-MF, and ReviewSim using NDCG@K, Recall@K, MRR, evidence coverage, ROUGE-1, sentiment agreement, and a term-attribution faithfulness score. On Luxury Beauty, RGXRec achieves the best ranking performance, reaching NDCG@10 of 0.3606 and outperforming the strongest single-view baseline. On Video Games, collaborative and metadata signals remain stronger for ranking, but RGXRec preserves competitive accuracy while providing non-zero review-grounded faithfulness that interaction-only baselines cannot offer. These findings show that review-grounded recommendation should be evaluated on both ranking quality and explanation faithfulness.
Self-Supervised Log Anomaly Detection with LogBERT-Style Transformers: Full Empirical Evaluation on a Reproducible SynHDFS Benchmark Xin, Qi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.3

Abstract

Log-based anomaly detection is a core problem in AIOps because system logs provide fine-grained evidence of failures, performance regressions, and security incidents. Recent work has shown that self-supervised sequence modeling substantially improves generalization compared with purely frequency-based detectors, especially when labeled anomalies are scarce. This paper presents a LogBERT-style transformer framework for session-level log anomaly detection and reports a complete, reproducible experimental evaluation. Due to download constraints of large archived log datasets in this environment, we construct a faithful fallback benchmark, SynHDFS-6k, which mimics HDFS-style block workflows by composing normal execution patterns and injecting five realistic anomaly types. SynHDFS-6k contains 6000 sessions with a fixed 5.0% anomaly rate and a vocabulary of 20 event templates. We train a two-layer transformer encoder with masked language modeling on normal sessions only and derive an anomaly score using pseudo log-likelihood (PLL) computed by masking each token position once. We compare against unigram and bigram probabilistic models, PCA reconstruction error, one-class SVM, isolation forest, a DeepLog-style GRU next-event predictor, and a supervised logistic regression upper bound. On the SynHDFS-6k test split, the proposed LogBERT-PLL achieves Precision=0.615, Recall=0.533, F1=0.571, ROC-AUC=0.898, and PR-AUC=0.594. We additionally analyze transformer scoring strategies (PLL mean, PLL top-k, PLL max, random masking, and CLS Mahalanobis), report runtime and model capacity trade-offs, and quantify per-anomaly-type detection behavior. The study provides an end-to-end blueprint for transformer-based self-supervised log anomaly detection under a fully specified protocol, and it highlights strengths and limitations that inform deployment on real-world HDFS logs.
Analysis of the Indonesian Tourist Destination Recommendation System Using User Profile-Based Collaborative Filtering Hamidah, Mas Nurul; Zainal, Rifki Fahrial; Tias, Rahmawati Febrifyaning; Ardiansyah, Tio Kukuh
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.6

Abstract

Tourism recommendation systems in Indonesia are challenged by highly heterogeneous user preferences and severe rating sparsity, which undermine the effectiveness of conventional collaborative filtering methods. However, prior studies predominantly rely on rating-based interactions and often utilize generic datasets, limiting their ability to capture the contextual and behavioural diversity of Indonesian tourism. Although user profile information is known to influence preferences, its integration with latent factor models is still fragmented and rarely evaluated in a unified, context-aware framework. Consequently, existing approaches often produce suboptimal accuracy and lack robustness in sparse and imbalanced data environments. This study proposes a unified user profile-enriched collaborative filtering framework that integrates Singular Value Decomposition (SVD), Jaccard similarity, and K-Nearest Neighbor (KNN) to jointly model latent preferences and contextual user characteristics. This integration constitutes the main novelty of this work, enabling simultaneous mitigation of sparsity and enhancement of personalization in a single pipeline. Experiments are conducted on an Indonesian tourism dataset, with performance evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and execution time. The results show that the proposed method consistently outperforms the rating-based baseline, achieving lower MAE (1.6994 vs. 1.7355) and RMSE (2.0653 vs. 2.1148), while maintaining comparable computational efficiency. Furthermore, the model demonstrates greater stability across varying neighbor sizes, indicating improved scalability and robustness. Practically, this approach provides a scalable and context-aware recommendation framework that can support more adaptive and personalized tourism services in Indonesia, particularly in real-world scenarios characterized by sparse and heterogeneous data.
Decision Support System for Selecting the Best Outsourcing Employee Using CRISUS and WASPAS Setiawansyah, Setiawansyah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.4

Abstract

The outsourcing employee selection process often faces problems such as high subjectivity in assessments, the involvement of multiple criteria with varying levels of importance, and inconsistencies in decision outcomes when using conventional methods. However, most previous studies still use weighting approaches that are subjective or have not integrated methods capable of optimally improving the objectivity and stability of decision results. These conditions have the potential to result in suboptimal employee selection that does not fully reflect the organization's needs. Based on these issues, this study proposes a Decision Support System for selecting the best outsourcing employees by combining the CRISUS method to objectively determine the criteria weights and the WASPAS method as a tool for evaluating and ranking alternatives. Data is collected through performance assessments based on a number of relevant criteria, and then the criteria weights are calculated using the CRISUS method to proportionally reflect the importance level of each criterion. Next, the WASPAS method is used to calculate the final preference values and generate the employee ranking order. The study results show that Employee A8 ranks first with a preference value of 1.00000, followed by Employee A3 in second place with a value of 0.96951, and Employee A5 in third place with a value of 0.94115. These findings indicate that the integration of CRISUS and WASPAS can produce rankings that are objective, consistent, and easy to interpret, so the proposed system can serve as an effective and reliable decision support tool in the outsourcing employee selection process.
Decision Support System for Selecting the Best Santri Using the Simple Additive Weighting (SAW) Method Fahlevi, Mohammad Reza; Kholil Rohman, Muhammad; Ali, Ircham; Muminin, Saeful
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.5

Abstract

The evaluation of Santri in Islamic boarding schools plays an important role in measuring academic and non-academic achievements; however, the assessment process often lacks transparency and tends to be centralized, leading to subjective and inefficient decision making. This study proposes a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to optimize the Santri evaluation process by considering 10 distinct academic and non-academic criteria in a systematic and objective manner. The research stages include requirement analysis, system design, implementation, and functional testing using the Black Box method to ensure reliable operation. The experimental results involving a dataset of 10 santri show that the proposed SAW-based approach is able to accurately automate the normalization and final score calculation processes. To validate the system's performance, the DSS-generated rankings were compared against the manual evaluation results from the boarding school administrators, achieving an accuracy rate of 80%. Furthermore, consistency testing demonstrated that the system maintains a stable and proportional ranking outcome when tested repeatedly. These findings confirm that the application of the SAW method significantly improves transparency, fairness, and efficiency in supporting decision making for Santri assessment.
Monitoring Air Quality Around Users With IOT Based NODEMCU ESP8266 Zaky Wahyu Oktavianto; Anton Breva Yunanda
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 7 No. 2 (2022): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v7i2.16

Abstract

For regional issues and issues related to a healthy environment, there were issues related to water and air quality. The biggest source of air pollution is vehicle exhaust fumes, and the increase in the number of vehicles in Sidoarjo City grows by an average of 18 percent per year. To overcome this problem, an air quality monitoring device is needed. The goal to be achieved by creating an air quality monitoring system as an Internet of Things application is to become a prototype for monitoring environmental health problems related to air quality in Sidoarjo City. Monitoring is carried out online through the Thingspeak IoT platform. This tool applies Ohm's law theory to reading conversion calculations when the MQ sensor operates as a CO, CO2, and Acetone gas detector. NODEMCU ESP8266 as microcontroller. This tool can be used as a prototype for monitoring in highway areas with high vehicle intensity.
Image Based Object Tracking Target on Ship Robot for Oil Waste Cleaner Richa Watiasih; Ahmadi; Adiananda
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 7 No. 2 (2022): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v7i2.17

Abstract

The existing oil waste water contains oil, solids, water and heavy metals. This oil waste is a contaminant material that can cause negative impacts to the aquatic environment as well as the existing living creatures around it so that it requires a careful and fast handling to clean. The research resulted in the tracking system on the image-based-ship robot by using the method of histogram and fuzzy logic controller that can detect the image of waste water well. The result of the testing of the ship robot done on the pool indicated that it took about ± 196.92 seconds for the robot to detect the image of oil waste objects. The oil waste suctioning process took a maximum of 60 seconds for once.
Energy System Audit Measurement at R.S. Bhayangkara H. S. Samsoeri Mertojoso Surabaya Agus Kiswanto; Edo Rahardian Arfelindo
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 7 No. 2 (2022): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v7i2.18

Abstract

Bhayangkara H.S. Samsoeri Mertojoso Surabaya Hospital is a public service agency (BLU) engaged in public health services and a member of the governmental police. The hospital certainly has electricity. This power source must be controlled honestly and as much as possible. This is used to get stable and maximum electrical energy. For this reason, weekly checks and measurements are required. In addition to inspection, of course it is necessary to clean every nook and cranny of electrical equipment such as wires. According to the researchers' observations, this time the power supply occurred at this hospital was noisy or unstable. This is proven to be because the filler material used is still suboptimal, which causes discomfort for the nurse and, of course, the patient. With this in mind, of course, we need a voltage and current that maintains maximum power.

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