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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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Self-Supervised Log Anomaly Detection with LogBERT-Style Transformers: Full Empirical Evaluation on a Reproducible SynHDFS Benchmark Qi Xin
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
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 Mas Nurul Hamidah; Rifki Fahrial Zainal; Rahmawati Febrifyaning Tias; Tio Kukuh Ardiansyah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
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): June
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.
Harmonic Performance in Hybrid AC-DC Microgrid Connected Bidirectional Converter with LCL Filter Hasti Afianti; Ahmadi Ahmadi; Saidah Saidah; Richa Watiasih; Bambang Purwahyudi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.7

Abstract

This paper presents the harmonic performance of a grid-connected hybrid AC-DC microgrid. Using solar power as a source in the DC microgrid and the grid in the AC microgrid eliminates the need for energy storage. Solar power generation is known to be intermittent, requiring a power supply to meet the load demand in the DC microgrid. Therefore, a converter that can operate as both an inverter and a rectifier is required. One way to address this issue is to use a bidirectional AC-DC converter (BC). To perform its function, this converter requires a filter to reduce the switching frequency ripple current injected into the grid. Among the available options, the LCL filter is widely recognized as the most effective solution for suppressing switching frequency harmonics. In this study, the performance of the LCL filter will be analyzed through simulations using MATLAB/Simulink. The results show that the bidirectional AC-DC converter designed with the LCL filter effectively suppresses resonances occurring at the converter output in rectifier or inverter mode., the voltage in the AC microgrid remained stable, the THD stayed below 5%, and the system frequency was well maintained. However, during the standby period, when the power transfer in both the DC and AC microgrids is zero, the THD on the grid remains high, up to 200%.
Energy Management of Battery–Supercapacitor Hybrid Storage in PV-Integrated DC Microgrids Using Predictive Control Madhusudan Nyaupane; Shanti Tiwari; Rajesh M. Pindoriya; Jeetendra Chaudhary; Asmita Rijal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 2 (2026): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i2.1

Abstract

This paper addresses the challenge of DC-link voltage instability and control conflicts in Hybrid Energy Storage Systems (HESS) for photovoltaic (PV)-integrated isolated DC microgrids, arising from the inherent variability of Renewable Energy Sources (RES). Existing control strategies often suffer from high computational complexity and inadequate coordination between battery and supercapacitor currents, limiting their effectiveness under dynamic operating conditions. To overcome these limitations, a HESS composed of batteries and supercapacitors is employed, leveraging their complementary characteristics: high energy density and high-power density, respectively. A predictive control strategy is proposed to optimize the current distribution between the battery and supercapacitor using DC-link voltage error and uncompensated power as control inputs. The proposed method is implemented in MATLAB/Simulink and evaluated under varying irradiance conditions (1000 W/m² to 500 W/m² at 25°C) with a 500 W load. The results demonstrate that the proposed approach achieves fast DC-link voltage recovery within approximately 0.1 s, maintains voltage deviation within ±2% of the nominal value, and reduces battery current stress by approximately 30% during transient conditions. Furthermore, the supercapacitor effectively handles rapid transient loads, significantly alleviating battery stress and improving system responsiveness. Additionally, a Bode-plot-based tuning method is employed to refine PI controller parameters, further enhancing energy management and overall system efficiency. These findings highlight the effectiveness of the proposed predictive control strategy as a computationally efficient, dynamically robust solution for the reliable, stable integration of renewable energy into isolated DC microgrids.
Decision Support System for Selecting the Best Santri Using the Simple Additive Weighting (SAW) Method Mohammad Reza Fahlevi; Muhammad Kholil Rohman; Ircham Ali; Saeful Muminin
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
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.
Rehabilitation Hand Exoskeleton Robot based on Soft Actuator and Adaptive Control Zahraa Al-Faeq; Hassanin Al-Fahaam
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.8

Abstract

Every year, millions of people suffer from severe motor impairment in the hand as a result of strokes or spinal cord injuries, which hinders their ability to perform simple daily activities. However, conventional rigid robots still face significant challenges related to their heavy weight and anatomical incompatibility with human joints, limiting their effectiveness in home-based rehabilitation. This research proposes a solution based on soft pneumatic actuators, Expansive Bending pneumatic actuators muscle (EBPAM) characterized by light weight and high flexibility. The methodology involves designing an intelligent control system that begins with fuzzy logic to track the therapist’s finger movements, and was subsequently developed using an Adaptive Fuzzy Neural Inference System (ANFIS) to compensate for the non-linearity of pneumatic systems. Experimental results demonstrated the system’s ability to achieve accurate motion tracking, with ANFIS successfully reducing tracking error by up to 50% compared to conventional control, whilst maintaining a total glove weight of less than 100 grams, making it ideal for domestic and clinical use.
Implementation of the Forward Chaining Algorithm in a Student Mental Health Detection System Ridwan Yulindra Megananda; Ghulam Asrofi Buntoro; Dyah Mustikasari
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 2 (2026): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i2.2

Abstract

Student mental health is an important aspect in supporting academic success and individual well-being. The various academic pressures, social challenges, and the transition to independent living make students a vulnerable group to mental health disorders such as stress, anxiety, and depression. However, many students are still reluctant or experience difficulties in accessing professional services to solve this problem. This research aims to develop an early detection system for student mental health based on an expert system using Forward Chaining. This method performs reasoning from symptoms toward a conclusion based on mental health condition using rules stored in the knowledge base. The system is developed using the DASS-21 (Depression, Anxiety, Stress Scale-21) instrument to assist in identifying mental health conditions. The dataset consists of 50 student respondents from Universitas Muhammadiyah Ponorogo who completed the DASS-21 questionnaire. System performance was evaluated by comparing the diagnostic outputs of the system against the standard DASS-21 score. The results were analyzed using a confusion matrix to calculate accuracy, precision, recall, and F1-score per severity class. The research results show that the system is capable of initially identifying students’ mental health conditions by presenting the severity level of the mental condition, a description of the condition, and appropriate handling recommendations. Black-box testing confirmed the accuracy of 96%, with precision and recall values above 90% across all severity classes. These results demonstrate that the implemented forward chaining system provides an accessible, automated, and standardized tool for early mental health detection in the Indonesian higher education context.
Design and Development of an Employee Database and Daily Reporting Information System Using Rapid Application Development (RAD) Febri Pratama; Abdul Kholik; Indah Pratiwi Putri
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.10

Abstract

Employee data management and daily operational reporting play an important role in supporting efficiency, accuracy, and decision-making processes in warehouse operations. However, many organizations still rely on spreadsheet-based reporting systems and manual data recording processes that are vulnerable to data redundancy, inconsistent updates, limited monitoring capabilities, and delays in report recapitulation, which negatively affect operational efficiency and information accuracy. Previous studies have generally focused only on employee administration systems or inventory management separately, while limited research has discussed the integration of employee databases, daily activity reporting, and operational monitoring within a unified information system. This study aims to design and develop an integrated employee database and daily reporting information system to improve operational efficiency and reporting accuracy in warehouse activities. The system integrates employee data management, daily activity recording, automated reporting, and role-based access control within a centralized platform. The Rapid Application Development (RAD) method was applied to support iterative development and faster adaptation to user requirements. System evaluation was conducted using Black Box Testing to verify functional reliability and system performance. The results show that the proposed system successfully reduces data duplication, accelerates report generation processes, improves information accessibility, and enhances operational transparency and managerial decision-making. The implementation results also demonstrate that the developed system provides a more structured, efficient, and integrated approach to warehouse personnel administration and daily operational reporting.
Analysis of the Task Technology Fit Suitability of the SiNonA Application and its Impact on Improving the Performance of Non-ASN Employees Melinda Kurnia Putri; Nining Ariati; Dhamayanti Dhamayanti
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.9

Abstract

The implementation of the Electronic-Based Government System (SPBE) encourages government institutions to utilize information technology to improve employee effectiveness and administrative efficiency. One implementation in Ogan Ilir Regency is the SiNonA application, which is used as a digital attendance system for Non-ASN employees. However, several problems are still encountered in its implementation, such as limited application features, difficulties in system usage, and the mismatch between system capabilities and employee work requirements. Previous studies on digital attendance systems mostly focused on usability, system quality, and technology acceptance, while studies examining the suitability between technology characteristics and employee task requirements using the Task Technology Fit (TTF) approach are still limited, especially in the government sector for Non-ASN employees. Therefore, this study aims to analyze the influence of Task Characteristics and Technology Characteristics on Task Technology Fit and its impact on employee performance improvement in using the SiNonA application. This study used a quantitative approach with a survey method involving 353 respondents selected using the Slovin formula. Data were collected through Likert-scale questionnaires and analyzed using the PLS-SEM method with SmartPLS software. The results showed that Task Characteristics and Technology Characteristics had a positive and significant effect on Task Technology Fit, while Task Technology Fit also had a positive and significant effect on employee performance improvement. These findings indicate that the suitability between technology and work tasks plays an important role in improving the effectiveness, efficiency, and productivity of Non-ASN employees in using the SiNonA application.

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