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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 and Construction of Infrastructure Asset Management Information Systems Using the Rapid Application Development (RAD) Method Mas Nurul Hamidah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.3

Abstract

The school administration system manages several things, such as the management, storage, and use of facilities and infrastructure items, which have been done traditionally. In contrast, the management must be well structured so that integration with information technology is needed. In previous studies, there were many areas for improvement in the asset information system, one of which was the use of traditional methods such as waterfall which had weaknesses in user flexibility and took a long time to process. Therefore, that a more relevant and accurate method is needed in its completion. SD Khaijah Surabaya built a facilities and infrastructure asset information system using the Rapid Application Development (RAD) method, which offers a more adaptive and interactive solution because it allows the development of rapid prototypes and continuous evaluation of the system. The results of this study show that building an asset management information system can simplify the process of managing infrastructure assets, improve data accuracy, and assist in making decisions related to asset management. The implementation of this system is expected to significantly improve organizational performance in terms of asset management, while system testing using the Black Box method shows accurate results and using the System Usability Scale (SUS) method manages to get a precise score of 80. The average person gives a score of 5 on the assessment of the questionnaire distributed to stakeholders.
Rewinding of 3 Phase Induction Motor Double Speed Linda Sartika; Abdul Muis Prasetia; Boby Setiawan; Tri Widodo
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.6

Abstract

A double-speed motor is a type of asynchronous AC motor designed with two or more windings. The presence of two separate windings causes three-phase double-speed motors to have a significantly larger physical size compared to three-phase single-speed motors of the same power rating. Numerous studies have investigated the impact of the rewinding process on the efficiency of single-speed induction motors. However, limited attention has been given to double-speed induction motors. Addressing this research gap, the present study focuses on two primary objectives: first, to analyze the impact of rewinding on the performance characteristics of double-speed induction motors; and second, to evaluate the operational performance of these motors after undergoing the rewinding process. In this study, the rewinding process utilized copper wire with a diameter of 0.50 mm, wound using a mold to create a total of 52 windings. Performance testing revealed the following results: under no-load conditions with slow rotation, the motor exhibited a current of 1.3 A, a frequency of 50.45 Hz, a power factor (cos φ) of 0.86, and a speed of 1515 RPM. When a load was applied under fast rotation, the motor demonstrated a current of 1.9 A, a frequency of 50.29 Hz, a power factor (cos φ) of 0.997, and a speed of 2949 RPM. The experimental results showed minimal variation in current and frequency between loaded and unloaded conditions, with significant differences primarily observed in rotational speed between slow and fast modes. This behavior is characteristic of double-speed motors, which are capable of operating at two distinct speeds. In fast rotation mode, the speed can reach approximately twice that of slow rotation, highlighting the design's capability to adapt to varying operational demands.
The Role of Data Science in Enhancing Web Security Ahmad Sanmorino
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.4

Abstract

With the rise of digital transformation, web security has become a critical concern for organizations, governments, and individuals. This study explores the role of data science in enhancing web security by leveraging machine learning algorithms and advanced analytics to predict and identify potential attacks in real-time. The main objective is to demonstrate how data-driven techniques, including predictive analytics, anomaly detection, and behavioral analysis, can be integrated into existing security frameworks to reduce vulnerabilities and strengthen defenses against cyber threats. The research gap addressed by this study lies in the insufficient application of comprehensive, data-driven methodologies for threat detection and classification in web security. The problem gap is the absence of integrated frameworks that combine feature engineering, classification models, and anomaly detection for both known and unknown threats. This study bridges these gaps by employing a structured dataset of web interactions to model, detect, and predict security threats using advanced data science techniques. Using a dataset of simulated web traffic and previous attack records, this research applies data preprocessing, feature engineering, and machine learning classification models, such as decision trees and random forests, to predict threat levels and identify anomalies. Results show that machine learning models can effectively classify threat levels, with a threat classification accuracy of 80 percent. This study contributes to the field by demonstrating how data science can improve web security practices, offering a proactive approach to detecting and mitigating cyber-attacks.
Implementation of Internet Network Load Balancing at the Faculty of Computer Science UNUSIDA Using the ECMP Method Nida Aulina; Mochammad Machlul Alamin
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 1 (2025): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i1.7

Abstract

A stable internet speed is essential in supporting worker and student task completion. Slow internet connectivity can become a persistent hindrance if it occurs continuously. Previous research indicates that conventional load balancing methods still have limitations in handling uneven traffic loads. This study, therefore, explores methods to optimize internet link utilization by distributing traffic load evenly, known as load balancing, using the Equal Cost Multi-Path (ECMP) method. This approach aims to maintain maximum data/packet flow, thereby preventing overload on a single link or connection failure. Testing was conducted by comparing network performance before and after ECMP implementation on two different ISPs. The test results showed that before implementing the ECMP method load balancing, the internet speed of ISP 1 was 29 Mbps, and the speed of ISP 2 was 16 Mbps. While after implementing load balancing using the ECMP method, the internet speed was 63 Mbps. Therefore, it is proven that implementing load balancing using the ECMP method can maintain and increase connection speed and share the load on both gateways so that overload does not occur.
Design of an Intelligent Computing-based Information System for Automated Decision Making Febri Pratama; Terttia Avini; Irfan Saputra; Melinda Kurnia Putri; Sultan Imam Fajri
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.2

Abstract

The rapid growth of information technology has increased the need for intelligent computing-based information systems across sectors, such as business, education, and government, to facilitate quick and accurate decision-making. Previous research primarily focused on data analysis without a seamless integration for automated decision support. This study aims to bridge this gap by designing an information system that leverages machine learning algorithms for automated decision-making. The system incorporates artificial intelligence and big data processing to provide accurate recommendations based on historical and real-time data patterns. Key processes include identifying user needs, selecting suitable algorithms, developing predictive models, and integrating them into a user-friendly, web-based platform. Results indicate that the intelligent system significantly enhances decision-making speed and accuracy, particularly in scenarios demanding real-time analysis. Tests with decision trees and neural network algorithms demonstrate the system's reliability and adaptability to various data types, supporting consistent, data-driven outcomes. This research concludes by highlighting the system's potential to address complex data challenges, enabling efficient decision-making in dynamic environments.
Sentiment Analysis of UINSU Students' Comfort Towards Trans Metro Deli Services at Taman Budaya Bus Stop Using the Naive Bayes Method Nurhidayati Nurhidayati; Dea Syahfira Hasibuan; Lailan Sofinah Harahap
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.10

Abstract

The large number of bus passengers at the Taman Budaya bus stop is one of the public transportation problems. Finding that the Metro Deli Bus Organizer is still operating. They are considered capable of meeting the requirements for choosing a mode of transportation. The purpose of this study is to determine the passenger transportation factors on the Trans Metro Deli Bus. The Trans Metro Deli Bus Passenger Transportation Factor is the purpose of this study. Data Collection Techniques Using Questionnaires Student comfort factors when using the Trans Metro Deli bus service. This study's methodology starts with problem identification and moves on to problem-solving techniques and assessment procedures. Respondents were given a questionnaire to fill out to collect data. The author of this study used Google Forms. The author of this study solved the problem using the Naïve Bayes algorithm. The Naïve Bayes algorithm model produces results with an accuracy of up to 71.43%, which is quite good. The accuracy results of 71.43% and approaching 100% show how accurate the sentiment analysis is using the Naïve Bayes classification. The accuracy results of 71.43% and approaching 100% show how accurate the sentiment analysis is using the Naïve Bayes classification. 'The bus took a long time to arrive' and 'didn't get a seat' were the most common negative reviews, indicating that some students felt uncomfortable. The Naïve Bayes results of the study showed that people who reviewed the Trans Metro Deli Bus expressed more positive opinions, with the highest score of 71.43%.
Hemorrhage Segmentation on Retinal Images for Early Detection of Diabetic Retinopathy Hendar Hermawan; Adithya Kusuma Whardana
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.5

Abstract

Diabetes mellitus is a chronic disorder that can lead to serious complications, including diabetic retinopathy, which affects the eyes and can potentially lead to blindness. Rapid identification of diabetic retinopathy is crucial to facilitate quicker and more efficient treatment for patients. This study aims to segment hemorrhages in retinal images using the Laplacian of Gaussian (LoG) approach in conjunction with threshold-based segmentation and analysis of region properties, including eccentricity. The LoG approach is utilized for its ability to detect edges, features, and abrupt variations in image intensity, thereby optimally highlighting the bleeding lesion area. With accurate segmentation, it is hoped that early detection and monitoring of diabetic retinopathy can be improved. This research uses the IDRiD, DR_2000, and DIARETDB1 datasets, recommending the use of IDRiD and DIARETDB1 for optimal results. Through this methodology, it is expected to make a significant contribution to reducing the risk of blindness in diabetes patients.
Modern Transformation in Agriculture for Onion Watering Automation with Solar Cell and IOT Technology Isac Ilham Akbar Habibi; Septriandi Wirayoga; Miftakhul Huda; Guntur Yanuar Astono
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.7

Abstract

Shallots are a key agricultural commodity in Indonesia, requiring careful water management for optimal growth. Excessive water can cause rot in shallot seedlings, while insufficient water leads to wilting and yellowing leaves, affecting crop quality. This research designs an IoT-based automatic monitoring and watering system to address these challenges. The system includes a soil moisture sensor, a DHT22 sensor for air temperature and humidity, an ESP32 microcontroller, a water pump, and a solar panel as its energy source, controllable via an Android application. The system maintains ideal soil moisture for each growth stage: 80-90% during germination, 60-70% for the vegetative stage, and 50-60% during the generative stage, with optimal air temperatures of 25-30°C. Real-time data from sensors are processed by the ESP32, which activates the water pump via a DC relay when soil moisture falls below the threshold. Testing shows the system operates continuously, with solar panels charging a VRLA battery daily to sustain nighttime operation. Battery voltage fluctuates between 12.5 V and 15.0 V, maintaining a charge above 12.0 V after sunset. To ensure reliability in extreme weather, the system employs waterproof enclosures for all components and uses sensors calibrated to maintain accuracy in saturated soils. An MPPT charge controller optimizes solar energy usage during low sunlight, while selected components operate in a wide temperature range, ensuring extreme heat or cold performance. This system improves water efficiency, reduces manual labor, and offers an environmentally friendly solution for shallot cultivation, particularly in remote areas without conventional power access. Its resilience to environmental challenges enhances productivity and supports sustainable agricultural practices.
Alzheimer Disease Prediction Through Guided Predictive Modeling With Machine Learning Mahesa Pramudya Alfayat; Adithya Kusuma Whardana
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 1 (2025): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i1.5

Abstract

Alzheimer's disease is a progressive neurodegenerative disorder characterized by the accumulation of misfolded brain proteins, especially beta-amyloid plaques, resulting in cognitive deterioration and memory impairment. However, there has been no effort of early detection to facilitate prompt intervention and preventive strategies. This research fulfills this essential need by employing the Open Access Series of Imaging Studies (OASIS) dataset supplied by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The research employs the Cross-industry Standard Process for Data Mining (CRISP-DM) methodology to create and assess a classification model utilizing Artificial Neural Networks (ANN). The model attains a remarkable accuracy rate of 96%, exhibiting elevated precision, recall, and F1-scores across all categories. A 10-fold cross-validation technique was utilized to assess the model's robustness, resulting in an average accuracy of 90.7%. These findings underscore the efficacy of artificial neural networks in identifying Alzheimer's disease in its initial phases. This research utilizes advanced data mining approaches to improve predictive capacities and highlights the promise of machine learning in tackling intricate healthcare issues.
Analysis of Malang University Student Achievement Grouping Using the K-Means Clustering Method Moh. Aqil Mukhtar Alfarera; Zaehol Fatah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.8

Abstract

With the increasing number of students and variations in achievement, managing achievement data in higher education has become more complex, so manual methods are insufficient. K-means clustering was chosen because of its ability to group data based on specific attributes, which makes it easier to identify patterns and trends. This research aims to prove K-Means' effectiveness in analyzing achievement data and adding to the literature regarding the application of data mining in education. The dataset includes student achievement indexes from various study programs at the University of Malang from 2018 to 2022. The data is processed to group student achievements efficiently. The clustering model was built using one of the algorithms in the clustering method, namely K-Means. This research produced the best cluster with a total of 3 clusters. The process was conducted to determine the best grouping by testing six cluster models. The best cluster was selected using the Davies Bouldin index test. Based on research with the results, these three groups can be categorized as cluster 0 in the low category with a value of 100, cluster 1 in the high category with a value of 4.100, and cluster 2 in the middle category with a value of 1.900.

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