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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 231 Documents
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Implementation of VLAN and ACL for Network Security at SDIT Ibnu Hajar Bekasi Rahman, Taufik; Aprianto, Qori
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.v7i2.12564

Abstract

In the age of modern technology, network security is essential to ensure that educational institutions operate properly. The purpose of this research is to improve the network security of SDIT Ibnu Hajar Bekasi City by applying technologies such as Virtual Local Area Network and Access Control List. This research uses the Network Development Life Cycle (NDLC) approach, which consists of the stages of initiation, planning, design, implementation, testing, and maintenance. By using vlan network segmentation, data traffic is divided into specific groups, such as administration, computer labs, instructors, and students. For now, Acls are used to set access rights between network segments according to user needs and authorization. Simulation testing on Cisco Packet Tracer shows that the implementation of vlans and acls improves network stability and security, reduces average latency, and decreases broadcast traffic. The acl configuration results show that students cannot access the teacher and administration networks. However, teachers can still access administration and laboratories. This study shows that the use of vlans and acls can improve the security and effectiveness of network management in schools.
A Product Recommendation-Based E-Commerce Application Using Collaborative Filtering at Izra Fashion Store Lubis, Nurul Amanda Khairani; Samsudin, Samsudin
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.v7i2.13000

Abstract

Digital transformation in the world of trade has shifted to E-Commerce in order to increase market share and efficiency. Izra Fashion Store, which is engaged in selling hijab, currently still relies on social media for marketing and interaction with customers. Although this method is quite effective, a more structured solution is needed so that the sales process can run more optimally. The use of a marketplace platform such as Shopee is actually an option, but tight competition, the number of similar stores, and the difficulty of getting positive reviews in a short time make it difficult for small stores to build customer trust. Therefore, this study aims to build a website-based e-commerce equipped with a product recommendation feature using the Collaborative Filtering method. Researching it using quantitative methods with the help of a descriptive approach. Data was collected through data collection and analysis of user needs, which were then used to implement the Collaborative Filtering algorithm in the product recommendation system. This system is assisted by the PHP programming language and MySQL database. The research findings show that the system built is able to improve the customer shopping experience by providing product recommendations that are more in line with their preferences. In addition, this system also helps business owners manage product and transaction data in a more organized manner. Through the existence of this e-commerce platform.
Solar Cell Energy Utilization Using SEPIC Converter with Fuzzy Logic Control for Electric Stove Sudiharto, Indhana; Suryono, Suryono; Adila, Ahmad Firyal; Budikarso, Anang
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.v7i2.12703

Abstract

In this modern era, most household appliances require electrical energy as an energy source. The use of energy in large and sustainable amounts will cause an energy crisis. Where fossil fuels will run out and cannot be renewed. Therefore, renewable alternative energy is needed that can be used as an energy substitute for one of the solutions. One of the alternative energies is solar cell energy that to supply the energy needs of electric stoves. This study discusses photovoltaic system that use 10 solar cells each with a power of 100 WP and 90 VDC. The electrical energy generated from the solar cell is 1000 WP. From the solar cell, the voltage is increased using a Single-Ended Primary-Inductor Converter (SEPIC) converter and controlled using Fuzzy Logic Control (FLC). The output voltage is used to meet the power needs of an electric stove with a maximum power of 650 W which has an average efficiency of 93%.
Pet Tracking System Using Telegram Notification Prasojo, Daeng Dwi; Ayuni, Shazana Dhiya; Anshory, Izza; Wisaksono, Arief
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.v8i1.12392

Abstract

This research aims to design and implement a pet tracking system using GPS Neo-6M and LoRa SX1278 modules with Telegram integration for real-time location monitoring. The system consists of a transmitter unit using Arduino Nano, a GPS module, and a LoRa module to send coordinates. The receiver uses a LoRa module and an ESP8266 microcontroller connected to the internet, which forwards the GPS data to a Telegram bot. The test results show that the system successfully sends accurate location data from the pet’s location to the owner via Telegram. This system is suitable for areas with limited internet coverage, offering low power consumption and long-range communication. It enhances the safety of pets through real-time monitoring and is highly applicable in various outdoor scenarios
Goal-Directed Design dalam Perancangan Antar Muka Pengguna: Studi Kasus Website Tax Corner Polije Yuana, Dia Bitari Mei; Ardhiarisca, Oryza; Wijanti, Rahma Rina; Harkat, Avisenna; Hartanto, Sugeng; Andini, Dessy Putri
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.v8i1.14103

Abstract

Prosedur perpajakan sering dianggap rumit, memerlukan banyak dokumen, dan membutuhkan pemahaman yang kuat tentang aturan yang berubah-ubah. Kondisi ini menyebabkan wajib pajak di kampus dan masyarakat sekitar mengalami kesulitan dalam mengakses informasi, melakukan perhitungan pajak, dan melaporkan kewajiban pajak mereka secara mandiri. Website perpajakan dibutuhkan untuk menjadi sarana layanan yang terintegrasi, informatif, dan mudah diakses kapan saja. Namun, pengembangan website perpajakan tidak hanya menekankan aspek teknologi, tetapi juga harus dapat membantu pengguna mencapai tujuannya. Goal-Directed Design (GDD) menekankan pemahaman mendalam tentang tujuan pengguna sehingga solusi yang dibangun tidak hanya memenuhi tugas administratif tetapi juga memahami tujuan, perilaku, dan kebutuhan pengguna. Melalui tahapan pengumpulan data pengguna, penyusunan persona, analisis skenario, hingga perancangan alur interaksi, GDD membantu menghasilkan desain yang berorientasi pada tujuan utama pengguna. Hasilnya didapatkan nilai 90,38% dengan System Usability Scale menunjukkan bahwa antar muka yang dibuat memiliki tingkat usability yang sangat baik yang disebut dengan excellent.
Multi-Channel Power Data Acquisition System for Solar Panel Monitoring Refly, Septia; BimaJaya, Adam; Harahap, Basyaruddin Ismail
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.v8i1.14224

Abstract

This study presents a low-cost and scalable multi-channel power data acquisition system for real-time solar photovoltaic (PV) panel monitoring, addressing the limitations of conventional single-channel approaches that provide only aggregate system measurements. The proposed system enables simultaneous per-panel measurement to support detailed performance analysis and improved fault localization. The system is implemented using an ESP32 microcontroller integrated with multiple calibrated INA219 sensors, which are connected via the I²C protocol to measure voltage, current, and electric power. A modular hardware design supports three independent PV channels, while data handling is achieved through dual-mode operation, consisting of local microSD card storage and wireless data transmission to the ThingSpeak IoT platform for real-time visualization. Calibration results demonstrate high measurement accuracy, with average errors below 1%, a voltage root mean square error (RMSE) of less than 0.07 V, and a current RMSE of less than 7 mA. Field testing conducted over two consecutive days confirms stable and uninterrupted operation, achieving 100% data acquisition reliability. The recorded data clearly reveal per-panel performance differences under real operating conditions, enabling effective identification of mismatch behavior among panels. The proposed system provides an affordable, reliable, and scalable solution for distributed PV monitoring, making it suitable for multi-panel and remote photovoltaic installations. Future improvements will involve temperature-based efficiency analysis and the integration of thermal management strategies to enhance photovoltaic performance.
Classification of Music for Study Based on Spotify Audio Features Using Random Forest with Feature Importance Analysis and Reduction Supraba, Laksmita Dewi; Sunyoto, Andi
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.v8i1.13200

Abstract

Music has a significant impact on the way a person thinks and feels in their daily activities. This study aims to categorize the types of music that are suitable for learning activities by using Spotify's audio feature, to create a more flexible and personalized music recommendation system. The dataset used comes from Spotify Study Music which consists of 172,819 songs with 12 audio features, which are grouped into three main categories, namely Pop tracks, Classical soundtracks, and Lo-fi tracks. The research process includes data pre-processing, handling class imbalances using SMOTE, data normalization, feature significance Analysis, Cross Validation, and feature reduction. Normalization results show that all features have been in the range of 0.0-1.0 without changing the characteristics of the original distribution. The Random Forest Model performed exceptionally well with an average accuracy rate of 99% on cross-validation and 99.9% on training data, indicating the model's ability to efficiently recognize musical patterns. Important Feature Analysis shows that energy, loudness, acousticness, instrumentalness, and liveness have the most significant influence in distinguishing music characteristics for learning, while mode, popularity, duration_ms, and danceability when removed using Feature Reduction analysis show a significant decrease in accuracy. This study recommends maintaining the features of acousticness, instrumentalness, and liveness because it plays an important role in maintaining the stability and accuracy of music classification models that support the learning process.
Design and Construction of Maternal and Infant Mortality Rate Mapping Using the K-Means Clustering Method Based on Geographic Information Systems (Case Study in Jember Regency) Rosidania, Nilla Putri; Utomo, Denny Trias
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.v8i1.13911

Abstract

Indonesia’s population continues to grow each year, including in Jember Regency, which reached 2,584,771 people in 2023. Population density contributes to various health issues, such as the high maternal mortality rate (MMR) and infant mortality rate (IMR), with 17 maternal deaths and 81 infant deaths recorded in 2023. The primary causes of MMR include pregnancy at too young or old an age, short birth spacing, and delays in referral, while IMR is mainly caused by asphyxia and low birth weight (LBW) due to premature birth. The government has implemented a midwife and traditional birth attendant partnership program to address this issue. However, information regarding high-risk areas remains inadequately conveyed. Therefore, this study develops a Geographic Information System (GIS)-based system using the K-Means Clustering method with a predefined number of clusters to classify high-risk maternal and infant mortality areas. The results show that the K-Means Clustering method with a fixed number of clusters (k = 5) successfully groups Jember Regency into five risk-level clusters, namely very high, high, medium, low, and very low. Visualization through GIS facilitates effective access to spatial information and supports the identification of priority areas for targeted health interventions, aiming to reduce maternal and infant mortality rates more effectively.
A Lora-Based Geofencing System For Real-Time Elephant Movement Monitoring And Early Warning Andika, Furqon; Azwar, Hamid; Indani, Wira; Diono, Muhammad
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.v8i1.14581

Abstract

This research aims to develop an elephant movement monitoring system capable of transmitting data from a gateway to a server using the MQTT protocol and displaying information in real time via a web-based dashboard. This system is designed to support remote monitoring with low latency and a wide communication range. Test results show that the average data transmission time from the gateway to the server via the MQTT protocol is 1.44 ms, while the average notification delivery time to the user is 0.89 seconds. The system is capable of operating up to 1 km with an RSSI value of -105 dB, indicating stable communication at that distance. In addition, the developed dashboard was successfully deployed online, allowing users to monitor sensor data and device status in real time through an interactive and easy-to-use interface. The results of this study indicate that the developed system has efficient communication performance, low latency, and ease of monitoring, making it potentially applicable to various IoT applications based on remote monitoring.
Retinocare: A Web-Based Intelligent System for Early Detection of Diabetic Retinopathy Using CNN Adrian, Angelia Melani; Pandelaki, Steven; Ratuliu, Gladys; Kamagi, Jonathan
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.v8i1.13568

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide and is becoming a significant public health concern in Indonesia due to the rising prevalence of diabetes. Early detection is critical, yet access to ophthalmologists and conventional fundus cameras remains limited in many primary healthcare facilities. To address these challenges, this study proposes a cost-effective, web-based intelligent system for early detection of DR using smartphone-based fundus adapters and deep learning.A hybrid dataset was employed, combining publicly available fundus image repositories with locally collected retinal images from Indonesian healthcare facilities, annotated by ophthalmologists. Images were preprocessed through normalization, cropping, artifact removal, and augmentation to address variability, particularly from smartphone acquisitions. A DenseNet-121 convolutional neural network was fine-tuned on this hybrid dataset to classify DR into five severity levels according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Model performance was evaluated using accuracy as the primary metric, with results compared against ophthalmologist annotations.The proposed system demonstrated promising performance in classifying DR severity levels, showing that combining public and local datasets improves contextual relevance and model robustness. Furthermore, integration into a web-based platform enables healthcare workers in primary care to upload fundus images, obtain real-time classification results, and facilitate referral decisions for severe cases.This study contributes to the development of an accessible and scalable screening tool for DR in Indonesia by integrating affordable imaging hardware, locally relevant datasets, and an AI-powered classification system. The approach has the potential to reduce reliance on expensive equipment and specialists, supporting national efforts to prevent diabetes-related blindness.

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