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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 5 Documents
 1 
Search results for , issue "Vol 8, No 1 (2026)" : 5 Documents clear
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.

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