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Electrical Engineering Department Faculty of Engineering State University of Gorontalo Jenderal Sudirman Street No.6, Gorontalo City, Gorontalo Province, Indonesia
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Jambura Journal of Electrical and Electronics Engineering
Published by Universitas Negeri Gorontalo
ISSN : 26547813     EISSN : 27150887     DOI : 10.37905/jjeee
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Jambura Journal of Electrical and Electronics Engineering (JJEEE) is a peer-reviewed journal published by Electrical Engineering Department Faculty of Engineering, State University of Gorontalo. JJEEE provides open access to the principle that research published in this journal is freely available to the public to support the exchange of knowledge globally. JJEEE published two issue articles per year namely January and July. JJEEE provides a place for academics, researchers, and practitioners to publish scientific articles. Each text sent to the JJEEE editor is reviewed by peer review. Starting from Vol. 1 No. 1 (January 2019), all manuscripts sent to the JJEEE editor are accepted in Bahasa Indonesia or English. The scope of the articles listed in this journal relates to various topics, including: Control System, Optimization, Information System, Decision Support System, Computer Science, Artificial Intelligence, Power System, High Voltage, Informatics Engineering, Electronics, Renewable Energy. This journal is available in online and highly respects the ethics of publication and avoids all types of plagiarism.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 135 Documents
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Sentiment Analysis of Local Sunscreen Skintific, Somethinc, and Avoskin with Naive Bayes and SVM Clarisha, Windi; Fani, A. Astri Merilsa; Surianto, Dewi Fatmarani; Fadilah, Nur
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.30257

Abstract

Indonesia’s beauty industry, particularly local sunscreen products, has experienced rapid growth alongside increasing public awareness of the importance of skin protection against ultraviolet rays. Consumer reviews on digital platforms have become a vital source of information to understand user perceptions and preferences. This study aims to analyze sentiment toward three local sunscreen brands—Skintific, Somethinc, and Avoskin—by comparing two text classification methods: Naïve Bayes and Support Vector Machine (SVM). To address the imbalance in the number of positive and negative sentiment data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The results show that applying SMOTE to Naïve Bayes significantly improved the accuracy from 81% to 93%, along with notable enhancements in precision, recall, and F1-score. Conversely, applying SMOTE to SVM slightly reduced accuracy from 92% to 91%, although the performance for positive sentiment remained stable. These findings indicate that the combination of Naïve Bayes and SMOTE is more effective in handling imbalanced data for sentiment analysis of beauty products. The implications of this study can serve as a basis for decision-making in product development and marketing strategies within the beauty industry, particularly in aligning with consumer sentiment.Industri kecantikan Indonesia, khususnya produk sunscreen lokal, menunjukkan pertumbuhan pesat seiring meningkatnya kesadaran masyarakat akan pentingnya perlindungan kulit dari sinar ultraviolet. Ulasan konsumen di platform digital menjadi sumber informasi penting untuk memahami persepsi dan preferensi pengguna. Penelitian ini bertujuan untuk menganalisis sentimen terhadap tiga merek sunscreen lokal—Skintific, Somethinc, dan Avoskin—dengan membandingkan dua metode klasifikasi teks, yaitu Naïve Bayes dan Support Vector Machine (SVM). Untuk mengatasi ketidakseimbangan jumlah data antara sentimen positif dan negatif, digunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Hasil menunjukkan bahwa penerapan SMOTE pada Naïve Bayes meningkatkan akurasi dari 81% menjadi 93%, serta memperbaiki precision, recall, dan F1-score secara signifikan. Sebaliknya, penerapan SMOTE pada SVM justru sedikit menurunkan akurasi dari 92% menjadi 91%, meskipun performa untuk kategori sentimen positif tetap stabil. Temuan ini menunjukkan bahwa kombinasi Naïve Bayes dengan SMOTE lebih efektif dalam menangani data tidak seimbang untuk analisis sentimen produk kecantikan. Implikasi dari penelitian ini dapat digunakan oleh pelaku industri kecantikan sebagai dasar pengambilan keputusan dalam pengembangan dan pemasaran produk berbasis persepsi konsumen.    
Energy Optimization and Protection of 3-Phase Electrical Systems Based on IoT Ratuhaji, Faruq; Mantasia, Mantasia
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.31248

Abstract

Three-phase electrical systems have advantages in efficiency and power stability but are susceptible to voltage disturbances such as overvoltage and undervoltage. Overvoltage is a condition where the voltage exceeds the upper threshold, while undervoltage occurs when the voltage drops below the minimum limit. Both of these conditions can lead to decreased efficiency, equipment damage, and operational disruptions. Therefore, an adaptive protection system capable of detecting abnormal conditions in real-time is needed. This research develops a three-phase electrical monitoring and protection system based on the Internet of Things (IoT). The system uses a PZEM-004T sensor to read the voltage and current on each phase, with the ESP32 microcontroller as the data processor and protection controller. Information is sent to the web-based user interface via a Wi-Fi network. Users can monitor system conditions online through a monitoring website that displays voltage and current parameters in real-time, as well as fault notifications. The test results indicate that the system accurately measures voltage and current, with average errors of just 0.35% and 0.45%, and it can automatically reduce power load within 5 to 6 seconds when there is a problem, while also sending real-time alerts about any issues through a web-based interface. Thus, this system is suitable for application in household and light industrial installations. Besides improving energy efficiency and the reliability of the electrical system, this system also promotes the utilization of IoT technology in modern electricity. Further research is recommended to develop a system with short-circuit current detection, harmonic analysis, and integration into a smart grid.Sistem kelistrikan tiga fasa memiliki keunggulan dalam efisiensi dan kestabilan daya, namun rentan terhadap gangguan tegangan seperti overvoltage dan undervoltage. Overvoltage adalah kondisi saat tegangan melebihi ambang batas atas, sedangkan undervoltage terjadi saat tegangan turun di bawah batas minimal. Kedua kondisi ini dapat menyebabkan penurunan efisiensi, kerusakan peralatan, dan gangguan operasional. Oleh karena itu, dibutuhkan sistem proteksi yang adaptif dan mampu mendeteksi kondisi abnormal secara real-time. Penelitian ini mengembangkan sistem monitoring dan proteksi kelistrikan tiga fasa berbasis Internet of Things (IoT). Sistem menggunakan sensor PZEM-004T untuk membaca tegangan dan arus pada masing-masing fasa, dengan mikrokontroler ESP32 sebagai pengolah data dan pengendali proteksi. Informasi dikirim ke antarmuka pengguna berbasis web melalui jaringan Wi-Fi. Pengguna dapat memantau kondisi sistem secara daring melalui website monitoring yang menampilkan parameter tegangan, arus secara real time, serta notifikasi gangguan. Hasil pengujian menunjukkan bahwa sistem memiliki akurasi pembacaan tegangan dan arus yang sangat baik dengan masing-masing nilai rata-rata Mean Absolute Percentage Error (MAPE) 0,35% dan 0,45%, dan waktu respon otomatis pemutusan beban ketika terjadi gangguan dalam rentang 5 hingga 6 detik, disertai dengan pengiriman informasi kondisi abnormal secara real-time melalui antarmuka berbasis web. Dengan demikian sistem ini layak diterapkan pada instalasi rumah tangga dan industri ringan. Selain meningkatkan efisiensi energi dan keandalan sistem kelistrikan, sistem ini juga mendorong pemanfaatan teknologi IoT dalam kelistrikan modern. Penelitian selanjutnya direkomendasikan untuk mengembangkan sistem dengan deteksi arus hubung singkat, analisis harmonisa, serta integrasi ke jaringan listrik pintar (smart grid). 
Analysis of 20 kV Distribution Network Reconfiguration PLN ULP Marisa Surusa, Frengki Eka Putra; Andrean, Yogi; Steven Humena, Steven Humena
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.33150

Abstract

This study aims to analyze the effect of 20 kV distribution network reconfiguration on voltage drops and power losses at the Marisa Substation feeder. In the world of electricity, the distribution network is the backbone that connects energy sources to end consumers. Therefore, it is important to ensure that the distribution network functions optimally to minimize energy losses and maintain good power quality. This study took place at the Marisa Substation, which is one of the vital electricity distribution centers in the area. The method used in this study is simulation with ETAP 12.6 software, which is widely known in electric power system analysis. ETAP (Electrical Transient Analyzer Program) is a tool that allows engineers to model and analyze power systems comprehensively. In this simulation, the Newton-Raphson algorithm is applied to solve the system of non-linear equations that arise in electric network analysis. This algorithm is very effective in overcoming convergence problems often encountered in complex distribution network simulations. The simulation results show that after the reconfiguration, active power losses are reduced by 1.3% and reactive power losses are reduced by 1.5%. Active power losses are the energy lost in the form of heat due to resistance in conductors, while reactive power losses relate to fluctuating energy in the system, which does not perform actual work but still contributes to the total power. The reduction in these power losses is significant, because although the percentage may seem small, on a large scale, it can result in substantial operational cost savings for electricity providers. Furthermore, the study also found that network reconfiguration increased the average voltage by 3%. This voltage increase is crucial because a stable and standard voltage will guarantee the quality of electricity supply to consumers. In this context, a stable voltage can prevent damage to sensitive electrical equipment and increase the operational efficiency of equipment used by consumers.Penelitian ini bertujuan untuk menganalisis pengaruh rekonfigurasi jaringan distribusi 20 kV terhadap jatuh tegangan dan rugi daya pada penyulang GI Marisa. Dalam dunia kelistrikan, jaringan distribusi merupakan tulang punggung yang menghubungkan sumber energi dengan konsumen akhir. Oleh karena itu, penting untuk memastikan bahwa jaringan distribusi berfungsi dengan optimal agar dapat meminimalisir kerugian energi serta mempertahankan kualitas daya yang baik. Penelitian ini mengambil lokasi di GI Marisa, yang merupakan salah satu pusat distribusi listrik yang vital di daerah tersebut. Metode yang digunakan dalam penelitian ini adalah simulasi dengan perangkat lunak ETAP 12.6, yang dikenal luas dalam analisis sistem tenaga listrik. ETAP (Electrical Transient Analyzer Program) adalah alat bantu yang memungkinkan insinyur untuk memodelkan dan menganalisis sistem tenaga secara komprehensif. Dalam simulasi ini, algoritma Newton-Raphson diterapkan untuk menyelesaikan sistem persamaan non-linear yang muncul dalam analisis jaringan listrik. Algoritma ini sangat efektif dalam mengatasi masalah konvergensi yang sering dihadapi dalam simulasi jaringan distribusi yang kompleks. Hasil simulasi menunjukkan bahwa setelah dilakukan rekonfigurasi, rugi daya aktif berkurang sebesar 1,3% dan rugi daya reaktif berkurang sebesar 1,5%. Rugi daya aktif adalah energi yang hilang dalam bentuk panas akibat resistansi pada konduktor, sedangkan rugi daya reaktif berkaitan dengan energi yang berfluktuasi dalam sistem, yang tidak melakukan kerja nyata tetapi tetap berkontribusi pada total daya. Penurunan rugi daya ini sangat signifikan, karena meskipun persentasenya terlihat kecil, dalam skala besar, hal ini dapat menghasilkan penghematan biaya operasional yang substansial bagi perusahaan penyedia listrik. Selain itu, penelitian ini juga menemukan bahwa rekonfigurasi jaringan meningkatkan tegangan rata-rata sebesar 3%. Peningkatan tegangan ini sangat penting karena tegangan yang stabil dan sesuai standar akan menjamin kualitas pasokan listrik kepada konsumen. Dalam konteks ini, tegangan yang stabil dapat mencegah kerusakan pada peralatan listrik yang sensitif, serta meningkatkan efisiensi operasional dari peralatan yang digunakan oleh konsumen.
Location and Single-Phase Fault Data Monitoring System Using ESP8266 via Blynk and Telegram Duhi, Sahrul; Tansa, Salmawaty; Dako, Rahmat Deddy R.; Wahab Musa, Wahab Musa; Abdussamad, Syahrir; Tolago, Ade Irawaty
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.33474

Abstract

The increasing consumption of electrical energy highlights the need for an efficient power monitoring system. This research develops an Internet of Things (IoT)-based single-phase fault monitoring system capable of detecting power outages and overcurrents, and providing real-time location notifications. The system is designed using a PZEM-004T power sensor for data acquisition, an ESP8266 microcontroller as a processing unit, a Blynk platform for visualization, and a Telegram-integrated NEO6M GPS module for automatic notification. Test results show that the system successfully detects and accurately informs power outages (current 0 A) and overcurrents (exceeding 1 A), sending notifications complete with a Google Maps link to the fault location. This system provides an effective and responsive solution for power outage management, contributing to improved operational and maintenance efficiency link via Telegram. The device testing was successfully conducted using the NEO 6M GPS module to determine the fault location.Peningkatan konsumsi energi listrik menyoroti kebutuhan akan sistem monitoring daya yang efisien. Penelitian ini mengembangkan sebuah sistem pemantauan gangguan satu fasa berbasis Internet of Things (IoT) yang mampu mendeteksi kondisi listrik padam dan arus lebih, serta memberikan notifikasi lokasi real-time. Sistem dirancang menggunakan sensor daya PZEM-004T untuk akuisisi data, mikrokontroler ESP8266 sebagai unit pemroses, platform Blynk untuk visualisasi, dan modul GPS NEO6M terintegrasi Telegram untuk notifikasi otomatis. Hasil pengujian menunjukkan bahwa sistem berhasil mendeteksi dan secara akurat menginformasikan kondisi listrik padam (arus 0 A) dan arus lebih (melebihi 1 A), mengirimkan notifikasi lengkap dengan link Google Maps lokasi gangguan. Sistem ini menyediakan solusi yang efektif dan responsif untuk manajemen gangguan listrik, berkontribusi pada peningkatan efisiensi operasional dan pemeliharaan
Effectiveness of Gradient Boosting Stacking Model in Predicting Electricity Costs: Residential Building Data Nadifa, Ulfatun; H, Haeriani; Abdussamad, Syahrir; Tolago, Ade Irawaty; Dako, Rahmat Deddy Rianto; Bonok, Zainudin; Asmara, Bambang Panji
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.33158

Abstract

Accurate electricity cost prediction is essential to support energy efficiency and resource management, particularly in residential and commercial buildings. This study aims to evaluate the effectiveness of the Gradient Boosting model in predicting monthly electricity costs. The model is built using the Stacking Ensemble method, a technique that combines multiple Gradient Boosting algorithms in a layered manner to improve prediction accuracy. To enhance the model’s performance, automatic selection of the best parameter values (Hyperparameter Optimization) is conducted using Optuna. The initial phase involves developing a tree-based preprocessing pipeline to address data variability and complexity. The model is evaluated using the K-Fold Cross Validation method, which divides the data into several subsets for more representative testing. The performance is assessed using the Root Mean Squared Logarithmic Error (RMSLE) metric to measure prediction accuracy. The evaluation results show that the model achieves an RMSLE score of 0.22, with an average prediction time of 0.00029 seconds. These findings suggest that although Gradient Boosting models are typically used on high-dimensional datasets, this approach remains effective for low-dimensional data. The combination of ensemble techniques and hyperparameter optimization yields accurate and efficient predictions. Therefore, this approach can be applied in real-world scenarios, such as urban energy management.Prediksi biaya listrik yang akurat penting untuk mendukung efisiensi energi dan pengelolaan sumber daya, terutama pada bangunan residensial maupun komersial. Penelitian ini bertujuan untuk menguji efektivitas model Gradient Boosting dalam memprediksi biaya listrik bulanan. Model dibangun dengan menggunakan metode Stacking Ensemble, yaitu teknik penggabungan beberapa algoritma Gradient Boosting secara bertingkat untuk meningkatkan akurasi prediksi. untuk meningkatkan kinerja model, digunakan pemilihan nilai parameter terbaik secara otomatis (Optimasi Hyperparameter) dengan bantuan Optuna. Tahapan awal dimulai dengan membangun pipeline preprocessing berbasis Tree Model untuk menangani variasi dan kompleksitas data. Model dievaluasi dengan menggunakan metode K-Fold Cross Validation, yaitu pembagian data menjadi beberapa bagian untuk pengujian yang lebih representatif, dan hasilnya diukur menggunakan metrik Root Mean Squared Logarithmic Error (RMSLE) untuk menilai ketepatan prediksi. Hasil evaluasi menunjukkan bahwa model mampu mencapai nilai RMSLE sebesar 0.22. Selain itu, waktu prediksi rata-rata adalah 0.00029 detik. Temuan ini menunjukkan bahwa meskipun model Gradient Boosting umumnya digunakan pada dataset berdimensi besar, pendekatan ini tetap efektif pada data berdimensi kecil. Kombinasi teknik ensemble dan Optimasi Hyperparameter mampu menghasilkan prediksi yang akurat dan efisien. Oleh karena itu, pendekatan ini dapat diterapkan dalam skenario nyata, seperti manajemen energi di kawasan perkotaan. 

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