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Jurnal Rekayasa Elektro Sriwijaya
Published by Universitas Sriwijaya
ISSN : -     EISSN : 27164063     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Jurnal Rekayasa Elektro Sriwijaya adalah peer-reviewed jurnal yang dipublikasikan oleh Jurusan Teknik Elektro Universitas Sriwijaya. Jurnal ini diterbitkan dua kali dalam setahun, yaitu pada bulan Mei dan November. Ruang lingkup jurnal berfokus pada bidang teknik elektro, namun tidak hanya terbatas pada tenaga listrik, tegangan tinggi, telekomunikasi, teknologi informasi, pengolahan sinyal, ataupun kecerdasan buatan saja. Jurnal Rekayasa Elektro Sriwijaya juga dapat mencakup beberapa bidang lainnya, seperti bidang pertanian dan ekonomi yang mana pada praktiknya dapat berkolaborasi dengan bidang teknik elektro.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 77 Documents
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Design and Implementation of an ESP32-Based Object Tracking Robot Using Ultrasonic and Infrared Sensors Syahrul, Elfitrin; Riefqi, Achmad; Yapie, Any Kurniawaty
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/z9zfkf19

Abstract

Abstract— This paper presents the design and implementation of an ESP32-based autonomous tracking robot that integrates ultrasonic and infrared sensing for target following and edge avoidance. The system utilizes HC-SR04 and HC-SR04P ultrasonic sensors mounted at the front, left, and right to estimate target distance, while infrared sensors detect floor edges to prevent accidental drop-offs. A dual-L298N motor driver configuration controls four DC motors, enabling real-time movement execution based on sensor feedback. Experimental testing shows that the ultrasonic modules achieved distance-measurement accuracy with an error rate of 1–5%. The infrared sensors reliably detected edges, with performance influenced by surface reflectivity. The integrated control system executed forward, reverse, turning, and stop maneuvers effectively in dynamic target-tracking scenarios. These results indicate that combining ultrasonic and infrared sensing with an ESP32 controller provides a functional, low-cost solution for autonomous tracking in constrained indoor environments.
Pengaruh Tingkat Tegangan DC Terhadap Pertumbuhan Tanaman Selada (Lactuca Sativa L.) di Dalam Green House Dengan Sumber Energi Panel Surya Caroline; Ningrum, Alvierina Azzahra; Hermawati; Ike Bayusari; Rahmawati
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/kbmb9h46

Abstract

Peningkatan kebutuhan pangan mendorong inovasi dalam teknologi pertanian berkelanjutan, salah satunya melalui penggunaan green house untuk mengendalikan lingkungan tumbuh tanaman. Penelitian ini mengkaji pengaruh variasi tegangan DC terhadap pertumbuhan tanaman selada (Lactuca sativa L.) dalam green house berbasis panel surya. Sistem ini menggunakan panel surya sebagai sumber energi utama, dengan kipas untuk pengatur suhu dan pompa DC untuk penyiraman otomatis. Delapan variasi tegangan (1V, 1.5V, 2V, 2.5V, 3V, 3.5V, 4V, and 4.5V) dan satu kontrol digunakan, dengan parameter pengamatan meliputi tinggi dan lebar tanaman. Hasil menunjukkan bahwa tegangan 4V menghasilkan tinggi tanaman terendah (16 cm), sedangkan tanaman kontrol memiliki tinggi tertinggi (21,7 cm). Lebar daun terbesar diperoleh pada tegangan 1,5V (10 cm). Tegangan rendah (<4V) cenderung lebih mendukung pertumbuhan awal tanaman. Kebutuhan energi harian sistem sebesar 68,189 Wh dapat dipenuhi oleh panel surya yang menghasilkan energi sebesar 87,85 Wh, sehingga membuktikan efisiensi dan kemandirian sistem dalam memenuhi kebutuhan energinya melalui sumber energi terbarukan.
Preventif Maintenance Generator Set 2288KVA pada PT. Krakatau Tirta Industri Adnyano, I Kadek Dwi Adnyano; Endi Permata
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/y3rd3m36

Abstract

Penelitian ini membahas tentang pelaksanaan preventif maintenance pada Generator Set 2288kVA di PT. Krakatau Tirta Industri, sebuah anak perusahaan PT. Krakatau Steel yang bergerak di bidang produksi air bersih. Dalam studi ini, dilakukan pengkajian menyeluruh menggunakan metode kualitatif yang meliputi observasi terperinci, wawancara mendalam dengan para ahli terkait, serta pemeliharaan langsung yang dilakukan secara cermat pada Generator Set. Generator Set yang menjadi fokus penelitian terdiri dari generator MAGNAMAX 744RSL7056 berkapasitas 2288 kVA dan mesin diesel MTU 16V4000G63 yang memiliki peran vital dalam operasional perusahaan. Penelitian ini terutama menekankan pada prosedur preventif maintenance yang dilakukan secara rutin, yaitu mingguan oleh tim mekanik dan elektrik, dan juga pemeliharaan per 6 bulan oleh tim mekanik untuk memastikan performa optimal dari Generator Set. Temuan yang dihasilkan dari penelitian ini menunjukkan bahwa tindakan preventif maintenance yang dilakukan dengan konsisten memberikan kontribusi penting dalam menjaga kinerja dan keandalan Generator Set. Data yang berhasil terkumpul selama periode Januari hingga Agustus 2024 merekam sejumlah indikator kunci seperti tegangan keluaran, frekuensi, suhu, dan total KWH Generator Set yang menunjukkan konsistensi dalam rentang normal. Adopsi praktik perawatan rutin yang mencakup pengecekan level oli, filter oli, level bahan bakar, air akumulator, air radiator, serta pembersihan komponen turut mendukung stabilitas operasional. Dengan demikian, kesimpulannya pun menguatkan bahwa melalui pelaksanaan preventif maintenance yang andal dan terstruktur, keberhasilan dalam mempertahankan performa dan keandalan peralatan seperti Generator Set di PT. Krakatau Tirta Industri dapat terjamin, sehingga pasokan listrik cadangan bagi operasional perusahaan tetap terjaga dengan baik.
Beach Litter Detection as an Environmental Conservation Effort Against Plastic Waste Using Artificial Intelligence Listyalina, Latifah; Mario Sarisky Dwi Ellianto; Midarto Dwi Wibowo
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/ap8wjq69

Abstract

The increasing presence of plastic debris in these areas not only disrupts biodiversity but also threatens the balance and sustainability of marine habitats. Addressing this problem requires innovative approaches that combine environmental science with modern technology. This study was conducted to develop a beach litter detection system as part of a broader effort to support environmental preservation and reduce the detrimental effects of plastic waste on coastal areas in Indonesia. The research employed a secondary dataset obtained from Kaggle.com, which consisted of labeled images of beach waste. A deep learning method was applied through the use of Convolutional Neural Networks (CNN), with MobileNetV2 selected as the primary architecture due to its lightweight design, computational efficiency, and proven effectiveness in image classification tasks. Experimental results demonstrated that the model performed exceptionally well, achieving a training accuracy of 100%, which indicates its strong ability to capture patterns in the dataset. More importantly, the validation accuracy reached 97.83%, reflecting the model’s robustness and capacity to generalize effectively to unseen data. These findings emphasize the potential of artificial intelligence in supporting environmental monitoring and management. In particular, automated detection and classification of plastic waste on beaches can enhance current conservation strategies and provide timely information for waste management interventions. Furthermore, this research serves as a foundation for future studies aimed at advancing intelligent waste management systems. The integration of AI in this domain remains relatively underexplored, and continued exploration could contribute significantly to mitigating the global challenge of plastic pollution in coastal environments.
Perancangan Robot Penyiraman pada Tanaman Tomat Beef menggunakan Metode Forward Chaining Siregar, Baginda; Daren Sulistio
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/bgcpt769

Abstract

Tanaman, khususnya tomat, memiliki nilai ekonomis tinggi dan membutuhkan pemeliharaan rutin, terutama penyiraman, untuk menjaga pertumbuhan optimal. Penelitian ini mengembangkan robot penyiram tanaman tomat beef otomatis bernama “Farmbot” berbasis metode forward chaining, yang memungkinkan monitoring dan pengaturan kebutuhan air secara cerdas. Penerapan forward chaining sebagai sistem pakar rule-based mempermudah pengambilan keputusan dalam penyiraman, dan alat ini berpotensi diterapkan pada tanaman lain dengan konfigurasi ulang parameter lingkungan. Penelitian ini dilakukan untuk membuktikan  metode forward chaining dalam proses perancangan robot penyiram tanaman. Mikrokontroller utama yang digunakan pada penelitian ini adalah NodeMCU ESP32, lalu untuk perangkat input menggunakan sensor kelembapan tanah untuk menghasilkan  data kelembapan dan sensor suhu DS18B20 untuk menghasilkan data suhu pada tanah. Berbagai fakta dan aturan digunakan untuk mengendalikan proses pergerakan dan penyiraman pada robot Farmbot. Hasil dari penelitian ini menunjukkan bahwa akurasi dari metode forward chaining yaitu sebesar 75,472% merupakan hasil yang baik dan tidak terpaut jauh dari metode fuzzy logic yang menghasilkan nilai MAE sebesar 4,5535 dan memiliki nilai akurasi pada kisaran 92,149%. Dari hasil tersebut, maka dapat disimpulkan bahwa metode forward chaining dapat digunakan sebagai metode perancangan robot penyiram tanaman.
Prediksi Iradiasi Matahari Menggunakan Machine Learning untuk Estimasi Output PLTS di Wilayah Malang Humaidi, Haneef Nouval Alannibras; Handayani, Sita Tri; Kasan, Nur; Hakim, Ermanu Azizul; Al Rasyid , Zya Jamaluddin; Ningtias, Dieta Wahyu Asry
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/hsar2478

Abstract

Accurate solar irradiance prediction is fundamental for planning and operating solar photovoltaic (PV) power systems. This study compares the performance of four machine learning algorithms — Random Forest (RF), Support Vector Regression (SVR), XGBoost, and Artificial Neural Network (ANN) — in predicting daily Global Horizontal Irradiance (GHI) in Malang, East Java. The dataset was obtained from NASA POWER spanning 10 years (2014–2023), comprising 3,646 daily records with 11 input features including meteorological parameters, temporal features, and autoregressive features. Data splitting was performed chronologically (70% training, 15% validation, 15% testing). Results show that XGBoost achieved the best performance with R² = 0.6797, RMSE = 0.5212 kWh/m²/day, and MAPE = 8.35%. Seasonal analysis reveals all models perform better during the dry season (R² = 0.74; MAPE = 6.63%) compared to the wet season (R² = 0.54; MAPE = 11.06%). A 5 kWp PV system in Malang is estimated to produce 7,626 kWh/year.
Multistage Fertile Egg Prediction via Texture Using Convolutional Neural Network Bimo, Muhammad; Dewi, Tresna; Maulidda, Renny; Oktarina, Yurni; Risma, Pola; Yudha, Hendra Marta
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/q58ezz91

Abstract

Accurate early detection of egg fertilisation status is necessary for effective incubation management in chicken production in order to avoid energy waste and decreased hatchery productivity brought on by infertile or non-viable eggs. Due to their comparable perceptual traits, conventional candling inspection relied on manual observation, which introduced subjectivity and made it challenging to distinguish between fertilised and blighted eggs early on. This study suggested an automated multistage fertilisation prediction method based on candling image analysis, utilising a convolutional neural network framework to get around this restriction. Rather than using traditional binary classification, the suggested system allowed for progressive monitoring of embryonic growth. On incubation days 1, 7, 14, and 21, candling photos were taken from native chicken eggs and classified into three groups: fertilised, infertile, and blighted. To enhance feature extraction efficiency under constrained dataset conditions, a transfer learning technique utilising the MobileNetV2 architecture was implemented. To guarantee consistent learning performance, image preprocessing, augmentation, model training, and validation were carried out. Precision, recall, F1-score, and classification accuracy were used as assessment measures. According to experimental findings, the suggested model produced consistent classification results for both fertilised and infertile eggs, with validation accuracy ranging from 90 to 95% throughout the incubation period. The results of multistage prediction showed consistent decision-making throughout the observation of embryo development. However, during intermediate incubation stages, visual uncertainty with fertilised eggs led to decreased performance in recognising blighted eggs. All things considered, the suggested method showed great promise as a nondestructive intelligent system for early fertilisation prediction. To increase the accuracy of blighted egg classification, more dataset expansion and model improvement were still required.

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