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All Journal Jambura Journal of Electrical and Electronics Engineering Jurnal Teknik Elektro dan Komputasi (ELKOM) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sibermas (Sinergi Pemberdayaan Masyarakat) Inverted KNOWLEDGE: Jurnal Inovasi Hasil Penelitian dan Pengembangan
Nadifa, Ulfatun
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Journal : Jambura Journal of Electrical and Electronics Engineering

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Design of Dual-Axis Sunflower Type Solar Tracker to Improve Solar Panel Efficiency Iti, Mohammad Rifki; Asmara, Bambang Panji; Abdussamad, Syahrir; Musa, Wahab; Bonok, Zainudin; Nadifa, Ulfatun
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.v7i2.31980

Abstract

This study aims to design and test a sunflower-type solar tracker system based on light sensors to improve energy conversion efficiency in solar panels. This system uses four LDR sensors and two servo motors controlled by a microcontroller to automatically direct the panel following the movement of the sun throughout the day. The research method used is an engineering experiment, which includes literature studies, design, implementation, and system testing in sunny and cloudy weather conditions. The test results show that in sunny conditions, the tracker system produces 15.60 Wh of energy, while the static panel is only 5.81 Wh. In cloudy conditions, the energy produced by the tracker system decreases to 0.68 Wh (a decrease of 95.61%), while the static panel only produces 0.047 Wh (a decrease of 99.19%). Overall, the tracker system shows an increase in efficiency of 34.25% compared to the static system. These findings indicate that the Sunflower Solar Tracking system is effective in increasing the efficiency of solar energy absorption, even in less than optimal lighting conditions.Penelitian ini bertujuan untuk merancang dan menguji sistem pelacak surya tipe sunflower berbasis sensor cahaya guna meningkatkan efisiensi konversi energi pada panel surya. Sistem ini menggunakan empat sensor LDR dan dua motor servo yang dikendalikan oleh mikrokontroler untuk mengarahkan panel secara otomatis mengikuti pergerakan matahari sepanjang hari. Metode penelitian yang digunakan adalah eksperimen rekayasa, yang mencakup studi literatur, perancangan, implementasi, serta pengujian sistem pada kondisi cuaca cerah dan mendung. Hasil pengujian menunjukkan bahwa pada kondisi cerah, sistem pelacak menghasilkan energi sebesar 15,60 Wh, sedangkan panel statis hanya 5,81 Wh. Pada kondisi mendung, energi yang dihasilkan oleh sistem pelacak menurun menjadi 0,68 Wh (penurunan 95,61%), sementara panel statis hanya menghasilkan 0,047 Wh (penurunan 99,19%). Secara keseluruhan, sistem pelacak menunjukkan peningkatan efisiensi sebesar 34,25% dibandingkan sistem statis. Temuan ini mengindikasikan bahwa sistem Sunflower Solar Tracking efektif dalam meningkatkan efisiensi penyerapan energi surya, bahkan pada kondisi pencahayaan yang kurang optimal.
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. 
Co-Authors Ade Irawaty Tolago Asri, Aristi Asri, Aristi Ayuningsi Ode Bambang Panji Asmara Dongka, Rahmad Hidayat Dongka, Rahmat Hidayat H, Haeriani H., Haeriani Haeriani, Haeriani Hermila A Iti, Mohammad Rifki Larosa, Esra Mutmainnah, Nur Ode Asri, Aristi Ayuningsi Pratama, Musfir Rizal Rahmat Deddy Rianto Dako Sri Ayu Ashari Suherman suherman Syahrir Abdussamad Wahab Musa Wahyu Saputra Yasin Mohamad Zainudin Bonok