Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Multitek Indonesia : Jurnal Ilmiah Journal of Electrical Engineering and Informatics Jurnal Teknik AMATA
Pasangkunan, Raya
Unknown Affiliation
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) METHOD TO FORECAST LONG-TERM ELECTRICITY LOAD (CASE STUDY: KARANRANG ISLAND) Pasangkunan, Raya
Journal of Electrical Engineering and Informatics Vol. 2 No. 2 (2025): Journal of Electrical Engineering and Informatics
Publisher : Fakultas Teknik Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jeeni.v2i2.7418

Abstract

In the islands, the use of electrical energy is not only for household purposes, but also to support the education, health, small industry and transport sectors. This increase in electrical energy demand is increasingly urgent, given the importance of electricity in driving technology, improving people's quality of life, and supporting the sustainability of life, thus requiring special attention to maintain the availability of electrical energy. Grid management involves proper planning of load requirements. Therefore, accurate load forecasting will ensure the safe, reliable, and economical operation of power systems. In recent years, researchers have proposed many models for electricity forecasting. In this study, the backpropagation ANN method is used to forecast electrical energy demand until 2035 on Karanrang island which will be compared with the prediction results using the linear regression method. Based on the results of research and discussion of the prediction results, it can be concluded that the MAPE value of the backpropagation ANN is 8% and from the linear regression method is 10.4% so it can be concluded that prediction with the backpropagation ANN method is more optimal. The MAPE value of 8% is categorised as a very good prediction result.
Potensi Pemanfaatan Energi Surya pada Rooftop Gedung Kampus Politeknik Amamapare Timika Pasangkunan, Raya; Buang, Nursahar; Rombekila, Aryani; Seruni, Monika; Yusuf, Sukmawati
Jurnal Teknik AMATA Vol. 6 No. 2 (2025)
Publisher : Politeknik Amamapare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55334/jtam.v6i2.344

Abstract

Penilitian ini menganalisis potensi pemanfaatan energi surya melalui sistem PLTS rooftop pada gedung kampus Politeknik Amamapare Timika, Papua Tengah, dengan menggunakan simulasi berbasis PVsyst. Sistem yang dirancang terdiri dari 77 panel surya, 28 baterai, dan 5 inverter, dan mampu memproduksi 11.103 kWh/tahun yang hanya mencukupi 19-20% dari total beban, sehingga sebagian energi masih bergantung pada PLN. Biaya investasi awal sistem diperkirakan Rp 490.733.150, sedangkan total emisi karbon siklus hidup tercatat 20.702 kgCO2, mencerminkan jejak karbon awal dari produksi dan instalasi. Hasil penelitian menunjukkan bahwa PLTS rooftop dapat secara signifikan mengurangi ketergantungan pada energi fosil dan menurunkan emisi karbon, sekaligus memberikan panduan praktis bagi institusi pendidikan di Papua untuk merancang sistem energi mandiri yang berkelanjutan.
Potential For Solar Energy Utilization On The Rooftop Of The Amamapare Polytechnic Campus Building In Timika Pasangkunan, Raya; Buang, Nursahar; Rombekila, Aryani; Seruni, Monika; Yusuf, Sukmawati
MULTITEK INDONESIA Vol 19 No 2 (2025): Desember
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v19i2.12630

Abstract

This study analyzes the potential for solar energy utilization through a rooftop solar power system at the Amamapare Polytechnic campus in Timika, Central Papua, using PVsyst-based simulations. The designed system consists of 77 solar panels, 28 batteries, and 5 inverters, and is capable of producing 11,103 kWh/year, which only meets 19-20% of the total load, so that some of the energy still depends on PLN. The initial investment cost of the system is estimated at IDR 490,733,150, while the total life cycle carbon emissions are recorded at 20,702 kgCO2, reflecting the initial carbon footprint from production and installation. The results of the study show that rooftop solar power plants can significantly reduce dependence on fossil fuels and lower carbon emissions, while providing practical guidance for educational institutions in Papua to design sustainable independent energy systems. Keywords: Rooftop Solar Power Plant, Solar Energy, PVsyst, Independent Energy, Campus Building
Co-Authors Buang, Nursahar Rombekila, Aryani Seruni, Monika Yusuf, Sukmawati