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All Journal Jurnal Teknik Industri JURNAL REKAYASA TEKNOLOGI INFORMASI
Oktavia, Andiny Trie
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering

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Capacitated Location Allocation Problem of Solar Power Generation in Indonesia using Particle Swarm Optimization Astungkatara, Arya Wijna; Fath, Hamzah; Putri, Oktaviana; Yana, Anak Agung Istri Anindita Nanda; Normasari, Nur Mayke Eka; Oktavia, Andiny Trie; Rifai, Achmad Pratama
Jurnal Teknik Industri Vol. 25 No. 1 (2024): February
Publisher : Department Industrial Engineering, University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/JTIUMM.Vol25.No1.55-72

Abstract

Indonesia has abundant potential for solar energy. The decrease in the cost of solar power generation components can bolster the development of solar power plants. Due to its geographical characteristics, it is essential to analyze the feasibility of using solar power plants as a primary renewable energy source in Indonesia, especially in Sumatra Island. One of the critical aspects of developing solar power plants is determining the suitable location of the power plant and allocating the electricity generated to the regions. Therefore, this study considers the Capacitated Location Allocation Problem (CLAP) to determine the optimal placement of solar power plants on Sumatra Island to minimize investment and transmission costs. To address the problem, we explore three metaheuristics, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Large Neighborhood Search (LNS). The results obtained by these metaheuristic methods show significant differences in cost, with SA providing the best solution with the lowest cost. The investment and transmission cost can be minimized by solving the CLAP to obtain optimal solar power plant placement while enhancing the region's resilience in implementing distributed generation.
Perbandingan Algoritma Temporal Convolutional Neural (TCN) dan Long Short-term Memory (LSTM) untuk Memprediksi Harga Saham Menggunakan Time Series Data Putra, Ryan Syahriel Maulana; Larasati, Aisyah; Salsabila, Titalia Trias; Oktavia, Andiny Trie
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 3 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i3.21683

Abstract

Penelitian ini bertujuan untuk memberikan wawasan tambahan bagi investor yang berinvestasi di pasar modal dengan membandingkan Algoritma TCN dan LSTM dalam memprediksi data deret waktu. Data yang digunakan mencankup periode 1 Januari 2023 hingga 31 Desember 2023 dan diambil dari Yahoo Finance, dengan variabel-variable seperti harga pembukaan, penutupan, dan volume. Proses penelitian melibatkan pembersihan data, pembagian data latih dan uji, serta pemodelan dengan pencarian parameter optimal menggunakan Hyperband. Hasil menunjukan bahwa TCN lebih efisien dengan RSME sebesar 167.06 dan MAPE 2,58%, sementara LSTM memperoleh RMSE 467.52 dan MAPE 7,05% dengan waktu peltihan TCN yang lebih singkat (40.8 detik) dibandingkan LSTM (252.5 detik).
Co-Authors Achmad Pratama Rifai Aisyah Larasati Astungkatara, Arya Wijna Fath, Hamzah Nur Mayke Eka Normasari Putra, Ryan Syahriel Maulana Putri, Oktaviana Salsabila, Titalia Trias Yana, Anak Agung Istri Anindita Nanda