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All Journal Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia
Andi Aris Mattunruang
Universitas Patompo, Makassar, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Reinforcement Learning for Portfolio Optimization: Evidence from the Indonesian Stock Market Rachmawaty; Rahmawati; Hartini; Andi Aris Mattunruang
Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Vol. 13 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jreksa.v13i1.14579

Abstract

Stock portfolio management in emerging markets such as Indonesia remains challenging due to high volatility, market inefficiencies, and the strong presence of retail investors. In this setting, conventional approaches, including buy-and-hold strategies, the Markowitz framework, and the Capital Asset Pricing Model (CAPM), often struggle to perform consistently under rapidly changing market conditions. While reinforcement learning (RL) has gained increasing traction in global finance, its application in the Indonesian stock market remains limited. This study examines the effectiveness of an RL-based approach, specifically the Deep Q-Network (DQN) algorithm, in optimizing stock portfolios on the Indonesia Stock Exchange (IDX). Using a quantitative experimental design, the analysis is based on back-testing simulations of IDX30 stocks over the 2022–2024 period, with samples selected purposively based on liquidity and market capitalization. The findings show that the DQN-based strategy consistently outperforms conventional methods, delivering higher returns, improved risk–return efficiency, and better control of downside risk. These results suggest that RL models are better suited to adapt to dynamic market conditions. Theoretically, this study extends portfolio optimization literature by incorporating adaptive, learning-based models into emerging market contexts. Practically, it offers evidence for investors and practitioners to consider AI-driven strategies as a more responsive alternative to traditional approaches in a volatile market.
Penerapan Pompa Air Tenaga Surya Untuk Irigasi Pertanian Pada Kelompok Tani Kelapa Muda Di Kampung Kaluku, Bantaeng Andi Imran; Aulia Sabril; Andi Aris Mattunruang; Muhammad Ruswandi Djalal
Jurnal Abdimas Indonesia Vol. 5 No. 4 (2025)
Publisher : Perkumpulan Dosen Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34697/jai.v5i4.2517

Abstract

Ketersediaan air irigasi merupakan kendala utama bagi petani di Kampung Kaluku, Kabupaten Bantaeng, terutama pada musim kemarau ketika sebagian besar lahan tidak dapat ditanami. Kegiatan ini bertujuan menyediakan sumber air irigasi yang berkelanjutan melalui penerapan pompa air tenaga surya serta meningkatkan kapasitas petani dalam pengoperasian dan perawatan teknologi tersebut. Pelaksanaan kegiatan dilakukan melalui observasi lapangan, perancangan sistem, instalasi pompa tenaga surya, pelatihan teknis, dan pendampingan langsung bersama kelompok tani. Sebanyak 37 anggota Kelompok Tani Kelapa Muda terlibat dalam proses instalasi dan pelatihan. Hasil kegiatan menunjukkan bahwa sistem mampu mengalirkan air 20–25 m³ per hari, memenuhi sekitar 70% kebutuhan irigasi, serta menghilangkan biaya operasional yang sebelumnya dikeluarkan untuk pompa diesel. Para petani juga memperoleh peningkatan kemampuan teknis dan kemandirian dalam mengelola sistem energi surya. Kegiatan ini menyimpulkan bahwa penerapan teknologi irigasi berbasis energi surya dapat meningkatkan keterlibatan, kemandirian, dan keberlanjutan praktik pertanian masyarakat desa.
Co-Authors Andi Imran Aulia Sabril Hartini Muhammad Ruswandi Djalal Rachmawaty Rahmawati