Model Prediksi Harga Acuan Ikan Berkeadilan dengan Pendekatan Artificial Inteligence

Abstract

Indonesia menghadapi berbagai tantangan dalam menjaga keberlanjutan sektor perikanan, termasuk perubahan iklim, degradasi habitat, polusi, overfishing, kegiatan ilegal, serta ketidakadilan dalam distribusi akses dan manfaat sumber daya laut. Untuk mengatasi tantangan ini, pemerintah mengimplementasikan kebijakan Penangkapan Ikan Terukur (PIT) melalui PP No. 11 Tahun 2023 dan peraturan terkait seperti Keputusan Menteri Kelautan dan Perikanan Nomor 21 Tahun 2021. Kebijakan ini bertujuan untuk menjaga keberlanjutan sumber daya laut, mengoptimalkan Pendapatan Negara Bukan Pajak (PNBP), serta mendukung kesejahteraan ekonomi nelayan dan pelaku industri perikanan. Namun, pelaksanaan kebijakan PIT menghadapi kendala utama pada akurasi penetapan harga acuan ikan, yang sering kali tidak mencerminkan kondisi pasar. Ketidakakuratan ini berdampak pada perencanaan PNBP, pendapatan nelayan, stabilitas harga, dan kelangsungan usaha pelaku industri perikanan. Penetapan harga yang lebih akurat memerlukan pendekatan berbasis data real-time dan teknologi digital seperti Artificial Intelligence (AI), yang mampu memproses data dengan cepat, akurat, dan terukur. AI menawarkan potensi besar dalam meningkatkan stabilitas harga ikan melalui teknologi machine learning dan deep learning. Algoritma seperti LSTM (Long Short Term Memory) dan GRU (Gated Recurrent Unit) dapat digunakan untuk memprediksi harga ikan secara real-time di pelabuhan-pelabuhan utama seperti PPS Nizam Zachman, PPN Karangantu, PPN Pekalongan, dan PPN Brondong. Pemilihan lokasi tersebut didasarkan pada tingginya keanekaragaman hayati serta potensi ekonomi yang besar dari aktivitas perikanan. Dalam konteks global, berbagai penelitian menunjukkan keberhasilan penerapan AI. Contohnya, model BATS dan ARIMA digunakan untuk prediksi produksi dan harga ikan di India, sementara pendekatan ensemble machine learning diterapkan di Malaysia untuk memprediksi produksi ikan laut dan budidaya. Teknologi AI juga telah membantu negara lain seperti Vietnam dalam memprediksi harga ekspor produk perikanan. Penggunaan sistem berbasis AI di Indonesia diharapkan dapat menyediakan prediksi harga ikan yang akurat, stabil, dan relevan, mendukung pengelolaan sumber daya perikanan secara berkelanjutan, mengoptimalkan PNBP sesuai dengan PP No. 85 Tahun 2021, meningkatkan kesejahteraan nelayan dengan mengurangi dampak fluktuasi harga, memberikan manfaat ekonomi bagi pelaku industri dan konsumen, mencegah overfishing melalui pengelolaan berbasis data. Dengan penerapan AI, pemerintah dapat lebih efektif menetapkan harga acuan ikan, meningkatkan kontribusi sektor perikanan terhadap perekonomian nasional, dan mendukung keberlanjutan ekosistem laut Indonesia.

Indonesia faces various challenges in maintaining the sustainability of the fisheries sector, including climate change, habitat degradation, pollution, overfishing, illegal activities, and inequities in the distribution of access and benefits from marine resources. To address these challenges, the government has implemented the Measured Fish Catch policy (PIT) through Government Regulation No. 11 of 2023 and related regulations such as Minister of Marine Affairs and Fisheries Decree No. 21 of 2021. This policy aims to sustainably manage marine resources, optimize Non-Tax State Revenue (PNBP), and support the economic welfare of fishermen and fisheries industry stakeholders. However, the implementation of the PIT policy faces major challenges in accurately determining benchmark fish prices, which often do not reflect market conditions. This inaccuracy impacts PNBP planning, fishermen's income, price stability, and the sustainability of fisheries businesses. More accurate price determination requires a data-driven, real-time approach and digital technologies such as Artificial Intelligence (AI), capable of processing data quickly, accurately, and measurably. AI offers significant potential to improve fish price stability through machine learning and deep learning technologies. Algorithms like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) can be utilized to predict fish prices in real-time at major ports such as PPS Nizam Zachman, PPN Karangantu, PPN Pekalongan, and PPN Brondong. These locations are chosen based on their biodiversity richness and substantial economic potential from fishing activities. Globally, various studies demonstrate the success of AI applications. For instance, models like BATS and ARIMA are used in India to forecast fish production and prices, while ensemble machine learning approaches are applied in Malaysia for predicting marine fish production and aquaculture. AI technology has also assisted countries like Vietnam in predicting export prices of fisheries products. The use of AI-based systems in Indonesia is expected to provide accurate, stable, and relevant fish price predictions, supporting sustainable fisheries resource management, optimizing PNBP as per Government Regulation No. 85 of 2021, enhancing fishermen's welfare by reducing price fluctuations, delivering economic benefits to industry players and consumers, and preventing overfishing through data-driven management. Through AI implementation, the government can more effectively set benchmark fish prices, increase the fisheries sector's contribution to the national economy, and support the sustainability of Indonesia's marine ecosystem.