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Amnur, Muh. Alfian
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Peramalan Produksi Perikanan Laut di Provinsi Jawa Tengah: Pendekatan Statistik dan Machine Learning Amnur, Muh. Alfian; Pramana, Setia
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2417

Abstract

Fisheries production in Central Java Province experiences seasonal fluctuations that affect supply stability and fishermen's income. This study aims to analyze the production trends from 2013 to 2023 and compare the performance of the SARIMA and Random Forest models in forecasting fishery production sold at Fish Auction Sites (TPI). Based on evaluation metrics including MAE, RMSE, and MAPE, the SARIMA(8,1,1)(1,1,0)[12] model demonstrated the best performance with values of 2930.12, 3749.83, and 15.40, respectively. Additionally, the SARIMA model was used to forecast production for January 2024, resulting in an estimated output of 26,210.63 tons. This forecast is expected to assist stakeholders in monitoring fishery production in Central Java Province.
Pembangunan Dataset Sintetis Klasifikasi Baku Lapangan Usaha Indonesia 2020 dengan Generative Artificial Intelligence Silmi Kaffah, M. Ihsan; Rahman, Dimas Haafizh; Amnur, Muh. Alfian; Montolalu, Cloudya Qashwah; Siregar, Amir Mumtaz; Sinulingga, Geraldo Benedictus; Ayu Alistin, Zharifah Dhiya; Raihannur, Cut Indah; Putri Arivia, Anggi Marya; Rahmawati, Arih; Nauli Sihombing, Fiona Audia; Salsabiela, Rahmadika Kemala; Bahy, Sabastian Alfons; Suadaa, Lya Hulliyyatus; Choir, Achmad Syahrul
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2581

Abstract

The limited quality datasets is a fundamental challenge in developing automatic classification of business description into the Indonesia Standard Industrial Classification (KBLI) using machine learning models. This research aims to develop a synthetic KBLI dataset using Generative AI via ChatGPT chatbot with a one-shot prompting technique. This technique is employed to generate business descriptions based on five-digit KBLI codes in order to address the limitations of labeled data and the variability of existing business descriptions. The dataset generated through prompt engineering and manual validation shows that 93,25% of the business descriptions align with the established KBLI standards. The average number of business descriptions per category demonstrates a fairly uniform distribution, ensuring sufficient representation for each five-digit code. This research makes a significant contribution in providing a dataset for training machine learning models in the automatic classification of business descriptions into the five-digit KBLI categories.
Co-Authors Ayu Alistin, Zharifah Dhiya Bahy, Sabastian Alfons Choir, Achmad Syahrul Montolalu, Cloudya Qashwah Nauli Sihombing, Fiona Audia Pramana, Setia Putri Arivia, Anggi Marya Rahman, Dimas Haafizh Rahmawati, Arih Raihannur, Cut Indah Salsabiela, Rahmadika Kemala Silmi Kaffah, M. Ihsan Sinulingga, Geraldo Benedictus Siregar, Amir Mumtaz Suadaa, Lya Hulliyyatus