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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Safruddin Safruddin
Universitas Asahan, Kisaran
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pemanfaatan Algoritma BFGS Quasi-Newton untuk Melihat Potensi Perkembangan Luas Tanaman Kopi di Pulau Sumatera Safruddin Safruddin; Elfin Efendi; Rita Mawarni; Anjar Wanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5524

Abstract

Coffee is one of Indonesia's essential export commodities and a foreign exchange source for the country. One crucial factor in coffee production development is the planted land area. Therefore, the availability of land for coffee plants in Indonesia needs to be maintained for the continuity of coffee production today and in the future. This study aimed to see the potential for the widespread development of coffee plants on the island of Sumatra. This is because the island of Sumatra is the largest coffee producer in Indonesia, so information about the potential for the development of this plant area needs to be known as early as possible, especially for the agriculture/plantation service and for coffee farmers, so that coffee production can be maintained. The algorithm proposed in this study is the Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton algorithm which can be used to solve data prediction (forecasting) problems. This study uses a dataset of coffee plant areas sourced from the Directorate General of Plantations for 2012-2021. This study was analyzed using 3 (three) network architecture models (4-9-1, 4-18-1, and 4-27-1). Based on the analysis, the results obtained from model 4-18-1 as the best architecture with 100% accuracy with minor MSE testing, which is 0.00036764820. Meanwhile, based on predictions made using the best architecture (predictions for 2022 and 2023), the area of coffee plantations has decreased slightly. So this needs serious attention from the respective provincial governments.
Pengelompokkan Produksi Tanaman Jagung di Sumatera Utara Menggunakan Algoritma K-Medoids Safruddin Safruddin; Joni Wilson Sitopu; Azwar Anas Manurung; Indra Satria; Anjar Wanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5562

Abstract

Corn is a strategic commodity with bright marketing prospects, especially in North Sumatra. Therefore efforts to increase corn production need great attention because, with sufficient availability, it is hoped that the community's need for corn can be fulfilled and the selling price remains stable. This study aims to classify corn production in North Sumatra based on districts/cities so that districts/cities can be identified and developed into corn production centers to reduce food imports, specifically corn crops. This research uses a corn production dataset based on districts/cities in North Sumatra consisting of 25 regencies and eight cities in 2019-2021 obtained from the Food Crops and Horticulture Service of North Sumatra Province. The algorithm used is the K-Medoids algorithm with Rapid Miner Studio tools. The results of this study were grouping corn production which was divided into 5 (five) groups, including Group 1 was an area with very high corn production consisting of 1 Regency, Group 2 was an area with high corn production consisting of 2 Regencies, Group 3 was an area with moderate corn production consisting of 4 regencies, Group 4 is an area with low corn production consisting of 3 regencies, and Group 5 is an area with very low corn production consisting of 15 regencies and seven cities. Based on these results, Karo, Dairi, and Simalungun districts can be used as centers for corn production in North Sumatra because these three districts alone produce corn production of 65.7% of the total corn production in North Sumatra.
Prediksi Produksi Tanaman Perkebunan Kelapa Sawit di Pulau Sumatera Tahun 2023 dengan Algoritma Bayesian Regulation Elfin Efendi; Surya Fajri; S Safruddin; Azwar Anas Manurung; Lokot Ridwan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 2 (2023): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i2.182

Abstract

Palm oil production has an essential role in the regional and national economy. Accurate and reliable predictions are fundamental in planning and decision-making in the oil palm plantation sector. This study aims to predict the production of oil palm plantations on the island of Sumatra in 2023 using the Bayesian Regulation algorithm. This method was chosen because it combines historical data and new information and considers the risk factors that affect palm oil production. Historical data on palm oil production on the island of Sumatra were obtained from the Central Bureau of Statistics and analyzed using the Bayesian Regulation algorithm. The oil palm production prediction model is evaluated using accuracy and prediction error metrics. The research results are expected to provide reliable and accurate predictions for palm oil production on the island of Sumatra in 2023. This research produces a reasonably high accuracy rate of 90% (10% margin of error) and a trim MSE level of 0.00388775674 with a target error of 0.009. The research results predict that palm oil production on the island of Sumatra will decrease compared to previous years (2018-2022). This prediction provides more precise insights to stakeholders such as farmers, producers, government, and industry players in production planning, resource management, budget allocation, and effective decision-making. This research also has the potential to contribute to the development of science, especially the development of more sophisticated and efficient prediction methods, both for palm oil production and other plantation sectors, using the Bayesian Regulation method.
Co-Authors Azwar Anas Manurung Azwar Anas Manurung Elfin Efendi Indra Satria Joni Wilson Sitopu Lokot Ridwan Rita Mawarni Surya Fajri Wanto, Anjar