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All Journal Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Azwar Anas Manurung
Universitas Asahan, Kisaran, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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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.
Analisis Perkembangan Produksi Tanaman Biofarmaka (Obat) di Indonesia Menggunakan Algoritma Resilient Indra Satria; Azwar Anas Manurung; Mhd Ali Hanafiah
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.285

Abstract

Biofarmaka (medicinal plants) in Indonesia play a crucial role in the pharmaceutical industry's development, providing natural resources for drug research, and supporting the utilization of traditional herbal remedies for public health. This research aims to analyze the development of biofarmaka plant production in Indonesia through predictions. This is essential for strategic planning, resource management, and future pharmaceutical industry development, ensuring an adequate supply of raw materials and supporting sustainable growth in the bio-pharmaceutical sector. The research dataset comprises biofarmaka plant production data in Indonesia by plant type, from 2018 to 2022, obtained from the Indonesian Central Statistics Agency. The research employs the Resilient algorithm, a machine learning technique. Architectural models used include 3-5-1, 3-10-1, 3-15-1, and 3-20-1. Among the four models, the 3-5-1 model is selected as the best due to its higher accuracy of 100%, and a lower Mean Squared Error (MSE) of 0.0023021, indicating the successful application of the Resilient algorithm in predicting the development of biofarmaka plant production in Indonesia.
JST: Prediksi Perkembangan Produksi Tanaman Sayuran Dalam Upaya Pemenuhan Gizi Masyarakat dengan Algoritma Resilient Azwar Anas Manurung; Indra Satria; Anjar Wanto
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.658

Abstract

Vegetable plants are very important in human life because they have a significant role as a source of nutrition and fulfillment of community nutrition. Therefore it is important to predict the production of vegetable crops. This study will use the Resilient algorithm which is one of the algorithms from Artificial Neural Networks (ANN) which is commonly used to predict data. This study uses times series data on vegetable crop production in North Sumatra Province from 2013 to 2022, obtained from the Indonesian Central Statistics Agency (BPS) website. The research topic will be analyzed using 5 ANN models, including: 8-8-1, 8-16-1, 8-24-1, 8-32-1 and 8-40-1. Based on the analysis results, model 8-32-1 was chosen as the best model, because it has an accuracy rate of 89% (the highest compared to other models). The results showed that the Resilient algorithm was able to predict vegetable crop production well. This research has important implications in supporting the sustainability of agricultural and food systems by providing information on developments in vegetable crop production to help farmers, producers and governments plan agricultural activities more effectively.
Co-Authors Efendi, Elfin Indra Satria Lokot Ridwan Mhd Ali Hanafiah Safruddin Safruddin Surya Fajri Wanto, Anjar