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Parini
Universitas Royal
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Penentuan Bibit Kelapa Sawit Unggul Dengan Metode ARAS Dan TOPSIS Sitti Nur Alam; Rolly Yesputra; Arridha Zikra Syah; Parini; Andi Ernawati
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2213

Abstract

The Industrial Era 4.0 opens up great opportunities to increase production, efficiency and sustainability of the palm oil industry. The problem faced by farmers is that farmers are often hampered by limited knowledge and lack of guidance in choosing plant seeds. Because seeds are an important factor in supporting satisfactory results. This research was carried out to help farmers who have difficulty in choosing oil palm seeds which could become a problem for farmers in the future. day. This research uses the ARAS and TOPSIS methods to evaluate seeds based on criteria that have been identified and analyzed, to assess 10 types of superior seeds based on 5 criteria: oil potential, pest resistance, seed price, productive planting period, and maintenance costs. It is hoped that this research can help oil palm farmers increase their productivity and profits, as well as support the sustainability of the palm oil industry in the Industry 4.0 era. The ARAS and TOPSIS methods have proven to be effective in helping farmers choose superior oil palm seeds. From the results of research conducted using the ARAS and TOPSIS methods, VIM 1 seeds were recommended as the best choice based on the points obtained.
Analysis of the Spatial Distribution of Rice Prices Acrossprovinces in Indonesia Using the K-Means Algorithm Parini; Yessica Siagian; Guntur Maha Putra; Julpan Hartono Suria Manja Manurung; Nuri Apriani; Putri Wulandari
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3146

Abstract

Differences in rice prices across provinces in Indonesia reflect variations in distribution and market conditions that can be analyzed using a data clustering approach. This study aims to map the characteristics of interprovincial rice prices using the K-Means Clustering algorithm. The dataset consists of prices for Premium Rice, Medium Rice, Non-SPHP Medium Rice, and SPHP Rice obtained from official government data. The research stages included data preprocessing, transformation, normalization using StandardScaler, determining the number of clusters using the Elbow Method and Silhouette Score, and visualizing the results using Principal Component Analysis (PCA). The Elbow Method results indicated an efficient point at K = 5, while the highest Silhouette Score of 0.5658 was obtained at K = 7; thus, seven clusters were selected as the best model. PCA visualization reveals that provinces in Indonesia exhibit rice price characteristics that form seven groups with varying degrees of similarity. The research results indicate that the K-Means algorithm is capable of effectively identifying rice price clustering patterns and producing a map that can support the analysis of inter-regional price characteristics as a basis for formulating food distribution policies and stabilizing rice prices in Indonesia.
Co-Authors Alam, Sitti Nur Andi Ernawati Arridha Zikra Syah Guntur Maha Putra Julpan Hartono Suria Manja Manurung Nuri Apriani Putri Wulandari Rolly Yesputra Yessica Siagian