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All Journal JITEK (Jurnal Ilmiah Teknosains) Jurnal TAM (Technology Acceptance Model) Jusikom : Jurnal Sistem Komputer Musirawas JMM (Jurnal Masyarakat Mandiri) JUTIM (Jurnal Teknik Informatika Musirawas) Martabe : Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Informatika Global Jurnal Informatika dan Rekayasa Elektronik Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Ngabdimas : Pengabdian Pada Masyarakat Knowbase : International Journal of Knowledge in Database Jurnal Pengabdian Kepada Masyarakat Ngabdimas Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal TAM (Technology Acceptance Model)
Dedi Setiadi
Institut Teknologi Pagar Alam, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Veterinary Other

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Journal : Knowbase : International Journal of Knowledge in Database

 1 
Optimization Of Agricultural Production In South Sumatera Using Multiple Linear Regression Algorithm Setiadi, Dedi; Sasmita, Sasmita; Mukti, Yogi Isro
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8754

Abstract

Rice is one of the agricultural commodities in South Sumatra whose productivity level still fluctuates. In 2000, rice production reached 1,863,643.00 kg, then increased to 3,272,451.00 kg, in 2010, but decreased again in 2020 to 2,696,877.46 kg. This instability is influenced by various factors such as land area, rainfall, pest attacks, and fertilizer use. This study aims to optimize rice production by applying machine learning using multiple linear regression algorithms, and the CRISP-DM method, with the stages being business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Data of 1,000 records obtained from farmers were analyzed using Google Collaboratory, resulting in an intercept of -3836,2639, and coefficients for land area of 5,7336, rainfall of 1,2710, pests of 6,1153, urea of 1,6226, and phonska of 1,2581. To evaluate the accuracy of rice production predictions based on these independent variables, calculations were made on the RMSE value and analysis of the coefficient of determination. The results were that the RMSE value was recorded at 17065084,9641, and the coefficient of determination (R²) was 0,6487, indicating that around 64,87 % of the variability in rice production can be explained by independent variables such as land area, rainfall, pest attacks, use of urea fertilizer, and phonska, while the remaining 35,13 % was influenced by other factors.
Analysis of Drug Inventory Patterns Using the K-Means Algorithm Setiadi, Dedi; Gusmaliza, Debi
Knowbase : International Journal of Knowledge in Database Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.10420

Abstract

Efficient drug inventory management is a critical challenge for the Sandar Angin Community Health Center to ensure the availability of drugs needed by customers without incurring excessive storage costs. Data mining with the K-Means algorithm was used to determine drug inventory more effectively. Drug data for the past year was used as a sample in this study. The Elbow method was used to determine the optimal number of clusters, and the results showed that three clusters were most appropriate for grouping drug sales data. The first cluster consisted of drugs with high and consistent sales, the second cluster included drugs with moderate and fluctuating sales, while the third cluster contained drugs with low and inconsistent sales. The results of this clustering provide clear guidance in drug inventory management. Drugs in the first cluster require larger stocks, the second cluster requires moderate stocks and promotional strategies tailored to the season, while the third cluster requires minimal stocks and regular evaluations to determine the continuation of its supply. The implementation of the K-Means method has proven effective in reducing storage costs, increasing customer satisfaction, and optimizing inventory management. This study concluded that data mining using the K-Means algorithm can help the Sandar Angin Community Health Center make better decisions regarding drug inventory. The results showed that out of a total of 506 drug data sets, 496 fell into cluster 0, or 98% of the data. One drug data set fell into cluster 1, and nine drug data set fell into cluster 2.
Co-Authors Abdi NAsrullah Asep Syaputra Dedy Syamsuar Dhiniati, Fameira Edowinsyah Efan Febriansyah . Gusmaliza, Debi Lesi Anggraini Lily Endah Diansari Masagus Taswin Masdalipa, Risnaini Melza Yolanda Rosmawati, Henni Sasmita Sasmita Sasmita Sasmita Syahri, Riduan Syerina Raihatul Jannah Tri Susanti Tri Susanti Tri Susanti Willy Wijayanti Yogi Isro Mukti Yogi Isro Mukti, Yogi Isro