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All Journal Building of Informatics, Technology and Science Bulletin of Informatics and Data Science Bulletin of Artificial Intelligence International Journal of Informatics and Data Science
Lubis, Ridha Maya Faza
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : building of informatics technology and science

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Mining Grouping Biopharmaceutical Plants in Indonesia Using the K-Means Algorithm: Application of Data Mining in Production Lubis, Ridha Maya Faza; Huang, Jen-Peng; Sigiro, Mula; Panjaitan, Joel
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3165

Abstract

There are various types of biopharmaceutical plants or medicinal plants in Indonesia, including ginger, galangal, kencur, turmeric, lempuyang and curcuma aeruginosa whose production is widespread in various provinces in Indonesia. reached 160 million USD annually. Then the application of data mining used to classify biopharmaceutical plant production data in Indonesia from this study resulted in 2 clusters namely cluster 0 which in this cluster is a cluster with low production value of biopharmaceutical plants in Indonesia, namely the Provinces of West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep. Bangka Belitung, Kep. Riau, DKI Jakarta, DI Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, Central Kalimantan, Southeast Sulawesi, Maluku, West Papua and Papua. While cluster 1 is the cluster where the production rate of biopharmaceutical plants is the highest in Indonesia, namely the Provinces of North Sumatra, West Java, Central Java, East Java and South Sulawesi. From the results that have been obtained, it is hoped that it will be useful for organizations, groups or individuals engaged in the biopharmaceutical plant sector so that they can review the existing deficiencies and can increase the production of biopharmaceutical plants in each province.
Agglomerative Hierarchical Clustering (AHC) Method for Data Mining Sales Product Clustering Lubis, Ridha Maya Faza; Huang, Jen-Peng; Wang, Pai-Chou; Khoifin, Kiki; Elvina, Yuli; Kusumaningtyas, Dyah Ayu
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3569

Abstract

Supermarkets are Indonesian terms that refer to large stores or supermarkets that offer a variety of daily needs such as food, drinks, cleaning products, household appliances, clothing, and so on. In contrast to stalls or small shops, supermarkets have a larger size and provide a variety of products. Because of this, many people prefer to shop for their daily needs at the supermarket rather than at the nearest shop because the existence of the supermarket makes it easier for consumers to buy various products in one place without having to move to another store. However, sales in supermarkets also pose a problem, namely how to sort or group products that are not selling well so they can be replaced with products that are selling better or reduce the number of suppliers. This is where data mining or data analysis techniques that use business intelligence are needed. The research was conducted to classify the best-selling products in supermarkets using the Agglomerative Hierarchical Clustering (AHC) method, in which alternatives with the same matrix or distance are grouped into certain clusters. In applying the AHC method, the number of clusters formed is 3. There are three different clusters, namely cluster 0, cluster 1, and cluster 2, each with a different alternative group. Each cluster has a different number of products and a different percentage. Cluster 0 is the cluster with the highest number of products and the largest percentage, namely 45% with a total of 9 products, followed by cluster 2, and cluster 1 has the smallest number of products and percentage, namely 0.30% with a total of 6 products and 0 .25% with a total of 5 products. In addition, sales data for several products each month are grouped based on certain price ranges
K-Means and AHC Methods for Classifying Crime Victims by Indonesian Provinces: A Comparative Analysis Lubis, Ridha Maya Faza; Huang, Jen-Peng; Wang, Pai-Chou; Damanik, Nurafni; Sitepu, Ade Clinton; Simanullang, Ceria D
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3630

Abstract

Crime is a common phenomenon that often occurs in society and has a negative impact both individually and collectively. Gaining a deeper understanding of crime can help us tackle the problem more efficiently. In an era that is increasingly complex and globally connected as it is now, crime has undergone significant developments and changes. Crime remains a serious threat to our security, integrity, and well-being. Some common types of crime include theft, robbery, fraud, physical abuse, and murder. Crime can happen anytime and anywhere. To tackle crime, data mining techniques can be used to analyze the surrounding situation and gain new knowledge. One approach is to classify provinces based on crime data from previous years so that crime-prone areas can be identified and security measures can be increased. In this study, two grouping methods were used, namely K-Means and AHC using the complete linkage mode. There are 34 provinces in Indonesia which are grouped based on the number of victims of crime from 2019 to 2021. The grouping results using the K-Means method yield two groups with 17 provinces each. However, using the AHC complete linkage method, there is a difference in the number of provinces between cluster 0 and cluster 1 compared to the K-Means results. In addition, there are differences in the location of the province in the cluster between the two methods. In the K-Means method, provincial data is located in cluster 0, while in the AHC method, the province's data is in cluster 1. Thus, this study provides insight into crime in Indonesia and provides information about the grouping of provinces based on crime rates using the K-Means method. Means and AHC
Co-Authors Batubara, Gunawan Damanik, Nurafni Elvina, Yuli Hetty Rohayani Huang, Jen-Peng Irvan Siahaan Joel Panjaitan Kiki Khoifin, Kiki Kusuma, Ade Ayunda Kusumaningtyas, Dyah Ayu Manalu, Hardi Giat Sofian Mesran, Mesran MULA SIGIRO Nurwahid, Fahri Sarumaha, Farel Notafelling Simanullang, Ceria D Sitepu, Ade Clinton Tarigan, Safrin Zulkarnain Wang, Pai-Chou