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All Journal Building of Informatics, Technology and Science Bulletin of Informatics and Data Science
Wang, Pai-Chou
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Computer Science & IT Electrical & Electronics Engineering Engineering

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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
The Process of Grouping Elementary School Students Receiving PIP Assistance uses the K-Means Algorithm Huang, Jen-Peng; Wang, Pai-Chou; Lubis, Ridha Maya Faza
Bulletin of Informatics and Data Science Vol 2, No 2 (2023): November 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v2i2.78

Abstract

As part of receiving support from the Smart Indonesia Program (PIP), this study intends to analyze and apply the K-Means algorithm in the process of grouping elementary school students. PIP is a government initiative that attempts to give money to elementary school pupils from disadvantaged or weaker homes. The effective and fair distribution of aid monies depends on the proper grouping of the students. The K-Means approach was selected because it can cluster data, allowing the grouping of pupils based on pertinent traits. Numerous characteristics that can affect kids' financial needs are included in the data utilized in this study, including family income, parental education level, proximity to the school, and other social and economic issues. This study makes use of empirical data from a PIP-affiliated elementary school in an urban setting. The data includes a large number of pertinent features and thousands of pupils. Based on how similar their characteristics are, pupils are divided into numerous clusters using the K-Means technique. The findings of this study will help us better identify the traits of students who are eligible for PIP support. By doing this, the government can allocate funds more wisely and guarantee that aid is given where it is most needed. The PIP program can benefit children in need more by streamlining the process of grouping the students. In addition, this research has broader implications for social aid and education policy. To guarantee effectiveness and equity in resource allocation, the K-Means algorithm can be used in a variety of additional aid initiatives. Data mining-based strategies, like those employed in this study, are becoming more crucial to boost the effectiveness of aid programs like PIP. The findings of this study can help the government and educational institutions improve the efficacy of aid initiatives designed to boost Indonesian children's education
Co-Authors Damanik, Nurafni Elvina, Yuli Huang, Jen-Peng Kiki Khoifin, Kiki Kusumaningtyas, Dyah Ayu Lubis, Ridha Maya Faza Simanullang, Ceria D Sitepu, Ade Clinton