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All Journal Inferensi Journal of Applied Science, Engineering, Technology, and Education Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology VARIANSI: Journal of Statistics and Its Application on Teaching and Research ARRUS Jurnal Pengabdian Kepada Masyarakat SMART: Jurnal Pengabdian Kepada Masyarakat
Rais, Zulkifli
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Journal : arrus journal of mathematics and applied science

 1 
K-Means Cluster Analysis for Grouping Districts in South Sulawesi Province Based on Village Potential Azrahwati; Nusrang, Muhammad; Aidid, Muhammad Kasim; Rais, Zulkifli
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience739

Abstract

Cluster analysis is an analysis in multivariable statistics that is used to group objects that have the same characteristics. One of the methods in cluster analysis used to group relatively large amounts of data is the K-Means method. In this study, the K-Means method was applied to classify sub-districts in South Sulawesi Province based on village potential. The variables used are the number of: Elementary School/Equivalent degree, Junior High School/Equivalent degree, Senior High School/Vocational School/Equivalent degree, Community Health Center/Pustu, Families without electricity, Villages/Urbans according to market presence, Villages/Towns that are passed by public transportation and Villages/Kelurahan that have lighting main road. The results of this study are that 3 groups are formed where the first cluster consists of 107 sub-districts that have high village potential, the second cluster consists of 16 sub-districts that have medium village potential and the third cluster consists of 184 sub-districts that have low village potential.
Classification Of Hypertension Using Methods Support Vector Machine Genetic Algorithm (SVM-GA) Fahmuddin S, Muhammad; Rais, Zulkifli; Yuniar, Eka Citra
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 1 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3976

Abstract

Support Vector Machine (SVM) is a machine learning method for classifying data that has been successfully used to solve problems in various fields. The risk minimization principle used can produce an SVM model with good generalization capabilities. The problem with the SVM method is the difficulty in determining the optimal SVM hyperparameters. This research uses Genetic Algorithm (GA) to optimize SVM hyperparameters. GA optimization on SVM is used to classify hypertension. From the result of classification analysis using GA, it shows good accuracy value performance, namely 100% compared to using only SVM.
K-Means Cluster Analysis for Grouping Districts in South Sulawesi Province Based on Village Potential Azrahwati; Nusrang, Muhammad; Aidid, Muhammad Kasim; Rais, Zulkifli
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience739

Abstract

Cluster analysis is an analysis in multivariable statistics that is used to group objects that have the same characteristics. One of the methods in cluster analysis used to group relatively large amounts of data is the K-Means method. In this study, the K-Means method was applied to classify sub-districts in South Sulawesi Province based on village potential. The variables used are the number of: Elementary School/Equivalent degree, Junior High School/Equivalent degree, Senior High School/Vocational School/Equivalent degree, Community Health Center/Pustu, Families without electricity, Villages/Urbans according to market presence, Villages/Towns that are passed by public transportation and Villages/Kelurahan that have lighting main road. The results of this study are that 3 groups are formed where the first cluster consists of 107 sub-districts that have high village potential, the second cluster consists of 16 sub-districts that have medium village potential and the third cluster consists of 184 sub-districts that have low village potential.
Cluster Analysis Using Ensemble ROCK Method in District/City Grouping in South Sulawesi Province based on People's Welfare Indicators Hidayat, Taufiq; Ruliana, Ruliana; Rais, Zulkifli; Botto-Tobar, Miguel
ARRUS Journal of Mathematics and Applied Science Vol. 3 No. 1 (2023)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience1761

Abstract

Cluster analysis is a data mining technique used to group data based on the similarity of attributes of object data. One of the problems that are often encountered in cluster analysis is data with a mixed categorical and numerical scale. The clustering stage for mixed data using the ensemble ROCK (Robust Clustering using links) method is carried out by combining clustering outputs from categorical and numeric scale data. The method used for categorical data is the ROCK method and the method used for numerical data is the Hierarchical Agglomerative method. The best clustering method is determined based on the criteria for the ratio between the standard deviations within the group (SW) and the smallest standard deviation between groups (SB). Based on 24 observation objects in the regencies and cities of the Province of South Sulawesi, the ROCK ensemble method with a value of 0.1 produces three clusters with a ratio value of 2,27 x10-16 based on the combination of the output results of the ROCK method and the Hierarchical Agglomerative method
K-Prototype Algorithm in Grouping Regency/City in South Sulawesi Province Based on 2020 People's Welfare Refaldy, Muhammad; Annas, Suwardi; Rais, Zulkifli
ARRUS Journal of Mathematics and Applied Science Vol. 3 No. 1 (2023)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience1763

Abstract

Clustering is something that is used to analyze data in machine learning, data mining, pattern engineering, image analysis, and bioinformatics. To produce the information needed for a data analysis using the clustering process, this is because the data has a large variety and amount. Researchers will use the K-Prototype method where this method becomes an efficient and effective algorithm in processing mixed-type data. The K-Prototype algorithm has problems in finding the best number of clusters. So, in this paper, researchers will conduct research by finding the best number of clusters in the K-Prototype method. There are many ways to determine this, one of which is the Elbow method. The determination of this method is seen from the SSE (Sum Square Error) graph of several number of clusters. The results of the clustering formed 2 clusters which were considered optimal based on the value of k that experienced the greatest decrease. The results showed that Cluster 1 is a cluster that has characteristics of people's welfare which is better than Cluster 2
A Hybrid Neural Network Approach Using SOM and LVQ for Mapping Crime Clusters in Indonesia Rais, Zulkifli; Meliyana, Sitti Masyitah; Hasbullah, Dinda Warfani
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4782

Abstract

Crime ratehigh crime rates in Indonesia are one of the important issues that need to be addressed with data-based strategies. This study aims to group provinces in Indonesia based on crime patterns using Self-Organizing Map (SOM) and classify the results using Learning Vector Quantization (LVQ). The results of the clustering analysis using SOM show that the optimal number of clusters is two, as supported by validation using Connectivity, Dunn Index, and Silhouette Score. Cluster 1 consists of 31 provinces with lower crime rates, while Cluster 2 includes 3 provinces with higher crime rates. To improve understanding of the clustering results, classification was carried out using the LVQ method, which produced an accuracy of 91.43%.
Classification of Family Welfare Card Recipients in Makassar City Using Decision Tree Algorithms Rais, Zulkifli; Fahmuddin S, Muhammad; Musfira, Musfira
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4783

Abstract

This study aims to analyze the factors influencing the determination of recipients of the Family Welfare Card (KKS) program in Makassar City and evaluate the level of accuracy of the decision tree model in the classification process. The KKS program is a government effort to accelerate poverty alleviation, so it is important to ensure that the selection process for program recipients is carried out on target. The decision tree method is used in this study because of its ability to simplify the decision-making process through an easy-to-understand tree structure. This study utilizes KKS recipient data with various variables, such as income, number of dependents, employment status, asset ownership, and education level, to build a classification model. The results of the study indicate that the variable of the Head of Household's (KRT) Highest Education Level (X4) has the highest level of importance in determining KKS recipients, followed by the variable Number of Family Members (X1), and the variable Ownership of Residential Buildings (X5). The decision tree model that was built has an accuracy level of 84.21%, which states the model's ability to classify KKS recipients effectively. This study also provides insight into the description of factors influencing KKS receipts, which can be used as a basis for formulating more efficient and targeted policies.
Co-Authors Abidin, Muh. Zulkifli Ahmar, Ansari Saleh Aidid, Muhammad Kasim Akbar Iskandar Alfairus, Muh. Qodri Amira, Husnul Annisa Syalsabila Asti Dewi Putri Aswi, Aswi Azrahwati Botto-Tobar, Miguel Bunga, Yhegi Rombe Fahmuddin S, Muhammad H.S, Rahmat Hardianti Hafid Haris, A. Sulfiana Hasbullah, Dinda Warfani Hasnita, Hasnita Ikhwana, Nur Indrayasaro Mar'ah, Zakiyah Muhammad Arif Tiro, Muhammad Arif Muhammad Kasim Aidid Mukhtazam Aqil Mukhtar Musfira, Musfira Nusrang, Muhammad Poerwanto, Bobby Purnamasari, A. Bida Rahmanda, Lalu Ramzy Rahmat, Rahmat Wahyudi Refaldy, Muhammad Rezky, Akhmad Rezky Ramadhana T Risqi, Shopia Ruliana Ruliana Ruliana, Ruliana Rusdianto Saida, Saida Sitti Masyitah Meliyana R. Sudarmin Sudarmin Sudarmin Suwardi Annas Syahputra, Akbar Taufiq Hidayat Triutomo, Agung Utomo, Agung Tri Yuniar, Eka Citra Yusniar Rasjid, Yusniar