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All Journal Inferensi Journal of Applied Science, Engineering, Technology, and Education Mattawang: Jurnal Pengabdian Masyarakat 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
Rais, Zulkifli
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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 Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Public Health Social Sciences Other

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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
Analisis Support Vector Regression (SVR) untuk meramalkan Indeks Kualitas Udara di Kota Makassar Rahmat, Rahmat Wahyudi; Annas, Suwardi; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm107

Abstract

Polusi udara merupakan salah satu permasalahan yang belum terselesaikan sampai saat ini terutama di kota besar di Indonesia. Kondisi ini tentu sangat mengkhawatirkan mengingat polutan yang dikeluarkan oleh kendaraan bermotor seperti karbon monoksida (CO), partikulat matter (PM), nitrogen oksida ( ), sulfur dioksida ), dan karbon dioksida ( ) sangat berbahaya bagi kesehatan manusia. Oleh karena itu perlu dilakukan penelitian untuk mengetahui peramalan indeks kualitas udara dimasa mendatang. Maka pada penelitian ini digunakan metode SVR untuk meramalkan indeks kualitas udara di Kota Makassar. SVR merupakan pengembangan Support Vector Machine (SVM) untuk kasus regresi. Dalam penelitian ini metode SVR digunakan dengan kernel terbaik sebagai bantuan penyelesaian masalah non-linier, metode Min – Max Normalization untuk normalisasi data, pembagian data training dan data testing yang digunakan yakni 80%:20%, pemilihan model terbaik dengan Grid Search Optimization. Hasil peramalan yang didapatkan bahwa kelima variabel indeks kualitas udara di kota makassar tergolong baik dengan nilai RMSE yaitu Partikulat (PM10) 0,12352, Sulfur Dioksida ( ) 0,11502, Ozon ( ) 0,13561, Nitrogen dioksida ( ) 0,11380, Karbon Monoksida (CO) 0,00699 artinya kemampuan model dapat mengikuti pola data dengan baik.
GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) IN MODELING THE RISK FACTORS OF PNEUMONIA DISEASE AMONG TODDLERS IN THE CENTRAL SULAWESI PROVINCE Mar'ah, Zakiyah; Rais, Zulkifli; Haris, A. Sulfiana
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm151

Abstract

This research was conducted to map and model the number of Pneumonia cases in Central Sulawesi Province using the Geographically Weighted Negative Binomial Regression (GWNBR) approach. The data used were Pneumonia case data in Central Sulawesi Province obtained from the Health Publication of Central Sulawesi Province in 2021. The analysis results with the GWNBR method indicated that predictor variables significantly influencing the number of Pneumonia cases in each district/city of Central Sulawesi Province were Exclusive Breastfeeding Percentage (X1), Complete Basic Immunization Percentage (X2), Percentage of Toddlers Receiving Vitamin A (X3), and Percentage of Coverage of Toddler Services (X5). Meanwhile, the variable Low Birth Weight (X4) does not significantly affect the cases.
Metode Radial Basis Function Neural Network Untuk Klasifikasi Kab/Kota Tertinggal Di Provinsi Sulawesi Selatan Ruliana, Ruliana; Rais, Zulkifli; Mar'ah, Zakiyah; Hasnita, Hasnita
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm197

Abstract

A disadvantaged area is an area that has the characteristics of tending to be left behind compared to other areas. Radial basis function neural networks are a part of Artificial Neural Networks, which use radial basis activation functions and are commonly used in classification cases. All districts/cities in South Sulawesi province have different characteristics from other districts/cities. Therefore, districts/cities are grouped into 2 groups to identify districts/cities that have characteristics that tend to be the same based on indicators of regional underdevelopment. The grouping results are then used as actual values ​​for classification using the RBFNN method, to determine the classification results and performance of the RBFNN method. In classifying districts/cities in South Sulawesi province based on indicators of regional underdevelopment using the radial basis function neural network method, an accuracy value of 91% was obtained using a comparison of 55% training data and 45% testing data and an f-measure value of 92% was obtained
Training on Village Website Management for Village Officials in Manimbahoi Village, Parigi District, Gowa Regency as Data Literacy Education: Pelatihan Pengelolaan Website Desa untuk Aparat Desa di Desa Manimbahoi, Kecamatan Parigi, Kabupaten Gowa sebagai Edukasi Literasi Data Ahmar, Ansari Saleh; Rais, Zulkifli; Iskandar, Akbar
Mattawang: Jurnal Pengabdian Masyarakat Vol. 4 No. 3 (2023)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.mattawang1993

Abstract

The training was held in the Meeting Room of Manimbahoi Village, Parigi District, Gowa Regency, South Sulawesi Province on August 6, 2023. The participants of the training were Manimbahoi Village officials, with the aim of understanding the importance of data to be published and also as an effort to educate village officials regarding data literacy. This service activity for Manimbahoi Village officials, in an effort to educate data literacy, runs smoothly, as expected. This can be seen from the increase in the ability and knowledge of village officials from not knowing about website management and publishing village news. Using this knowledge, village data can be published on village websites. Abstrak Pelatihan ini dilaksanakan di Ruang Pertemuan Desa Manimbahoi, Kecamatan Parigi, Kabupaten Gowa, Provinsi Sulawesi Selatan pada tanggal 6 Agustus 2023. Peserta dari pelatihan ini adalah aparat Desa Manimbahoi, dengan tujuan agar aparat desa tersebut paham mengenai pentingnya data untuk dipublikasikan dan juga sebagai upaya untuk edukasi aparat desa terkait literasi data. Kegiatan pengabdian untuk aparat Desa Manimbahoi ini sebagai upaya untuk edukasi literasi data berjalan lancer dan sesuai dengan yang diharapkan. Hal ini terlihat terjadinya peningkatan kemampuan dan pengetahuan aparat desa dari tidak tahu menjadi tahu pengelolaan website dan penerbitan berita desa. Dengan adanya pengetahuan ini maka data-data desa dapat dipublikasikan di website desa.
Application of Bisecting K-Means Method in Grouping Earthquake Data (Case Study: Earthquakes in Indonesia 2023) Rais, Zulkifli; Hafid, Hardianti; Risqi, Shopia
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23335

Abstract

Earthquakes are natural disasters that frequently occur in Indonesia, threatening the safety and resilience of its communities. This study aims to analyze the descriptive and clustering results of earthquake data in Indonesia. The data used in this study include various variables such as latitude, longitude, magnitude, and depth as the main features. The method used in this study is Bisecting K-means, and the Davies Bouldin Index test is used to determine the number of clusters. The study results indicate the formation of 3 groups, where cluster 1 falls into the deep earthquake category, cluster 3 falls into the intermediate earthquake category, and cluster 2 falls into the shallow earthquake category, with an average Davies-Bouldin Index value of 0.4758.
Co-Authors Ahmar, Ansari Saleh Aidid, Muhammad Kasim Akbar Iskandar Amira, Husnul Azrahwati Botto-Tobar, Miguel Fahmuddin S, Muhammad Hardianti Hafid Haris, A. Sulfiana Hasnita, Hasnita Indrayasaro Mar'ah, Zakiyah Nusrang, Muhammad Purnamasari, A. Bida Rahmat, Rahmat Wahyudi Refaldy, Muhammad Risqi, Shopia Ruliana Ruliana Ruliana, Ruliana Rusdianto Saida, Saida Sitti Masyitah Meliyana R. Sudarmin Sudarmin Sudarmin Suwardi Annas Syahputra, Akbar Taufiq Hidayat Triutomo, Agung Yuniar, Eka Citra Yusniar Rasjid, Yusniar