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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal Ibriez : Jurnal Kependidikan Dasar Islam Berbasis Sains BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Variance : Journal of Statistics and Its Applications Jurnal Varian Proceedings of Annual Conference for Muslim Scholars Jurnal Statistika dan Aplikasinya Parameter: Journal of Statistics WISDOM: Jurnal Pendidikan Anak Usia Dini Journal of Research Applications in Community Services Eksponensial STATISTIKA
M. Fathurahman
Universitas Mulawarman
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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An Implementation of K-Medoids Method in Provincial ClusteringBased on Education Indicator Data Apridayanti, Annisa Zuhri; Fathurahman, M; Prangga, Surya
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3205

Abstract

Data mining is searching for interesting patterns or information by selecting data using specific techniques or methods. One method that can be used in data mining is K-Medoids. K-Medoids is a method used to group objects into a cluster. This research aimed to obtain the optimal number of clusters using the K-Medoids method based on Davies-Bouldin Index (DBI) validity on education indicators data by province in Indonesia in 2021. The results showed that the optimal number of clusters using the K-Medoids method based on DBI validity is 5 clusters. Cluster 1 consists of 1 province with a higher average dropout rate, average length of schooling, and well-owned classrooms compared to other clusters. Cluster 2 consists of 15 provinces with an average proportion of school libraries lower than Clusters 3 and 4 and higher than Clusters 1 and 5. Cluster 3 consists of 9 provinces with an average proportion of school libraries, proportions of school laboratories, net enrollment rates, and higher school enrollment rates than other clusters. Cluster 4 consists of 8 provinces with a higher average enrollment rate than the other clusters. Cluster 5 consists of 1 province with a higher average repetition rate and student-per-teacher ratio than other clusters.
Prediksi Curah Hujan di Kabupaten Berau Menggunakan Support Vector Regression Patiallo, Surya Randang; Fathurahman, M.; Prangga, Surya; Nadhilah Widyaningrum, Erlyne
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/2098zg59

Abstract

Machine learning is an analytical approach that is able to predict the output of a system based on patterns that have been formed from previous data. One of the machine learning methods used in this research is Support Vector Regression (SVR). SVR is the application of the support vector machine method in the case of regression. The concept of the SVR algorithm is to obtain a function with the minimum error rate so as to produce a good predictive value. The advantage of SVR lies in its ability to handle nonlinear data using the kernel functions. This study aims to determine the results of rainfall prediction in Berau Regency using the SVR method. The data used is rainfall data in Berau Regency from January 2014 to December 2023 as much as 120 data, and uses five predictor variables namely temperature, humidity, air pressure, wind speed, and solar irradiation. The kernel function used is a polynomial kernel with parameter values  and . The results showed that the best SVR model to predict rainfall in Berau Regency is the SVR model with parameter values  and . This model provides good prediction performance, with an RMSE value of 0,1786.
Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indeks kemahalan Konstruksi Tahun 2020-2024  Menggunakan Algoritma Spatio Temporal-DBSCAN Irfan, Muh.; Suyitno, Suyitno; Fathurahman, M.
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/76nh3g16

Abstract

The Construction Cost Index (CCI) is an indicator that describes the level of cost of construction in a region compared to the national average. The CCI between districts/cities in Kalimantan Island in 2024 still shows a considerable difference. To understand the pattern and similarity of CCI values between districts/cities, a clustering approach is needed. Clustering is a data analysis technique to group data based on similarity. The clustering algorithm used in this research is the Spatio Temporal Density Based on Spatial Clustering of Application with Noise (Spatio Temporal-DBSCAN) algorithm which forms clusters based on density in spatial and temporal aspects simultaneously. The purpose of this study is to obtain the optimal cluster in clustering districts/cities on Kalimantan Island based on spatial aspects (longitude and latitude data) and temporal aspects (IKK value from 2020-2024) based on the Silhouette Coefficient (SC) value of the Eps and MinPts combinations that were tried. Based on the clustering results, 2 clusters and also noise were obtained from the combination of Eps1=2, Eps2=13 and MinPts=8 with an SC value of 0.3179 which means that the optimal cluster formed has a weak structure.
Co-Authors Abidin, Ahmad Aliful Aisha, Putri Andrea Tri Rian Dani Annisa Zuhri Apridayanti Apridayanti, Annisa Zuhri Arif, Zainul Astafira, Ilyas Dani, Andrea Tri Rian Darnah Darnah, Darnah Dini Elizabeth Erlyne Nadhilah Widyaningrum Fachrian Bimantoro Putra Fauziyah, Meirinda Goenjatoro, Rito Hasanah, Siti Hadijah Ika Purnamasari Khairun Nida Khasanah, Lisa Dwi Nurul Meiliyani Siringoringo Meirinda Fauziyah Memi Nor Hayati Memi Nor Hayati Muh. Irfan Murdani, Endah Mulia Nariza Wanti Wulan Sari Ni'matuzzahroh, Ludia Ningsih, Eva Lestari Nurmayanti, Wiwit Pura Oroh, Chiko Zet Orvanita Patiallo, Surya Randang Putra, Fachrian Bimantoro Putri, Ditha Reginna Ramadhini, Laila Thalia Rasidia, Fikri Ratna Nila Puspitasari Regita Putri Permata Rito Goejantoro, Rito Roejantoro, Rito Rut Esra Sekar Nur Utami Sifriyani Sifriyani Sinaga, Julia Oriana Siringoringo, Meiliyani Sri Wahyuningsih Surya Prangga Suryaningsih, Ruliq Suyitno Suyitno Suyitno Suyitno Suyono, Ari Krisna Umi Kalsum Yusrian Paliling