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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika UNP Journal of Statistics and Data Science d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Statistics and Its Applications
Zamahsary Martha
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Grouping Of Universities In Indonesia In 2025 Based On The Qs World University Rankings Ranking Indicator Using The Kohonen Self-Organizing Maps Algorithm Raihan Athaya Wudd; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/427

Abstract

Increasing the competitiveness of higher education is one of the main focuses in facing global competition. One of the important indicators in assessing the quality of higher education institutions is the QS World University Rankings which assesses universities based on indicators such as academic reputation, citations per lecturer, sustainability, and international collaboration. This study aims to group universities in Indonesia that are included in the QS World University Rankings in 2025 using the Kohonen Self-Organizing Maps (SOM) algorithm. The data used consisted of 10 QS assessment indicators for 26 universities in Indonesia. The normalization process is carried out using the min-max method, and the optimal number of clusters is determined using internal validation indices such as Connectivity, Dunn, and Silhouette. The results of the analysis show that the best models form three main clusters. Cluster 1 contains universities with superior performance in reputation and research, cluster 2 contains universities with a fairly balanced medium performance, and cluster 3 consists of universities with low performance in key indicators. The results of this study are expected to be the basis for policy makers and university managers to develop strategies to improve the quality of higher education in a targeted manner.
Classification of Recipients of the Family Hope Program in West Sumatra Province Using the Random Forest Algoritma Nini Erdiani; Dwi Sulistiowati; Nonong Amalita; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/431

Abstract

According to the Central Statistics Agency (BPS), the percentage of poor people in West Sumatra Province increased by 0.02% in 2024. One of the government's efforts to overcome poverty is a social assistance program issued by the government to help people who are economically disadvantaged. The targeted distribution of social assistance is an important challenge in improving community welfare, especially for families receiving PKH benefits. This study aims to classify households receiving the Family Hope Program (PKH) in West Sumatra Province using a random forest algorithm with Synthetic Minority Oversampling Technique (SMOTE). This study uses data on PKH recipient households in West Sumatra Province in 2024, which has a significant class imbalance. Therefore, the SMOTE method was applied to balance the data. The data was divided into training and testing data with a ratio of 80%:20%, then parameter tuning was performed to optimize mtry and ntree. The model was evaluated using a confusion matrix to compare model performance. The results show that the accuracy obtained is 76%. The precision value is 72%, the recall is 84%, and the f1-score is 78%. Based on the Mean Decrease Gini value, the head of household's diploma became the main attribute in determining whether a household received PKH or not. This study concluded that the use of SMOTE in the random forest algorithm performed well in classifying PKH recipients in West Sumatra Province, where the model performed well and was quite reliable in identifying PKH recipients.
Comparison of K-Means and Ward Methods in Clustering Indonesian Provinces Based on Household Basic Service Access Nurul Mulya; Fajri Juli Rahman Nur Zendrato; Muhammad Arief Rivano; Zamahsary Martha; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss1/449

Abstract

Disparities in household basic service access across provinces in Indonesia remain a key issue in regional development. Basic services such as access to improved drinking water, proper sanitation, electricity, and adequate housing are essential indicators of household welfare, making regional classification necessary to identify similarities and disparities among provinces. This study aims to cluster Indonesian provinces based on household basic service access indicators and to compare the performance of the K-Means method and Hierarchical Clustering using the Ward approach. The analysis was conducted using numerical data with Euclidean distance as a measure of similarity. The optimal number of clusters was determined using the Silhouette plot and further validated using the Silhouette Coefficient. The results indicate that both K-Means and Ward methods produce two optimal clusters representing provinces with relatively high and relatively low levels of household basic service access. Centroid analysis reveals clear differences between clusters across all indicators, particularly in electricity access and sanitation. Furthermore, the evaluation of clustering quality shows that the Ward method yields a higher Silhouette Coefficient than the K-Means method, indicating more compact clusters and better separation between clusters. Therefore, the Ward method is considered more effective in mapping patterns of household basic service access across provinces. The findings of this study can support regional planning by providing a clearer understanding of disparities in household basic service access in Indonesia.
Comparison of Agglomerative Hierarchical Clustering Methods for Grouping Indonesian Provinces Based on Community Literacy Development Index Olga Afrilly Putri; Bunga Nafandra; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss1/470

Abstract

Community literacy development is one of the important indicators in improving the quality of human resources in Indonesia. This study aims to group provinces in Indonesia based on the Community Literacy Development Index by considering the equity of library services, the adequacy of library collections, and the level of community visits per day. The method used is agglomerative hierarchical cluster analysis. Before grouping, the data is standardized to overcome differences in units and scales between variables. The selection of the best cluster method is done using the cophenetic correlation coefficient, while the determination of the optimal number of clusters uses the silhouette method. The results of the analysis show that the Average Linkage method is the most optimal hierarchical cluster method with the best number of clusters being four clusters. Each cluster has different characteristics, reflecting variations in community literacy levels, service equity, collection adequacy, and library visit intensity between provinces. These findings indicate disparities in community literacy development between regions in Indonesia. Therefore, the results of this study are expected to serve as a basis for consideration in formulating more effective and targeted literacy and library development policies.
Co-Authors Afifa Lufti Insani Al Aziz, Saddam Anjelisni, Nining Annisa Ramadhani Annisa Rizki Amalia april leniati Arssita Nur Muharromah Asra Dinul Haq Atus Amadi Putra Aurumnisva Faturrahmi Bimbim Oktaviandi Bunga Nafandra Desi Rahmawati Dina Fitria Dina Fitria Dina Fitria, Dina Dio Afdal Putra Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Dzakyyah Rahma Fadhilah Fitri Fadhilah Fitri Fadhillah Meisya Carina Fajri Juli Rahman Nur Zendrato Febi Febiola Putri Fitri, Fadhilah Futiah Salsabila Gezi Fajri Hefiani Mustika Hasanah Khairisa Putri, Nadya Lubis, Riskiani Maulidya Hernanda Muhamad Rayhan Nanda P Muhammad Arief Rivano Muharromah, Arssita Nur Mukthi, Tessy Octavia Nadha Ovella Syaqhasdy natasyalinggaa Natasya Dwi Ovalingga Nazifatul Azizah Nindi Syahfitrri Nini Erdiani Nonong Amalita Novi, Yoli Marda Nurul Mulya Oktaviani, Bernadita Olga Afrilly Putri Permana, Dony Rahmad Wanizal Pastha Rahmadani Rahmadani Rahmanesta, Frandito Raihan Athaya Wudd Riska 01 Salma, Admi silfia wisa fitri Suherman, Suherman Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Wahda Aulia Assara Yenni Kurniawati Yoli Marda Novi Yonggi Septa Pramadia Yonggi Zilrahmi, Zilrahmi