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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Padjadjaran Journal of Dentistry Jurnal Matematika Sains dan Teknologi Journal of Regional and Rural Development Planning BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Inferensi International Journal of Advances in Data and Information Systems Jurnal Statistika dan Aplikasinya Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Natural Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Yenni Angraini
Department Of Statistics And Data Science, Faculty Of Mathematics And Natural Science, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

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K-Means Optimization Algorithm to Improve Cluster Quality on Sparse Data Yully Sofyah Waode; Anang Kurnia; Yenni Angraini
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3936

Abstract

The aim of this research is clustering sparse data using various K-Means optimization algorithms. Sparse data used in this research came from Citampi Stories game reviews on Google Play Store. This research method are Density Based Spatial Clustering of Applications with Noise-Kmeans (DB-Kmeans), Particle Swarm Optimization-Kmeans (PSO-Kmeans), and Robust Sparse Kmeans Clustering (RSKC) which are evaluated using the silhouette score. Clustering sparse data presented a challenge as it could complicate the analysis process, leading to suboptimal or non-representative results. To address this challenge, the research employed an approach that involved dividing the data based on the number of terms in three different scenarios to reduce sparsity. The results of this research showed that DB-Kmeans had the potential to enhance clustering quality across most data scenarios. Additionally, this research found that dividing data based on the number of terms could effectively mitigate sparsity, significantly influencing the optimization of topic formation within each cluster. The conclusion of this research is that this approach is effective in enhancing the quality of clustering for sparse data, providing more diverse and easily interpretable information. The results of this research could be valuable for developers seeking to understand user preferences and enhance game quality.
Latent Household Food Security in Raja Ampat Marine Protected Areas: A Binary CFA Approach Indah Ratih Anggriyani; I Made Sumertajaya; Khairil Anwar Notodiputro; Yenni Angraini
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40979

Abstract

This study examines household food security in four marine protected areas in Raja Ampat using repeated cross-sectional household survey data. Data were collected between 2010 to 2024, grouped into five monitoring periods. This study aims to provide a measurement framework for household food security as a latent construct based on binary indicators representing dimensions of food access and to estimate latent household food security scores in the four analyzed areas. In addition to applying confirmatory factor analysis to new empirical data, this study also presents a systematic estimation framework for measuring the latent construct using binary household indicators in repeated cross-sectoral survey data. The framework includes indicator threshold estimation, tetrachoric correlation estimation, parameter estimation using the robust diagonally weighted least squares method, and derivation of latent scores based on posterior expectations using the Gauss–Hermite quadrature approach. The analysis results indicate that the one-factor model provides acceptable fit and adequate construct reliability across the analyzed area-period groups. Estimates of factor loadings and thresholds provide information on the relative contribution and severity of each indicator in representing variations in household food access conditions. Overall, the goodness-of-fit indices indicate that the one-factor structure provides a reasonable representation of the relationships among the observed indicators under the fitted measurement model.
A Comparative Study of Generalized Linear Mixed Model and Mixed Effects Random Forest for Analyzing Data with Outliers Arianti, Reza; Notodiputro, Khairil Anwar; Angraini, Yenni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5407

Abstract

This study compares MERF and GLMM-NB in analyzing hierarchical data and focusing on the role of residual outliers and the application of winsorization. A two-stage analytical pipeline was implemented: (1) winsorization to reduce extreme residual values, and (2) model training using MERF and GLMM-NB. The dataset comes from the 2021 National Socio-Economic Survey (Susenas) in West Java Province, measuring tobacco consumption intensity. Two statistical approaches are compared, MERF and GLMM with a Negative Binomial distribution (GLMM-NB). Models were trained under two conditions: without winsorization (WIN0) and with two-sided 5% winsorization (WIN5). Winsorization was applied to the training data, and the test data were adjusted using thresholds from the training set. Model performance was assessed using Root Mean Squared Error (RMSE) and the train-test ratio. Under WIN0, GLMM recorded an RMSE of 49.65 for training and 42.27 for testing, while MERF achieved 35.96 and 39.94, respectively. After WIN5, GLMM showed a larger error reduction, with RMSE values of 34.90 (train) and 30.20 (test), while MERF dropped to 26.63 (train) and 28.64 (test). These results indicate that MERF provides higher predictive accuracy, whereas GLMM benefits more from winsorization. Household expenditure, employment status, age, and gender consistently emerged as key variables linked to tobacco consumption intensity. This study is the first to compare MERF and GLMM-NB with winsorization using Indonesia’s hierarchical data. The analytical framework helps inform public health policies aligned with SDG 3: Good Health and Well-being, particularly in reducing tobacco-related health risks.
Co-Authors Aam Alamudi Achmad Noerkhaerin Putra Adelia Putri Pangestika Akbar Rizki Akbar Rizki Al Maida, Mahda Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Anang Kurnia Andika Putri Ratnasari Anisa, Rahma Anistia Iswari Antique Yusuf, Rakesha Putra Arbaynah, Siti Arianti, Reza Ariesanti, Yessy ASEP SAEFUDDIN Azahran, Muhammad Ryan Azkiya, Azka Al Bagus Sartono Berliana Apriyanti Billy, Billy Cintani, Meavi Dian Kusumaningrum Dzulhij Rizki, Muhammad Abshor Eka Dewi Pertiwi Else Virdiani Fachry Abda El Rahman Fadhilah, Nur Anggraini Fadillah, Maulana Ahsan Fira Nurahmah Al Aminy Fitri, Zafira Ilma Fitrianti, Dwi Fitrianto, Anwar Ghiffary, Ghardapaty Ghaly Gunawan, Windi Hakim, Bashir Ammar Hari Wijayanto Hasanah, Mauizatun Hilali Moh’d, Fatma I Made Sumertajaya Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indah Ratih Anggriyani Indahwati Isnaini, Mardatunnisa Itasia Dina Sulvianti Jamaluddin Rabbani Harahap Kenia Maulidia Kurnadipare, Aleytha Ilahnugrah Kusman Sadik Lia Ratih Kusuma Dewi Magfirrah, Indah Maghfiroh, Firda Aulia Mahesa Ahmad Rahmawan Mahesa, Hakim Zoelva Maulidiyah, Wildatul Moh'd, Fatma Hilali Mohammad Abror Gustiansyah Mohammad Masjkur Mualifah, Laily Nissa Atul Mualifah, Laily Nissa Atul  MY, Hadyanti Utami Nabila Ghoni Trisno Hidayatulloh Nabila Ghoni Trisno Hidayatulloh Nensi, Andi Illa Erviani Nickyta Shavira Maharani Nizar, Yeky Abil Nugraha, Adhiyatma Nur Aziza, Vivin Nurhambali, Muhammad Rizky Oksi Al Hadi Oktaviani Aisyah Putri Pratiwi, Windy Ayu Putri Zainal Putri, Adelia Putri, Mega Ramatika Putri, Rizki Alifah Raffael Julio Roger Roa Rahmasari, Hazelita Dwi Rahmi, Salsabila Dwi Ramadhani, Dini Ramdani, Indri Riana Riskinandini Riska Yulianti, Riska Rizki, Akbar Rizki, Anwar Fajar Setyowati, Silfiana Lis Siregar, Indra Rivaldi Steven Kurniawan Suci Pujiani Prahesti Suwarso, Dhiya Khalishah Tsany Syam, Ummul Auliyah Tendi Ferdian Diputra Tias Amalia Safitri Tsabitah, Dhiya Tsabitah, Dhiya Ulayya Ulfia, Ratu Risha Utami Dyah Syafitri Wahyudina, Salsa Putri Wiwiek Poedjiastoeti, Wiwiek Wiwik Andriyani Lestari Ningsih Wiwik Andriyani Lestari Ningsih Yanuari, Eka Dicky Darmawan Yully Sofyah Waode Zulhijrah, Zulhijrah