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All Journal MEDIA PETERNAKAN - Journal of Animal Science and Technology Agromet FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Media Statistika IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Scientific Journal of Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan ComTech: Computer, Mathematics and Engineering Applications Inferensi Jurnal Tanah dan Iklim Jurnal Natural Jurnal Teknik Informatika (JUTIF) Jurnal Natural Xplore: Journal of Statistics STATISTIKA Jurnal Veteriner dan Biomedis PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Statistics and Its Applications
Khairil Anwar Notodiputro
Department Of Statistics, IPB University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Social Sciences Transportation Veterinary Other

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Journal : PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS

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GLMM and GLMMTree for Modelling Poverty in Indonesia Suseno Bayu; Khairil Anwar Notodiputro; Bagus Sartono
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.333

Abstract

GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMMTree for the actual data applications of poverty in Indonesia and confirms that the GLMMTree algorithm method has better precision than GLMM. The significant predictors that affect poverty in Indonesia are the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding subgroups of provinces with electricity lighting access and clean drinking water sources variables.
Cost-Sensitive Boosting Algorithm for Classifying Underdeveloped Regions in Indonesia Bayu Suseno; Bagus Sartono; Khairil Anwar Notodiputro
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.373

Abstract

Imbalanced classes are indicated by having more instances of some classes than others. The cost-sensitive boosting algorithm is a modification of the AdaBoost algorithm, which aims to solve the problem of imbalanced classes. In this study, we evaluate the cost-sensitive Boosting algorithm AdaC2 using Indonesia's underdeveloped region's data. This study confirms that the cost-sensitive boosting algorithm (AdaC2) performs better in classifying the instances in the minority classes than standard classifiers algorithms.
Co-Authors . Barizi . SUTIKNO A. Bey Adlina Khairunnisa Agus Mohamad Soleh Aji H. Wigena Anang Kurnia Annisa Putri Utami ASEP SAEFUDDIN Asfar Aulya Permatasari Ayunda Pratiwi Azlam Nas Bagus Sartono Bayu Suseno Budi Susetyo Bukhari, Ari Shobri Cahyani Oktarina Christiana Anggraeni Putri D Diapari Dani Al Mahkya Dwi Jayanti Erfiani, Erfiani Etis Sunandi FAHREZAL ZUBEDI FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar H. M H Bintoro Hari Wijayanto Hari Wijayanto Hari Wijayanto Herdina Kuswari I Las I Made Sumertajaya I. LAS Indahwati Indahwati, Indahwati Intan Nirmala J Jachja Jajah K. Wagiono Kurnia, Anang Kusman Sadik Laily Nissa Atul Mualifah M S Saeni Muhammad Ilham Abidin Muhammad Nur Aidi Nurfadilah, Khalilah Pardomuan Robinson Sihombing Pratiwi, Windy Ayu R Boer R. BOER Rahardiantoro, Septian Rini Irfani Rizky Zulkarnain Sachnaz Desta Oktarina Septian P Palupi Sri Wahyuni Stephan Chen Suseno Bayu Tri Listianingrum Ulfah Sulistyowati Utami Dyah Syafitri Yekti Widyaningsih