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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Pelita Eksakta ILKOMNIKA: Journal of Computer Science and Applied Informatics GAUSS: Jurnal Pendidikan Matematika Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications
Zilrahmi, Zilrahmi
Universitas Negeri Padang
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences Other

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Journal : Rangkiang Mathematics Journal

 1 
Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia Fajrin Putra Hanifi; Syafriandi; Chairina Wirdiastuti; Nonong Amalita; Zilrahmi
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.75

Abstract

Inflation is defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable Inflation, defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable economic growth. The importance of controlling inflation is based on the consideration that high and unstable inflation hurts the socio-economic conditions of the community. In this context, government and economic agents must know the future inflation rate. The backpropagation algorithm forecasting method can be a mathematical tool to forecast future inflation rates. The best forecasting model is obtained from applying the backpropagation algorithm, namely ANN BP (12,2,1), with a mean square error value of 0.15 and an absolute percentage error value of 11.09%. Based on these results, the back-propagation algorithm in artificial neural networks can accurately forecast the inflation rate. Thus, it is hoped that this research can be used in economic decision-making.
Multidimensional Poverty Clustering using K-Means Algorithm with Dimensionaly Reduction by Principal Component Analysis Salma, Admi; Zilrahmi, Zilrahmi
Rangkiang Mathematics Journal Vol. 4 No. 2 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i2.101

Abstract

The level of Multidimensional poverty in each province in Indonesia varies, similar policies is ineffective to reduce the poverty. Several poverty indicators also influence other factors. General policies established to overcome poverty have proven ineffective, making it urgent to identify the needs of each province in overcoming this condition. Grouping provinces based on similar multidimensional poverty which use cluster analysis, will help address this situation. The aim of this study is to group provinces based on multidimensional poverty indicators using the k-means clustering method. Principal Component Analysis (PCA) was also used to reduce variables and multicollinearity. The clustering results showed seven clusters. The highest multidimensional poverty was found in cluster 2, which consisted of one province, namely Papua Pegunungan. This province shows deficiencies in education, health, and living standards compared to other clusters. Meanwhile, the lowest multidimensional poverty was found in cluster 7. There are three provinces in this cluster, namely Bali, Jakarta, and DIY Jogjakarta. These provinces experience minimal multidimensional poverty which is able to provide a better quality of life. The policies and development strategies in these provinces could serve as role models to develop other provinces based on their specific deficiencies and needs.   Each cluster is well separated, as Davies Bouldin Index (DB) is lover, at 0.4.
Co-Authors Adinda Dwi Putri Aditya, Muhammad Fadhil Aditya Admi Salma Admi Salma Afifa Lufti Insani Amanda, Abilya Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dony Permana Fadhilah Fitri Fadhillah Fitri Fadlan Rafly, Muhammad Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fitri Mudia Sari Fitri, Fadhilah fitri, silfia wisa Hamida, Zilfa Hari Wijayanto Hendrawan, Muhammad Ibnul farizi, Gilang Ichlas Djuazva Ihsanul Fikri Khasanah, Nurviqotun Khoirun Nisa Maharani, Listia Manja Danova Putri martha, Ully Martha Martia Rosada Meliani Putri Melin Wanike Ketrin Miftahurrahmi, Syifa Moh. Erkamim Muhammad Alif Yustin Muhammad Faisal Mukhti, Tessy Octavia Muslimah, Nailul Amani Mutiara Amazona Sosiawati nazhiroh, hanifah Nilda Yanti Nonong Amalita Nurdalia Nurwijayanti Permana, Dony Putri, Fadhira Vitasha Putri, Lathifa Putri, Sindy Amelia Rahmad Wanizal Pastha Rahmadani Iswat Rahmanesta, Frandito Rizal Bakri Rizqa Fajriaty Fitri MY Said Thaufik Rizaldi Salma, Admi Sepriano Sepriano Sulhatun Sulhatun Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Nabilah Wandira Tessy Octavia Mukhti Ulya Syafitri.J Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha