Article Per Year (5 Year)
p-Index From 2021 - 2026
7.445
P-Index
This Author published in this journals
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
Department Of Statistics, 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

Published : 59 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 59 Documents
Search

 2   3   4   5   6 
STUDY ON EMD METHOD FOR PREDICTING THE PRICE OF CURLY RED CHILI IN INDONESIA Zilrahmi Zilrahmi; Hari Wijayanto; Farit M Afendi; Rizal Bakri
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i2.600

Abstract

The fluctuations of curly red chili price affect the inflation rate in Indonesia. So that, the basic characteristics of price movement and correctly prediction for curly red chili price become concern in various studies. Empirical Mode Decomposition (EMD) method helps to examine behavioral characteristics of curly red chili prices in Indonesia easily. Ensemble EMD (EEMD) and modified EEMD are the decomposition method of time series which is development of EMD method. The decomposed data with EMD methods can also used for price forecast. The forecasting with ARIMA and trend polynomial performed to assess the effect of decomposition with EMD methods for forecast stability of curly red chili price in Indonesia under various conditions. The results show the most influence factor for price fluctuation of curly red chili in Indonesia is season and growing season. In this case, the ability of a decomposition method to produce the actual components that describe the pattern of data signals affect the accuracy of the predicted value obtained using the model. The predicted value using the decomposed data by modified EEMD always better than EEMD on the overall condition.
Comparison of The Singular Spectrum Analysis and SARIMA for Forecasting Rainfall in Padang Panjang City Fadhira Vitasha Putri; Fadhilah Fitri; Yenni Kurniawati; Zilrahmi Zilrahmi
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p61-74

Abstract

Indonesia is an area with a tropical climate, so it has two seasons, namely the rainy season and the dry season. The rainy season lasts from November to March and during this period rainfall tends to be high in several areas. Padang Panjang City is one of the cities with the smallest area in West Sumatra Province, which has the nickname Rain City. This is because the city of Padang Panjang has cool air with a maximum air temperature of 26.1 °C and a minimum of 21.8 °C, so this city has a fairly high level of rainfall with an average of 300 to 400 mm/year. This article discusses rainfall forecasting for Padang Panjang City by comparing the Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average methods. The data used spans 8 years, from January 2016 to December 2023. Forecasting results are obtained from the best method selected based on the smallest Mean Absolute Percentage Error value. The Singular Spectrum Analysis method has a Mean Absolute Percentage Error value of 5.59% and Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average  has a value 7.43%. The best forecasting method is obtained by the Singular Spectrum Analysis method.
Implementation of Fuzzy C-Means Algorithm for Clustering Provinces in Indonesia Based on Micro and Small Industry Ratio in Village Areas Frandito Rahmanesta; Zamahsary Martha; Dodi Vionanda; Zilrahmi Zilrahmi
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p178-190

Abstract

Post-economic crisis, the micro and small industries contribute the most labor compared to other industries. Regional development sourced from small micro industries is a strategic force in developing a country because the development of small micro industries leads to realizing equitable welfare to reduce income inequality. Development in village areas is an important factor for regional development, reducing inequality between regions, and alleviating poverty. However, based on the 2018 PODES survey, there are regional imbalances in Indonesia in the small micro industry which is centralized on Java Island. Therefore, clustering and characteristics of the province were carried out based on the PODES survey of the small micro industry sector. This research uses the Fuzzy C-Means algorithm to cluster 34 provinces in Indonesia based on the ratio of small micro industries in village areas in 2021, to see how the development of small micro industries in village areas in each province in Indonesia. Fuzzy C-Means is one of the data clustering techniques that uses a fuzzy clustering model, where cluster formation is based on a membership degree value that varies between 0 and 1. The Fuzzy C-Means algorithm generates 4 clusters, cluster 1 and 2 represents provinces with high and very high micro and small industry development in village areas and cluster 3 and 4 represents provinces with medium and low micro and small industry development in village areas. The Fuzzy C-Means algorithm produces a good cluster structure with a silhouette coefficient value of 0,6406.
Application of Singular Spectrum Analysis in Predicting Rupiah Exchange Yuan Muhammad Hendrawan; Zilrahmi Zilrahmi; Yenni Kurniawati; Dina Fitria
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p75-85

Abstract

The exchange rate between two countries is the price of the currency used by residents of these countries to trade with each other, the relationship between the Rupiah exchange rate and the Yuan is one of the important aspects in the dynamics of international trade. Therefore, forecasting the exchange rate is important as an effort to predict the exchange rate of Rupiah against Yuan in the future. The method used for forecasting is Singular Spectrum Analysis, namely decomposition and reconstruction. The accuracy of the resulting forecast is measured using the Mean Absolute Percentage Error criterion. The exploration results obtained are forecasting accuracy based on the Mean Absolute Percentage Error value of 2.15% with a window length of 23 which identifies that the forecasting results are accurate and effective. Forecasting is said to be accurate if the Mean Absolute Percentage Error value is lower than 10% and close to 10%
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 Admi Salma; 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.
Penerapan Metode Multivariate Adaptive Regression Spline untuk Memahami Dinamika Kemiskinan di Indonesia Nurviqotun Khasanah; Zilrahmi; Syafriandi
GAUSS: Jurnal Pendidikan Matematika Vol. 8 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/gauss.v8i1.10569

Abstract

Abstrak Kemiskinan masih menjadi tantangan besar bagi pembangunan di negara berkembang, khususnya Indonesia. Berbagai faktor seperti pendidikan, kesehatan dan pendapatan masyarakat diketahui mempengaruhi tingkat kemiskinan, namun hubungan antar faktor tidak sederhana. Studi ini dilakukan untuk memprediksi Presentase Penduduk Miskin Di Indonesia berdasarkan faktor sosial ekonomi menggunakan metode Mulitivariate Adaptive Regression Spline yang mampu menangkap hubungan nonlinear dan interaksi antar variabel. Penelitian menggunakan Data dan Informasi Kemiskinan Kab/Kota di Indonesia Tahun 2023 dari publikasi Badan Pusat Statistik (BPS) yang telah melalui proses Pre-processing data. Model terbaik dibangun dari 0.8 data training dan 0.2 data testing dengan kombinasi BF=26, MI=3, MO=1 dengan Generalized Cross Validation (GCV) terkecil sebesar 0.160211 dan dari 13 variabel prediktor yang diteliti menunjukkan bahwa variabel Persentase Pengeluaran Rata-Rata per Orang untuk Makanan Kategori Miskin dan Tidak Miskin (X5) dan variabel Persentase Pengeluaran Rata-Rata per Orang untuk Makanan Kategori Miskin dan Tidak Miskin (X6) yang mempunyai skor tertinggi sebesar 100% untuk menurunkan nilai GCV model dan menurunkan Residual Sum of Squares (RSS) pada model. Selain itu, model MARS mampu menjelaskan variasi tingkat kemiskinan dengan nilai R-squared sebesar 83,7% yang mengidentifikasikan prediksi cukup akurat. Kata kunci : Kemiskinan, MARS, GCV Abstract Poverty remains a major challenge for development in developing countries, especially Indonesia. Various factors such as education, health and income are known to affect the poverty rate, but the relationship between factors is not simple. This study aims to predict the percentage of poor people in Indonesia based on socioeconomic factors using the Mulitivariate Adaptive Regression Spline method which is able to capture nonlinear relationships and interactions between variables. The research uses data and information on poverty in districts / cities in Indonesia in 2023 obtained from the Central Statistics Agency (BPS) which has gone through a process of cleaning, standardisation and handling outliers. The best model was built from 0.8 training data and 0.2 testing data with a combination of BF=26, MI=3, MO=1 with the smallest Generalised Cross Validation (GCV) of 0.160211 and of the 13 predictor variables studied showed that the variable Percentage of Average Expenditure per Person on Food for Poor and Non-Poor Categories (X5) and the variable Percentage of Average Expenditure per Person on Food for Poor and Non-Poor Categories (X6) which had the highest score of 100% to reduce the GCV value of the model and reduce the Residual Sum of Squares (RSS) in the model. In addition, the MARS model is able to explain the variation in poverty rates with an R-squared value of 83.7%, which identifies a fairly accurate prediction. Keywords: Poverty, MARS, GCV
Extended Cox Model for Analyzing Factors Influencing Time to First Employment After Graduation in West Sumatra M. Anfasa Prana Karil; Zilrahmi; Rita Diana; Tessy Octavia Mukhti; Dina Fitria; Retno Lis Megawati
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (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-iss3/587

Abstract

The transition from education to employment has become one of the employment challenges in West Sumatra Province. This study aims to analyze the factors affecting the duration of obtaining a first job using the Extended Cox Proportional Hazard model. The data used were obtained from the August 2025 National Labor Force Survey (Sakernas) with variables including age, gender, educational attainment, regional classification, training, and work experience. The results show that age, educational attainment, and regional classification significantly affect the duration of obtaining a first job. Age has a positive effect that decreases over time, while higher educational attainment tends to increase job waiting time. Individuals living in rural areas tend to obtain jobs faster than those in urban areas. Meanwhile, gender and work experience are not significant, whereas training is significant through its interaction with time. Overall, the duration of obtaining a first job is influenced by individual factors, regional characteristics, and time-varying effects of the variables
Classification of Toddler Stunting Status Using Naïve Bayes Classifier with K-Fold Cross Validation Vania Riski Afifah; Zilrahmi; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (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-iss3/558

Abstract

The growth of toddlers that doesn’t meet age standards can affect their quality of life in the future. In Indonesia, one of the nutritional problems that continues to receive significant attention is stunting, which is generally indicated by a mismatch between a child's height and age (height-for-age, HAZ/TB/U). Considering that determining stunting status requires a high level of accuracy, a data-driven approach is needed to support the identification and evaluation of children's nutritional conditions in a more systematic manner. This study aims to classify stunting status among toddlers using the Naïve Bayes Classifier (NBC) algorithm with a 10-Fold Cross Validation method. NBC was selected because it is simple, efficient, and suitable for probability-based classification of health data. The data were obtained from the Health Office of Pasaman Regency in 2022. The variables used include gender, birth weight, birth height, age at measurement, body weight, body height, Mid-Upper Arm Circumference (MUAC), and height-for-age (HAZ) status. Stunting status was divided into two categories: stunted and severely stunted. The analysis showed that 53.91% of toddlers were classified as stunted, while 46.10% were classified as severely stunted. Based on the evaluation using 10-Fold Cross Validation, the NBC model showed good performance with an accuracy of 86,26%, precision of 73,33%, recall of 68,75%, and an F1-score of 70,97%. The analysis also showed that height, weight, and MUAC at the time of measurement were the characteristics that most clearly distinguished the stunted and severely stunted categories. These findings indicate that anthropometric indicators can support the detection and monitoring of stunting among toddlers. Overall, this study is expected to help health workers identify toddlers who require further nutritional assessment and support more targeted stunting management efforts.
Co-Authors Abilya Amanda Adinda Dwi Putri Afifa Lufti Insani Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Devi Yopita Sipayung Dila Sari Dina Fitria Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Dony Permana Dwi Sulistiowati Fadhilah Fitri Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhira Vitasha Putri Fajri Juli Rahman Nur Zendrato Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fedisha Elfiri Fedisha Fitri Mudia Sari Fitri, Fadhilah Frandito Rahmanesta Gilang Ibnul farizi Hadid Habiburrahman Hamida, Zilfa Hanifah Nazhiroh Hari Wijayanto Ichlas Djuazva Ihsanul Fikri Khoirun Nisa Lathifa Putri Listia Maharani M. Anfasa Prana Karil Manja Danova Putri Martia Rosada Meliani Maya Sari Meliani Putri Melin Wanike Ketrin Mellisa Ayuningtyas Moh. Erkamim Muhammad Alif Yustin Muhammad Fadhil Aditya Aditya Muhammad Fadlan Rafly Muhammad Faisal Muhammad Hendrawan Muslimah, Nailul Amani Mutiara Amazona Sosiawati Naila Marettania Nilda Yanti Nonong Amalita Nurdalia Nurviqotun Khasanah Nurwijayanti Permana, Dony Rahmad Wanizal Pastha Rahmadani Iswat Retno Lis Megawati Rita Diana Rizal Bakri Rizqa Fajriaty Fitri MY Said Thaufik Rizaldi Salma, Admi Sepriano Sepriano silfia wisa fitri Sindy Amelia Putri Sri Wahyu suci Sulhatun Sulhatun Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Miftahurrahmi Syifa Nabilah Wandira Tessy Octavia Mukhti Tessy Octavia Mukhti Ully Martha martha Ulya Syafitri.J Vania Riski Afifah Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha Zamahsary Martha