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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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Classification the Characteristics of Traffic Accident Victims in Pariaman Using the Chi-square Automatic Interaction Detection Algorithm Manja Danova Putri; Dina Fitria; Yenni Kurniawati; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): 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/vol2-iss1/127

Abstract

Traffic accidents are incidents that occur when motor vehicles collide on the road, resulting in damage to vehicles and road infrastructure, as well as the potential for material losses, injuries, physical damage, and even death for those involved. Data from the Indonesian National Police show that the number of traffic accident victims between 2010 and 2020 ranged from 147.798 to 197.560 people, with fatalities predominantly occurring among individuals aged 15-34. The high number of traffic accident victims has negative impacts on various aspects of life, ranging from material losses to physical damage to the victims. Classification is a technique used to group objects or data into pre-defined classes or categories based on their attributes or features. One method in the field of classification is Chi-Square Automatic Interaction Detection (CHAID). The results of the classification using this method indicate that the age of the victims and the type of accident are the most significant variables influencing the condition of traffic accident victims. The evaluation of the model using a confusion matrix yielded an accuracy rate of 92%. This indicates that the model performs well in overall data classification.
Prediction of Palm Oil Production Results PT.KSI South Solok Using Ensemble k-Nearest Neighbor Nilda Yanti; Atus Amadi Putra; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): 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/vol2-iss1/136

Abstract

PT. KSI experienced production decrease that the cause of replanting that happened in 2022. In managing palm oil production PT. KSI has problems with palm oil production results not reaching out the targets so it can affect the Company's Work Plan and Budget, therefore it is very necessary to predict palm oil production results so that all palm oil production and processing activities can run according to plan. The ensemble technique is a method that is capable of making accurate predictions and is used very effectively in the kNN method, therefore there is no need to search for the best k value.Based on the results of the analysis that has been carried out, it can be seen that by using an ensemble the level of accuracy is 9.36%, which is considered high accuracy compared to just using a single kNN with k = 1 of 10.84%. So it can be concluded that the model has worked well with the data.
Implementation of an Artificial Neural Network Based on the Backpropagation Algorithm in Forecasting the Closing Price of the Jakarta Composite Index (IHSG) Muhammad Fadhil Aditya Aditya; Zilrahmi; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): 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/vol2-iss1/137

Abstract

Investing is highly common in Indonesia. Continuous investment activities carried out by the community will increase economic activity and employment opportunities, increase national income, and increase the level of prosperity of the community. In carrying out share buying and selling transactions, there is a means for companies to obtain funds from official financiers or investors, which is called the capital market. One of the indices issued by the IDX is the Jakarta Composite Index (IHSG). Statistics can be used to help investors, the government, or related institutions to predict the value of the IHSG. One method that can be used to predict data is an Artificial Neural Network (ANN). Backpropagation method is a multi-layer ANN method that works in a supervised learning. The idea of the Backpropagation algorithm is that the input of the neural network is evaluated against the desired output results. The purpose of this research is to give forecasting values with high accuracy to describe the movement of IHSG close price values using the ANN method based on the Backpropagation algorithm. The research showed that the BP (4,6,1) model produced an RMSE value of 28,24024 and a MAPE value of 0.00342%. Based on the results of this research, an Artificial Neural Network model based on the Backpropagation Algorithm can be applied to predict the IHSG Closing Price value.
Klasifikasi Karies Gigi di Rumah Sakit Gigi dan Mulut Baiturrahmah Menggunakan Metode Random Forest Martia Rosada; Zilrahmi; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): 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/vol2-iss2/155

Abstract

The mouth cavity is the main gate through which germs and bacteria enter. Therefore, it is important to maintain oral hygiene. When dental and oral hygiene is not maintained it will cause dental and oral problems or diseases such as periodontitis, dental caries, tooth abscess, gingivitis and other dental and oral health problems. The dental and oral problems that many people experience are caries or cavities. West Sumatra itself has a fairly high prevalence of dental caries. Prevention of dental caries needs to be done by making the public aware of dental and oral hygiene in order to reduce the problem of dental caries in West Sumatra. Therefore, it is necessary to have a method that is able to classify dental caries based on its symptoms. The classification method is very useful for knowing the main factors that cause dental caries. One classification method that can be used is random forest. Random forest is an ensemble method, namely the development of several methods using bootstrap sampling. The results of this research use the smallest OOB level and the Variable Importance Measure (VIM). Random forest classification using dental and oral pain medical record data at Baiturrahmah Padang Hospital produces an OOB error rate of 32.08% or an accuracy rate of 67.92%. The optimal model is obtained using mtry=2 and ntree=200. From this research it can be concluded that dental plaque, age, and tooth brushing habits are the importance variables or main factors that influence dental caries.
Classification of Poor Households in West Sumatra Province using Decision Tree Algorithm C4.5 Dinda Fitriza; Atus Amadi Putra; Dodi Vionanda; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): 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/vol2-iss2/157

Abstract

The significant and increasingly complex issue of poverty poses a considerable challenge to Indonesia's development, including West Sumatra Province, with a poverty rate was 5.92% in 2022. The government has initiated programs to address poverty by focusing on the criteria of impoverished households. Data on impoverished households can be obtained through the National Socio-Economic Survey (Susenas). One method that can classify impoverished households is the decision tree. Decision tree is a flowchart that resembles a tree. The C4.5 algorithm used in this research has the ability handle discrete and continuous data, manage variables with missing values, and prune decision tree branches. The result of the analysis shows that the variables affecting the classification of poor households are the number of household members, then the age of the household head, type of house floor, type of house wall, source of drinking water, and cooking fuel. The accuracy of the test data using a confusion matrix is 69.89%, sensitivity of 71.15% for classifying regular households, and specificity of 68.72% for classifying impoverished households.
K-Modes Analysis with Validation of the DBI in Grouping Provinces in Indonesia based on Indicators of Poor Households Syifa Azahra; Zilrahmi; Dodi Vionanda; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): 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/vol2-iss2/165

Abstract

Poverty is the most pressing social problem in Indonesia. Efforts to alleviate poverty are to group provinces in Indonesia based on indicators of poor households using the K-modes algorithm. The data used is data from the 2017 Indonesian Demographic and Health Survey (IDHS) on the Household List. The analysis includes data noise detection, data clustering using K-Modes algorithm, and cluster validation with Davies Bouildin Index (DBI). Based on the clustering that has been done, two clusters are obtained, where cluster 1 consists of 26 provinces and cluster 2 consists of 8 provinces. cluster 1 is a cluster that fulfills 9 indicators of poor households and cluster 2 only a few indicators of poor households. So that the government can prioritize these 8 provinces to overcome poverty in Indonesia. For the DBI value obtained is 1.89 which means that 2 clusters are already well used in the algorithm.
Classification of Program Keluarga Harapan Recipient Households in Padang Using K-Nearest Neighbors Yurivo Rianda Saputra; Syafriandi Syafriandi; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): 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/vol2-iss2/167

Abstract

Program Keluarga Harapan (PKH) is a social assistance program from the government aimed at providing social protection in the central government's efforts to promote social welfareas. PKH provides benefits to poor families, especially pregnant women and children, by utilizing various health and education services available. PKH benefits also include people with disabilities and the elderly by maintaining their level of social welfare in accordance with the Constitution and the Nawacita of the Republic of Indonesia. The implementation of PKH that experiences distribution errors needs to be classified to ensure its proper distribution. Classification is performed by comparing the number of  neighbors (k) in K-Nearest Neighbors (KNN). The Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN) is applied to balance classes in the target classification and Recursive Feature Elimination Cross Validation (RFECV) is applied to select attributes in the dataset used. The data source was obtained from SUSENAS 2023 data in Padang City. The research results show that KNN with k = 3 is a good algorithm for classifying households recieiving PKH using 10 attributes. KNN with k = 3 achieves an Accuracy of 91,12%, Precision of 89,29%, and Recall of 96,77%.
Application of Principal Component Analysis in Identifying Factors Affecting the Human Development Index Faisal, Muhammad; Fitri, Fadhilah; Zilrahmi
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.26

Abstract

This study examines the Human Development Index (HDI) in West Sumatra Province in 2023. The HDI is an essential indicator for measuring the success of efforts to improve the quality of human life. This research aims to identify the key factors that influence the HDI. The HDI is constructed from three fundamental dimensions that indicate human quality of life: health, education, and economy. The factors within each dimension tend to be strongly correlated, as they mutually influence one another, potentially leading to multicollinearity issues. Therefore, an analysis is conducted to reduce the number of original variables into new orthogonal variables while preserving the total variance of the original variables using Principal Component Analysis (PCA). Based on this background, the study applies PCA to address multicollinearity and to identify new, more representative variables. The study findings indicate that the factors influencing the HDI are the education and economic and health welfare indexes.
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.
Penerapan Metode Multivariate Adaptive Regression Spline untuk Memahami Dinamika Kemiskinan di Indonesia Khasanah, Nurviqotun; 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
Co-Authors Abilya Amanda Adinda Dwi Putri Afendi, Farit M Afifa Lufti Insani Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Devi Yopita Sipayung Dila Sari Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fajri Juli Rahman Nur Zendrato Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fedisha Elfiri Fedisha Fitri Mudia Sari Fitri, Fadhilah Gilang Ibnul farizi Hadid Habiburrahman Hamida, Zilfa Hanifah Nazhiroh Hari Wijayanto Hari Wijayanto Hendrawan, Muhammad Ichlas Djuazva Ihsanul Fikri Khasanah, Nurviqotun Khoirun Nisa Lathifa Putri Manja Danova Putri Martia Rosada Meliani Maya Sari Meliani Putri Melin Wanike Ketrin Moh. Erkamim Muhammad Alif Yustin Muhammad Fadhil Aditya Aditya Muhammad Fadlan Rafly Muhammad Faisal Muslimah, Nailul Amani Mutiara Amazona Sosiawati Nilda Yanti Nonong Amalita Nurdalia Nurwijayanti Permana, Dony Putri, Fadhira Vitasha Rahmad Wanizal Pastha Rahmadani Iswat Rahmanesta, Frandito 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 Syifa Azahra Syifa Miftahurrahmi Syifa Nabilah Wandira Tessy Octavia Mukhti Tessy Octavia Mukhti Ully Martha martha Ulya Syafitri.J Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha