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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal Edukasi Matematika dan Sains Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Gantang NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Basicedu Eksakta : Berkala Ilmiah Bidang MIPA FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Pelita Eksakta Jurnal Ilmiah Pendidikan Matematika Al Qalasadi Jurnal Basicedu Journal of Mathematics UNP UNP Journal of Statistics and Data Science International Journal of Trends in Mathematics Education Research (IJTMER)
Dony Permana
Jurusan Matematika Universitas Negeri Padang
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Pemetaan Indikator Pertumbuhan Ekonomi Di Provinsi Sumatera Barat Menggunakan Analisis Korespondensi Berganda Vidhiya Addini; Dony Permana; Nonong Amalita; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/190

Abstract

Economic growth is a key factor in sustainable regional development. This study employs Multiple Correspondence Analysis (MCA) to explore the relationships among economic growth indicators in the districts/cities of West Sumatra Province. Data from 2022 provided by the Central Statistics Agency are used to analyze economic growth indicators, including Gross Regional Domestic Product (GRDP) at Constant Prices (X1), Human Development Index (X2), Labor Force Participation (X3), Domestic Investment (X4), Government Expenditure (X5), and Balance Fund Allocation (X6). The results of MCA reveal complex relationships among these variables, with the first and second dimensions explaining approximately 44.43% of the data variance. The MCA plots visualize clusters of districts/cities based on their economic characteristics. From these plots, it is concluded that there are disparities in economic growth indicators in West Sumatra Province, with 11 districts/cities requiring special attention to achieve equitable and sustainable economic growth. This study contributes to a deeper understanding of regional economic disparities in West Sumatra Province and their relevance to more targeted and sustainable development policies.
K-Medoids Cluster Analysis for Grouping Provinces in Indonesia Based on Agricultural Households ST2023 Riska 01; Zamahsary Martha; Dony Permana; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/193

Abstract

Agriculture plays a crucial role in Indonesia's national development, providing essential resources such as raw materials, household income, and contributing significantly to Gross Domestik Product (GDP). According to the 2023 Agricultural Census (ST2023), there has been an increase in the number of Agricultural Household Enterprises (RTUP) across various agricultural subsectors. However, the welfare of agricultural entrepreneurs remains low, with 48.68% of poor household heads working in this sector. Therefore, an analysis is needed to understand the patterns and characteristics of RTUPs in each province. This study aims to cluster the provinces in Indonesia based on the number of Agricultural Household Enterprises (RTUP) using K-Medoids cluster analysis. K-Medoids, an extension of K-Means, was chosen for its ability to handle outliers by using medoids as cluster centers instead of means. The research utilized data from the 2023 Agricultural Census, covering 38 provinces and eight variables representing different agricultural subsectors. The optimal number of clusters was determined using the Elbow method, resulting in four distinct clusters. The findings revealed that Cluster 1 consists of 12 provinces with moderate RTUP numbers, Cluster 2 includes 23 provinces with low RTUP numbers, Cluster 3 comprises one province with high RTUP numbers, and Cluster 4 contains two provinces with very high RTUP numbers. The cluster validation using the Davies-Bouldin Index (DBI) yielded a value of 0.722, indicating that the clustering results are optimal.
Comparison Of Extreme Learning Machine And Holt Winter’s Exponential Smoothing Methods In Railway Passenger Forecasting Meil Sri Dian Azma; Dony Permana; Fadhilah Fitri; Atus Amadi Putra
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/211

Abstract

Forecasting the number of passengers on the Pariaman Express train is an activity that is considered to have the potential to help PT KAI in maximizing passenger service facilities and comfort. It is estimated that the number of train passengers in Indonesia will always increase along with the increasing population of Indonesia. The high interest of users of this mode of transportation can be seen from historical data that continues to increase every year. PT KAI (Persero) as a single train transportation provider company needs to have several strategies in providing and meeting passenger needs every day. In the study of forecasting the number of passengers on the Pariaman Express train using the Holt Winters exponential smoothing method and one of the artificial neural network methods, namely the extreme learning machine. The purpose of this study was to determine the comparison of the accuracy values ​​of the forecast results produced by the two methods, and to find out which method is good to use in this forecast. The data used is data on the number of Pariaman Express train passengers from 2021-2023. The results of the study show that the comparison of the accuracy values ​​of the forecasting of the number of train passengers shows that the Holt Winter's and ELM methods have error values ​​above 10%, meaning that the Holt Winter's and ELM methods are good at forecasting for 4 periods. Holt Winter's has a MAPE value of 17.10% and ELM has a MAPE value of 20%.
Evaluasi Faktor-Faktor Yang Memengaruhi Indeks Pembangunan Manusia Tahun 2023 Menggunakan Metode SEM-PLS Sindy Amelia Putri; Zilrahmi; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/214

Abstract

The human development index (HDI) is a measure of the success of development in a country. Indonesia as a developing country in 2022 has an HDI value that ranks 112 out of a total of 193 countries in the world. This indicates that there is an urgent need for evaluation in increasing the HDI value in Indonesia which leads to an increase in the quality of human development. The evaluation can be done using the Structural Equation Modeling-Partial Least Square (SEM-PLS) analysis method. With 34 Indonesian provinces as observations, there are three dimensions as variables analyzed in this paper, namely economy, education, and health. These variables are analyzed based on each indicator variable. The results of the analysis show that in the economic variable, the influential indicators are the Open Unemployment Rate, GRDP per Capita at Constant Prices, and Average Wage per Hour Worker. Then in the education variable, the influential indicators are the School Participation Rate Age 7-12, the School Participation Rate Age 13-15, the Pure Enrollment Rate for Elementary/Middle School/Package A, the Pure Enrollment Rate for Junior High School/MTs/Package B, and the Pure Enrollment Rate for Senior High School/SMK/MA/Package C. Furthermore, in the health variable, there are indicators of the Percentage of Households by Province and Source of Adequate Drinking Water, and the Percentage of Ever-Married Women Aged 15-49 Years whose Last Childbirth Processed in a Health Facility which affect the value of HDI in Indonesia in 2023.
Pemodelan Tingkat Partisipasi Angkatan Kerja Terhadap Persentase Penduduk Miskin di Jawa Timur Tahun 2023 Menggunakan Metode B-Spline Gilang Ibnul farizi; Zilrahmi; Dony Permana; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/215

Abstract

Poverty is a common issue in Indonesia. Data on the Percentage of Poor Population against the Labor Force Participation Rate (LFPR) per district/city, consisting of 38 districts/cities in East Java Province in 2023, indicates that the highest percentage of poverty in East Java Province in 2023 was 21,760. Employment is considered the most effective solution to alleviate poverty. The data in this study shows a distribution pattern that does not form a specific pattern, making it difficult to analyze using parametric methods. Therefore, the appropriate approach is Nonparametric Regression. In this study, the nonparametric regression used is the B-Spline regression model. The suitability of the model is based on the Mean Squared Error (MSE) value of the model. The analysis results indicate that the B-Spline regression model achieves an MSE value of 20.11447. The optimal MSE value is obtained from B-Spline estimation with order 2. This suggests that the B-Spline method provides a good explanation in addressing the issue
Estimation of Poverty in North Sumatera in 2022 using Truncated and Penalized Spline Regression Kurnia Andrea Diva; Fadhilah Fitri; Dony Permana; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/217

Abstract

The Sustainable Development Goals' main goal is to reduce poverty (SDGs). Low human capital is the cause of poverty. The Human Development Index is one indicator that can be used to assess human capital (HDI). Despite having the largest population on the island of Sumatra, North Sumatra continues to have the fifth highest poverty rate. Because the pattern of the relationship between poverty and HDI based on previous research is still unclear because the results are inconsistent, nonparametric regression modeling was used in this study because it is flexible in following the pattern of data relationships and can avoid model prespecific errors. This study aims to compare the Spline Truncated and Penalized Spline regression methods. The results of the comparison between the Truncated Spline regression model and the P-Spline regression model by looking at the smallest MSE value showed that a better estimator for modeling the Human Development Index in North Sumatera in 2022 is non-parametric regression using the truncated spline estimaor. where the best truncated spline modeling is at order 2 with one knot point located at X = 66.93 with a GCV value of 6.0543.
Penerapan Metode Choice-Based Conjoint Analysis pada Preferensi Pekerjaan Mahasiswa Departemen Statistika Universitas Negeri Padang M. Farel Rusde Putra; Dodi Vionanda; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/221

Abstract

In the realm of psychology studies, it is widely assumed that the age range between 18 and 25 represents a critical period during which individuals preferences begin to take shape. This developmental phase encloses college students who despite their academic pursuits, remain relatively unfamiliar with the dynamic job market, particularly in the context of rapid technological advancements. Statistics as a discipline with broad applicability across both social and scientific domains, offers student of statistics significant career prospects. This research would likely estimate the job preferences of statistics students using one of the most common use methods called choice-based conjoint (CBC) analysis. The analysis reveals that work hours were the most substantial influence on statistics students’ job preferences, with a percentage of 40.29%. In addition, other factors that influence the preferences of statistics students are such as first salary (36.87%), correlation with the field of statistics (12.04%), work environment (7.18%), and type of workplace (3.62%).
PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM) hanifah nazhiroh; Dina Fitria; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/223

Abstract

The movement of the share price of PT Telkom (Tbk) fluctuates so it is necessary to do a forecasting analysis. Forecasting the share price of PT Telkom (Tbk) can be done using the Long Short Term Memory (LSTM) method. LSTM is a development of the Recurrent Neural Network (RNN) method. In this study using PT.Telkom (Tbk) stock price data for 2018-2023 and PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023). The purpose of this research is to determine the movement of PT.Telkom (Tbk) stock prices in 2024, to find out the difference in forecasting using PT.Telkom (Tbk) 2018-2023 stock price data with PT.Telkom (Tbk) stock price data after covid-19 2021-2023, and to determine the level of accuracy of forecasting PT.Telkom (Tbk) stock prices using the LSTM method. The results showed that both data have a small MAPE value. to forecast the share price of PT.Telkom for 1 year, PT.Telkom (Tbk) share price data for 2018-2023 is used which has more data to analyze long-term forecasting. From the analysis results obtained MAPE of 1.016% with the optimal parameter combination of neuron 4, batch size 64, and epoch 80. The results of forecasting the share price of PT.telkom (Tbk) in 2024 experienced very rapid fluctuations with an average share price of PT.Telkom (Tbk) in 2024 Rp 4,668 / sheet.
Analysis of Factors Influencing the Number of Families at Risk of Stunting in Merangin Regency Using Mixed Geographically Weighted Regression Muhammad Fadlan Rafly; Zilrahmi; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/236

Abstract

The number of families at risk of stunting is among the significant concerns that have been a negative impact on developing superior human resources in Merangin Regency. The number of families at risk of stunting is sought to be solved by identifying the contributing components. MGWR is among the methods that may be employed to obtain a specific model that affects each obesrvasion location locally and a comprehensive model that is global. Multiple linear regression and GWR are used to create models MGWR used when data has the influence of spatial heterogeneity. This project aims to develop an MGWR model which will be used to calculate the amount families at risk of stunting in each sub-district in Merangin Regency who are at risk of stunting in 2022. A fixed gaussian kernel weighting matrix is used in MGWR modeling. At the very least CV of 0.6152241, A fixed gaussian kernel is utilized as the weighting function. The results indicate that the model obtained has an accuracy rate of 99.18%, which means that the predictor variables can explain the model by that percentage. Families with insufficient access to drinking water is one factor that significantly affects how many families are at risk of stunting, families with inadequate sanitation, maternal age less than 20 years and families with babies under five years old.
Library Book Lending Recommendation Using Association Rules with Frequent Pattern Growth (FP-Growth) Algorithm Fakhri Kamil; Dony Permana; Dodi Vionanda; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/284

Abstract

College libraries are libraries managed by higher education institutions such as university libraries. The library functions as an information center management forum for students which includes learning resource functions, access functions, librarian functions, ethical functions, and evaluation functions.  Students prefer to read through e-books rather than reading books or library collections. Limited knowledge of literature is the cause of students choosing to look for books on search engines rather than in the library. Managed book loan circulation history data will be able to improve library services that can assist in finding library collections. Book recommendation services using association rules, can find patterns of borrowing behavior of book titles that have the highest association as the most recommended titles to be borrowed together. The FP-Growth or Frequent Pattern Growth is an algorithm of associations rule that is able to generate association rules as personalized book borrowing recommendations. The results of book recommendations found as many as 50 rules that meet the chi-square assumption test where the recommendation items are independent. The results of 50 rules for book title choices that can be used by students as suggestions for determining books that have a relationship to be borrowed together to enrich references. For students who wish to borrow the books 'Professional Teacher: Mastering Teaching Methods and Skills' is recommended to also borrow the book 'Participatory Learning Methods and Techniques'. With the book recommendation service, the library provides advice to students in choosing related book titles to borrow at the library.
Co-Authors Ade Eriyen Saputri Afdhal Rezeki Afdhal Afifah Hardi Afifah Zafirah Ahmad Fauzan Alandra, Cindy Resha Aldi Prajela Ali Asmar Andini Diva Luthfiyah april leniati Armiati Arnellis Arnellis Arssita Nur Muharromah Asra Dinul Haq Atus Amadi Putra AULIA YUSWITA Bahri Annur Sinaga Bonita Nurul Afifah Denny Armelia Dewi Febiyanti DHEA PUTRI RIZKIA Dina Fitria Dina Fitria Dodi Vionanda Dodi Vionanda Dwi Putri Amilia Dwi Ratih Listiani Yusri Dwi Sulistiowati Edwin Musdi Elita Zusti Jamaan Elsa Oktaviani Elvina Catria Emi Suryani Putri Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhillah Meisya Carina Fakhri Kamil Fanni Rahma Sari Farras Luthfyah Nisa Fauzan Al-Hamdani Siregar Fauzan Arrahman Febri Ramayanti Fenni Kurnia Mutiya Gilang Ibnul farizi Hana Rahma Trifanni Hana Zafirah Hanif Khairi Hanifa Hasna Hanifah Nazhiroh haniyathul husna Hefiani Mustika Hasanah Helma Helma Huriati Khaira I Made Arnawa I Made Arnawa iin aini fitri Indonesia Irma Surya Anisa Isra Miraltamirus Kerin Hagia Aidillah Kurnia Andrea Diva M. Farel Rusde Putra Media Rosha Meidiani Sandra Meil Sri Dian Azma Meliani Maya Sari Meliani Putri Mohammad Reza febrino Muhammad Fadlan Rafly Muslimah, Nailul Amani Muthia Sakhdiah Mutiara Amazona Sosiawati nabillah putri Nadya Nadya Nahda Maesya Tsani Nilda Yanti Nisa Ulkhairat Asfar Nonong Amalita Nufhika Fishuri Nur Nur Fadillah Nurdalia Nurul Afifah rahmad revi fadillah Rahmadina Adityana rama novialdi Refenia Usman Refina Rintani Revina Rahmadani Ridha Fajria rios Riry Sriningsih Riska 01 Ronald Rinaldo roza maylinda Salma, Admi Salsabilla Khairani Septrina Kiki Arisandi Siltima Wiska Sindy Amelia Putri Sofni Fajriani SRI RAHAYU Suherman Suherman Suwanda Risky Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Titin Mardianingsih Tri Wahyuni Nurmulyati Ully Martha martha Vidhiya Addini Vinka Haura Nabilla Wahda Aulia Assara Welgi Okta Irawan Widia Handa Riska Widya Febriani Widya Yarman Yarman Yatri Asri Yenni Kurniawati Yerizon Yerizon Yerizon Yoga Perdana Yuli Andari Wulan Yulia Pertiwi Yulia Utami Putri Yulyanti Harisman Yurivo Rianda Saputra Zamahsary Martha Zilrahmi, Zilrahmi