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All Journal Jurnal Ampere PENDIPA Journal of Science Education JUTIS : Jurnal Teknik Informatika Pelita Eksakta Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Abdimas Bina Bangsa Publikasi Hasil Pengabdian Kepada Masyarakat. UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Dina Fitria, Dina
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Journal : UNP Journal of Statistics and Data Science

 1 
Evaluasi Faktor-Faktor Yang Memengaruhi Indeks Pembangunan Manusia Tahun 2023 Menggunakan Metode SEM-PLS Putri, Sindy Amelia; Zilrahmi; Permana, Dony; Fitria, Dina
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.
Implementation of Association Rule on Agricultural Commodity Exports in Indonesia Using Apriori Algorithm Dinul Haq, Asra; Fitria, Dina; Dony Permana; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): 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/vol3-iss1/336

Abstract

Exports of agricultural commodities in Indonesia have the smallest contribution to state revenues and the movement of export values ​​in the last decade has not shown a significant increase compared to other export sectors. This shows that there are weaknesses in the export of agricultural commodities so that an analysis is needed to optimize export results to other countries. These weaknesses can be seen in terms of quality, price, infrastructure and technology. This study uses association rule analysis with the apriori algorithm with the aim of finding out what agricultural commodities are exported simultaneously and the resulting association rules. The apriori algorithm is an algorithm used to find association rules between items in a database by considering two main parameters, namely Support and Confidence. The data used is agricultural commodity export data obtained from the publication of the Central Statistics Agency in Indonesia in 2023. Based on the analysis carried out, there are 32 association rules generated with a minimum Support of 25% and a minimum Confidence of 80%. Then after the Lift Ratio test was carried out, all the rules generated met the Lift Ratio test with a value of more than 1. The association rules produced must have at least 2 to 4 agricultural export commodities in each rule. By knowing the association rules for agricultural commodity exports, it is hoped that export distribution in the agricultural sector can be further optimized for trading abroad so that it can cover existing weaknesses.
Classification of Determining Factors for Eligibility of Extreme Poverty Social Assistance Recipients in Dumai City for 2024 Using CHAID Pajrini, Nurul Hasni; Fitria, Dina; Mukhti, Tessy Octavia
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): 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/vol3-iss1/354

Abstract

Poverty is one of the goals of the Sustainable Development Goals (SDGs). Poverty is a condition in which an individual falls below the standard minimum value of basic needs, both food and non-food. One of the efforts by the Indonesian government to alleviate poverty is through fulfilling needs in various sectors. Although the distribution of social assistance has been successfully implemented, there are still issues in determining beneficiaries who are not properly targeted. Therefore, it is necessary to identify the significant factors influencing the eligibility of social assistance recipients. The application of the CHAID method in classifying the determining factors for eligibility of extreme poverty social assistance recipients in Dumai City for 2024 shows that the significant factors influencing the eligibility status of extreme poverty social assistance recipients in Dumai City for 2024 are house size and neighbors' testimonies. The classification model's accuracy in determining the eligibility factors for extreme poverty social assistance recipients in Dumai City for 2024 is 87.70%.
Co-Authors Abdillah, Muhammad Mukhlis Abdul Aziz, Ahmad Malik Abdur Rahim, Abdur Al Aziz, Saddam Alamin, M. Anggari Linda Destiana Antasya, Faridah Darussalam, Muhammad Illyas Day, Dafzel Dewi Rawani Dinul Haq, Asra Dodi Vionanda Dony Permana Fathonah, Deffya Ghaly, Fayyadh Hendrawan, Muhammad Indri Astuti Joni Joni Khalida, Rizqiana Maulana, Muhammad Arif Moh. Wahyu Aminullah Moulita, R.A. Nurul Muhni Pamuji, Muhni Mujaidiah Mukhti, Tessy Octavia Nahdah, Athaya Nisrina Nonong Amalita Nugroho, Agung Pajrini, Nurul Hasni Permana, Dony Putri, Sindy Amelia Revelino, Imanuel Ridho, Fakhru Rini Afrianti Salma, Admi Suhady, Suhady Suherman, Suherman Suparmin Suparmin Syafriandi Syafriandi Turrohmah, Azizah Yarman Yarman, Yarman Yenni Kurniawati Yudi Irwansi, Yudi Yuwon, Yuwon zafira, Rufaidah Zamahsary Martha Zilrahmi, Zilrahmi