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Tessy Octavia Mukhti
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tessyoctaviam@fmipa.unp.ac.id
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tessyoctaviam@fmipa.unp.ac.id
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LPPM Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Barat, Kota Padang, Sumatera Barat 25131
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INDONESIA
UNP Journal of Statistics and Data Science
Published by Universitas Negeri Padang
ISSN : -     EISSN : 2985475X     DOI : 10.24036/ujsds
Core Subject : Science, Education, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences
UNP Journal of Statistics and Data Science is an open access journal (e-journal) launched in 2022 by Department of Statistics, Faculty of Science and Mathematics, Universitas Negeri Padang. UJSDS publishes scientific articles on various aspects related to Statistics, Data Science, and its application. Articles can be in the form of research results, case studies, or literature reviews. All papers were reviewed by peer reviewers consisting of experts and academicians across universities.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 17 Documents
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Search results for , issue "Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science" : 17 Documents clear
Panel Data Regression on Gross Regional Domestic Product in West Sumatra Eujeniatul Jannah; Admi Salma; Syafriandi Syafriandi
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-iss1/328

Abstract

Economic growth is assessed by the amount of gross regional domestic product (GRDP) as part of the development of people's welfare. West Sumatra Province needs a development plan that is able to produce GRDP per capita population of 9 to 11 times the current economic growth. To examine the economic growth of a country, not only using cross section data, because it is important to observe the behavior of the research unit over several periods of time. So that research is carried out whether there is an influence on the level of labor force participation, average length of schooling, life expectancy, and the number of poor people on GRDP per capita in districts / cities in West Sumatra in 2020-2023 using panel data regression. This research is an applied research with secondary data obtained from the Regency / City RPJPD document and the official website of the West Sumatra Statistics Agency consisting of 19 districts / cities as objects and the period 2020-2023.   The factors that are significant to GRDP per capita are average years of schooling and life expectancy with the selected model, namely the fixed effect model. The model has a good ability to explain the dependent variable with a value of 82.72%
Analysis of The Effect of Unemployment, Economic Growth and Inflation on Poverty in West Sumatra Province Ulya Syafitri.J; Zilrahmi; Admi Salma
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-iss1/329

Abstract

Poverty remains a major challenge in West Sumatra, although various efforts have been made to improve community welfare. In this context, it is important to understand the factors that influence poverty levels. Unemployment, economic growth and inflation are several important variables that can have a significant effect on poverty levels. Unemployment is one of the problems that is often associated with poverty. On the other hand, strong economic growth has the potential to reduce poverty levels by creating new job opportunities and increasing people's incomes. However, non-inclusive economic growth can increase social inequality and uneven income distribution, which in the end can worsen poverty. Apart from that, inflation can also affect poverty levels by reducing people's purchasing power, especially those with low incomes. This research aims to analyze the effect of unemployment, economic growth and inflation on poverty levels. The multiple linear regression analysis method is used to test the relationship between the independent variables (unemployment, economic growth and inflation) and the dependent variable (poverty). Based on the research findings, it can be concluded that unemployment, economic growth and inflation contribute to poverty in West Sumatra at 49,35% and the remainder 50,65% is explained by other factors outside the model.The analysis indicates a significant linear influence on unemployment and economic growth on poverty in West Sumatra and there is no significant linear impact of inflation  on poverty in West Sumatra.
Peramalan Harga Bawang Merah di Kota Padang Menggunakan Metode SARIMA Larissa, Dwika; Fitri, Fadhilah; Dina Fitria
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-iss1/330

Abstract

The fluctuation of shallot prices in Padang City has become a major concern for consumers, producers, and the government. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast shallot prices from January 2020 to August 2024, using monthly time-series data. The analysis identifies ARIMA(1,1,2)(0,1,1)12 as the optimal model for predicting shallot prices in Padang City, effectively capturing seasonal and non-seasonal patterns. Predictions for the period from September 2024 to August 2025 indicate a price increase trend, peaking in May 2025 before declining. The findings are expected to serve as a reference for planning production, distribution, and price control of shallots.
Error Correction Model Approach for Analysis of Original Regional Income in West Sumatra Herlena Purnama Sari; Fadhilah Fitri; Nonong Amalita; Tessy Octavia Mukhti
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-iss1/332

Abstract

In this research, an error correction model approach is used, namely looking at long-term and short-termrelationships. Meanwhile, Original Regional Income (PAD) is all regional income originating from original regionaleconomic sources. Sources of Original Regional Income according to Law Number 33 of 2004 Chapter V Article 6consist of Regional Taxes, Regional Levies, Separated Regional Wealth Management Results and Other Legal PAD.because this approach uses long-term and short-term relationships, it is known that only variables x1 and x3 have along-term relationship and variables x1 and x3 have a short-term relationship. so it can be concluded that not allindependent variables have a connection with the dependent variable
Implementation of the Self Organizing Maps (SOMS) Method in Grouping Provinces in Indonesia Based on the Number of Crimes by Type of Crime fajriyanti nur, Putri; Tessy Octavia Mukhti; Nonong Amalita; Admi Salma
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-iss1/334

Abstract

Crime cases are often the main topic of daily news in various media in Indonesia. Some of these crime cases are detrimental to the surrounding community and some are detrimental and these actions cannot be avoided in human life because they have become one type of social phenomenon. To protect the community by providing a sense of security and peace, the Indonesian government, especially the police, must pay attention to conditions like this. The results of this study used the Self Organizing Maps (SOMs) method to obtain 3 clusters with the characteristics of each cluster. The first cluster with a low impact crime rate consists of 29 provinces. The second cluster with a moderate impact consists of 3 provinces showing the most dominant crime rate, namely crimes related to fraud, embezzlement, smuggling & corruption compared to other clusters. The third cluster with a high impact consists of 2 provinces with the most prominent characteristics by showing almost all indicators of the number of crimes according to the type of crime experiencing the highest average crime cases compared to other clusters.
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.
Analisis Pemilihan Model Regresi Konversi Metanol Berdasarkan Suhu, Waktu Tinggal, Konsentrasi, Rasio Oksigen, dan Sistem Reaktor Marvero, Andre; Amri, Fahmi; Fadhil Irsyad, Muhammad; Kurniawati, Yenni
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-iss1/339

Abstract

This study aims to determine the best regression model that explains the effect of temperature, residence time, methanol concentration, oxygen to methanol ratio, and reactor system on methanol conversion in supercritical water. Preliminary analysis showed a violation of the multicollinearity assumption, which affected the validity of the multiple linear regression model. To overcome this and determine the optimal model, variable selection was performed using the stepwise selection method. This method was evaluated based on predictive power, model accuracy and statistical validity. The results showed that the stepwise method produced an optimal model in predicting conversion. Reactor system and temperature were the most significant variables affecting methanol conversion. The conclusion of this study shows that the variable selection approach with stepwise selection can be effectively used to identify the best regression model, when classical assumptions are met. These findings make an important contribution to the optimization of supercritical water-based chemical processes.
Classification of Factors Affecting Preeclampsia in Pregnant Women at RSUP. Dr. M. Djamil Padang using the CART Algorithm YUSWITA, AULIA; Dina Fitria; Dony Permana; Admi Salma
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-iss1/341

Abstract

Preeclampsia is a pregnancy-specific disease characterized by hypertension and proteinuria that occurs after 20 weeks of gestation. Preeclampsia itself is caused by various factors that can influence the occurrence of preeclampsia in pregnant women, including age, parity, history of hypertension, obesity, and kidney disorders. This study aims to determine the risk factors influencing preeclampsia based on preeclampsia diagnosis at RSUP Dr. M. Djamil Padang by classifying each variable using a decision tree. This research employs the CART (Classification and Regression Tree) algorithm. The CART algorithm has a binary nature and can analyze response variables that are either categorical or continuous, handle data with missing values, and produce an interpretable tree structure. The study results indicate that the primary risk factor for preeclampsia is parity. The model developed using the CART algorithm was tested using a confusion matrix, yielding an accuracy of 54%, a precision of 33.3% in correctly classifying patients with mild preeclampsia (PER), and a recall of 23.8% in classifying patients with severe preeclampsia (PEB).
Analisis Sentimen Penggunaan Aplikasi YouTube Menggunakan Metode Naïve Bayes Putri, Triana; Siti Nurhaliza; Dodi Vionanda
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-iss1/343

Abstract

This study aims to analyze user sentiment towards the YouTube application using the Naive Bayes method. With the rapid growth of YouTube users worldwide, understanding user preferences and experiences is crucial. Sentiment analysis, a process of processing or extracting textual data to obtain information by categorizing positive or negative sentiment The Naive Bayes algorithm, a statistical approach commonly used in natural language processing and sentiment analysis, was applied due to its simplicity and efficiency. The research involved data collection through web scraping, followed by preprocessing steps such as cleaning, case folding, tokenization, stopword removal, and stemming. Feature selection was performed using TF-IDF (Term Frequency-Inverse Document Frequency) to assign weights to words based on their importance. The Naive Bayes classifier was then trained on the preprocessed data, and its performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed an accuracy of 82%, precision of 83%, recall of 98%, and an F1-score of 89%, indicating the effectiveness of the Naive Bayes method in sentiment analysis for the YouTube application. This study provides valuable insights into user sentiment towards YouTube, enabling developers and content creators to enhance user experiences and marketing strategies.
Analisis Klaster K-means dalam mengelompokan Kabupaten/Kota di Provinsi Sumatera Barat Berdasarkan Jenis Kekerasan Terhadap Perempuan Tahun 2023 Febiola, Latifah Jayatri; Fadhilah Fitri; Fenni Kunia Mutiya
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-iss1/344

Abstract

Violence against women is a serious social issue and a violation of human rights. Women are often vulnerable to violence, whether physical, psychological, or sexual, which negatively impacts their physical and mental health. To understand the distribution of violence cases against women in West Sumatra Province, an analytical method is needed to classify regions based on the number of reported cases. K-Means Clustering is one of the clustering analysis methods used to group districts/cities based on similarities in the number of violence cases. This study aims to classify districts/cities in West Sumatra based on the number of female violence victims using the K-Means Clustering algorithm. The optimal number of clusters was determined using the silhouette method, resulting in three clusters. Cluster 3 has the highest average number of physical and sexual violence cases, consisting of four districts/cities: Solok Regency, Lima Puluh Kota, Solok City, and Payakumbuh City. Cluster 2 represents areas with a moderate level of violence, dominated by psychological abuse, and consists of five districts/cities. Meanwhile, Cluster 1 comprises ten districts/cities with the lowest recorded violence cases. This classification provides insight into the regional distribution of violence against women in West Sumatra, identifying areas that require more attention. The findings suggest that the government should prioritize regions with high levels of violence through stricter law enforcement, the provision of support services for victims, gender equality campaigns, and increased awareness of women's rights

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