cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tessy Octavia Mukhti
Contact Email
tessyoctaviam@fmipa.unp.ac.id
Phone
+6282283838641
Journal Mail Official
tessyoctaviam@fmipa.unp.ac.id
Editorial Address
LPPM Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Barat, Kota Padang, Sumatera Barat 25131
Location
Kota padang,
Sumatera barat
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 202 Documents
 8   9   10   11   12 
Classification of Unemployment at West Sumatra Province in 2021 using Algorithm Classification and Regression Tree Nur Fadillah, Nur; Syafriandi Syafriandi; Nonong Amalita; Dony Permana
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-iss2/166

Abstract

Unemployment is a problem that often occurs in developing countries. This is caused by the imbalance between the number of labor force and the number of working population. According to the Central Bureau of Statistics, West Sumatra Province in 2021 is the eighth province with a high open unemployment rate of 6,52%, which is higher than the average Indonesian open unemployment rate of 6,49%. The increase in unemployment has occurred from 2017 to 2021 which is caused by educated unemployment. This is due to the habit of job seekers who tend to pick and choose the types of jobs available, while business needs are very limited. The problem of unemployment will get higher if it is not resolved. As a result, unemployment can lead to poverty and other social problems. In this study, CART analysis is used to classify unemployment in West Sumatra Province in 2021 which aims to determine the factors that affect unemployment. CART is a decision tree that shows the relationship between the response variable and one or more predictor variables. The purpose of CART analysis is to obtain the right data group for classification purposes. Based on the analysis obtained, the variables that affect unemployment in West Sumatra Province in 2021 are marital status, gender, household status, education level, age, and place of residence with an accuracy value of 71,73%.
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%.
Artificial Neural Networks to Forecasting the Retail Price of Beras Solok in Padang City using Backpropagation Algorithm Rivani, Putri; Tessy Octavia Mukhti; Dodi Vionanda; Dina Fitria
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-iss2/168

Abstract

Strengthening rice production is an important step as the population continues to grow. Padang City is only able to meet 30% of the community's needs, so to fulfill the community's needs, rice is also imported from Solok. Forecasting can be done especially in order to see the movement of the average retail price of Anak Daro Solok Rice in Padang City which has decreased and increased in rice prices due to the lack of rice availability in Padang City. In this research, the forecasting method that will be used is the Artificial Neural Network Backpropogation Algorithm. Artificial Neural Networks are widely used for forecasting nonlinear time series data. Based on the results of the research that has been done, forecasting the average retail price of Anak Daro Solok Rice in Padang City using the Backpropagation Algorithm Artificial Neural Network obtained the optimal network architecture has the best model, namely BP (1,6,1) which model produces a MAPE of 0.03121%, indicating that the network performance of the model that has been formed shows very good results because it manages to achieve an accuracy rate (MAPE) of less than 10%. Artificial Neural Network Model based on Backpropagation Algorithm can be applied to predict the average retail price of Anak Daro Solok Rice in Padang City. Comparison of the results of forecasting the average retail price of Anak Daro Solok Rice in Padang City for the next 12 months period, namely an increase from the previous 12 months period.
Analisis Sentimen Pengguna Aplikasi X terhadap Konflik antara Israel dan Palestina Menggunakan Algoritma Support Vector Machine Carina, Fadhillah Meisya; Admi Salma; Dony Permana; Zamahsary Martha
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-iss2/170

Abstract

The conflict between Israel and Palestine is the Middle East's longest-running conflict since 1917 and is still ongoing today. This is one of the international conflicts that involves many Arab countries and Western countries in the dispute. The conflict between Israel and Palestine has caused countries in the world to be divided into two camps, namely the pro Palestinian independence camp and the contra camp. The impact of this conflict also creates polarization among Indonesians and forms diverse public opinions on the social media application X. The purpose of this research is to find out how the classification of sentiment of X application users affects the conflict between Israel and Palestine. An analysis that is utilized to convert text-based public opinion data into information is sentiment analysis. The chosen algorithm to separate data classes is the Support Vector Machines algorithm, which can classify data by determining the best hyperplane to provide a separator between opinions that are pro Israel or pro Palestine. After the preprocessing stage, 1000 tweets data were obtained with 800 training data and 200 testing data. The accuracy rate is 93%, precision is 92.93%, recall is 100%, and f-measure is 96.33%. From the results of testing 200 data points, there were 198 pro Palestine opinions and 2 pro Israel opinions, so that it might be said that more individuals favor or support Palestinian independence in the conflict that occurred between Israel and Palestine.
Pemetaan Intensitas Gempa Bumi di Wilayah Sumatera Barat Menggunakan Model Epidemic Type Aftershock Sequence Spatio-Temporal Fikra, Hidayatul; Fitria, Dina; Nonong Amalita; 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-iss2/171

Abstract

The random spatial and temporal occurrence of earthquakes means that this are still being researched from a seismological and stochastic perspective. Point processes are examples of stochastic processes which explain seismic activity, one of them is Epidemic Type Aftershock Sequence (ETAS) model. It lackness ignores the location or spatial component of. Consequently, the components of time, location, and magnitude will be taken into consideration when discussing the ETAS model in this study. The spatio-temporal model is the name given to this concept. Therefore, in this research,mapping of earthquake intensity will be carried out in the West Sumatra region using the spatio-temporal ETAS model stated in conditional intensity function with eight parameters. The data used are earthquake events in the West Sumatra region with a magnitude threshold of 4 SR and a depth of ≤ 70 km for the period January 2000 to January 2024. Parameter model estimated using the maximum likelihood method and solved using the Davidon Fletcher Powell algorithm. The result shows area of West Sumatra with high earthquake intensity is coastal area, namely West Pasaman, Padang, Mentawai Islands and the South Pesisir. This makes the area vulnerable to seismic disasters
Perbandingan Algoritma C4.5 dan C5.0 Dalam Klasifikasi Status Gizi Balita Stunting harelvi, dhea afrila; Admi Salma; Yenni Kurniawati; 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-iss2/172

Abstract

Stunting is one of the health conditions that reflect aspects of nutrition and child growth, allowing us to observe the nutritional status of toddlers. The aim of this study is to determine the classification results of the C4.5 and C5.0 algorithms in cases of stunted toddler nutritional status and to compare the results between the C4.5 and C5.0 algorithms in classifying stunted toddler nutritional status using k-fold cross-validation. The data in this study are secondary data. Which is collected from Puskesmas IV Pesisir Selatan Regency. The research variables are divided into two, namely the response variable Y, which is Toddler Nutritional Status, and predictor variables X including Age, Toddler Gender, Toddler Weight, and Toddler Height. The result of the study obtain the algorithm C5.0 produse accuracy value of the C5.0 algorithm is higher than that of the C4.5 algorithm. The C5.0 algorithm provides an average accuracy result of 83% while the C4.5 algorithm provides an accuracy result of 79%. Thus, it can be concluded that the C5.0 algorithm is better at classifying stunted toddler nutritional status.
Random Forest Implementation for Air Pollution Standard Index Classification in DKI Jakarta 2022 Hasna, Hanifa; Nonong Amalita; Dony Permana; Admi Salma
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-iss2/173

Abstract

Air pollution is a serious challenge in various cities, including DKI Jakarta. Based on measurements of the Air Pollution Standard Index carried out by the DKI Jakarta Environmental Service, the air quality in DKI Jakarta is considered moderate to unhealthy. Deteriorating air quality in the Jakarta metropolitan area is very dangerous for humans and living things. Therefore, to prevent the problem, the classification of air quality based on pollutant content is carried out using Random Forest (RF). The application of RF will form several trees that can provide better predictions and are able to produce low errors. The result of this study obtained optimal tree formation, namely tree formation using a combination of mtry (any input variables randomly selected in one sorting node)=2 and ntree (number of trees in the forest) as many as 5000 trees. The resulting accuracy was 99.17% with an OOB error rate of 0.83%. This research identifies that particulate pollutants are the main factor causing air pollution in DKI Jakarta. Based on these results, it shows that RF is able to provide accurate predictions about the level of air pollution in DKI Jakarta and can be identify important factors that affect air pollution.
PEMODELAN INDEKS PEMBANGUNAN GENDER (IPG) PROVINSI JAWA BARAT DENGAN PENDEKATAN REGRESI NONPARAMETRIK DERET FOURIER RIZKIA, DHEA PUTRI; Fadhilah Fitri; Dony Permana; Dina Fitria
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-iss2/174

Abstract

Gender equality is a development target in many countries. The ideal condition in human development that is expected is that male and female population groups have equal access to play a role in development, control over existing development resources, and receive benefits from development equally and fairly. The gender gap still occurs today in all aspects. The condition of the gender gap can be known by looking at the Gender Development Index . In observing the data curve, between the Gender Development Index and each independent variable does not form a certain pattern. In addition, the data patterns that are formed tend to repeat. Nonparametric regression analysis is the solution. Fourier series is a nonparametric analysis used for repetitive data. Modeling was performed using 1, 2, and 3 oscillation parameters. Of the three parameters, the best model resulted from the K=3 oscillation parameters with a GCV value of 2.8084 and a coefficient of determination of 42.39%.
Comparison of Estimate Method of Moment and Least Trimmed Squares in Models Robust Regression Tri Wahyuni Nurmulyati; Dony Permana; Nonong Amalita; Martha, Zamahsary
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-iss2/176

Abstract

The poverty line is the minimum income that a person must earn to be considered to have a decent standard of living in a particular area. In 2022, the poverty line in West Sumatra Province was higher than the poverty line in Indonesia as a whole. An analysis was conducted to identify the factors influencing the poverty line in West Sumatra Province. However, the observational data on the poverty line and its influencing factors contained outlier. Therefore, robust regression analysis was performed to address the data containing outlier, comparing two estimates: MM estimation and LTS estimation. By examining the value, the best estimate was found to be MM estimation, with significant factors being average net wages/salaries, TPT, APM, and AMH. If the average net wages/salaries, TPT, APM, and AMH increase, the poverty line in West Sumatra will rise. With an of 0.9582, the model can explain 95.82% of the variation in the poverty line, while the remaining variation is explained by other factors not included in the model.
Markov Chain Model Application for Rainfall Pattern in Padang City haniyathul husna; Dony Permana; Nonong Amalita; 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-iss3/179

Abstract

Rainfall is a natural phenomenon that includes climate variables and is observed every time in every place. Daily rainfall data is a time series data, which is random. It is a data transfer from one time to another which can be expressed as a state of light, medium, heavy or very heavy rainfall intensity. Rainfall prediction is needed for people's lives and supports the economy. In addition, rainfall prediction is an anticipation of prevention if high rain intensity will occur in a long time. One of the rainfall prediction methods that can be used is the stochastic process approach. Markov chain is part of the stochastic process that can be used for prediction of rainfall at the present time based on one previous time. The focus of this research is the application of Markov Chains for rainfall prediction. Through Markov chains, long-term opportunities for rainfall phenomena are obtained. This study will look at the rainfall pattern of Padang City using Markov chains and also to predict rainfall in Padang City. The results of predicting the weather conditions of Padang City with any rainfall conditions today are 36.9% for the chance of no rain tomorrow, 46% for the chance of light rain tomorrow, 10% for the chance of moderate rain tomorrow, 5.3% for the chance of heavy rain tomorrow, and 1.8% for the chance of very heavy rain tomorrow.The results of this study are expected to be a recommendation for parties directly involved in taking preventive measures due to rainfall.

Page 10 of 21 | Total Record : 202

 8   9   10   11   12 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 3 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science More Issue