UNP Journal of Statistics and Data Science
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.
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<![CDATA[Pemodelan Tingkat Partisipasi Angkatan Kerja Terhadap Persentase Penduduk Miskin di Jawa Timur Tahun 2023 Menggunakan Metode B-Spline ]]>
<![CDATA[Ibnul farizi, Gilang]]>;
<![CDATA[Zilrahmi]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/215
<![CDATA[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]]>
<![CDATA[Estimation of Poverty in North Sumatera in 2022 using Truncated and Penalized Spline Regression ]]>
<![CDATA[Kurnia Andrea Diva]]>;
<![CDATA[Fadhilah Fitri]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/217
<![CDATA[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.]]>
<![CDATA[Optimization of Sentiment Analysis for MBKM Program using Naïve Bayes with Particle Swarm Optimization ]]>
<![CDATA[Diva Aliyah]]>;
<![CDATA[Zilrahmi]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/220
<![CDATA[In early 2020, Kemendikbudristek launched the MBKM program with the aim of improving the quality of higher education through a student-focused learning approach. The launch of this program triggered various reactions on social media, especially on Twitter, both positive and negative. This study aims to analyze the sentiment of Twitter users towards the MBKM program using the Naive Bayes algorithm optimized with Particle Swarm Optimization (PSO). The data used are Indonesian tweets containing the keywords "MBKM" and "Merdeka Campus" from the period July to December 2022. The research stages include data collection through crawling, manual labeling of data into positive and negative sentiments, data preprocessing, application of the Naive Bayes algorithm, and feature selection with PSO. The results showed that the group of tweets categorized based on positive and negative sentiments towards the implementation of the MBKM program in Indonesia in 2022, showed that the NB-PSO experiment achieved an accuracy of 90.87%, an increase of 7.12% compared to the Naive Bayes algorithm alone. Thus, the use of Particle Swarm Optimization algorithm in Naive Bayes classification algorithm is proven to improve classification performance, especially in the case of sentiment analysis. Keywords: Sentiment Analysis, Merdeka Belajar Kampus Merdeka, Twitter, Naive Bayes, Particle Swarm Optimization.]]>
<![CDATA[Penerapan Metode Choice-Based Conjoint Analysis pada Preferensi Pekerjaan Mahasiswa Departemen Statistika Universitas Negeri Padang ]]>
<![CDATA[Putra, M. Farel Rusde]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/221
<![CDATA[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%).]]>
<![CDATA[Penerapan Metode Rating-Based Conjoint Analysis dalam Preferensi E-Wallet Mahasiswa Departemen Statistika Universitas Negeri Padang ]]>
<![CDATA[Putra, Dio Afdal]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Zamahsary Martha]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/222
<![CDATA[The rapid development of technology in the era of globalization has influenced the evolution of society's life in terms of economy, social, culture, and education, with the aim of facilitating daily activities, one of which is the ease of transactions using e-wallets. An e-wallet is a payment tool that uses a server-based system. Many factors influence a person's decision to use an e-wallet as a payment method, one of which is the level of security. To identify the factors that affect someone's use of e-wallets, one method is Rating-Based Conjoint Analysis (RBC). Therefore, this study aims to determine what influences a person to use an e-wallet, with the subjects being active students of the Statistics Department at Padang State University. The results of this RBC study indicate that the most influential factor on the e-wallet preferences of statistics students is security level, with a value of 37.70%, followed by transaction speed 23.17%, transfer fees at at 23.07%, features provided at 11.78%, and the least influential factor being promotions at 4.28%.]]>
<![CDATA[PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM) ]]>
<![CDATA[nazhiroh, hanifah]]>;
<![CDATA[Dina Fitria]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Zilrahmi]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/223
<![CDATA[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.]]>
<![CDATA[Implementation of CART Method with SMOTE for Household Poverty Classification in Mentawai Islands 2023 ]]>
<![CDATA[Dewi Adiningtiyas, Rheizma]]>;
<![CDATA[Admi Salma]]>;
<![CDATA[Syafriandi Syafriandi]]>;
<![CDATA[Fadhilah Fitri]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/232
<![CDATA[Poverty is a condition in which individuals or groups are unable to fulfill their basic needs due to economic pressure or limited resources. The Classification and Regression Trees (CART) method is a classification technique in the form of a classification tree, which describes the relationship between independent and dependent variables. Data imbalance can lead to low sensitivity values and area under curve (AUC) values. One method that can overcome unbalanced data is to perform Synthetic Minority Oversampling Technique (SMOTE). SMOTE is a technique with the addition of artificial data in the minority class at a stage before analyzing the data. The purpose of this research is to compare the model without and with SMOTE in CART method. The use of SMOTE is applied to balance the amount of data on each poor household. The accuracy value of the method without SMOTE is 89% while with the SMOTE method is 79%. However, the sensitivity value has increased by 80%. Meanwhile, the AUC value in the CART method with SMOTE increased by 31%. So in this study it can be concluded that CART classification analysis with SMOTE is able to provide better performance compared to CART classification analysis without SMOTE.]]>
<![CDATA[Analysis of Factors Influencing the Number of Families at Risk of Stunting in Merangin Regency Using Mixed Geographically Weighted Regression ]]>
<![CDATA[Fadlan Rafly, Muhammad]]>;
<![CDATA[Zilrahmi]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/236
<![CDATA[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.]]>
<![CDATA[Early Marriage Factors Indonesian Using Spatial Regression Analysis ]]>
<![CDATA[permana, yazid]]>;
<![CDATA[Dina Fitria]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Fadhilah Fitri]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/239
<![CDATA[Marriage is a sacred union recognized socially and religiously to form a family, as regulated by Law No. 16 of 2019. The percentage of early marriages in Indonesia continues to rise, reaching 21.5% in 2022, placing Indonesia 8th in the world according to UNICEF 2023 data. The increase in early marriages has significant impacts on maternal and child health and often leads to high divorce rates, with 516,334 cases in 2022. The aim of this research is to provide information and knowledge for students about early marriage and spatial regression. The main factors influencing early marriages are low education levels, economic difficulties, and environmental factors. Research shows that early marriages are highest in Kalimantan and Sulawesi, with spatial effects influencing the percentage of early marriages between regions.Spatial regression analysis, such as the Spatial Autoregressive (SAR) model, is used to examine the interactions between regions affecting early marriage. Spatial autocorrelation tests and spatial dependency effects show a spatial dependency effect, making the SAR model with queen contiguity weights the most suitable. The resulting model is considered quite good considering the R-squared value of 40.97%. The best-formed model shows that the Open Unemployment Rate (TPT) of youth is a significant variable that greatly impacts the percentage of early marriages. Therefore, the central and provincial governments are expected to pay more attention to the open youth unemployment factor to control and reduce the rate of early marriages in Indonesia.]]>
<![CDATA[Classification of Poor Households in Padang City Using the Naïve Bayes Algorithm with Synthetic Minority Oversampling Technique ]]>
<![CDATA[kartika, anice]]>;
<![CDATA[Dina Fitria]]>;
<![CDATA[Syafriandi Syafriandi]]>;
<![CDATA[Tessy Octavia Mukhti]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/241
<![CDATA[Poverty is a condition where a person is unable to meet minimum basic needs or a condition caused by the influence of development policies that have not been able to reach all levels of society. In Indonesia, the government has designed various programs to overcome poverty, but these programs are often not on target. One method to improve the effectiveness of the program is through proper classification of poor and non-poor households. This study uses the Naïve Bayes classification method which is popular in data mining to predict data categories based on the probability distribution of its features. However, challenges arise when the data is unbalanced between different classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) method is used to balance the data. Based on the analysis that has been carried out To determine the performance of Naïve Bayes using SMOTE and without SMOTE in classifying poor households in Padang City in 2023, classification using the Naïve Bayes method without SMOTE produced an accuracy value of 98%, precision of 0%, and recall of 0%. Meanwhile, the classification using the Naïve Bayes method with SMOTE produces an accuracy value of 90%, precision of 87%, and recall of 92% and the results of the criteria for poor households in Padang City in 2023 using Naïve Bayes can be seen from the results that the probability of poor households is much greater than that of non-poor households, therefore the data is classified as group of households that are classified as poor.]]>
