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[Comparison of the Chen and Sinsgh’s Fuzzy Time Series Methods in Forecasting Farmer Exchange Rates in Indonesia ]]>
<![CDATA[Okia Dinda Kelana]]>;
<![CDATA[Atus Amadi Putra]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/36
<![CDATA[Chen and Singh's Fuzzy Time Series Model is a forecasting method that uses the basi fuzzy logic in the process. The differences in the models are in the fuzzy logic relations. Chen's model uses Fuzzy Logical Relationship Groups. Meanwhile, the Singh model uses only Fuzzy Logical Relationships in the forecasting process. To find out the best model between the two models, forecasting the Farmer's Exchange Rate is carried out. Farmers' exchange rates are the option for observers of agricultural development in assessing the level of welfare of farmers in Indonesia. With changes in farmer exchange rates every month, it is necessary to forecast data in order to obtain an overview for the following month. Research used is applied research where the initial step is to study and analyze the theories related to our research, then colect the necessary data. The data used is data secondary data obtained online from the official website of the Badan Pusat Statistika (BPS). the forecasting results of the two models were compared using MAPE. The results of the comparison of the accuracy of the prediction accuracy of Chen and Singh's fuzzy time series models on farmers' exchange rates obtained Chen's MAPE fuzzy time series values of 0.679% and Singh's fuzzy time series models of 0.354%. This means that the best forecasting model for farmer exchange rates in Indonesia is the Singh model.]]>
<![CDATA[Pemodelan Waktu Survival Pasien Tuberkulosis menggunakan Regresi Cox Proportional Hazard dengan Data Tersensor ]]>
<![CDATA[Elsa Oktaviani]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Atus Amadi Putra]]>;
<![CDATA[Dony Permana]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/65
<![CDATA[Tuberculosis is an infectious disease that needs to be watched out for in West Sumatra Province. West Sumatra Province is the province with the 12th highest TB case in Indonesia in 2021 with a total of 8,216 TB cases and a TB treatment cure rate that is still far from the target of the Indonesian Ministry of Health. The purpose of this study is to determine the Cox proportional hazard regression model and factors that affect the survival time of tuberculosis patients at Dr. M. Djamil Padang Hospital. The survival period used is the time when the patient is taking TB treatment at RSUP Dr. M. Djamil Padang in 2021 until the patient is declared dead. The method used in the Cox Proportional Hazard Regression analysis is the Maximum Partial Likelihood Estimation Method. By using the cox proportional hazard regression model, the factors that influence the survival time of tuberculosis patients at RSUP Dr. M. Djamil's BMI , leukocytes , fever , shortness of breath , and decreased appetite . ]]>
<![CDATA[Sentiment Analysis of Electric Cars Using Naive Bayes Classifier Method ]]>
<![CDATA[NURUL AFIFAH]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/68
<![CDATA[In recent years, electric cars have become increasingly popular as an alternative to environmentally friendly vehicles in the automotive industry. These vehicles use electric power as an energy source that can mitigate the reliance on fossil fuels contribute to efforts to minimize greenhouse gas emissions and air pollution. However, the presence of electric cars raises pro and con opinions from the public. the conversation about electric cars has become one of the hot on social media. Twitter is a social media microblogging that permits its users to create short messages and share them easily and quickly. These opinions require sentiment analysis. The purpose of conducting sentiment analysis is to find out how people's perceptions and opinions on electric cars are leading in a favorable or unfavorable direction. Thus, sentiment analysis can help companies marketing strategies, and better business decisions. Then the opinions will be classified based on positive and negative categories. This investigation employs the naive classifier method to generate positive and negative sentiment towards electric cars on Twitter. The accuracy results of naive bayes obtained by using a confusion matrix in this research are 77.8%, with a dataset split composition of 70%:30%.]]>
<![CDATA[Comparison of Error Rate Prediction Methods in Classification Modeling with Classification and Regression Tree (CART) Methods for Balanced Data ]]>
<![CDATA[Fitria Panca Ramadhani]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Syafriandi Syafriandi]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/73
<![CDATA[CART (Classification and Regression Tree) is one of the classification algorithms in the decision tree method. The model formed in CART is a tree consisting of root nodes, internal nodes, and terminal nodes. After the model is formed, it is necessary to calculate its accuracy. The aim is to see the performance of the model. The accuracy of this model can be determined by calculating the predicted error rate in the model. The error rate prediction method works by dividing the data into training data and testing data. There are three methods in the error rate prediction method: Leave One Out Cross Validation (LOOCV), Hold Out (HO), and K-Fold Cross Validation. These methods have different performance in dividing data into training data and testing data, so there are advantages and disadvantages to each method. Therefore, a comparison was made between the three error rate prediction methods with the aim of determining the appropriate method for the CART algorithm. This comparison was made by considering several factors, for instance, variations in the mean, the number of variables, and correlations in normally distributed random data. The results of the comparison will be observed using a boxplot by looking at the median error rate and the lowest variance. The results of this study indicate that the K-Fold Cross Validation method has the lowest median error rate and the lowest variance, so the most suitable error prediction method for the CART method is the K-Fold Cross Validation method]]>
<![CDATA[Comparasion of Error Rate Prediction Methods of C4.5 Algorithm for Balanced Data ]]>
<![CDATA[Ichlas Djuazva]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Zilrahmi]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/74
<![CDATA[C4.5 is a highly effective decision tree algorithm for classification purposes. Compared to CHAID, Cart, and ID3, C4.5 generates the decision tree faster and is easier to understand. However, C4.5 algorithm is also not exempt from errors in classification, which can impact the accuracy of the resulting model. Model accuracy could be measured by predicting the error rate. One commonly used method for error rate prediction is cross-validation. The cross-validation method divides data into two parts: training set to build model and testing set to test the model. There are several cross-validation techniques commonly used to predict the error rate, such as Leave One Out Cross Validation (LOOCV), Hold Out (HO), and k-fold cross-validation. LOO has unbiased estimation but takes a long time and depends on the data size; HO could avoid overfitting and work faster; and k-folds cross validation has a smaller error rate prediction. This study uses artificially generated data with a normal distribution, including univariate, bivariate, and multivariate datasets with various combinations of mean differences and different correlations. Different correlation structures are applied to see the impact of these different correlations on the error rate prediction method. Considering these factors, this research focuses on comparing three cross-validation methods to predict error rates for the decision tree model generated by C4.5 algorithm. This research found that k-folds cross-validation is the most suitable cross-validation method to apply when testing the model generated by C4.5 algorithm with balanced data]]>
<![CDATA[Comparison of Fuzzy Time Series Markov Chain and Fuzzy Time Series Cheng to Predict Inflation in Indonesia ]]>
<![CDATA[Ihsanul Fikri]]>;
<![CDATA[Admi Salma]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Zilrahmi]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/76
<![CDATA[Inflation is one of the main microeconomic problems which is a very important economic indicator. Unstable inflation has a negative impact on people’s welfare, thus controlling inflation is important thing for a country. Forecasting is needed to monitor future movements in the inflation rate. In this study, the Fuzzy Time Series Markov Chain and fuzzy time series Cheng methods will be compared in forecasting inflation. The advantage of the fuzzy time series method is that it does not have any special assumptions thet must be met. The purpose of this study is to determine the results of forecasting based on the results of the comparison of the two methods. The result of the comparison of the two methods based on the MAPE value is that fuzzy time series Markov Chain has the smallest value of 6,97%. The result of inflation forecasting for the next 5 periods using the fuzzy time series Markov Chain method is 5,42; 5,71; 5,95; 5,82 and 6,10.]]>
<![CDATA[Step Function Intervention Analysis Model to Estimate Number of Aircraft Passengers in Minangkabau International Airport ]]>
<![CDATA[Velya Rahma Putri]]>;
<![CDATA[Zilrahmi]]>;
<![CDATA[Syafriandi Syafriandi]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/77
<![CDATA[Pandemic of Covid-19 had a quite big impact in air transportation. Minangkabau International Airport (BIM) has also felt the impact of this pandemic, namely a drastic decrease in the number of airplane passengers or there was an intervention event.a stable of airplane passengers is needed to indicate a stable economy in the transportation sector. If there are no passengers or flight activity in an area, it means that there are no entry and exit of economic activities, industrial activities, tourism and trade which help economic development. For this reason, it is necessary to do forecasting so that the problems that arise as a result of the drastic decline can be resolved by making new policies. Forecasting was carried out in this study to obtain an intervention model that will be used for forecast the next 12 months and predict how long the effect of the intervention will last for avoid further losses due to the continued decline in the number of passengers. The intervention model is considered better for data that has intervention variable compared to SARIMA models. The results of forecasting showed that the SARIMA model (0,1,1)(1,1,1)12 b = 0, s = 8, r = 1 is the best model that can be used for forecasting data containing interventions. This is evidenced by the small MAPE of 36.34% so that the model is feasible to use because the accuracy is quite high and close to the actual value.]]>
<![CDATA[Analysis of the Poverty Level Model for West Sumatra Province Using Geographically Weighted Binary Logistic Regression ]]>
<![CDATA[april leniati]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Zamahsary Martha]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/80
<![CDATA[T West Sumatra Province (West Sumatra) ranks third lowest in terms of the poverty rate on the island of Sumatra in 2022, with a figure of 5.92%. Although this figure is lower than the national average, the Province of West Sumatra is targeting a reduction in the poverty rate to 5.62% in 2024 in the vision of the 2021–2026 Regional Development Plan. The purpose of this study is to analyze the factors that contribute to the poverty rate in West Sumatra Province based on geography in 2022. The method used to address poverty problems is Geographically Weighted Binary Logistic Regression (GWBLR), which takes geographical influences into account in the analysis. This study uses data on the percentage of poor people (Y) and the influencing factors, namely life expectancy (X1), literacy rate (X2), labor force participation (X3), and economic growth (X4). The results showed that based on the lowest Akaike Information Criterion Corrected (AICc) value, the GWBLR model with a Fixed Gaussian Kernel weight is the best at modeling the problem of poverty in West Sumatra in 2022. According to the model, the life expectancy variable will have a significant impact on the level of poverty in 13 districts and cities in West Sumatra Province in 2022.]]>
<![CDATA[Comparison of Error Rate Prediction Methods in Classification Modeling with the CHAID Method for Imbalanced Data ]]>
<![CDATA[Seif Adil El-Muslih]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/81
<![CDATA[CHAID (Chi-Square Automatic Interaction Detection) is one of the classification algorithms in the decision tree method. The classification results are displayed in the form of a tree diagram model. After the model is formed, it is necessary to calculate the accuracy of the model. The aims is to see the performance of the model. The accuracy of this model can be done by calculating the predicted error rate in the model. There are three methods, such as Leave one out cross-validation (LOOCV), Hold-out, and K-fold cross-validation. These methods have different performances in dividing data into training and testing data, so each method has advantages and disadvantages. Imbalanced data is data that has a different number of class observations. In the CHAID method, imbalanced data affects the prediction results. When the data is increasingly imbalanced the prediction result will approach the number of minority classes. Therefore, a comparison was made for the three error rate prediction methods to determine the appropriate method for the CHAID method in imbalanced data. This research is included in experimental research and uses simulated data from the results of generating data in RStudio. This comparison was made by considering several factors, for the marginal opportunity matrix, different correlations, and several observation ratios. The results of the comparison will be observed using a boxplot by looking at the median error rate and the lowest variance. This research finds that K-fold cross-validation is the most suitable error rate prediction method applied to the CHAID method for imbalanced data.]]>
<![CDATA[Penerapan Metode Self Organizing Maps (SOM) dalam Pengklasteran Berdasarkan Indikator Pemerlu Pelayanan Kesejahteraan Sosial (PPKS) Provinsi Jawa Barat ]]>
<![CDATA[Maulidya Hernanda]]>;
<![CDATA[Admi Salma]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Zamahsary Martha]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss4/82
<![CDATA[The province of West Java in Indonesia has witnessed a rise in its impoverished population. Being the most populous province in Indonesia, West Java faces complex social welfare issues due to its large population. This study aims to conduct cluster analysis to identify district/city clusters in West Java province and determine the characteristics of these groups based on the indicators of the Need for Social Welfare Services (PPKS). The self-organizing maps (SOM) method will be utilized for this analysis. SOM is an unsupervised learning method, in which the training process does not require supervision (target output) which produces input representations in two dimensions (maps). In this study, the results obtained were 3 clusters where cluster 1 which consisted of 24 districts/cities had a relatively high average score for each member in the cluster, then cluster 2 which consisted of Cianjur and Karawang districts showed high social welfare problems compared to other clusters, and for cluster 3 which consists of Bandung regency, it shows that the most prominent social welfare problem is the indicator of socio-economic vulnerability of women, with an average of 34,549 cases/year. Based on the results obtained, it is necessary to make the right decisions regarding allocations, resources, more effective service planning, and the development of more targeted social welfare programs.]]>
