Articles
<![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[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[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.]]>
<![CDATA[Implementation of the Self Organizing Maps (SOM) Method for Grouping Provinces in Indonesia Based on the Earthquake Disaster Impact ]]>
<![CDATA[Ihsan Dermawan]]>;
<![CDATA[Admi Salma]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Tessy Octavia Mukhti]]>
<![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-iss4/83
<![CDATA[Indonesia's strategic geological location causes Indonesia to be frequently hit by earthquake disasters, which are a series of events that disturb and threaten the safety of life and cause material and non-material losses. The number of earthquake events in Indonesia causes casualties, both fatalities and injuries, destroying the surrounding area as well as destroying infrastructure and causing property losses. Therefore, it is important to cluster the impact of earthquake disasters in Indonesia as a disaster mitigation effort in order to determine the characteristics of each province. The clustering method used is Kohonen Self Organizing Maps (SOM). SOM is a high-dimensional data visualization technique into a low-dimensional map. The results of this study obtained 3 clusters with the characteristics of each cluster. The first cluster with low impact of earthquake disaster consists of 32 provinces. The second cluster with moderate impact consists of 1 province characterized by the highest number of missing victims and the highest number of injured victims. The third cluster with a high impact consists of 1 province with the most prominent characteristics being the number of earthquake events, the number of deaths, the number of injured, the number of displaced, the number of damaged houses, the number of damaged educational facilities, the number of damaged health facilities and the number of damaged worship facilities is the highest of the other clusters.]]>
<![CDATA[Comparison of Error Rate Prediction Methods in Binary Logistic Regression Modeling for Imbalanced Data ]]>
<![CDATA[Bahri Annur Sinaga]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Dony Permana]]>;
<![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-iss4/86
<![CDATA[Binary logistic regression is a regression analysis used in classification modeling. The performance of binary logistic regression can be seen from the accuracy of the model formed. Accuracy can be measured by predicting the error rate. One method of predicting the error rate that is often used is cross-validation. There are three algorithms in cross-validation: leave one out, hold out, and k-fold. Leave one out is a method that divides data based on the number of observations so that each observation has the opportunity to become testing data but requires a long time in the analysis process when the number of observations is large. Hold out is the simplest algorithm that only divides the data into two parts randomly, so there is a possibility that important data does not become training data. K-fold is an algorithm that divides data into several groups, but k-fold is not suitable for data that has a small number of observations. In reality, real data is often imbalanced. In logistic regression,when the data is increasingly imbalanced, the prediction results will approach the number of minority classes. This research focuses on the comparison of error rate prediction methods in binary logistic regression modeling with imbalanced data. This study uses three types of data, namely univariate, bivariate, and multivariate, which are generated by differences in population mean and correlation between independent variables.The results obtained show that the k-fold algorithm is the most suitable error rate prediction algorithm applied to binary logistic regression.]]>
<![CDATA[Application of the Fuzzy Time Series-markov Chain Method to the Rupiah Exchange Rate Against the US Dollar (USD) ]]>
<![CDATA[rahmad revi fadillah]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Yenni Kurniawati]]>;
<![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-iss4/91
<![CDATA[The exchange rate plays an important role in evaluating the Indonesian economy due to how much it affects the nation's overall financial situation. Activities for projecting future exchange rates can be conducted based on their dynamic characteristics. The purpose of this study is to predict the exchange rate of the Indonesian Rupiah (IDR) against the United States Dollar (USD) using the Fuzzy Time Series Markov chain (FTS-MC) method. Researchers apply the FTS-MC approach to analyze the connection between every bit of historical data and the direction in which it moved in order to forecast future data movements. While the rupiah exchange rate Forecast against the USD between January 2 and January 31, 2023, with a MAPE value of 2.41% and a forecast accuracy score of 97.58% result. During up to 8 forecasted periods, the forecasting value gained by the FTS-MC approach is close to the actual value, and the next period is higher than the current value. The forecasting results graph further shows that the FTS-MC approach gives forecast values fluctuate around IDR15,800.]]>
<![CDATA[Sentiment Analysis of TikTok Application on Twitter using The Naïve Bayes Classifier Algorithm ]]>
<![CDATA[Denia Putri Fajrina]]>;
<![CDATA[Syafriandi Syafriandi]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss5/103
<![CDATA[TikTok is a popular social media platform that has gained a lot of attention lately. People of all ages are using this application to share short videos with their friends and followers. The content on TikTok is diverse and can be tailored to individual preferences, but there have been concerns about the presence of vulgar content that can be viewed by minors as there are no age restrictions. This has led to public scrutiny of the application on social media platforms like Twitter. To address this issue, sentiment analysis was conducted on reviews of the TikTok application to help users make informed decisions about its use. The aim of this analysis was to determine whether people's opinions about TikTok were positive or negative. To achieve this goal, researchers used the hashtag "TikTok Application".The results were classified into two categories positive and negative using the Naïve Bayes Classifier method. The analysis was carried out using 80% training data and 20% testing data, and the results showed an accuracy rate of 80.32%, with a recall value of 97% and a precision value of 78%. In general, positive feedback from Indonesians on the TikTok application is related to the invitation to download the TikTok application, while in negative feedback, information is obtained that the TikTok application is based on content that is inappropriate for TikTok users to download This information can help users make informed decisions about using the TikTok application.]]>
<![CDATA[Prediction Of Bogor City Rainfall Parameters Using Long Short Term Memory (LSTM) ]]>
<![CDATA[Sherly Amora Jofipasi]]>;
<![CDATA[Admi Salma]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss5/110
<![CDATA[Bogor is a city that has high intensity of rainfall and has erratic rainfall. So it is necessary to predict Bogor's rainfall. Rainfall prediction can be done using the LSTM algorithm. In the LSTM algorithm, there are neuron hidden layer and epoch parameters. Neuron hidden layer and epoch greatly affect the resulting prediction results, therefore it is necessery to determine the best neuron hidden layer and epoch values to produce good prediction results in Bogor rainfall. The prediction parameters results obtained by LSTM have worked well using optimal neuron hidden values of 256, optimal epoch of 150, MAPE of 1,64%, and the comparison of actual data patterns and prediction data already has the same data patterns.]]>
<![CDATA[Bitcoin Price Prediction Using Support Vector Regression ]]>
<![CDATA[Wulan Septya Zulmawati]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Syafriandi Syafriandi]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol1-iss5/121
<![CDATA[Cryptocurrency provides the most return compared to other investment instruments, causing many novice traders to be attracted to crypto as a tool to make significant profits in the short term. One of the most widely used cryptocurrencies is Bitcoin. Trading is closely related to technical analysis. Various techniques in technical analysis cause beginner traders to have difficulties choosing the right technique. Machine learning methods can be an alternative to overcoming the barriers of beginner traders in the crypto market with predictive methods. One method of machine learning for prediction is Support Vector Regression (SVR). Using the grid search algorithm shows that this method has a good predictive accuracy value of 99,25% and MAPE 0,1206%.]]>
