UNP Journal of Statistics and Data Science最新文献_第5页

Comparison of the Chen and Sinsgh’s Fuzzy Time Series Methods in Forecasting Farmer Exchange Rates in Indonesia Chen和Sinsgh模糊时间序列方法预测印尼农民汇率的比较

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/36

None Okia Dinda Kelana, None Atus Amadi Putra, None Nonong Amalita, None Admi Salma

{"title":"Comparison of the Chen and Sinsgh’s Fuzzy Time Series Methods in Forecasting Farmer Exchange Rates in Indonesia","authors":"None Okia Dinda Kelana, None Atus Amadi Putra, None Nonong Amalita, None Admi Salma","doi":"10.24036/ujsds/vol1-iss4/36","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss4/36","url":null,"abstract":"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.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Error Rate Prediction Methods in Classification Modeling with Classification and Regression Tree (CART) Methods for Balanced Data 平衡数据分类建模错误率预测方法与分类回归树(CART)方法的比较

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/73

None Fitria Panca Ramadhani, None Dodi Vionanda, None Syafriandi Syafriandi, None Admi Salma

{"title":"Comparison of Error Rate Prediction Methods in Classification Modeling with Classification and Regression Tree (CART) Methods for Balanced Data","authors":"None Fitria Panca Ramadhani, None Dodi Vionanda, None Syafriandi Syafriandi, None Admi Salma","doi":"10.24036/ujsds/vol1-iss4/73","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss4/73","url":null,"abstract":"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 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. 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, such as 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 for 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, number of variables, and correlations in normal 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 has the median error rate and the lowest variance, so the most suitable error prediction method used for the CART method is the K-Fold Cross Validation method.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Step Function Intervention Analysis Model to Estimate Number of Aircraft Passengers in Minangkabau International Airport 估计米南卡保国际机场飞机旅客数量的阶跃函数干预分析模型

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/77

None Velya Rahma Putri, None Zilrahmi, None Syafriandi Syafriandi, None Dina Fitria

引用次数: 0

Implementation of the Self Organizing Maps (SOM) Method for Grouping Provinces in Indonesia Based on the Earthquake Disaster Impact 基于地震灾害影响的印尼省份分组自组织地图(SOM)方法的实现

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/83

Ihsan Dermawan, None Admi Salma, None Yenni Kurniawati, None Tessy Octavia Mukhti

{"title":"Implementation of the Self Organizing Maps (SOM) Method for Grouping Provinces in Indonesia Based on the Earthquake Disaster Impact","authors":"Ihsan Dermawan, None Admi Salma, None Yenni Kurniawati, None Tessy Octavia Mukhti","doi":"10.24036/ujsds/vol1-iss4/83","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss4/83","url":null,"abstract":"Indonesia is a country prone to earthquakes. This is because the territory of Indonesia is located between the confluence of three tectonic plates (the Eurasian plate, the Indo-Australian plate, and the Pacific Ocean plate) and is also in the Ring of Fire. The number of earthquakes in Indonesia varies in each province due to the different characteristics of the location of the plates of each province in Indonesia. Earthquake disasters have a very detrimental impact on society, such as causing casualties, damaged houses or damage to public facilities. Therefore it is important to grouping the impact of earthquakes in Indonesia as a disaster mitigation effort in order to find out the characteristics of each province in each cluster. The grouping method used is Kohonen Self Organizing Maps (SOM). SOM is a high-dimensional data visualization technique in the form of a low-dimensional map. The results 3 clusters with the characteristics of each cluster. Cluster 1 consisting of 24 provinces has the characteristics of the highest number of earthquake incidents, the number of missing victims, the number of victims suffering and the number of damaged houses. Cluster 2 consisting of 7 provinces does not show any prominent characteristics of the cluster. Cluster 3 consists of 3 provinces with very prominent characteristics, namely the number of victims killed, the number of injured victims, the number of displaced victims, the number of damaged educational facilities, the number of damaged health facilities and the number of damaged worship facilities the most of the other clusters.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Error Rate Prediction Methods in Binary Logistic Regression Modeling for Imbalanced Data 非平衡数据二元Logistic回归模型错误率预测方法比较

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/86

None Bahri Annur Sinaga, None Dodi Vionanda, None Dony Permana, None Admi Salma

{"title":"Comparison of Error Rate Prediction Methods in Binary Logistic Regression Modeling for Imbalanced Data","authors":"None Bahri Annur Sinaga, None Dodi Vionanda, None Dony Permana, None Admi Salma","doi":"10.24036/ujsds/vol1-iss4/86","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss4/86","url":null,"abstract":"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, namely 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 found 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 are k-fold algorithm is the most suitable error rate prediction algorithm applied to binary logistic regression.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Fuzzy Time Series Markov Chain and Fuzzy Time Series Cheng to Predict Inflation in Indonesia 模糊时间序列马尔可夫链与模糊时间序列程预测印尼通货膨胀的比较

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/76

Ihsanul Fikri, None Admi Salma, None Dodi Vionanda, None Zilrahmi

引用次数: 0

Comparasion of Error Rate Prediction Methods of C4.5 Algorithm for Balanced Data 平衡数据C4.5算法错误率预测方法比较

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/74

None Ichlas Djuazva, None Dodi Vionanda, None Nonong Amalita, None Zilrahmi

{"title":"Comparasion of Error Rate Prediction Methods of C4.5 Algorithm for Balanced Data","authors":"None Ichlas Djuazva, None Dodi Vionanda, None Nonong Amalita, None Zilrahmi","doi":"10.24036/ujsds/vol1-iss4/74","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss4/74","url":null,"abstract":"C45 is a highly effective decision tree algorithm widely used for classification purposes. Compared to CHAID, Cart, and ID3, C4.5 generates decision trees that are easier to understand and does so in a faster manner. This is due to C4.5 selecting attributes based on their information content during each stage of the process. After generating the decision tree model, its performance needs to be evaluated. One commonly used method is the prediction error rate, which assesses the model's performance. The prediction error rate consists of two approaches: the train error rate, which employs the same data for both building and testing the model, potentially leading to overfitting, and the test error rate, which divides the data into training and testing sets. The test error rate includes cross validation techniques such as Leave One Out Cross Validation (LOOCV), Hold Out (HO), and k-folds cross validation. Considering these factors, this research focuses on comparing the three cross-validation methods for predicting error rates applied to the C4.5 algorithm. The study utilizes artificially generated data with a normal distribution, including univariate, bivariate, and multivariate datasets with various combinations of mean differences and correlations. Different correlation structures are applied between two relevant variables and between relevant and irrelevant variables in the bivariate and multivariate data, including three correlation levels: no correlation, moderate correlation, and high correlation. This research findings that k-folds cross validation is the most suitable cross validation method to apply to C4.5.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penerapan Metode Self Organizing Maps (SOM) dalam Pengklasteran Berdasarkan Indikator Pemerlu Pelayanan Kesejahteraan Sosial (PPKS) Provinsi Jawa Barat 根据西爪哇省社会福利委员会(PPKS)的指标，在分类表中进行自我组织(SOM)的实施

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/82

Maulidya Hernanda, Admi Salma, Dodi Vionanda, Zamahsary Martha

{"title":"Penerapan Metode Self Organizing Maps (SOM) dalam Pengklasteran Berdasarkan Indikator Pemerlu Pelayanan Kesejahteraan Sosial (PPKS) Provinsi Jawa Barat","authors":"Maulidya Hernanda, Admi Salma, Dodi Vionanda, Zamahsary Martha","doi":"10.24036/ujsds/vol1-iss4/82","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss4/82","url":null,"abstract":"Salah satu Provinsi di Indonesia yang mengalami peningkatan jumlah penduduk miskin adalah provinsi Jawa Barat. provinsi jawa barat merupakan provinsi dengan jumlah penduduk miskin dan provinsi dengan jumlah penduduk terbanyak di indonesia, wilayah dengan populasi penduduk yang banyak justru memiliki masalah kesejahteraan sosial yang lebih kompleks. Pada penelitian ini dilakukan analisis klaster bagaimana hasil klaster kabupaten/kota di provinsi Jawa Barat serta mengidentifikasi karakteristik kelompok yang dihasilkan berdasarkan indikator Pemerlu Pelayanan Kesejahteraan Sosial (PPKS) menggunakan metode self organizing maps (SOM). SOM merupakan suatu metode unsupervised learning, dimana pada proses pelatihannya tidak memerlukan pengawasan (target output) yang menghasilkan representasi input kedalam dua dimensi (maps). pada penelitian ini diperoleh hasil 3 klaster dimana pada klaster 1 yang beranggotakan 24 kabupaten/kota memiliki nilai rataan relatif tinggi untuk setiap anggota dalam klaster, lalu pada klaster 2 yang beranggotakan kabupaten Cianjur dan kabupaten Karawang menunjukkan masalah kesejahteraan sosial yang tinggi dibandingkan klaster lain, dan untuk klaster 3 yang beranggotakan kabupaten bandung menunjukkan bahwa masalah kesejahteraan sosial yang paling menonjol adalah pada indikator perempuan rawan sosial ekonomi yaitu dengan rataan kasus sebanyak 34.549 jiwa/tahun. berdasarkan hasil yang diperoleh maka diperlukan pengambilan keputusan yang tepat terkait alokasi, sumber daya, perencanaan pelayanan yang lebih efektif, dan pengembangan program kesejahteraan sosial yang lebih tepat sasaran.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emprical Study for Algorithms Comparison of Classification and Regression Tree and Logistic Regression Using Combined 5×2cv F Test 结合5×2cv F检验的分类回归树与逻辑回归算法比较实证研究

UNP Journal of Statistics and Data Science Pub Date : 2023-08-28 DOI: 10.24036/ujsds/vol1-iss4/85

None Fayza Annisa Febrianti, None Dodi Vionanda, None Yenni Kurniawati, None Fadhilah Fitri

{"title":"Emprical Study for Algorithms Comparison of Classification and Regression Tree and Logistic Regression Using Combined 5×2cv F Test","authors":"None Fayza Annisa Febrianti, None Dodi Vionanda, None Yenni Kurniawati, None Fadhilah Fitri","doi":"10.24036/ujsds/vol1-iss4/85","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss4/85","url":null,"abstract":"Classification is a method to estimate the class of an object based on its characteristics. Several learning algorithms can be applied in classification, such as Classification and Regression Tree (CART) and logistic regression. The main goal of classification is to find the best learning algorithm that can be applied to get the best classifier. In comparing two learning algorithms, a direct comparison by seeing the smaller prediction error rate may be possible when the difference is very clear. In this case, direct comparison is misleading and resulting inadequate conclusions. Therefore, a statistical test is needed to determine whether the difference is real or random. The results of the 5×2cv paired t-test sometimes reject and sometimes fail to reject the hypothesis. It is distracting because the changing of the error rate difference should not affect the test result. Meanwhile, the overall results of the combined 5×2cv F test show that the tests fail to reject the hypothesis. This indicates that CART and logistic regression perform identically in this case.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Haversine and Euclidean Distance Formula for Calculating Distance Between Regencies in West Sumatra 计算西苏门答腊县间距离的哈弗辛和欧几里得距离公式的比较

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/39

Vinka Haura Nabilla, Dina Fitria, D. Permana, F. Fitri

{"title":"Comparison of Haversine and Euclidean Distance Formula for Calculating Distance Between Regencies in West Sumatra","authors":"Vinka Haura Nabilla, Dina Fitria, D. Permana, F. Fitri","doi":"10.24036/ujsds/vol1-iss3/39","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/39","url":null,"abstract":"Jarak adalah angka yang menunjukkan seberapa jauh jarak antara dua tempat. Manfaat penggunaan jarak banyak digunakan dalam penelitian, salah satunya dalam penerapan matriks pembobot spasial. Matriks pembobot spasial diperoleh berdasarkan informasi kedekatan antar wilayah. Terdapat dua jenis pembobot spasial yaitu, berdasarkan persinggungan (contiguity) dan jarak (distance). Penentuan kedekatan wilayah di Sumatera Barat lebih baik menggunakan pembobot spasial berdasarkan jarak, sebab di Sumatera Barat terdapat wilayah kepulauan dan pegunungan yang membatasi antar wilayah. Beberapa persamaan estimasi jarak yang dapat digunakan adalah jarak Haversine dan Euclidean. Hubungan antara dua titik dalam Haversine memperhitungkan kelengkungan bumi ketika menghitung jarak, yang merupakan perbedaan antara kedua rumus tersebut. Sebaliknya, metode jarak Euclidean menggunakan garis lurus untuk menghubungkan dua titik. Tujuan dari penelitian ini adalah untuk memastikan apakah rumus jarak Haversine dan Euclidean menghasilkan hasil yang berbeda secara signifikan dalam hal jarak. Perhitungan jarak titik koordinat memanfaatkan garis lintang dan garis bujur yang di dapat dari Google Maps. Jarak yang diukur menggunakan kedua rumus tersebut dinyatakan dalam kilometer (km), kemudian data tersebut diolah dengan menggunakan uji z. Temuan menunjukkan bahwa rumus Haversine dan rumus Euclidean Distance tidak berbeda secara signifikan dalam proses penghitungan jarak.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123589005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0