UNP Journal of Statistics and Data Science最新文献_第2页

Categorical Data Clustering with K-Modes Method on Fire Cases in DKI Jakarta Province 用 K-Modes 方法对雅加达省火灾案例进行分类数据聚类

UNP Journal of Statistics and Data Science Pub Date : 2024-02-25 DOI: 10.24036/ujsds/vol2-iss1/115

Widia Handa Riska, D. Permana, Atus Amadi Putra, dan Zilrahmi

引用次数: 0

Twitter Data Sentimen Analysis 2024 Presidential Candidate Using Algorithm Naïve Bayes Classifier By Methods K-Fold Cross Validation 使用 Naïve Bayes 分类器算法和 K-Fold 交叉验证法分析 2024 年总统候选人的 Twitter 数据句子

UNP Journal of Statistics and Data Science Pub Date : 2024-02-25 DOI: 10.24036/ujsds/vol2-iss1/149

Aldi Prajela, Syafriandi Syafriandi, Dony Permana, Dina Fitria

{"title":"Twitter Data Sentimen Analysis 2024 Presidential Candidate Using Algorithm Naïve Bayes Classifier By Methods K-Fold Cross Validation","authors":"Aldi Prajela, Syafriandi Syafriandi, Dony Permana, Dina Fitria","doi":"10.24036/ujsds/vol2-iss1/149","DOIUrl":"https://doi.org/10.24036/ujsds/vol2-iss1/149","url":null,"abstract":"Indonesia implements a democratic system by involving the public in General Elections (Pemilu) for specific political positions. The active community expresses opinions on social media, especially regarding the 2024 Presidential Election (Pilpres) and respective presidential candidates, which have become trending topics on Twitter. The analysis used to absorb these tweets into information is sentimen analysis using the Naïve Bayes Classifier algorithm with the K-fold Cross-Validation method. Through the stages of pre-processing, weighting, labeling, classification using NBC, and testing using a Confusion Matrix, The results of the classification from NBC showed that Anies got 80% positive tweets and 20% negative tweets from 1186 tweets, Prabowo Subianto got 78% positive tweets and 22% negative tweets from 1149 tweets, and Ganjar Pranowo got 77% positive tweets and 23% negative tweets from 1075 tweets. Testing process was carried out using the NBC algorithm with the K-Fold Cross Validation method using values k=1 to k=10. The function of K-Fold Cross Validation is to maximize the confusion matrix result. It can be conclude that Anies Baswedan has the highest score in iteration 4, namely a precision value of 90%, a recall value of 99%, and the accurary value of 91%. Furthemore, Ganjar Pranowo had the highest score in iteration 9, namely a precision value of 95%,a recall value of 97%, and an accuracy value of 92%. Meanwhile, Prabowo Subianto had the highest score in iteration 9, namely a precision value of 97%, a recall value of 99%, and an accuracy value of 94%.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"10 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432181","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

Biplot and Procrustes Analysis of Poverty Indicators By Province in Indonesia in 2015 dan 2019 2015 年至 2019 年印度尼西亚各省贫困指标的双曲线图和 Procrustes 分析

UNP Journal of Statistics and Data Science Pub Date : 2024-02-25 DOI: 10.24036/ujsds/vol2-iss1/124

Ade Eriyen Saputri, Admi Salma, N. Amalita, D. Permana

{"title":"Biplot and Procrustes Analysis of Poverty Indicators By Province in Indonesia in 2015 dan 2019","authors":"Ade Eriyen Saputri, Admi Salma, N. Amalita, D. Permana","doi":"10.24036/ujsds/vol2-iss1/124","DOIUrl":"https://doi.org/10.24036/ujsds/vol2-iss1/124","url":null,"abstract":"Poverty is one of the country's problems that the government should overcome. Poverty is influenced by several indicators. The success of a government can be seen from changes in poverty. This study compares the percentage of Indonesia's poverty indicators at the beginning of office (2015) and the end of office (2019) of one government period. The indicators that most affect the poverty rate in 2015 and 2019 are seen using biplot analysis while to measure the similarity and the magnitude of the percentage change in poverty from 2015 to 2019 can use procrustes analysis. The results of the biplot analysis show that households have access to proper sanitation services as the indicator with the highest diversity in 2015 while in 2019 it is the poor young population (15-24 years old) who are not in school and working or attending training and households have access to decent drinking water sources. Riau Islands, DKI Jakarta, DI Yogyakarta, and Bali are the provinces that have the highest values in almost all poverty indicators except the indicator of the percentage of young people (15-24 years old) who are not in school, working or attending training. The results of the procrustean analysis show an increase of 9.7% in Indonesia's poverty indicators in 2019 compared to 2015. So it can be said that the two configurations are very similar.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"44 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432714","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 Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis 在 RSUD 中应用 Naive Bayes 算法进行登革热分类 Achmad Darwis 博士

UNP Journal of Statistics and Data Science Pub Date : 2024-02-25 DOI: 10.24036/ujsds/vol2-iss1/128

Viola Yuniza, Atus Amadi Putra, Nonong Amalita, Fadhilah Fitri

{"title":"Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis","authors":"Viola Yuniza, Atus Amadi Putra, Nonong Amalita, Fadhilah Fitri","doi":"10.24036/ujsds/vol2-iss1/128","DOIUrl":"https://doi.org/10.24036/ujsds/vol2-iss1/128","url":null,"abstract":"Dengue Hemorrhagic Fever (DHF) is a disease transmitted through the bite of the Aedes Aegypti mosquito. Limapuluh Kota Regency BPS stated that the morbidity rate due to dengue fever was 14.40% per 100,000 population, this figure jumped high from the previous year with a morbidity rate of 3.30% per 100,000 population. The main symptoms of dengue fever are fever that lasts for 2-7 days, pain felt in the muscles and joints accompanied by a rash or no rash, dizziness, and even vomiting blood. Dengue infection can cause various clinical symptoms, ranging from dengue fever, dengue hemorrhagic fever, to dengue shock syndrome. Based on this, there is a need for a classification method that can help and facilitate early diagnosis of dengue fever. The method used is Naive Bayes by classifying dengue positive and dengue negative patients. The aim of this research is to determine the results of the classification of patients suffering from dengue fever, as well as to determine the level of accuracy using the Naive Bayes method. Based on research that has been carried out, the results of the classification of patients are 58 correct and 14 patients classified incorrectly. The accuracy results obtained in this algorithm were quite high, namely 80%, while the sensitivity was 65% and the specificity was 86.5%.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"100 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140433595","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

Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm 利用支持向量回归和网格搜索算法预测印度尼西亚黄金价格

UNP Journal of Statistics and Data Science Pub Date : 2024-02-25 DOI: 10.24036/ujsds/vol2-iss1/145

Nindi Syahfitri, N. Amalita, Dodi Vionanda, Zamahsary Martha

引用次数: 0

Sentiment Analysis of DANA Application Reviews on Google Play Store Using Naïve Bayes Classifier Algorithm Based on Information Gain 使用基于信息增益的奈夫贝叶斯分类器算法对 Google Play 商店中的 DANA 应用程序评论进行情感分析

UNP Journal of Statistics and Data Science Pub Date : 2024-02-25 DOI: 10.24036/ujsds/vol2-iss1/147

Cindy Caterine, Syafriandi Yolanda, Yenni Kurniawati, Dina Fitria

{"title":"Sentiment Analysis of DANA Application Reviews on Google Play Store Using Naïve Bayes Classifier Algorithm Based on Information Gain","authors":"Cindy Caterine, Syafriandi Yolanda, Yenni Kurniawati, Dina Fitria","doi":"10.24036/ujsds/vol2-iss1/147","DOIUrl":"https://doi.org/10.24036/ujsds/vol2-iss1/147","url":null,"abstract":"DANA is a digital payment platform that provides various features to make it easier for users to make payments, transfers, and balance replenishment online. DANA application users provide a variety of reviews that include both constructive and critical opinions, which can be valuable input for DANA application developers. The purpose of this research is to evaluate the results of sentiment classification of DANA application user reviews on the Google Play Store service using the Naïve Bayes Classifier method and Information Gain feature selection. In addition, this study aims to assess the effect of applying IG feature selection on the performance of the resulting model. In this study, reviews are divided into two categories, namely positive and negative based on lexicon-based labeling. Furthermore, data weighting, feature selection, and data division are carried out with a proportion of 80% train data and 20% test data before model building. There are two models, namely a model without feature selection (NBC model) and a model with feature selection (NBC-IG model). The evaluation results showed that the NBC model with 1106 features performed well, with 82.91% accuracy, 83.96% precision, and 90.23% recall. Meanwhile, the NBC-IG model with 536 features showed higher performance, with 85.09% accuracy, 85.79% precision, and 92.09% recall. The application of IG feature selection with the IG value limit parameter > 0.01 in the NBC model successfully reduced the number of features by 570, and improved model performance with an increase in accuracy by 2.18%, precision by 1.83%, and recall by 1.86%.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"42 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432739","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

Prediction Of Bogor City Rainfall Parameters Using Long Short Term Memory (LSTM) 利用长短期记忆（LSTM）预测茂物市降雨量参数

UNP Journal of Statistics and Data Science Pub Date : 2023-11-30 DOI: 10.24036/ujsds/vol1-iss5/110

Sherly Amora Jofipasi, Admi Salma, Dodi Vionanda, Dina Fitria

引用次数: 0

Backpropagation Neural Network Application in Predicting The Stock Price of PT Bank Rakyat Indonesia Tbk 反向传播神经网络在预测 PT Bank Rakyat Indonesia Tbk 股价中的应用

UNP Journal of Statistics and Data Science Pub Date : 2023-11-30 DOI: 10.24036/ujsds/vol1-iss5/113

Dewi Febiyanti, N. Amalita, D. Permana, Tessy Octavia Mukhti

{"title":"Backpropagation Neural Network Application in Predicting The Stock Price of PT Bank Rakyat Indonesia Tbk","authors":"Dewi Febiyanti, N. Amalita, D. Permana, Tessy Octavia Mukhti","doi":"10.24036/ujsds/vol1-iss5/113","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss5/113","url":null,"abstract":"Investors often make mistakes when making stock transactions even though having chosen good company stocks. The thing that needs to be considered in making stock transactions is to see the movement of stock prices. The movement of the stock price in PT Bank Rakyat Indonesia Tbk has changed in the form of a decrease or increase. An increasing stock price will provide benefits for investors by selling stocks. But, investors actually decide to make stock purchases. The existence of stock purchase transactions causes investors to take a high risk because stock prices fluctuate. To anticipate the occurrence of high risk to investor, stock price predictions are made using a Backpropagation Neural Network (BPNN). BPNN can adapt quickly and is able to predict nonlinear data such as stock prices and produce a high level of accuracy. The results of this study obtained the best BPNN model, namely the BP(5,3,1) model with a Mean Absolute Percentage Error (MAPE) of 0,8193%. These results show that the model has good network performance so that it can predict stock prices well because it gets a small prediction error.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"165 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139201853","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

Sentiment Analysis of TikTok Application on Twitter using The Naïve Bayes Classifier Algorithm 使用奈维贝叶斯分类器算法对 Twitter 上的 TikTok 应用程序进行情感分析

UNP Journal of Statistics and Data Science Pub Date : 2023-11-30 DOI: 10.24036/ujsds/vol1-iss5/103

Denia Putri Fajrina, Syafriandi, N. Amalita, Admi Salma

{"title":"Sentiment Analysis of TikTok Application on Twitter using The Naïve Bayes Classifier Algorithm","authors":"Denia Putri Fajrina, Syafriandi, N. Amalita, Admi Salma","doi":"10.24036/ujsds/vol1-iss5/103","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss5/103","url":null,"abstract":"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. 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%. This information can help users make informed decisions about using the TikTok application.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139207228","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

Bitcoin Price Prediction Using Support Vector Regression 使用支持向量回归预测比特币价格

UNP Journal of Statistics and Data Science Pub Date : 2023-11-30 DOI: 10.24036/ujsds/vol1-iss5/121

Wulan Septya Zulmawati, Nonong amalita, Syafriandi Syafriandi, Admi Salma

引用次数: 0