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Tessy Octavia Mukhti
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tessyoctaviam@fmipa.unp.ac.id
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LPPM Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Barat, Kota Padang, Sumatera Barat 25131
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UNP Journal of Statistics and Data Science
Published by Universitas Negeri Padang
ISSN : -     EISSN : 2985475X     DOI : 10.24036/ujsds
Core Subject : Science, Education, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences
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.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 17 Documents
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Search results for , issue "Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science" : 17 Documents clear
Penerapan Metode Regresi Kuantil pada Data yang Mengandung Outlier untuk Tingkat Kejahatan di Jabodetabek Arssita Nur Muharromah; Zamahsary Martha; Dony Permana; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/94

Abstract

The problem of crime is increasingly widespread in Indonesia. The crime rate in Jabodetabek is the second highest in Indonesia. In this study containing outliers, the appropriate method for this research is quantile regression. Quantile regression is the development of median regression or the Least Absolute Deviation (LAD) method which is useful for dividing data into two parts to minimize errors. however, this LAD is considered not good for modeling, therefore comes the quantile regression. Quantile regression is useful for overcoming the problem of unfulfilled assumptions in classical regression, namely the phenomenon of heteroscedasticity and quantile regression can model data that contains outliers. The quantile regression method approach is to separate or divide the data into certain parts or quantiles where it is suspected that there are differences in estimated values. The resulting measurement of the goodness of the model uses the coefficient of determination or R2 in each quantile. In this study, five quantiles were used, namely 0,05; 0,25; 0,50; 0,75; and 0,95. From the results of the analysis it is known that the best parameter estimation model is found in the 0,95 quantile with all independent variables having a significant effect on the dependent variable (crime rate). whereas in the 0,25 and 0,50 quantiles there are no independent variables that have a significant effect, this may be due to the influence of other factors not present in the study that affect each quantile.
Classification of Nutrition Problems for Indonesian Toddler With Decision Tree Algorithm C4.5 Nadha Ovella Syaqhasdy; Zamahsary Martha; Nonong Amalita; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/98

Abstract

Having excellent human resources is essential for Indonesia's development. The development of Indonesia is the key to improving the quality of life for its citizens, and a focus on this development can have a positive impact on the health and economy of the community. A healthy and educated generation is fundamental for the expected progress of this nation, as nutritional status is a significant factor affecting the quality of human resources. Nutritional problems can lead to serious consequences, such as abnormal physical growth, a decline in IQ quality, and even death. The objective of this research is to analyze the factors that influence the nutritional status of toddlers by classifying each variable using a decision tree. A decision tree is a flowchart resembling a branching tree structure. The C4.5 algorithm was utilized in this study. This algorithm can process both numeric and categorical data, handle missing attribute values, and generate easily interpretable rules. After conducting the analysis, it was found that the decision tree's results indicated that the attribute "Stunting < 20%" is a determining factor for acutechronic malnutrition issues in toddlers. There are 392 districts and cities in Indonesia where the prevalence of stunted toddler nutritional status is less than 20%. The model created using the C4.5 algorithm was evaluated using a confusion matrix, resulting in an accuracy of 99.8% and a kappa value close to 1. This indicates that the model is capable of accurately classifying toddler nutrition problems in Indonesia.
Sentiment Analysis of Prabowo Subianto as 2024 Presidential Candidate on Twitter Using K-Nearest Neighbor Algorithm Aurumnisva Faturrahmi; Zamahsary Martha; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/101

Abstract

The presidential election is one of the most talked topics at this moment. Based on many surveys, Prabowo Subianto is one of the strongest candidates for the upcoming 2024 presidential election. This research aims to see how the public sentiment towards Prabowo Subianto as the presidential candidate tends to be positive or negative. Sentiment classification was conducted using the K-Nearest Neighbor (KNN) algorithm. This algorithm classifies sentiment based on the k value of the nearest neighbor. This analysis was conducted in several stages such as data collection, text preprocessing, data labelling, data classification using the KNN algorithm, and evaluating the accuracy of the model in classifying sentiment. In this research, the results of the sentiment classification were 2731 positive sentiments and 76 negative sentiments. Where the accuracy rate produced by the model using the value of k = 3 on the division of training data and testing data of 80:20 is 97,33%.
Naive Bayes Classifier Method on Sentiment Analysis of Bibit Application Users in Play Store Afifa Lufti Insani; Zamahsary Martha; Yenni Kurniawati; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/102

Abstract

The Bibit app is one of the most widely used investment apps these days. This application is widely used by novice investors because of its convenience in opening accounts, disbursing funds, purchasing mutual funds and easy-to-understand application design. However, there are still many people who doubt and worry about the quality of the Bibit application due to the lack of understanding of the advantages and disadvantages of the Bibit application. So, review data on the application is used which is available in the play store with the aim of knowing user reviews of the application and being a consideration for prospective users before using the application. Because reviews on the application have a large number and can be positive or negative, so sentiment analysis is needed that can help classify these reviews quickly. Then classification is carried out to obtain a classification model that can be used to predict user sentiment using the Naive Bayes Classifier method. The results obtained by Bibit application users tend to have positive sentiments with an accuracy value of 79.45%.
Sentiment Analysis of TikTok Application on Twitter using The Naïve Bayes Classifier Algorithm Denia Putri Fajrina; Syafriandi Syafriandi; Nonong Amalita; Admi Salma
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/103

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. 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.
Classification of Coronary Heart Disease at Semen Padang Hospital using Algorithm Classification And Regression Trees (CART) defal aditya defran; Atus Amadi Putra; Dodi Vionanda; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/104

Abstract

Cardiovascular disease is a degenerative disease caused by decreased function of the heart and blood vessels. One of the heart diseases that is very popular today is coronary heart disease (CHD). The main factors that cause CHD include age, gender, hypertension, blood sugar and cholesterol. One method that can be used to group CHD is classification. Classification And Regression Trees (CART )is a decision tree that describes the relationship between a response variable and one or more predictor variables. The goal of CART is to obtain an accurate data group as a characteristic of a classification. Based on the results of the optimal tree, the attribute that is the main characteristic in classifying CHD patients at Semen Padang Hospital is age. The determination of the classification results using the confusion matrix produced an accuracy value of 66.67%, a sensitivity of 56.52% for classifying CHD patients, and a specificity of 84.61% for classifying non-CHD patients.
Fuzzy Geographically Weighted Clustering Method for Grouping Provinces in Indonesia Based on Welfare Indicators Aspects of Information and Communication Technology (ICT) Hefiani Mustika Hasanah; Dina Fitria; Dony Permana; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/108

Abstract

The welfare of the people is a task and goal that must be realized by the Republic of Indonesia. To find out the condition of the welfare of the Indonesian people, it can be seen in eight areas of Indonesia's welfare indicators. Indicators The welfare of the Indonesian people is undergoing a digital transformation of information and communication technology (ICT) in 2021. However, there was a gap in ICT development due to geographical conditions and the distribution and dynamics of each region's society. Cluster analysis is a solution for target setting for better future decisions. Fuzzy Geographically Weighted Clustering (FGWC) is one of the cluster methods with fuzzy logic that considers geographical and population elements in grouping targets. The results of the research resulted in three optimum clusters with different characteristics for  each cluster based on indicators of ICT aspects of people's welfare. Cluster 1 has a medium status of ICT indicators of people's welfare and is located in the middle or at the end of the island, provinces from cluster 2 have a low status of ICT indicators of people's welfare with a medium area, while cluster 3 has a high status of ICT indicators of people's welfare with a large area or dense populations.
Fuzzy Geographically Weighted Clustering Analysis for Sectoral Potential Gross Regional Domestic Product in West Sumatera Syifa Nabilah Wandira; Zilrahmi; Syafriandi Syafriandi; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/109

Abstract

Gross Regional Domestic Product (GRDP) is the sum of the added value of all goods and services produced or produced in an area that arises as a result of various economic activities in a certain period. Each region certainly has its own advantages and potential, such as in sectors or business fields. GRDP inequality occurs due to differences in geographical conditions and natural resources in each region. The method that can be used to overcome this inequality is cluster analysis. Cluster analysis can group data objects that have the same characteristics so that the inequality that occurs can be seen from the clusters formed. Fuzzy Geographically Weighted Clustering is a clustering method using fuzzy logic which gives a geographic effect to each cluster so that it can better describe the actual cluster situation. The results of  research obtained 3 optimum clusters with different characteristics. Cluster 1 has high potential, cluster 2 has low potential and cluster 3 has medium potential in forming GRDP.
Prediction Of Bogor City Rainfall Parameters Using Long Short Term Memory (LSTM) Sherly Amora Jofipasi; Admi Salma; Dodi Vionanda; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/110

Abstract

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.
Backpropagation Neural Network Application in Predicting The Stock Price of PT Bank Rakyat Indonesia Tbk Dewi Febiyanti; Nonong Amalita; Dony Permana; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/113

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. The increase in stock price will provide benefits for investors by selling stocks. However, the occurrence of mistakes when choosing the time to make stock transactions results in investors being able to take high risks because stock prices fluctuate. Therefore, to anticipate the occurrence of high risk to investors, stock price predictions is 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

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