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All Journal Media Statistika Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Journal of Natural Science and Integration JURNAL EKSAKTA PENDIDIKAN (JEP) Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Pembelajaran Sains Pelita Eksakta Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Leibniz: Jurnal Matematika Journal of Mathematics UNP UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Yenni Kurniawati
Jurusan Matematika, FMIPA Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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Journal : UNP Journal of Statistics and Data Science

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Implementation of the Self Organizing Maps (SOM) Method for Grouping Provinces in Indonesia Based on the Earthquake Disaster Impact Ihsan Dermawan; Admi Salma; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (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-iss4/83

Abstract

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.
Comparing Classification and Regression Tree and Logistic Regression Algorithms Using 5×2cv Combined F-Test on Diabetes Mellitus Dataset Fashihullisan; Dodi Vionanda; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (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-iss4/84

Abstract

Classification is the process of finding a model that describes and distinguishes data classes that aim to be used to predict the class of objects whose class labels are unknown. There are several algorithms in classification, such as classification trees and regression trees (CART) and logistic regression. The k-fold cross validation method has a weakness for algorithm comparison problems it is possible at different folds to produce different error predictions, so that the results of comparing algorithm performance will also be different. There for in the problem of comparison of algorithms, the researcher will apply the 52cv t test method and the 52cv combined F test. Out of 100 iterations the 10-fold cross validation method was only consistent three times which shows that the k-fold cross validation method has poor consistency in comparing the CART algorithm and logistic regression for diabetes mellitus data. In addition, 52cv combined F test and 52cv t test methods that have been carried out show that 52cv combined F test is better used to get conclusions from the results of a comparison of the two algorithms because it only produces one decision, in contrast to 52cv t test which has the possibility to get different decisions from 10 test statistics which results makes it difficult for researchers to draw conclusions in comparing the cart algorithm and logistic regression
Emprical Study for Algorithms Comparison of Classification and Regression Tree and Logistic Regression Using Combined 5×2cv F Test Fayza Annisa Febrianti; Dodi Vionanda; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (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-iss4/85

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.
Perbandingan Metode Prediksi Laju Galat dalam Pemodelan Klasifikasi Algoritma C4.5 untuk Data Tidak Seimbang Yunistika Ilanda; Dodi Vionanda; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (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-iss4/89

Abstract

Classification modeling can be formed using the C4.5 algorithm. The model formed by the C4.5 algorithm needs to be seen for its prediction accuracy using the error rate prediction method. Imbalanced data causes an increase in the classification error of the C4.5 algorithm because the prediction results do not represent the entire data and worsen the performance of the error rate prediction method. Meanwhile, the case of data with different correlations is carried out to find out whether different correlations affect the performance of the error rate prediction method. The purpose of the research is to find out the most suitable error rate prediction method applied to the C4.5 algorithm in the case of imbalanced data and the influence of different correlations. The results show that the K-Fold CV method is the most suitable prediction method applied to the C4.5 algorithm for imbalanced data cases compared to the HO and LOOCV methods. In addition, high correlation can worsen the performance of error rate prediction methods.
Application of the Fuzzy Time Series-markov Chain Method to the Rupiah Exchange Rate Against the US Dollar (USD) rahmad revi fadillah; Dony Permana; Yenni Kurniawati; Admi Salma
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (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-iss4/91

Abstract

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.
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%.
Forecasting the Exchange Rate of Yen to Rupiah Using the Long Short-Term Memory Method Anggi Adrian Danis; Yenni Kurniawati; Nonong Amalita; 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-iss5/114

Abstract

Long Short-Term Memory (LSTM) is a modification of the Recurrent Neural Network (RNN) to address the problems of exploding and vanishing gradients and make it possible to manage long-term information. To tackle these problems, modifications were made to the RNN by providing memory cells that can store information for long periods. This study aimed to forecast the exchange rate of  Yen to Rupiah using the LSTM method. The data used in this research is daily purchasing rate data from January 2020 to May 2023, which consists of 848 observations. The data was divided into two sets: 80% for training and 20% for testing. For the forecasting process, experiments were conducted to identify the best model by adjusting several hyperparameters. The performance of each model was evaluated using the Mean Absolute Percentage Error (MAPE). According to the experimental results, the best model was the LSTM model with a batch size of 20, 150 epochs, and 50 neurons per layer, which yielded an MAPE value of 1,5399.
Implementation Self Organizing Maps Method In Cluster Analysis Based on Achievement Suistainable Development Goal/SDG’s West Sumatera Province AL Rezki Ivansyah; Fadhilah Fitri; Yenni Kurniawati; 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-iss5/118

Abstract

Indonesian government's commitment to implementing the Sustainable Development Goals (SDG’s) agenda, particularly in West Sumatra. The government of West Sumatra supports the objectives and targets of achieving SDG’s by optimizing the implementation of SDG indicators in the Rencana Aksi Daerah (RAD) for SDG’s of West Sumatra Province for the years 2022-2026. However, in its execution, there is a need for annual monitoring and evaluation of the RAD for SDG’s in West Sumatra Province. Clustering is employed to serve as a consideration for evaluating the implementation of RAD for SDG’s in West Sumatra Province for the years 2022-2026. The clustering method used is Self Organizing Map (SOM), an effective tool for visualizing high-dimensional data and can be used to map high-dimensional data into one, two, or three dimensions, representing connected units or neurons. The data used consist of 14 SDG indicator variables across 19 regencies/cities in West Sumatra in the year 2022, sourced from the official website and publications of the Badan Pusat Statistika (BPS) of West Sumatra Province. The analysis results in the formation of 3 clusters with different characteristics, which can be used as references in making policy decisions and effective strategies to enhance the implementation performance of SDG’s programs in West Sumatra Province.
Fuzzy K-Nearest Neighbor to Predict Rainfall in Padang Pariaman District Rizki Amalia, Annisa; Nonong Amalita; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): 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/vol2-iss1/126

Abstract

Information about rainfall levels at a time and in a region is very important because rainfall influences human activities. Rainfall is the amount of water that falls to the earth in a certain period of time, measured in millimeters. One piece of information related to rainfall is daily rainfall predictions. In this study, an attempt was made to classify daily rainfall at the Padang Pariaman climatology station into 5 categories, namely very light rain, light rain, moderate rain, heavy rain and very heavy rain. There are 4 weather parameters used, namely air temperature, humidity, wind speed and duration of sunlight. One of the methods used to predict rainfall is data mining, a computer learning to analyze data automatically thus obtaining a perfect new model. One of the best prediction algorithms in data mining is Fuzzy K-Nearest Neighbor (FK-NN). FK-NN uses the largest membership degree value of the test data in each class to predict the class. The number of sample classes for rainfall data in Padang Pariaman Regency has an imbalance class. To overcome the imbalance class, Synthetic Minority Over-sampling Technique (SMOTE) method is used to generate minority data as much as majority data. The results of this study by using FK-NN classification with 343 test data, parameters K = 12, and euclidean distance is quite good at the accuracy level of 76,38%..
Co-Authors Abdullah Herman Aditya, Muhammad Fadhil Aditya Admi Salma Afifa Lufti Insani AL Rezki Ivansyah Alya Aufa, Wafiq Amelia Susrifalah Anang Kurnia Anggara, Rudi Anggi Adrian Danis Anita Fadila Anjelisni, Nining Annisa Ramadhani Aprotama, Celsy Ardhi, Sonia Ardiyatul Putri Arnellis Arnellis arrahmi, nailul Atus Amadi Putra Aulia Wanda Aulia, Yuke Aurumnisva Faturrahmi Baehaqi Berliana Nofriadi Bimbim Oktaviandi Celsy Aprotama Chairina Wirdiastuti Cindy Caterine Yolanda Darwas Deska Warita Devi Yopita Sipayung Dewi Murni Dina Fitria Dina Fitria Dina Fitria, Dina Disti Harlin Diva Aliyah Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Elfiani Sarian Bur Elfin Innaka Hamidah Elza Vinora Eujenniatul Jannah Fachri Dermawan Fadhil Irsyad, Muhammad Fadhilah Fitri Fahmi Amri, Fahmi Fashihullisan Fatimah Depi Susanty Harahap Fayyadh Ghaly Fayza Annisa Febrianti Febiola Putri, Febi Fitri, Fadhilah Fitri, Fitri Hayati fitri, silfia wisa Ghaly, Fayyadh Hadiyanti Riskha harelvi, dhea afrila Harpidna, Riska Harpidna Hary Merdeka Helma Helma Helma Helma Hendrawan, Muhammad Ihsan Dermawan Irwan Irwan Khairani, Putri Rahmatun Kusman Sadik Lutfian Almash M Fathoni Arnas Manja Danova Putri Marvero, Andre Maya Ifra Shobia Meira Parma Dewi Minora Longgom Nasution Muhammad Arief Rivano Mukhti, Tessy Octavia NA Mentacem Natasya Dwi Ovalingga, natasyalinggaa Nonong Amalita Oktaviani, Bernadita Permana, Dony permana, yazid Prida Nova Sari Putra, Dio Afdal Putri Yeni, Dicha Putri, Fadhira Vitasha Rahma, Dzakyyah rahmad revi fadillah Revina Rahmadani Rizki Amalia, Annisa Rizkiah, Niswatul Ronald Rinaldo Rosa Salsabila Azarine Salma, Admi Salsabilla Khairani Sasmita, Riza Sepniza Nasywa Septrina Kiki Arisandi Silvia Triana Siskha Maulana Basrul Siti Nurhaliza Sondriva, Wilia SRI RAHAYU Sri Wahyuni Susrifalah, Amelia Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tsani, Nahda Maesya Wimmi Sartika Windi Dwi Saputra Wita, Wita Resfi Ananta Yunistika Ilanda Zamahsary Martha Zilrahmi, Zilrahmi