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Tessy Octavia Mukhti
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Pemetaan Intensitas Gempa Bumi di Wilayah Sumatera Barat Menggunakan Model Epidemic Type Aftershock Sequence Spatio-Temporal Fikra, Hidayatul; Fitria, Dina; Nonong Amalita; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (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-iss2/171

Abstract

The random spatial and temporal occurrence of earthquakes means that this are still being researched from a seismological and stochastic perspective. Point processes are examples of stochastic processes which explain seismic activity, one of them is Epidemic Type Aftershock Sequence (ETAS) model. It lackness ignores the location or spatial component of. Consequently, the components of time, location, and magnitude will be taken into consideration when discussing the ETAS model in this study. The spatio-temporal model is the name given to this concept. Therefore, in this research,mapping of earthquake intensity will be carried out in the West Sumatra region using the spatio-temporal ETAS model stated in conditional intensity function with eight parameters. The data used are earthquake events in the West Sumatra region with a magnitude threshold of 4 SR and a depth of ≤ 70 km for the period January 2000 to January 2024. Parameter model estimated using the maximum likelihood method and solved using the Davidon Fletcher Powell algorithm. The result shows area of West Sumatra with high earthquake intensity is coastal area, namely West Pasaman, Padang, Mentawai Islands and the South Pesisir. This makes the area vulnerable to seismic disasters
Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia Yonggi, Yonggi Septa Pramadia; Zamahsary Martha; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/194

Abstract

One indicator to determine the economic stability of a country can be seen from the inflation rate of a country. Inflation is an economic symptom in the form of a general increase in prices or a tendency to increase the prices of goods and services in general and continuously. In an effort to anticipate the impact of inflation in the future, an analysis is needed to find out how the development of the inflation rate is by forecasting. Extreme Learning Machine (ELM) is a feed-forward artificial neural network (ANN) algorithm with one hidden layer called Single Hidden Layer Neural Networks (SLFNs). Based on the research, forecasting the inflation rate in Indonesia using the Extreme Learning Machine algorithm obtained the best architecture  (12,48,1) with a MAPE value of 11%. These results show good forecasting because the resulting MAPE is relatively low.
Comparison of Linear Discriminant Analysis with Robust Linear Discriminant Analysis Fitri, Fitri Hayati; Dodi Vionanda; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/206

Abstract

Discriminant analysis is a multivariate method for dividing things into discrete groups and assigning new objects to existing categories. A discriminant function, which is a linear combination of independent variables used to categorize things into two or more groups or categories, is the result of discriminant analysis. The independent variables in a linear discriminant analysis must be multivariate normally distributed, and the covariance matrices for each group must be equal. In linear discriminant analysis, it is also essential to identify outliers because their existence in the data set can undermine the assumptions made by the method and lead to incorrect classification results. Therefore, in discriminant analysis, handling outliers with robust approaches is required. One such robust method in discriminant analysis is the Minimum Covariance Determinant (MCD), which is highly effective in dealing with outliers and relatively easier to apply compared to other robust methods. The aim of this study is to compare the classification results of linear discriminant analysis with robust linear discriminant analysis on the dataset of diabetes patients at RSUD Padangsidimpuan in 2023. The results obtained from this dataset indicate that linear discriminant analysis achieved an accuracy of 85,71%, while robust linear discriminant analysis achieved an accuracy of 80,95%. These findings suggest that the use of liniar discriminant analysis and robustt linear discriminant analysis can yield different results depending on the characteristics of the data and the number of outliers in the dataset.
Mixed Geographically Weighted Regression Modeling of Gender Development Index in Indonesia Nikma Hasanah; Dodi Vionanda; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/207

Abstract

The Gender Development Index (GDI) is one of the primary measures of gender equality in the field of human development. Indonesia's GDI statistics for 2023 show the development gap between men and women. Using Mixed Geographically Weighted Regression (MGWR), a blend of regression and Geographically Weighted Regression (GWR) models, to identify the factors influencing GDI is one approach to closing the gap. The results showed that when it came to value selection using the Akaike Information Criterion (AIC), the MGWR model outperformed the GWR model. Population with health complaints and adjusted per capita expenditure were found to be globally influential factors, while female participation in parliament, open unemployment rate, and labor force participation rate were found to be locally influential factors by the MGWR model with Adaptive Kernel Bisquare weights.
Implementation of the Fuzzy C-Means Clustering Method in Grouping Provinces in Indonesia based on the Types of Goods Sold in E-commerce Businesses in 2022 Bimbim Oktaviandi; Tessy Octavia Mukhti; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/210

Abstract

The internet facilitates e-commerce by enabling efficient transactions and building consumer trust. With internet users in Indonesia reaching 204 million in 2022, it is crucial to Cluster provinces based on the types of goods and services sold online to design effective marketing strategies. The Fuzzy C-Means (FCM) method is used for Cluster analysis, allowing objects to have different membership degrees in multiple Clusters and providing accurate Cluster center placement. This study applies Fuzzy C-Means to Cluster 34 provinces in Indonesia based on the sale of goods/services in e-commerce in 2022, aiming to provide insights into market preferences and assist companies in developing more effective strategies. The results show that the method forms two Clusters. By evaluating standard deviation values and ratios, Fuzzy C-Means proves effective in Clustering provinces in Indonesia based on e-commerce sales data. Cluster validation reveals a standard deviation ratio of 0.14, indicating clear and significant Cluster separation.
Classification of Poor Households in Padang City Using the Naïve Bayes Algorithm with Synthetic Minority Oversampling Technique kartika, anice; Dina Fitria; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/241

Abstract

Poverty is a condition where a person is unable to meet minimum basic needs or a condition caused by the influence of development policies that have not been able to reach all levels of society. In Indonesia, the government has designed various programs to overcome poverty, but these programs are often not on target. One method to improve the effectiveness of the program is through proper classification of poor and non-poor households. This study uses the Naïve Bayes classification method which is popular in data mining to predict data categories based on the probability distribution of its features. However, challenges arise when the data is unbalanced between different classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) method is used to balance the data. Based on the analysis that has been carried out To determine the performance of Naïve Bayes using SMOTE and without SMOTE in classifying poor households in Padang City in 2023, classification using the Naïve Bayes method without SMOTE produced an accuracy value of 98%, precision of 0%, and recall of 0%. Meanwhile, the classification using the Naïve Bayes method with SMOTE produces an accuracy value of 90%, precision of 87%, and recall of 92% and the results of the criteria for poor households in Padang City in 2023 using Naïve Bayes can be seen from the results that the probability of poor households is much greater than that of non-poor households, therefore the data is classified as  group of households that are classified as poor.
Perbandingan Metode Naïve Bayes Dan K-Nearest Neighbors Dalam Mengklasifikasikan Indeks Pembangunan Manusia Menurut Kabupaten/ Kota di Indonesia Tahun 2022 Anggara, Rudi; Tessy Octavia Mukhti; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/319

Abstract

The Human Development Index (HDI) is an indicator used to measure the success of efforts to improve the quality of human life in a particular region. Indonesia's HDI has increased every year, but the HDI in several districts/cities in Indonesia remains in the low category. The low HDI in these districts/cities is due to unequal development between regions in Indonesia. This disparity in development is influenced by HDI indicators as well as other factors. To address this issue, a decision system is needed to determine HDI categories using the Naive Bayes and KNN methods. Naive Bayes is applied with the assumption of Gaussian distribution, while KNN is implemented with the optimization of the nearest K value. Model performance evaluation is conducted to determine the best accuracy of the two methods using a confusion matrix. The analysis results show that the Naïve Bayes model outperforms the KNN algorithm in classifying the Human Development Index (HDI) by district/city in Indonesia for the year 2022, with Naïve Bayes achieving an accuracy of 93%. Therefore, the Naïve Bayes algorithm show good performance in terms of accuracy.
Mapping Indonesian Provinces Based on Leading Plantation Commodities with Export Potential Using Multidimensional Scaling Analysis Putri Yeni, Dicha; Tessy Octavia Mukhti; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/327

Abstract

Indonesia, as an agrarian country, benefits significantly from its plantation subsector, which contributes substantially to the national economy. However, the processing of plantation products in Indonesia remains largely limited to raw or semi-finished goods, resulting in low added value and restricted income for both farmers and the nation. This study aims to map Indonesia's provinces based on the production of key plantation commodities with high export potential, utilizing the Multidimensional Scaling (MDS) analysis method. The research focuses on commodities such as pepper, palm oil, coconut, rubber, coffee, cocoa, clove, and tea. It seeks to group 34 Indonesian provinces based on similarities in plantation production, providing valuable insights for policymakers to enhance production and increase export value. The analysis calculates inter-provincial similarities to determine distances between objects and evaluates the accuracy of the MDS mapping using STRESS and R2 values. The findings indicate that 12 provinces share similarities in cocoa production, while 7 provinces are closely aligned in the production of pepper, rubber, and coffee. Furthermore, 5 provinces exhibit similarities in palm oil production, and 9 provinces demonstrate commonalities in the production of coconut, clove, and tea. The analysis achieved a STRESS value of 0.024 (2.4%) and an R2 value of 0.9994, indicating that the MDS mapping is highly reliable. However, the results do not fully align with field data, suggesting the need for orthogonal transformation through Principal Component Analysis (PCA) to improve accuracy.
Error Correction Model Approach for Analysis of Original Regional Income in West Sumatra Herlena Purnama Sari; Fadhilah Fitri; Nonong Amalita; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): 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/vol3-iss1/332

Abstract

In this research, an error correction model approach is used, namely looking at long-term and short-termrelationships. Meanwhile, Original Regional Income (PAD) is all regional income originating from original regionaleconomic sources. Sources of Original Regional Income according to Law Number 33 of 2004 Chapter V Article 6consist of Regional Taxes, Regional Levies, Separated Regional Wealth Management Results and Other Legal PAD.because this approach uses long-term and short-term relationships, it is known that only variables x1 and x3 have along-term relationship and variables x1 and x3 have a short-term relationship. so it can be concluded that not allindependent variables have a connection with the dependent variable
Implementation of the Self Organizing Maps (SOMS) Method in Grouping Provinces in Indonesia Based on the Number of Crimes by Type of Crime fajriyanti nur, Putri; Tessy Octavia Mukhti; Nonong Amalita; Admi Salma
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): 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/vol3-iss1/334

Abstract

Crime cases are often the main topic of daily news in various media in Indonesia. Some of these crime cases are detrimental to the surrounding community and some are detrimental and these actions cannot be avoided in human life because they have become one type of social phenomenon. To protect the community by providing a sense of security and peace, the Indonesian government, especially the police, must pay attention to conditions like this. The results of this study used the Self Organizing Maps (SOMs) method to obtain 3 clusters with the characteristics of each cluster. The first cluster with a low impact crime rate consists of 29 provinces. The second cluster with a moderate impact consists of 3 provinces showing the most dominant crime rate, namely crimes related to fraud, embezzlement, smuggling & corruption compared to other clusters. The third cluster with a high impact consists of 2 provinces with the most prominent characteristics by showing almost all indicators of the number of crimes according to the type of crime experiencing the highest average crime cases compared to other clusters.
Co-Authors Adinda Putri Aditya, Muhammad Fadhil Aditya Admi Salma Afifah Nabilah AL Rezki Ivansyah Anggara, Rudi Annisa Ramadhani Ardhi, Sonia Arssita Nur Muharromah Atus Amadi Putra Azizah Apriyerni Berliana Nofriadi Bimbim Oktaviandi Bunga Miftahul Barokah Bunga Nafandra defal aditya defran Dewi Febiyanti Dina Fitria Dina Fitria, Dina Dodi Vionanda Dony Permana Ervi Dayana Putri Fadhilah Fitri fajriyanti nur, Putri Fikra, Hidayatul Fitri, Fadhilah Fitri, Fitri Hayati Hariati Ainun Nisa Herlena Purnama Sari Ihsan Dermawan Inna Auliya kartika, anice Martia Rosada Nikma Hasanah Nonong Amalita Nurul Mulya Syahwa Olga Afrilly Putri Putri Yeni, Dicha Rahmatul Annisa Rahmika Alya Rivani, Putri RR. Ella Evrita Hestiandari Sari Agustin Sepniza Nasywa Siti Nurhaliza Sri Wahyuni Suci Rahmadani Syafriandi Syafriandi Wafiq Alya Aufa Yenni Kurniawati Yoli Marda Novi Yonggi, Yonggi Septa Pramadia Zamahsary Martha Zilrahmi, Zilrahmi