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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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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.
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 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.
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
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.
Implementation of an Artificial Neural Network Based on the Backpropagation Algorithm in Forecasting the Closing Price of the Jakarta Composite Index (IHSG) Aditya, Muhammad Fadhil Aditya; Zilrahmi; Yenni Kurniawati; Tessy Octavia Mukhti
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-iss1/137

Abstract

Investing is highly common in Indonesia. Continuous investment activities carried out by the community will increase economic activity and employment opportunities, increase national income, and increase the level of prosperity of the community. In carrying out share buying and selling transactions, there is a means for companies to obtain funds from official financiers or investors, which is called the capital market. One of the indices issued by the IDX is the Jakarta Composite Index (IHSG). Statistics can be used to help investors, the government, or related institutions to predict the value of the IHSG. One method that can be used to predict data is an Artificial Neural Network (ANN). Backpropagation method is a multi-layer ANN method that works in a supervised learning. The idea of the Backpropagation algorithm is that the input of the neural network is evaluated against the desired output results. The purpose of this research is to give forecasting values with high accuracy to describe the movement of IHSG close price values using the ANN method based on the Backpropagation algorithm. The research showed that the BP (4,6,1) model produced an RMSE value of 28,24024 and a MAPE value of 0.00342%. Based on the results of this research, an Artificial Neural Network model based on the Backpropagation Algorithm can be applied to predict the IHSG Closing Price value.
Comparison of K-Means and Fuzzy C-Means Algorithms for Clustering Based on Happiness Index Components Across Provinces in Indonesia Inna Auliya; Fitri, Fadhilah; Nonong Amalita; Tessy Octavia Mukhti
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-iss1/150

Abstract

Cluster analysis is a statistical technique used to group objects based on their shared characteristics. This research aims to assess how 34 provinces in Indonesia are clustered using happiness index indicators for the year 2021. The study compares two non-hierarchical cluster analysis methods, K-Means and Fuzzy C-Means. K-Means categorizes objects into clusters based on their proximity to the nearest cluster center, while Fuzzy C-Means employs a fuzzy grouping model assigning membership degrees from 0 to 1. The results indicate that both methods form three clusters. Evaluating standard deviation values and ratios, Fuzzy C-Means proves superior, displaying a larger standard deviation between groups and a smaller ratio (0.6680004) compared to K-Means. Consequently, the study concludes that the Fuzzy C-Means method is more optimal than K-Means.
Klasifikasi Karies Gigi di Rumah Sakit Gigi dan Mulut Baiturrahmah Menggunakan Metode Random Forest Martia Rosada; Zilrahmi; Syafriandi Syafriandi; 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-iss2/155

Abstract

The mouth cavity is the main gate through which germs and bacteria enter. Therefore, it is important to maintain oral hygiene. When dental and oral hygiene is not maintained it will cause dental and oral problems or diseases such as periodontitis, dental caries, tooth abscess, gingivitis and other dental and oral health problems. The dental and oral problems that many people experience are caries or cavities. West Sumatra itself has a fairly high prevalence of dental caries. Prevention of dental caries needs to be done by making the public aware of dental and oral hygiene in order to reduce the problem of dental caries in West Sumatra. Therefore, it is necessary to have a method that is able to classify dental caries based on its symptoms. The classification method is very useful for knowing the main factors that cause dental caries. One classification method that can be used is random forest. Random forest is an ensemble method, namely the development of several methods using bootstrap sampling. The results of this research use the smallest OOB level and the Variable Importance Measure (VIM). Random forest classification using dental and oral pain medical record data at Baiturrahmah Padang Hospital produces an OOB error rate of 32.08% or an accuracy rate of 67.92%. The optimal model is obtained using mtry=2 and ntree=200. From this research it can be concluded that dental plaque, age, and tooth brushing habits are the importance variables or main factors that influence dental caries.
Impelementation of Subtractive Fuzzy C-Means Method in Clustering Provinces in Indonesia Based on Factors Causing Stunting in Toddlers Hariati Ainun Nisa; Admi Salma; Dodi Vionanda; 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-iss2/164

Abstract

Indonesia in 2022 has a stunting rate that is still relatively high at 21.6%. For this reason, it is necessary to make various efforts to reduce the stunting rate. One of the efforts that can be made is to understand the characteristics of each province in Indonesia with cluster analysis. This study aims to cluster provinces in Indonesia based on factors that cause stunting in children under five. The method used is Subtractive Fuzzy C-Means which has advantages in terms of speed, iteration, thus producing more stable and accurate results. The results of the validity test with Silhouette Coefficient Index, the optimum number of clusters is 8 clusters with a radius (r) of 0.70. There are 8 provinces that have provided maximum handling and efforts in reducing stunting rates, namely the provinces of Bangka Belitung Islands, Riau Islands, DKI Jakarta, DI Yogyakarta, Bali, East Kalimantan, South Kalimantan, and South Sulawesi. Meanwhile, 7 provinces namely East Nusa Tenggara, South Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, and West Papua, still need special attention from the government in reducing stunting rates based on the factors that cause stunting discussed in this study.
Artificial Neural Networks to Forecasting the Retail Price of Beras Solok in Padang City using Backpropagation Algorithm Rivani, Putri; Tessy Octavia Mukhti; Dodi Vionanda; Dina Fitria
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-iss2/168

Abstract

Strengthening rice production is an important step as the population continues to grow. Padang City is only able to meet 30% of the community's needs, so to fulfill the community's needs, rice is also imported from Solok. Forecasting can be done especially in order to see the movement of the average retail price of Anak Daro Solok Rice in Padang City which has decreased and increased in rice prices due to the lack of rice availability in Padang City. In this research, the forecasting method that will be used is the Artificial Neural Network Backpropogation Algorithm. Artificial Neural Networks are widely used for forecasting nonlinear time series data. Based on the results of the research that has been done, forecasting the average retail price of Anak Daro Solok Rice in Padang City using the Backpropagation Algorithm Artificial Neural Network obtained the optimal network architecture has the best model, namely BP (1,6,1) which model produces a MAPE of 0.03121%, indicating that the network performance of the model that has been formed shows very good results because it manages to achieve an accuracy rate (MAPE) of less than 10%. Artificial Neural Network Model based on Backpropagation Algorithm can be applied to predict the average retail price of Anak Daro Solok Rice in Padang City. Comparison of the results of forecasting the average retail price of Anak Daro Solok Rice in Padang City for the next 12 months period, namely an increase from the previous 12 months period.
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