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All Journal JURNAL PENDIDIKAN TAMBUSAI Pelita Eksakta Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications
Dodi Vionanda
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences Other

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Classification of Poor Households in West Sumatra Province using Decision Tree Algorithm C4.5 Dinda Fitriza; Atus Amadi Putra; Dodi Vionanda; Zilrahmi
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-iss2/157

Abstract

The significant and increasingly complex issue of poverty poses a considerable challenge to Indonesia's development, including West Sumatra Province, with a poverty rate was 5.92% in 2022. The government has initiated programs to address poverty by focusing on the criteria of impoverished households. Data on impoverished households can be obtained through the National Socio-Economic Survey (Susenas). One method that can classify impoverished households is the decision tree. Decision tree is a flowchart that resembles a tree. The C4.5 algorithm used in this research has the ability handle discrete and continuous data, manage variables with missing values, and prune decision tree branches. The result of the analysis shows that the variables affecting the classification of poor households are the number of household members, then the age of the household head, type of house floor, type of house wall, source of drinking water, and cooking fuel. The accuracy of the test data using a confusion matrix is 69.89%, sensitivity of 71.15% for classifying regular households, and specificity of 68.72% for classifying impoverished households.
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.
K-Modes Analysis with Validation of the DBI in Grouping Provinces in Indonesia based on Indicators of Poor Households Syifa Azahra; Zilrahmi; Dodi Vionanda; Fadhilah Fitri
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-iss2/165

Abstract

Poverty is the most pressing social problem in Indonesia. Efforts to alleviate poverty are to group provinces in Indonesia based on indicators of poor households using the K-modes algorithm. The data used is data from the 2017 Indonesian Demographic and Health Survey (IDHS) on the Household List. The analysis includes data noise detection, data clustering using K-Modes algorithm, and cluster validation with Davies Bouildin Index (DBI). Based on the clustering that has been done, two clusters are obtained, where cluster 1 consists of 26 provinces and cluster 2 consists of 8 provinces. cluster 1 is a cluster that fulfills 9 indicators of poor households and cluster 2 only a few indicators of poor households. So that the government can prioritize these 8 provinces to overcome poverty in Indonesia. For the DBI value obtained is 1.89 which means that 2 clusters are already well used in the algorithm.
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.
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.
Penerapan Metode Choice-Based Conjoint Analysis pada Preferensi Pekerjaan Mahasiswa Departemen Statistika Universitas Negeri Padang Putra, M. Farel Rusde; Dodi Vionanda; Dony Permana; 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-iss4/221

Abstract

In the realm of psychology studies, it is widely assumed that the age range between 18 and 25 represents a critical period during which individuals preferences begin to take shape. This developmental phase encloses college students who despite their academic pursuits, remain relatively unfamiliar with the dynamic job market, particularly in the context of rapid technological advancements. Statistics as a discipline with broad applicability across both social and scientific domains, offers student of statistics significant career prospects. This research would likely estimate the job preferences of statistics students using one of the most common use methods called choice-based conjoint (CBC) analysis. The analysis reveals that work hours were the most substantial influence on statistics students’ job preferences, with a percentage of 40.29%. In addition, other factors that influence the preferences of statistics students are such as first salary (36.87%), correlation with the field of statistics (12.04%), work environment (7.18%), and type of workplace (3.62%).
Penerapan Metode Rating-Based Conjoint Analysis dalam Preferensi E-Wallet Mahasiswa Departemen Statistika Universitas Negeri Padang Putra, Dio Afdal; Dodi Vionanda; Yenni Kurniawati; Zamahsary Martha
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-iss4/222

Abstract

The rapid development of technology in the era of globalization has influenced the evolution of society's life in terms of economy, social, culture, and education, with the aim of facilitating daily activities, one of which is the ease of transactions using e-wallets. An e-wallet is a payment tool that uses a server-based system. Many factors influence a person's decision to use an e-wallet as a payment method, one of which is the level of security. To identify the factors that affect someone's use of e-wallets, one method is Rating-Based Conjoint Analysis (RBC). Therefore, this study aims to determine what influences a person to use an e-wallet, with the subjects being active students of the Statistics Department at Padang State University. The results of this RBC study indicate that the most influential factor on the e-wallet preferences of statistics students is security level, with a value of 37.70%, followed by transaction speed 23.17%, transfer fees at at 23.07%, features provided at 11.78%, and the least influential factor being promotions at 4.28%.
Library Book Lending Recommendation Using Association Rules with Frequent Pattern Growth (FP-Growth) Algorithm Kamil, Fakhri; Dony Permana; Dodi Vionanda; 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-iss4/284

Abstract

College libraries are libraries managed by higher education institutions such as university libraries. The library functions as an information center management forum for students which includes learning resource functions, access functions, librarian functions, ethical functions, and evaluation functions.  Students prefer to read through e-books rather than reading books or library collections. Limited knowledge of literature is the cause of students choosing to look for books on search engines rather than in the library. Managed book loan circulation history data will be able to improve library services that can assist in finding library collections. Book recommendation services using association rules, can find patterns of borrowing behavior of book titles that have the highest association as the most recommended titles to be borrowed together. The FP-Growth or Frequent Pattern Growth is an algorithm of associations rule that is able to generate association rules as personalized book borrowing recommendations. The results of book recommendations found as many as 50 rules that meet the chi-square assumption test where the recommendation items are independent. The results of 50 rules for book title choices that can be used by students as suggestions for determining books that have a relationship to be borrowed together to enrich references. For students who wish to borrow the books 'Professional Teacher: Mastering Teaching Methods and Skills' is recommended to also borrow the book 'Participatory Learning Methods and Techniques'. With the book recommendation service, the library provides advice to students in choosing related book titles to borrow at the library.
Prediksi Harga Emas Dunia Menggunakan Metode k-Nearest Neighbor Nanda P, Muhamad Rayhan; Zamahsary Martha; Dodi Vionanda; Admi Salma
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-iss4/314

Abstract

This research aims to predict world gold prices using the k-nearest neighbor (KNN) method with secondary data from the London Bullion Market Association (LBMA) in the form of monthly time series data from January 2019 to December 2023. In the analysis process, the data is divided into two parts: 80% for training data (January 2019 - December 2022) and 20% for testing data (January - December 2023). The analysis results show that the Mean Absolute Percentage Error (MAPE) value of the KNN method is 4.5%, which indicates a very good level of accuracy. With a MAPE below 10%, the KNN model is proven to be able to accurately predict world gold prices. Gold price predictions for the period January to December 2024 show a consistent upward trend, which is influenced by factors such as global economic fluctuations, increased gold demand, and geopolitical uncertainty. These results show that the KNN model is reliable as a tool for forecasting future world gold prices.
Co-Authors Admi Salma Admi Salma Afdhal, Afdhal Rezeki Afifah Salsabilah Putri Aidillah, Kerin Hagia Aldwi Riandhoko Alfathan, Muhammad Luthfi Amanda, Abilya Amannia zeze Andini Yulianti Aprilla Suhada Ardhi, Sonia Atus Amadi Putra Bahri Annur Sinaga Cindy Pratiwi, Cindy defal aditya defran Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Dony Permana Dwi Sulistiowati, Dwi Eujeniatul Jannah Fadhilah Fitri Fadhillah Fitri Fashihullisan Fayyadh Ghaly Fayza Annisa Febrianti FAZHIRA ANISHA Febri Ramayanti Findri Wara Putri Fitri, Fadhilah Fitri, Fitri Hayati Fitria Panca Ramadhani Hariati Ainun Nisa Husni, Nabila Ichlas Djuazva Ihsanul Fikri Isra Miraltamirus Jimmi Darma putra Jumiati, Susi Kamil, Fakhri Larissa, Dwika Latifah Jayatri Febiola Lifia Zullani Mardhiatul Azmi martha, Ully Martha Muhammad Ravi Azzaki Muhammad Tibri Syofyan Mukhti, Tessy Octavia nabillah putri Nanda P, Muhamad Rayhan Nazifatul Azizah Nikma Hasanah Nonong Amalita Nufhika Fishuri Nur Leli Nurul Afifah Permana, Dony Putra, Dio Afdal Putra, M. Farel Rusde Putri, Triana Rahmadina Adityana Rahmanesta, Frandito rama novialdi Rivani, Putri Rizki Akbar Robiati, Silfi Salma, Admi Seif Adil El-Muslih Shavira Asysyifa S Sherly Amora Jofipasi Silvia Agustina Siti Nurhaliza Susrifalah, Amelia Syafriandi Syafriandi Syafriandi Syahfitrri, Nindi Syifa Azahra Tessy Octavia Mukhti Wood, Raihan Attaya Yarman Yarman, Yarman Yenni Kurniawati Yunistika Ilanda Zamahsary Martha Zilrahmi, Zilrahmi Zulzila, Alivia