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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika Tsaqofah: Jurnal Penelitian Guru Indonesia UNP Journal of Statistics and Data Science Journal of Authentic Research d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Statistics and Its Applications
Zamahsary Martha
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Penerapan Metode Rating-Based Conjoint Analysis dalam Preferensi E-Wallet Mahasiswa Departemen Statistika Universitas Negeri Padang Dio Afdal Putra; 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%.
Prediksi Harga Emas Dunia Menggunakan Metode k-Nearest Neighbor Muhamad Rayhan Nanda P; 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.
Regularized Ordinal Regression with LASSO: Identifying Factors in Students' Public Speaking Anxiety at Universitas Negeri Padang natasyalinggaa Natasya Dwi Ovalingga; Nonong Amalita; 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-iss4/316

Abstract

Public speaking anxiety is a common issue faced by students, particularly in academic settings. It may arise from a range of factors, including humiliation, physical appearance, preparation, audience interest, personality traits, rigid rules, unfamiliar role, negative result, and mistakes. This research seeks to determine the factors influencing different levels of public speaking anxiety among students at Universitas Negeri Padang through the application of ordinal regression with LASSO regularization. This method allows for automatic selection of significant variables and addressesmulticollinearity issues. The results indicate that eight factors influence low public speaking anxiety levels, while only six factors impact high public speaking anxiety levels. The ordinal regression model with LASSO penalty demonstrates good performance in classifying public speaking anxiety levels, achieving an accuracy of 71.33%. This study is expected to help students and educators better understand and manage public speaking anxiety, thereby enhancing public spekaing competence among students
Implementation of Association Rule on Agricultural Commodity Exports in Indonesia Using Apriori Algorithm Asra Dinul Haq; Dina Fitria; Dony Permana; Zamahsary Martha
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-iss1/336

Abstract

Exports of agricultural commodities in Indonesia have the smallest contribution to state revenues and the movement of export values ​​in the last decade has not shown a significant increase compared to other export sectors. This shows that there are weaknesses in the export of agricultural commodities so that an analysis is needed to optimize export results to other countries. These weaknesses can be seen in terms of quality, price, infrastructure and technology. This study uses association rule analysis with the apriori algorithm with the aim of finding out what agricultural commodities are exported simultaneously and the resulting association rules. The apriori algorithm is an algorithm used to find association rules between items in a database by considering two main parameters, namely Support and Confidence. The data used is agricultural commodity export data obtained from the publication of the Central Statistics Agency in Indonesia in 2023. Based on the analysis carried out, there are 32 association rules generated with a minimum Support of 25% and a minimum Confidence of 80%. Then after the Lift Ratio test was carried out, all the rules generated met the Lift Ratio test with a value of more than 1. The association rules produced must have at least 2 to 4 agricultural export commodities in each rule. By knowing the association rules for agricultural commodity exports, it is hoped that export distribution in the agricultural sector can be further optimized for trading abroad so that it can cover existing weaknesses.
Grouping of Provinces in Indonesia Based on Active Family Planning Participants Using Modern Methods Using Fuzzy C-Means Annisa Ramadhani; Tessy Octavia Mukhti; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/365

Abstract

Indonesia’s rapid population growth presents a significant challenge to national welfare and public health. One of the key strategies implemented by the government to address this issue is the Family Planning (FP) program, which emphasizes the use of modern contraceptive methods. However, the utilization of these methods remains uneven across provinces. This study aims to cluster Indonesian provinces based on the number of active participants using modern contraceptive methods in 2023 by applying the Fuzzy C-Means (FCM) clustering algorithm. FCM was selected due to its ability to handle overlapping data characteristics, allowing for a more flexible and representative analysis. The clustering results reveal two main clusters: Cluster 1, which consists of provinces with high levels of active modern contraceptive users, and Cluster 2, which includes provinces with low participation levels. These findings are expected to serve as a reference for more targeted policy formulation to enhance the equity and effectiveness of the FP program across the country.
Comparison of Nadaraya-Watson Method with Local Polynomial in Modeling HDI and Poverty Relationship in Java Island Yoli Marda Novi; Fadhilah Fitri; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (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-iss3/380

Abstract

Poverty remains a critical issue in Indonesia, with the number of poor people reaching 24.06 million in September 2024. The Human Development Index (HDI), which indicates the level of human resource quality, is one of the factors influence poverty. This analysis focuses on the correlation involving HDI also this number of poor people in districts/cities in Java Island by comparing two kernel regresokesion methods, namely Nadaraya-Watson Estimator and Local Polynomial Estimator. Nonparametric regression was chosen thus it does not necessitate this presumption of a certain form of connection among variables, so it is more flexible in capturing complex relationship patterns. Secondary data from Statistics Indonesia (BPS) in 2024 was used in this study. Initial exploration shows, the data distribution does not have a clear pattern, so nonparametric methods are more suitable for use. Modeling is done using the optimal bandwidth obtained through the dpill function in R software. The analysis results show that the local polynomial estimator produces smoother regression curves and lower MSE values. In addition, comparison of different polynomial degrees shows that higher polynomial degrees tended to improve model performance. Among the tested polynomial degrees, the local polynomial with degree five (p=5) produced the lowest MSE value and the highest coefficient of determination. Therefore, the local polynomial estimator with degree 5 is the best method for modeling the relationship between the HDI and poverty levels in Java in 2024
Applications of Panel Data Analysis on Human Development Index Indicators in Districts/Cities of Lampung 2022 – 2024 Rahmad Wanizal Pastha; Zilrahmi; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (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-iss3/411

Abstract

This paper aims to identify the determinants affecting the Human Development Index (HDI) in Lampung Province, Indonesia, during the periode 2022-2024 using panel data regression. Lampung consistenly ranks among the provinces with the lowest HDI scores in Sumatera, indicating developmental disparties across regions. The research employs secondary data from 15 districts/cities and includes variables such as life expectancy, expected years of schoolingm mean years of schooling, and expenditure per capita. Panel data regression models fixed effect, random effect, and common effect were evaluated using chow, hausman, and lagrang multiplier tests to select the most approriate model. The random effect model was chosen, supported by a high R-Squared value of 92,71% indicating strong explanatory power. The analysis found that life expectancy and mean years of schooling significantly influence HDI, while expected years of schooling and expenditure per capita were not statistically significant in this model. The analysis shows that ensuring equal opportunities in health and education significantly contributies to better human development. Future research is recomended to incorporate qualitative approaches and more recent variables to enrich the analysis.
Inflation Prediction In Indonesia Using Extreme Learning Machine and K-Fold Cross Validation Wahda Aulia Assara; Zamahsary Martha; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (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-iss3/412

Abstract

Inflation rate forecasting is an important aspect in supporting economic policies and price control by the government. This study aims to evaluate the performance of the Extreme Learning Machine (ELM) algorithm in forecasting the inflation rate in Indonesia and provide inflation prediction results for 2025. The data used is historical data on Indonesia's inflation rate for the period 2003–2024. The analysis process begins with data normalization to ensure a uniform scale, followed by data partitioning using 10-Fold Cross Validation. The ELM model was built with 30 hidden neurons, a sigmoid activation function, and a regularization parameter of 0.8. The test results show that the ELM algorithm has superior performance. This is evidenced by the average MAPE value of 1.71%, RMSE of 0.0359, and coefficient of determination (R²) of 0.9833, indicating very high accuracy. The inflation prediction for January to December 2025 is in the range of 1.517%–1.761%, with an average approaching 1.663%, indicating a relatively stable pattern throughout the year. Based on these results, the ELM algorithm can be used as an effective alternative method for forecasting time series data, particularly in the context of inflation. This research is expected to serve as a reference for the government in establishing inflation control policies and for other researchers interested in applying artificial intelligence models to economic analysis.
Grouping Of Universities In Indonesia In 2025 Based On The Qs World University Rankings Ranking Indicator Using The Kohonen Self-Organizing Maps Algorithm Raihan Athaya Wudd; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/427

Abstract

Increasing the competitiveness of higher education is one of the main focuses in facing global competition. One of the important indicators in assessing the quality of higher education institutions is the QS World University Rankings which assesses universities based on indicators such as academic reputation, citations per lecturer, sustainability, and international collaboration. This study aims to group universities in Indonesia that are included in the QS World University Rankings in 2025 using the Kohonen Self-Organizing Maps (SOM) algorithm. The data used consisted of 10 QS assessment indicators for 26 universities in Indonesia. The normalization process is carried out using the min-max method, and the optimal number of clusters is determined using internal validation indices such as Connectivity, Dunn, and Silhouette. The results of the analysis show that the best models form three main clusters. Cluster 1 contains universities with superior performance in reputation and research, cluster 2 contains universities with a fairly balanced medium performance, and cluster 3 consists of universities with low performance in key indicators. The results of this study are expected to be the basis for policy makers and university managers to develop strategies to improve the quality of higher education in a targeted manner.
Classification of Recipients of the Family Hope Program in West Sumatra Province Using the Random Forest Algoritma Nini Erdiani; Dwi Sulistiowati; Nonong Amalita; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/431

Abstract

According to the Central Statistics Agency (BPS), the percentage of poor people in West Sumatra Province increased by 0.02% in 2024. One of the government's efforts to overcome poverty is a social assistance program issued by the government to help people who are economically disadvantaged. The targeted distribution of social assistance is an important challenge in improving community welfare, especially for families receiving PKH benefits. This study aims to classify households receiving the Family Hope Program (PKH) in West Sumatra Province using a random forest algorithm with Synthetic Minority Oversampling Technique (SMOTE). This study uses data on PKH recipient households in West Sumatra Province in 2024, which has a significant class imbalance. Therefore, the SMOTE method was applied to balance the data. The data was divided into training and testing data with a ratio of 80%:20%, then parameter tuning was performed to optimize mtry and ntree. The model was evaluated using a confusion matrix to compare model performance. The results show that the accuracy obtained is 76%. The precision value is 72%, the recall is 84%, and the f1-score is 78%. Based on the Mean Decrease Gini value, the head of household's diploma became the main attribute in determining whether a household received PKH or not. This study concluded that the use of SMOTE in the random forest algorithm performed well in classifying PKH recipients in West Sumatra Province, where the model performed well and was quite reliable in identifying PKH recipients.
Co-Authors Afifa Lufti Insani Al Aziz, Saddam Anjelisni, Nining Annisa Ramadhani Annisa Rizki Amalia april leniati Arssita Nur Muharromah Asra Dinul Haq Atus Amadi Putra Aurumnisva Faturrahmi Bimbim Oktaviandi Bunga Nafandra Desi Rahmawati Diah Uswatun Hasanah Dina Fitria Dina Fitria Dina Fitria, Dina Dio Afdal Putra Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Dzakyyah Rahma Fadhilah Fitri Fadhilah Fitri Fadhillah Meisya Carina Fajri Juli Rahman Nur Zendrato Febi Febiola Putri Fitri, Fadhilah Frandito Rahmanesta Futiah Salsabila Gezi Fajri Hefiani Mustika Hasanah Khairisa Putri, Nadya Maulidya Hernanda Muhamad Rayhan Nanda P Muhammad Arief Rivano Muharromah, Arssita Nur Mukthi, Tessy Octavia Nadha Ovella Syaqhasdy natasyalinggaa Natasya Dwi Ovalingga Nazifatul Azizah Nindi Syahfitrri Nini Erdiani Nonong Amalita Novi, Yoli Marda Nurul Mulya Oktaviani, Bernadita Olga Afrilly Putri Permana, Dony Putri, Deya Junida Rahmad Wanizal Pastha Rahmadani Rahmadani Raihan Athaya Wudd Riska 01 Riskiani Lubis Salma, Admi silfia wisa fitri Suherman, Suherman Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Wahda Aulia Assara Yenni Kurniawati Yoli Marda Novi Yonggi Septa Pramadia Yonggi Zilrahmi, Zilrahmi