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All Journal PROSIDING SEMINAR NASIONAL JURNAL EKSAKTA PENDIDIKAN (JEP) Beta: Jurnal Tadris Matematika JURNAL EDUCATION AND DEVELOPMENT Eksakta : Berkala Ilmiah Bidang MIPA Menara Ilmu Pelita Eksakta ILKOMNIKA: Journal of Computer Science and Applied Informatics Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Leibniz: Jurnal Matematika Journal of Mathematics UNP Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Nonong Amalita
Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Journal : UNP Journal of Statistics and Data Science

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Regularized Ordinal Regression with LASSO: Identifying Factors in Students' Public Speaking Anxiety at Universitas Negeri Padang Natasya Dwi Ovalingga, natasyalinggaa; 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
Sentiment Analysis of The Constitutional Court Decision Regarding Changes to The Age Limit for Presidentian and Vice Presidential Candidates Using Support Vector Machine Amanda, Abilya; Nonong Amalita; Dodi Vionanda; Zilrahmi
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-iss4/321

Abstract

The Constitutional Court (MK) as a judicial institution granted a judicial review on October 16, 2023 related to the Election Law Article 169 (q) Law No.7 of 2017 number 90/PUU-XXI/2023. The Constitutional Court approved the material test, leading to changes in the age limit for presidential and vice presidential candidates. This change caused controversy because it was considered to benefit one of the candidate pairs. This research aims to see the trend of public opinion towards policy changes by the government. This research uses the Support Vector Machine (SVM) method which divides the data into two classification classes. The application of linear, Radial Bias Function (RBF), and polynomial kernels resulted in the highest accuracy of 84%. The calculation of accuracy, precision, and recall is 84%, 22%, and 90%, respectively. Based on the resulting wordcloud, Positive words indicate backing for presidential and vice presidential candidates. Meanwhile, negative sentiments express disapproval of the Constitutional Court's decision concerning the changes to the age limit requirements for presidential and vice presidential candidates.
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.
Penanganan Ketidakseimbangan Multikelas pada Dataset Survei Kerangka Sampel Area menggunakan Metode SCUT Sondriva, Wilia; Kurniawati, Yenni; Amalita, Nonong; Salma, Admi
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-iss2/163

Abstract

Area Sampling Frame (ASF) is a survey used by the Indonesian government to measure rice productivity in Indonesia. ASF survey is important data because accurate and high-quality rice productivity data is highly needed. There is extreme imbalance in the ASF survey data, thus requiring handling of this imbalance. SMOTE and Cluster-based Undersampling Technique (SCUT) is a method that can be used to address the dataset imbalance. SCUT combines oversampling using SMOTE and undersampling using CUT. The results from SCUT show that the number of data points in each class becomes balanced. Subsequently, a two-sample mean test is conducted to observe the mean differences between the original dataset and the dataset after handling. The results show that in the early vegetative, late vegetative, and harvest phases, the means are significantly similar between the original dataset and the dataset after handling, but in the generative phase, the means are not significantly similar. Therefore, synthetically generated data using the SCUT method generally exhibit similar mean characteristics.
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 Addini, Vidhiya Ade Eriyen Saputri Adinda Dwi Putri Admi Salma Aldwi Riandhoko Ali Asmar Amanda, Abilya Amelia Fadila Rahman Andini Yulianti Anggi Adrian Danis Anjelisni, Nining april leniati Arnellis Arnellis Atika Ahmad Atus Amadi Putra Azwar Ananda Chairina Wirdiastuti Cindy Febrianita Denia Putri Fajrina Dewi Febiyanti Dewi Murni Dina Fitria Dina Fitria Dina Fitria, Dina Dodi Vionanda Dony Permana Dwi Sulistiowati Edwin Musdi Elita Zusti Jamaan Elsa Oktaviani Fadhilah Fitri Fajrin Putra Hanifi fajriyanti nur, Putri Fatma Yulia Sari Faulina FAZHIRA ANISHA Fikra, Hidayatul Fitri, Fadhilah Gezi Fajri Ghaly, Fayyadh Hamida, Zilfa Hana Rahma Trifanni haniyathul husna Hasna, Hanifa Helma Helma Helma Helma Herlena Purnama Sari Huriati Khaira Ichlas Djuazva Inna Auliya Jihe Chen Juwita Juwita Khairani, Putri Rahmatun Lilis Sulistiawati Media Rosha Media Rosha Meira Parma Dewi Melly Kurniawati Miftahurrahmi, Syifa Minora Longgom Mohammad Reza febrino Mudjiran Mudjiran Muhammad Tibri Syofyan Mukhti, Tessy Octavia nabillah putri Nadha Ovella Syaqhasdy Natasya Dwi Ovalingga, natasyalinggaa Nini Erdiani Nur Fadillah, Nur Nurhizrah Gistituati Okia Dinda Kelana Oktaviani, Bernadita Permana, Dony Prida Nova Sari Puti Utari Maharani Rahma, Dzakyyah Resti Febrina Retsya Lapiza Rizki Amalia, Annisa Rizqia Salsabila Rusdinal Rusdinal Saddam Al Aziz Safitri, Melda Salma, Admi Seif Adil El-Muslih Shavira Asysyifa S Sondriva, Wilia Sujantri Wahyuni Suparman Suparman Swithania Rizka Putri Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syahfitrri, Nindi Tamur, Maximus Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Venny Oktarinda Viola Yuniza Wella Saputri Wulan Septya Zulmawati Yarman Yarman, Yarman Yenni Kurniawati Yulia Pertiwi Zamahsary Martha Zilla Zalila Zilrahmi, Zilrahmi