Article Per Year (5 Year)
p-Index From 2021 - 2026
1.819
P-Index
This Author published in this journals
All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) ELP (Journal of English Language Pedagogy) Jurnal Riset Statistika Emasains : Jurnal Edukasi Matematika dan Sains Journal of Mathematics UNP Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Jurnal Pengabdian Masyarakat Olahraga dan Kesehatan Mathematical Journal of Modelling and Forecasting
Chairina Wirdiastuti
Universitas Negeri Padang
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Pengelompokkan Sembilan Kabupaten/Kota di Provinsi Sumatera Barat Berdasarkan Tingkat Kriminalitas dengan Menggunakan Analisis Gerombol Chairina Wirdiastuti; Helma Helma
Journal of Mathematics UNP Vol 4, No 2 (2019): Journal Of Mathematics UNP
Publisher : UNIVERSITAS NEGERI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (236.123 KB) | DOI: 10.24036/unpjomath.v4i2.6296

Abstract

Abstract Crime is an act that violates the law. Sumatra Barat Province have a high criminal. Criminals can make big problem for criminal victims, such as death, disability, material losses and others. The increase of crime is caused by several problem such as economic, social, conflict and etc. Crime is an element that explains the quality of sociaty and the law of a region. For that, it is necessary to know the groups of regencies/cities in the Province of Sumatera Barat based on crime rates so that the government can evaluate crime and improve some policies towards a region that has high crime. Data was obtained from the Central Agency on Statistics which showed the number of cases that occurred in regencies/cities in Sumatra Barat Province. Using cluster analysis with hirarchical method, it was concluded that regencies/cities in Sumatra  Barat province were divided into three groups where group one consist of Solok Regency, group two consist of Tanah Datar Regency, Padang Pariaman Regency, Lima Puluh Kota Regency, Solok Selatan Regency, Dharmasraya Regency, Pariaman City, and Sawahlunto City and group three consists of Bukittingi City.KeywordsCrime, Cluster Analysis,  Hierarchical Method
STUDENTS’ LISTENING COMPREHENSION IN TOEFL: A STUDY ON STUDENTS’ PERFORMANCE ACROSS THE SECTIONS Sepyanda, Marsika; Fenni Kurnia Mutiya; Chairina Wirdiastuti; Titiek Fujita Yusandra; Lucy Oktavani; Yummi Meirafoni
ELP (Journal of English Language Pedagogy) Vol. 10 No. 1 (2025): ELP (Journal of English Language Pedagogy)
Publisher : Universitas Mahaputra Muhammad Yamin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36665/elp.v10i1.994

Abstract

      This study examines students’ performance in the TOEFL listening comprehension test, focusing on their strengths and weaknesses across three sections: short conversations (Part A), long conversations (Part B), and short talks (Part C). There are 45 students registered in the course “Aplikasi Pendidikan Bahasa dalam Pembelajaran PJOK” on the odd semester in the 2024/2025 academic year participated in this study. The participants are chosen because the course integrates TOEFL-based language instruction, as outlined in the semester learning plan (RPS), where one of the students’ difficulties is listening skills. A descriptive quantitative research design was conducted over a three-week study period, with listening tests administered weekly during three class meetings. The tests used questions adopted from standardized TOEFL practice materials, which were selected based on their alignment with the test's objectives and validated by experienced English instructors for relevance and difficulty. Data analysis discovered that students consistently performed best in Part C, with average scores increasing from 53% to 65% across three tests, while Part B presented the most significant challenges, with scores improving from 43% to 54%. Part A showed moderate performance, with scores rising from 49% to 60%. These results show that there is also an improvement on each part of the test, attributed to repeated exposure to the test format and the development of listening strategies. However, the persistent difficulty in Part B suggests the need for targeted instructional interventions to address challenges in extended dialogues. It can be concluded that the students demonstrated general improvement in TOEFL listening comprehension across all sections. The repeated exposure can be an effective strategy performance in TOEFL listening comprehension.  
Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia Fajrin Putra Hanifi; Syafriandi; Chairina Wirdiastuti; Nonong Amalita; Zilrahmi
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.75

Abstract

Inflation is defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable Inflation, defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable economic growth. The importance of controlling inflation is based on the consideration that high and unstable inflation hurts the socio-economic conditions of the community. In this context, government and economic agents must know the future inflation rate. The backpropagation algorithm forecasting method can be a mathematical tool to forecast future inflation rates. The best forecasting model is obtained from applying the backpropagation algorithm, namely ANN BP (12,2,1), with a mean square error value of 0.15 and an absolute percentage error value of 11.09%. Based on these results, the back-propagation algorithm in artificial neural networks can accurately forecast the inflation rate. Thus, it is hoped that this research can be used in economic decision-making.
Pengelompokkan Kabupaten/Kota di Provinsi Sumatera Barat Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Algoritma SOM Winartha, Mardia; Wirdiastuti, Chairina; Salma, Admi
Jurnal Riset Statistika Volume 5, No. 1, Juli 2025, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v5i1.6707

Abstract

Abstract. People's welfare is the main indicator in measuring the success of a region's development. Welfare reflects the fulfillment of people's basic needs, both material and spiritual, as measured through indicators of people's welfare. West Sumatra Province still shows a welfare gap between districts/municipalities, which can be seen from significant differences in indicators such as employment and poverty. Therefore, the purpose of this article is to cluster districts/cities in West Sumatra Province and recognize the characteristics of each cluster according to the people's welfare indicators in 2023 using the self-organizing maps algorithm. The results of the analysis show that 3 clusters are the optimal number of clusters. Cluster 1 includes 7 districts/cities with higher welfare levels, cluster 2 includes 7 districts/cities with medium welfare levels, and cluster 3 includes 5 districts/cities with lower welfare levels. This article is expected to help create better and more equitable policies that will support the improvement of people's welfare in West Sumatra Province. Abstrak. Kesejahteraan rakyat menjadi indikator utama dalam mengukur keberhasilan pembangunan suatu wilayah. Kesejahteraan mencerminkan kondisi terpenuhinya kebutuhan dasar masyarakat, baik material maupun spiritual yang diukur melalui indikator-indikator kesejahteraan rakyat. Provinsi Sumatera Barat masih menunjukkan kesenjangan kesejahteraan antar kabupaten/kota yang terlihat dari perbedaan signifikan pada indikator seperti ketenagakerjaan dan kemiskinan. Oleh sebab itu, tujuan dari artikel ini adalah untuk mengelompokkan kabupaten/kota di Provinsi Sumatera Barat dan mengenali karakteristik setiap cluster sesuai dengan indikator kesejahteraan rakyat pada tahun 2023 menggunakan algoritma self-organizing maps. Hasil analisis menunjukkan bahwa 3 cluster adalah jumlah cluster optimal. Cluster 1 meliputi 7 kabupaten/kota dengan tingkat kesejahteraan yang lebih tinggi, cluster 2 meliputi 7 kabupaten/kota dengan  tingkat kesejahteraan menengah, dan cluster 3 meliputi 5 kabupaten/kota dengan tingkat kesejahteraan yang lebih rendah. Artikel ini diharapkan dapat membantu menciptakan kebijakan yang lebih baik dan merata yang akan mendukung peningkatan  kesejahteraan rakyat di Provinsi Sumatera Barat.
Grouping of Regencies/Cities In West Sumatra Province Based On Economic Development Indicators Using The Self-Organizing Maps (SOM) Algorithm Wiwil Dzil Izzatil; Chairina Wirdiastuti; Syafriandi
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.57741

Abstract

Economic development is an important aspect in improving the standard of living of the community. To measure economic development progress in a region, relevant indicators are needed, one of which is Gross Regional Domestic Product (GRDP) per capita. In West Sumatra Province, there are disparities in RDP per capita between regions. Therefore, clustering is necessary to assist local governments in determining development priorities, formulating more targeted development policies, and allocating resources efficiently. This study aims to cluster West Sumatra regions using the Self-Organizing Maps algorithm based on economic development indicators. The analysis results identified three clusters: Cluster 1 consists of 6 districts/cities categorized as having moderate economic development, Cluster 2 includes 7 districts/cities with high economic development, and Cluster 3 consists of 6 other districts/cities categorized as having low economic development.
Application of Algorithm Learning Vector Quantization for Air Quality Classification Roufsaldiaz Nawfal; Dina Fitria; Chairina Wirdiastuti
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.48

Abstract

This study aims to classify air quality using the Learning Vector Quantization (LVQ) algorithm based on the Air Quality and Pollution Assessment dataset obtained from Kaggle. The dataset comprises 5,000 observations, of which 4,000 were used for training and 1,000 for testing. The analytical process includes data preprocessing (normalization), the construction and training of the LVQ model, and performance evaluation using a confusion matrix. The experimental results demonstrate that the LVQ model successfully classified 903 of 1,000 test samples, yielding an overall accuracy of 90.3%. This level of accuracy indicates that the LVQ algorithm can capture relevant patterns in air quality variables and perform reliable classification across different air quality categories. The findings suggest that LVQ can serve as a potential foundation for developing automated air quality monitoring and decision-support systems. Future studies are encouraged to compare LVQ with other machine learning classification techniques to build a more optimal model and to gain deeper analytical insights.
Eksperimentasi Air: Pengembangan Pariwisata Olahraga Berkelanjutan di Kepulauan Mentawai Andri Gemaini; Hadi Pery Fajri; Ariando Ariston; Chairina Wirdiastuti
JURNAL PENGABDIAN MASYARAKAT OLAHRAGA DAN KESEHATAN (JASO) Vol. 4 No. 2 (2024): Jurnal Pengabdian Masyarakat Olahraga dan Kesehatan
Publisher : DEPARTEMEN KESEHATAN DAN REKREASI UNIVERSITAS NEGERI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kepulauan Mentawai merupakan salah satu destinasi unggulan pariwisata bahari di Indonesia yang memiliki potensi besar dalam pengembangan pariwisata olahraga berbasis air. Namun, optimalisasi potensi tersebut masih menghadapi berbagai kendala, terutama terkait keterbatasan sumber daya manusia (SDM), aspek keselamatan wisata, serta pengelolaan lingkungan yang berkelanjutan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kapasitas masyarakat lokal dalam pengelolaan pariwisata olahraga melalui edukasi dan pelatihan aktivitas snorkeling, stand up paddle (SUP), serta water rescue. Metode yang digunakan meliputi pelatihan, pendampingan, dan praktik langsung di lapangan dengan melibatkan masyarakat Desa Tua Pejat sebagai mitra. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan dan keterampilan masyarakat dalam aspek keselamatan, teknik dasar olahraga air, serta kesadaran terhadap konservasi lingkungan laut. Program ini juga memberikan dampak positif terhadap kesiapan masyarakat dalam mendukung pengembangan pariwisata olahraga yang berkelanjutan. Dengan demikian, kegiatan ini berkontribusi dalam meningkatkan kualitas SDM serta mendukung pengembangan destinasi pariwisata berbasis sport tourism di Kepulauan Mentawai.
Nonparametric Fourier Series Regression for Unemployment Analysis in Banten Province Barokah, Bunga Miftahul; Fitri, Fadhilah; Wirdiastuti, Chairina
Rangkiang Mathematics Journal Vol. 5 No. 1 (2026): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v5i1.90

Abstract

The Open Unemployment Rate (OUR) is a vital indicator of regional economic performance, particularly in Banten Province, which faces disparities in education and poverty. This study models the unemployment rate using two predictors: average years of schooling and poverty level, through a nonparametric Fourier series regression for the 2017–2024 period. This method provides greater flexibility in capturing the nonlinear and fluctuating patterns often observed in socio-economic data. The analysis used secondary data from Statistics Indonesia (BPS), beginning with descriptive statistics and data visualization. Models were evaluated using Generalized Cross-Validation (GCV) and the coefficient of determination (R²). The optimal model was found at K = 3, with a GCV of 2.4057 and an R² of 0.5155. The model effectively captured the non-linear relationships between unemployment, education, and poverty. Although the R² value is moderate, this indicates that including additional explanatory variables could enhance the model’s performance. These findings support the use of Fourier series regression as an alternative approach for labor market analysis, especially when linear methods fall short and provide insights for developing more targeted employment policies.
Analisis Faktor-Faktor yang Mempengaruhi Prevalence of Undernourishment (PoU) di Indonesia Menggunakan Model Spatial Autoregressive (SAR) Annisa, Rahmatul; Ervi Dayana Putri; Chairina Wirdiastuti
Emasains : Jurnal Edukasi Matematika dan Sains Vol. 15 No. 2 (2026): Juli 2026 (On Progres)
Publisher : Program Studi Pendidikan Matematika dan Pendidikan Biologi Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/emasains.v15i2.6191

Abstract

Tujuan Pembangunan Berkelanjutan (Sustainable Development Goals/SDGs) merupakan kerangka pembangunan global yang dirancang untuk mewujudkan pembangunan berkelanjutan hingga tahun 2030. Di Indonesia, tingkat kerawanan pangan nasional mencapai 8,27 persen pada tahun 2024, menurun sekitar 0,26 poin dibandingkan tahun 2023 yang sebesar 8,53 persen. Meskipun terjadi penurunan secara nasional, prevalensi kekurangan gizi di beberapa provinsi di Indonesia masih tergolong tinggi. Hasil penelitian menunjukkan bahwa model Spatial Autoregressive (SAR) memiliki kinerja yang lebih baik dibandingkan model Ordinary Least Squares (OLS) dan Spatial Error Model (SEM). Dengan demikian, model SAR merupakan model terbaik dalam menganalisis faktor-faktor yang memengaruhi prevalensi kekurangan gizi antar provinsi di Indonesia tahun 2024. Hasil uji signifikansi model menunjukkan bahwa pendapatan per kapita, persentase rumah tangga dengan sanitasi layak, serta persentase rumah tangga dengan akses air bersih merupakan faktor yang berpengaruh terhadap kerawanan pangan di Indonesia. Nilai koefisien determinasi yang dihasilkan sebesar 75,56%, yang menunjukkan bahwa variabel independen dalam model mampu menjelaskan variasi variabel dependen secara cukup kuat.
Classification of Stroke Desease Using the Learning Vector Quantization Algorithm Andriarmi Andriarmi; Chairina Wirdiastuti; Syafriandi Syafriandi
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): 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/vol4-iss2/499

Abstract

Stroke is one of the leading causes of death and disability worldwide, thereby making early detection crucial for timely and appropriate medical treatment. In clinical practice, stroke diagnosis is generally carried out through medical examinations and patient history analysis, but this process is time-consuming and depends on the subjective judgment of medical personnel. Therefore, machine learning approaches can be utilized to support disease classification more quickly and objectively. This study aims to analyze the performance of the Learning Vector Quantization (LVQ) method in classifying stroke disease using a dataset obtained from Kaggle. The dataset used in this study is imbalanced;therefore, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to handle class imbalance. The research stages included data preprocessing, splitting data into training and testing sets, LVQ model training, parameter optimization using learning rate and maximum epoch, and model evaluation using accuracy and sensitivity. The results show that the LVQ model trained on the original dataset achieved an accuracy of 95,72%, but failed to detect stroke cases with a sensitivity of 0%. After applying SMOTE, the best model achived a stroke sensitivity of 90%, although the accuracy decreased to 49,49% due to the high number of false positives. These findings indicate that LVQ is highly sensitive to data distribution and model parameters, making its performance on this dataset less optimal for stroke classification and more suitable as an initial screening tool.
Co-Authors Andri Gemaini Andriarmi Andriarmi Annisa, Rahmatul Ariando Ariston Barokah, Bunga Miftahul Bunga Miftahul Barokah Dina Fitria Ervi Dayana Putri Fadhilah Fitri Fajrin Putra Hanifi Fenni Kurnia Mutiya Fitri, Fadhilah Hadi Pery Fajri Helma Helma Lucy Oktavani nailul arrahmi Nonong Amalita Riska Harpidna Harpidna Roufsaldiaz Nawfal Salma, Admi Sepyanda, Marsika Syafriandi Syafriandi Syafriandi Titiek Fujita Yusandra Winartha, Mardia Wiwil Dzil Izzatil Yummi Meirafoni Zilrahmi, Zilrahmi