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All Journal ELP (Journal of English Language Pedagogy) Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science
Chairina Wirdiastuti
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Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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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.
K-Means Cluster Analysis for Grouping Small and Medium Enterprises (SMEs) in Pesisir Selatan Regency arrahmi, nailul; Chairina Wirdiastuti; Yenni Kurniawati
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-iss2/364

Abstract

Small and Medium Industries (SMEs) play an important role in national economic growth through job creation, improving regional economies, and triggering entrepreneurial spirit. Although most SMEs operate on a limited scale with simple technology, this sector has great potential to grow if it receives sustainable support. However, SMEs in Pesisir Selatan Regency face various challenges, such as limited human resources, difficulty in accessing capital, and low utilization of technology. This study aims to analyze the grouping of SMEs in Pesisir Selatan Regency using the clustering method. Using secondary data on six types of SMEs in 15 sub-districts in 2023, this study applies the K-Means algorithm to group SMEs based on the characteristics of the dominant sector. The clustering results produce three main groups: first, sub-districts with high SME activity in the textile and food sectors; second, sub-districts with low SME activity in almost all sectors; and third, sub-districts with balanced SME activity in various sectors, such as apparel, beverages, furniture, and non-metallic minerals. These findings are expected to provide insight for local governments in formulating more targeted policies for the development of SMEs and equitable distribution of economic growth in Pesisir Selatan Regency.
Nonparametric Regression with Local Polynomial Kernel on Relationship Between Schooling Years and Unemployment Rate in Banten Miftahul Barokah, Bunga; Fadhilah Fitri; Chairina Wirdiastuti
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-iss3/372

Abstract

The Open Unemployment Rate (TPT) is a key indicator in assessing the economic performance of Banten Province. One of the factors suspected to influence TPT is education, which is measured by the average years of schooling. This study aims to analyze the relationship between the average years of schooling and TPT using the Local Polynomial Kernel Nonparametric Regression method for the period 2017–2024. This method was chosen for its flexibility in modeling nonlinear relationships without requiring strict assumptions about the data. The optimal bandwidth parameter for smoothing was determined using the Direct Plug-In (DPI) method through the dpill function in the R software. The results show that the nonparametric model has a coefficient of determination (R²) of 0.2841, which is higher than that of the Ordinary Least Squares (OLS) linear regression model, which only reached 0.1710. This indicates that the nonparametric approach is better at capturing the complex relationship between education and unemployment. However, the low R² values in both models indicate the presence of other factors that influence the unemployment rate, such as economic conditions, labor market structure, and education policy. Therefore, increasing the average years of schooling alone may not be sufficient to significantly reduce the unemployment rate. More comprehensive policies are needed, such as job skill enhancement, vocational training, and economic strategies focused on job creation. The findings of this study are expected to provide useful insights for policymakers in formulating more effective strategies to address unemployment in Banten Province.
Aplication Algorithm Learning Vector Quantization for Classification of Hypertention in Padang Laweh Health Center Harpidna, Riska Harpidna; Chairina Wirdiastuti; Yenni Kurniawati
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-iss3/408

Abstract

Hypertension is a health condition characterized by blood vessel disorders, in which there is a chronic increase in blood plessure of 140/90 mmHg. There are several factors that influence hypertension, including unhealthy eating patterns, lack of physical activity, smoking, stress and excess weight. Hypertension does not show clear symptoms, but it has the potential to cause other diseases such as heart failure, stroke, and premature death. Therefore, a study was conducted to classify the risk of hypertension based on hypertension diagnoses at the Padang Laweh Health Center, Dharmasraya Regency, using the Learning Vector Quantiazation (LVQ) Algorithm. The advantage of LVQ is its ability to achieve high accuracy in processing data with numerous numerical and categorical features. The analysis results show that the use of the Learning Vector Quantization Algorithm on the test data produces very good accuracy, namely 95.17% correct classification of hypertensive patients
Co-Authors arrahmi, nailul Fadhilah Fitri Fajrin Putra Hanifi Fenni Kurnia Mutiya Harpidna, Riska Harpidna Lucy Oktavani Miftahul Barokah, Bunga Nonong Amalita Sepyanda, Marsika Syafriandi Titiek Fujita Yusandra Yenni Kurniawati Yummi Meirafoni Zilrahmi, Zilrahmi