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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JET (Journal of English Teaching) Adi Buana Jurnal Aplikasi Statistika & Komputasi Statistik
Karim, Rafidah Abd
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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THEORETICAL PERSPECTIVES AND PRACTICES OF MOBILE-ASSISTED LANGUAGE LEARNING AND MIND MAPPING IN THE TEACHING OF WRITING IN ESL CLASSROOMS Abu, Abdul Ghani; Karim, Rafidah Abd
JET ADI BUANA Vol 2 No 01 (2017)
Publisher : English Education Department, Faculty of Teacher Training and Education, Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jet.v2.n01.2017.706

Abstract

Mobile assisted language learning (MALL) is a highly popularmultidisciplinary study field which increasingly attracts the attention ofscholars around the world. Moreover, it has attracted the scholars whohave realized the potential to apply mobile technologies to enhancelearning. This paper explores the perspectives and practices of mobileassistedlanguage learning and mind mapping and their practices inteaching of writing in the ESL classrooms. Few aspects are covered likedefining MALL, theoretical perspectives drawn from MALL, relatingthese to the practice of MALL and mind mapping in writing, pedagogicalapproaches used in MALL and issues faced in the ESL writingclassrooms. Thus, it is showed that MALL can be incorporated intowriting by using it with several writing approaches and techniques whichcounterparts the pedagogical advantages in mobile language learningcontexts. The paper concludes with a brief discussion of the reviewedstudies and it implicates that mobile learning and mind mapping has goodprospects for teaching writing to ESL students.
Spatial Dependencies in Environmental Quality: Identifying Key Determinants Samosir, Omas Bulan; Karim, Rafidah Abd; Fauzi, M. Irfan; Berliana, Sarni Maniar
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.802

Abstract

Introduction/Main Objectives: Environmental quality is essential to human development because it reflects the condition of our natural surroundings. Background Problems: Understanding the determinants of environmental quality is crucial for Indonesia as it helps identify the key factors influencing environmental quality. Novelty: This study seeks to identify the determinants of environmental quality in regencies and municipalities on Java Island, incorporating spatial effects into the analysis. Research Methods: The dependent variable is environmental quality index. The independent variables are GRDP in industrial sector, GRDP in agricultural sector, urban population rate, population density, and poverty rate. We applied spatially lag regression model using contiguity spatial weight matrix. Finding/Results: This study shows the spatially lag regression model outperforms the OLS model. GRDP in the industrial sector, GRDP in the agricultural sector, urban population rate, and population density have negative effects, suggesting the increases in these variables were associated with lower environmental quality.
A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION AND SMOTE ALGORITHM Berliana, Sarni Maniar; Samosir, Omas Bulan; Karim, Rafidah Abd; Valenzuela, Victoria Pena; Wahyuni, Krismanti Tri; Alfian, Andi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1409-1420

Abstract

Suicide, a global health challenge identified in Goal 3 of the global agenda for enhancing worldwide well-being, demands urgent attention. This study focused on predicting suicidal thoughts using machine learning, leveraging the 2021 National Women's Life Experience Survey (SPHPN) involving women aged 15 to 64. Analyzing 11,305 ever-married women, 504 (4.5%) reported experiencing suicidal thoughts. The outcome variable was binary (1 for suicidal thoughts, 0 for none). The study used seven predictors: age, education level, residence type, physical and sexual violence, smoking frequency, alcohol consumption, and depression. Ordinary logistic regression and SMOTE-based logistic regression were applied. The former identified physical violence, depression, and sexual violence as crucial factors, while the latter emphasized physical violence, sexual violence, and age. In cases of class imbalance, the SMOTE-enhanced model exhibited improved performance in terms of sensitivity, false positive rate, balanced accuracy, and Kappa statistic, with lower standard errors of parameter estimates. The findings highlight the importance of addressing violence and mental health in policies aimed at reducing suicidal thoughts among women. Policymakers can use these insights to develop targeted interventions, and healthcare providers can identify high-risk individuals for timely interventions. Community programs and public health campaigns should promote mental well-being and prevent suicidal behaviors using these findings. Future research should include more predictors, diverse populations, and longitudinal data to better understand causal relationships and timing. Interdisciplinary collaboration and advanced machine learning techniques can enhance predictive accuracy and model interpretability.
Co-Authors Abdul Ghani Abu, Abdul Ghani Alfian, Andi Fauzi, M. Irfan Samosir, Omas Bulan Sarni Maniar Berliana, Sarni Maniar Valenzuela, Victoria Pena Wahyuni, Krismanti Tri