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p-Index From 2020 - 2025
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All Journal International Journal of Evaluation and Research in Education (IJERE) Indonesian Journal of Electrical Engineering and Computer Science
Razak, Nor Asiah
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Education

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A predictive model for postpartum depression: ensemble learning strategies in machine learning Fazraningtyas, Winda Ayu; Rahmatullah, Bahbibi; Naparin, Husni; Basit, Mohammad; Razak, Nor Asiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp443-451

Abstract

Postpartum depression (PPD) presents a significant mental health challenge for mothers following childbirth. While the precise cause of this condition remains unknown, preventive measures and treatments are available. This study aims to employ ensemble learning techniques, utilizing C4.5 decision tree (DT), gradient boosting tree (GBT), and extreme gradient boosting (XGBoost), to predict the occurrences of PPD in the Banjarmasin, South Kalimantan, Indonesia. The predictive model developed encompasses a dataset comprising 317 records gathered from postpartum mothers in hospitals, community health services, and midwifery clinics (referred to as Model 1). Furthermore, resampling techniques (Model 2) were employed to address class imbalance. Additionally, feature selection including forward selection and backward elimination (Model 3) were implemented to enhance model performance. The findings reveal that XGBoost, combined with resampling methods, achieved the highest accuracy rate at 87.57%. Feature selection identified five crucial factors associated with PPD incidence: marital status, number of living children, history of depression, fear of delivery, and family relationships. The utilization of ensemble learning strategies for PPD prediction yields reliable outcomes that can be applied within clinical settings. Exploring alternative ensemble learning strategies such as random forest and adaptive boosting could further optimize model performance and warrant consideration in future research endeavours.
Bridging technology and humanity: humanizing online pedagogy in digital environments Razak, Nor Asiah; Zulkifli, Che Zalina; Abdullah, Yusri; Khairuddin, Ahmad Zulfadhli; Misron, Aervina; Somasundram, Piriya; Shakirdjanovna, Azizova Gulnora
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i3.31937

Abstract

Comprehensive analyses on incorporating the intersection of online education, humanizing teaching approaches, and digital tools remain scarce. To the best of the authors' knowledge, limited comprehensive studies integrate online pedagogy and digital tools to humanize teaching methods, enabling students to become engaged and personalized learners, while fostering empathy among educators. A systematic literature review (SLR) was conducted, utilizing databases from the Scopus, Web of Science (WoS), and Google Scholar. The study employed content and comparative analysis and advocated a grounded theory approach to inductively analyses and navigate the articles’ data for addressing three research questions. Based on a set of criteria for inclusion and exclusion, 34 research articles written in English between 2010 and 2024 were reviewed. Results indicated the community of inquiry (CoI) framework has been prominent over the past two decades and is considered suitable for integration with any digital tools when investigating pedagogical strategies at all education levels, aiming to make online learning student-centered or human-centered with the principle of ‘no child left behind'. The review offers significant implications for humanizing online learning to the educational technology community, particularly for policymakers and practitioners, to strategies, reflect on, and, if necessary, improve their practices for future sustainable education and efficient pedagogical performance. 
Co-Authors Abdullah, Yusri Khairuddin, Ahmad Zulfadhli Misron, Aervina Mohammad Basit, Mohammad Naparin, Husni Rahmatullah, Bahbibi Shakirdjanovna, Azizova Gulnora Somasundram, Piriya Zulkifli, Che Zalina