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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Journal of Education and Learning (EduLearn) AL-ATHFAL : JURNAL PENDIDIKAN ANAK Insight: Jurnal Ilmiah Psikologi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pendidikan Islam
Rahmatullah, Bahbibi
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Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences Other

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The Digital Teaching Landscape: Investigating the Roles of Interest, Self-Efficacy, and Experience in Shaping TPACK Salsabila , Sarah; Rahmatullah, Bahbibi; 'Aziz, Hafidh
Al-Athfal: Jurnal Pendidikan Anak Vol. 9 No. 2 (2023)
Publisher : Islamic Early Childhood Education Study Program, Faculty of Tarbiyah and Education, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/al-athfal.2023.92-06

Abstract

Purpose –This study aims to analyze the relationship between interest, self-efficacy, and practical teaching experience concerning prospective early childhood education teachers' perceptions of Pedagogical Knowledge of Technology Content. Design/methods/approach – This research employs an associative correlational quantitative design, involving 84 students from the Early Childhood Islamic Education Study Program at UIN Sunan Kalijaga Yogyakarta. Data was collected through both offline and online questionnaires and analyzed using multiple linear regression techniques. This analysis assesses the predictive capacity of interest, self-efficacy, and teaching experience in determining TPACK within the perceptions of prospective ECCE teachers. Data analysis was aided by the statistical software SPSS 26 for Windows. Findings – The statistical tests indicate that interest, when considered individually, does not exhibit a positive and significant effect on TPACK. Conversely, self-efficacy and teaching practice experience, when examined separately, demonstrate a positive and significant influence on TPACK. Moreover, when these variables are collectively examined, interest, self-efficacy, and teaching practice experience collectively impact TPACK, showcasing a robust relationship of 75.4%. However, it's worth noting that there are some inconsistencies in the results concerning the impact of interest on TPACK, which may be attributed to a lack of specific context and a detailed examination of respondents' interest levels. Research implications/limitations – This study primarily focuses on elucidating the interplay between interest, self-efficacy, and practical teaching experience in TPACK development. Practical implications – These practical implications are envisioned to enhance the quality of early childhood education and prepare prospective ECCE teachers to confront the evolving technological landscape. Originality/value – This research contributes to an enhanced understanding of the interrelationships between interest, self-efficacy, and practical teaching experience in shaping prospective teachers' perceptions of TPACK, offering novel insights into the professional development of prospective ECCE teachers. Paper type Research paper  
A systematic review on research trends, datasets, algorithms, and frameworks of children’s nutritional status prediction Swastina, Liliana; Rahmatullah, Bahbibi; Saad, Aslina; Khan, Hussin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1868-1877

Abstract

The monitoring of children's nutritional status serves as a crucial tool for assessing the health of both children and society as a whole. In this regard, machine learning has been employed to predict nutritional status for monitoring purposes. This topic has been extensively discussed; however, the question remains as to which algorithm or machine learning framework can yield the highest accuracy in predicting the nutritional status of children within a specific region. Furthermore, determining the appropriate dataset for predictions is also crucial. Therefore, this review aims to identify and analyze the research trends, dataset characteristics, algorithms, and frameworks utilized in studies pertaining to the nutritional status of children under the age of five from 2017 to early 2022. The selected papers focus on the application of machine learning techniques in predicting nutritional status. The findings of this research reveal that the Bangladesh DHS 2014 dataset is among the popular choices for machine learning applications in this field. The most commonly employed algorithms include Neural Networks, Random Forests, Logistic Regression, and Decision Trees which demonstrated promising performance. Lastly, the data preprocessing stage within a framework plays a significant role in models aimed at predicting nutritional status.
Recent advancements in postpartum depression prediction through machine learning approaches: a systematic review Fazraningtyas, Winda Ayu; Rahmatullah, Bahbibi; Dwi Salmarini, Desilestia; Arrieya Ariffin, Shamsul; Ismail, Azniah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7185

Abstract

Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followed by support vector machine (SVM), logistic regression (LR), XGBoost, and AdaBoost. The development of advanced data techniques in PPD has encouraged interdisciplinary collaboration between researchers in psychiatry and computer science that holds great potential for refining the accuracy and reliability of PPD predictive models, ultimately resulting in improved outcomes for mothers and their families through early detection, intervention, and support.
The Impact of Islamic Religious Education on the Development of Early Childhood Religious and Moral Values During the COVID-19 Pandemic in Indonesia and Malaysia Munastiwi, Erni; Rahmatullah, Bahbibi; Marpuah
Jurnal Pendidikan Islam Vol. 10 No. 1 (2021)
Publisher : Faculty of Tarbiyah and Education, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jpi.2021.101.49-66

Abstract

When the world is busy with the COVID-19 virus outbreak, various sectors experienced a significant impact, especially in the education sector, which requires children to carry out learning activities from home. This study aims to determine the effect of Islamic religious education on early childhood's religious and moral values during the COVID-19 pandemic. This study uses a quantitative analysis method of product-moment correlation through a t-test to test the significance of the effect of the independent variable on the dependent variable. The study results found that the hypothesis using the t-test showed that the independent variable of Islamic religious education was proven to significantly influence the dependent variable on the development of religious and moral values of early childhood. Then through the t-test, it can be seen that the significance value of 0.534 is greater than the 0.05 significance level. Thus, Ha is accepted, religious education has a significant effect on the development of religious and moral values of early childhood during the COVID-19 pandemic. This study contributes to a deep understanding that Islamic religious education affects the development of religious and moral values of early childhood even in the Covid-19 pandemic situation. The role of parents is a good role model for children to apply religious and moral values through Islamic religious education.
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.
A systematic review of students’ awareness on cyberbullying at high school level of education Raja Hassan, Raja Muhammad Faiz; Rahmatullah, Bahbibi; Saad, Aslina; Tareq, Ziadoon
Insight: Jurnal Ilmiah Psikologi Vol. 24 No. 1 (2022): FEBRUARY 2022
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/psikologi.v24i1.1978

Abstract

Today's cyber world is becoming more accessible to anyone. Lack of education and awareness related to cyber security including bullying has contributed to the misuse of available facilities, especially social media. Cyberbullying is a serious concern among secondary students, and owing to the pandemic COVID-19, the majority of secondary students are required to attend class online in order to complete their studies. However, the use of technology in secondary school has a detrimental influence, as seen by the rise in cyberbullying instances among secondary pupils. Using the Scopus database, we discovered 36 publications connected to the term that was used. Following the screening, a total of 17 academic documents that are entirely connected to the study issue were obtained and examined. Thematic analysis performed shows several important aspects studied which looks at the impact, prevention, and knowledge of cyberbullying among secondary students, teachers, and parents. The findings also point towards raising awareness about the impact of cyberbullying on secondary students and how to prevent it. The goal of this systematic literature review is to raise awareness regarding cyberbullying's impact on high school or secondary level students.
Integrating computational thinking into English writing: development of a computational thinking-integrated module Saad, Aslina; Hashim, Haslinda; Rahmatullah, Bahbibi
Journal of Education and Learning (EduLearn) Vol 20, No 1: February 2026
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v20i1.23260

Abstract

This study addresses challenges in teaching English writing skills in English as a second language (ESL) classrooms, proposing a novel approach through computational thinking (CT). A CT-integrated writing module was developed for primary school ESL teachers using the analysis, design, development, implementation, and evaluation (ADDIE) model and qualitative research. Incorporating constructivist and experiential learning theories, the module uses visualization tools like circle maps and flow maps across 8 units, combined with an inquiry-based approach, scaffolding, and localized materials. The 5 CT elements-decomposition, pattern recognition, abstraction, algorithmic thinking and logical reasoning-are embedded to enhance learning. Focus group interviews with 4 ESL experts indicate strong acceptance, highlighting the module’s usability, content, and teaching activities. The study provides a framework for CT-based instructional modules to improve problem-solving and cooperative learning in English writing education.
Co-Authors 'Aziz, Hafidh Arrieya Ariffin, Shamsul Dwi Salmarini, Desilestia Hashim, Haslinda Ismail, Azniah Khan, Hussin Liliana Swastina Marpuah Mohammad Basit, Mohammad Munastiwi, Erni Naparin, Husni Raja Hassan, Raja Muhammad Faiz Razak, Nor Asiah Saad, Aslina Salsabila , Sarah Tareq, Ziadoon