Article Per Year (5 Year)
p-Index From 2021 - 2026
5.123
P-Index
This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) Matematika dan Sains Tadris: Jurnal keguruan dan Ilmu Tarbiyah Indonesian Journal of Educational Review (IJER) Jurnal Tadris Kimiya Seminar Nasional Pendidikan IPA Orbital: Jurnal Pendidikan Kimia JKPK (Jurnal Kimia dan Pendidikan Kimia) Jurnal Riset Pendidikan Kimia (JRPK) Jurnal Lentera Pendidikan Pusat Penelitian LPPM UM Metro Jurnal Riset Sains dan Kimia Terapan Journal of Educational Chemistry (JEC) ABSYARA: Jurnal Pengabdian Pada Masyarakat International Journal of Social and Management Studies (IJOSMAS) International Journal of STEM Education for Sustainability Chemistry and Materials Prosiding Seminar Nasional Pengabdian Kepada Masyarakat LAVOISIER: Chemistry Education Journal Mitra Teras: Jurnal Terapan Pengabdian Masyarakat Journal of Educational Management and Learning LAVOISIER: Chemistry Education Journal Proceeding of the International Conference on Global Education and Learning
Maria Paristiowati
Universitas Negeri Jakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

Published : 53 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search
Journal : Journal of Educational Management and Learning

 1 
Optimizing University Admissions: A Machine Learning Perspective Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Paristiowati, Maria; Suhendra, Rivansyah; Yandri, Erkata; Satrio, Justinus; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.46

Abstract

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.
Augmented Reality and Student Learning: Analysis of Mental Models of Salt Hydrolysis at SMAN 51 Jakarta, Indonesia Umayah, Anisa; Paristiowati, Maria; Dianhar, Hanhan; Hasibuan, Nur Azizah Putri
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.53

Abstract

This study aimed to ascertain students' mental models while learning about salt hydrolysis through augmented reality (AR). The study comprised 36 participants from Public High School 51 in Jakarta. A descriptive qualitative approach was adopted for this research, employing various data collection methods such as written drawings, interviews, classroom observations, teacher notes, student worksheets, and final tests. In categorizing students' mental models, three main types emerged: scientific, synthetic, and initial mental models. The findings revealed that 7.20% of students fell into the initial mental model category, 53.90% exhibited synthetic mental models, and 38.90% demonstrated scientific mental models. Notably, incorporating AR into salt hydrolysis learning predominantly influenced the development of synthetic mental models. The study's results also indicated that the utilization of AR positively enhanced students' spatial abilities in understanding submicroscopic representations.
Evaluating Learning Motivation: An Analysis of Students' Engagement in Online Learning Environments Karyadi, Prita Atria; Paristiowati, Maria
Journal of Educational Management and Learning Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i1.193

Abstract

This study analyzes students' learning motivations, including concentration, curiosity, enthusiasm, independence, readiness, encouragement, consistency, and self-confidence during online learning at Pahoa High School. Using a qualitative descriptive method, the research was conducted from February to April 2022, involving 53 students from class XI. Data were collected through observations, interviews, reflective journals, and questionnaires adapted from various research journals, focusing on learning motivation. The data analysis involved stages of data reduction, coding, presentation, and conclusion. Results were categorized into three areas: (1) student learning motivation during online learning, (2) advantages of online learning, and (3) disadvantages of online learning. Questionnaire data revealed that 78% of students exhibited excellent concentration, 57% had good curiosity, 41% displayed fair enthusiasm, 92% showed excellent independence, 54% demonstrated good readiness, 93% had excellent encouragement, 78% maintained excellent consistency, and 61% had good self-confidence. Overall, 69% of students were found to have good learning motivation. These findings suggest that students generally possess strong learning motivations in an online learning environment, providing valuable insights for educators to enhance online teaching strategies and improve student learning outcomes. This study also serves as a reference for future research on learning motivation in online education.
Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Zahriah, Zahriah; Paristiowati, Maria; Emran, Talha Bin; Ilyas, Mukhlisuddin; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i2.233

Abstract

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As generative AI tools become more popular, there is growing interest in how AI can improve teaching, learning, and research. Higher education faces many challenges, such as meeting diverse learning needs and preparing students for fast-changing careers. Generative AI offers solutions by personalizing learning experiences, making education more engaging, and supporting skill development through adaptive content. It can also help researchers by automating tasks like data analysis and hypothesis generation, making research faster and more efficient. Moreover, generative AI can streamline administrative tasks, improving efficiency across institutions. However, using AI also raises concerns about privacy, bias, academic integrity, and equal access. To address these issues, institutions must establish clear ethical guidelines, ensure data security, and promote fairness in AI use. Training for faculty and AI literacy for students are essential to maximize benefits while minimizing risks. The paper suggests a strategic framework for integrating AI in higher education, focusing on infrastructure, ethical practices, and continuous learning. By adopting AI responsibly, higher education can become more inclusive, engaging, and practical, preparing students for the demands of a technology-driven world.
Developing Digital Microlearning Content on Reaction Rates Using Wix for Senior High School Fajriah, Sari Nur; Paristiowati, Maria; Nanda, Elsa Vera
Journal of Educational Management and Learning Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i1.281

Abstract

Reaction rate concepts in high school often lack submicroscopic depth and are not supported by digital media that match Generation Z's preferences for interactive learning. This study developed a Wix-based microlearning platform for 11th-grade students featuring videos, infographics, quizzes, worksheets, games, and modules. Using the ADDIE model, the platform was evaluated by experts and tested in the classroom. Expert validation showed high feasibility (91.94% from content and language experts, 90.40% from media experts). Large-scale trials with students and teachers also yielded high acceptance (93.25% and 93.23%, respectively). These findings support the platform’s feasibility and effectiveness in enhancing chemistry learning, especially for teaching reaction rates.
Techniques and Tools in Learning Analytics and Educational Data Mining: A Review Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Paristiowati, Maria; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i1.308

Abstract

Learning analytics and educational data mining are rapidly evolving fields that leverage data-driven methods to enhance teaching, learning, and institutional decision-making. This review provides a comprehensive overview of the key analytical techniques and tools employed in learning analytics and educational data mining, including classification, clustering, regression, association rule mining, and data visualization. It also highlights the integration of advanced methods such as deep learning and adaptive systems for personalized education. The paper examines various platforms and technologies, including learning management systems, open-source tools, and AI/ML libraries, to evaluate their capabilities, scalability, and practical adoption. Key application areas, such as dropout prediction, engagement analysis, personalized learning, and curriculum design, are examined through selected case studies spanning K–12 and higher education. The review emphasizes the growing importance of ethical considerations, interpretability, and usability in the application of educational analytics. By synthesizing current practices and trends, this work aims to inform educators, researchers, and developers seeking to harness educational data for improved learning outcomes and strategic planning.
The Role of Study Habits, Parental Involvement, and School Environment in Predicting Student Achievement: A Machine Learning Perspective Noviandy, Teuku Rizky; Paristiowati, Maria; Isa, Illyas Md; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i2.350

Abstract

This study explores the application of machine learning techniques to predict student achievement based on study habits, parental involvement, and school environment. Using a dataset from Kaggle comprising academic, behavioral, and contextual variables, four machine learning algorithms, namely K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Random Forest, were implemented and evaluated. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curve, and Precision–Recall curves. Results show that all models effectively classified students into low- and high-achievement categories, with SVM achieving the highest accuracy (94.02%) and the strongest overall performance. The findings highlight the potential of machine learning-driven predictive analytics in educational settings, enabling early identification of at-risk students and supporting evidence-based interventions. By integrating diverse factors influencing academic performance, this study demonstrates how data-driven approaches can enhance educational management, inform policy, and promote equitable learning outcomes.
Co-Authors , Woro Sumarni - Afrizal Afrizal Afrizal Aftuni Aftuni Agung Imaduddin, Agung Ahmad Syakur Al Husna Alysa Hestaviana Amyyana, Afwu Hayyi Andini Lestari Syah Putri Annisa Nur Fitria Arinda Putri Nurhaliza Ariyatun, Ariyatun Aulia, Faizah Auliya, Annisa BUDI SETIADI DARYONO Catur Ahda Darojatun Cecep Kustandi Chaeruman, Uwes Anis Cyntia Melawati Darsef Darwis Deby Virgiawan Deswara, Raka Dewi Fitriyani Diska Ariani Silalahi Edith Allanas Emran, Talha Bin Erdawati Erdawati Erdawati Erdawati, Erdawati Erkata Yandri Fahmi Anhar Muladi Fajriah, Sari Nur Fitriani, Roisyah Ghazi Mauer Idroes Ghina Imani Rofi Hanhan Dianhar Hasibuan, Nur Azizah Putri Ilmiyati, Dian Irma Ratna K Irwanto Irwanto Isa, Illyas Md Istianah Istianah Jumila Jumila Karepesina, Mohammad Asrul Ashmi Karyadi, Prita Atria Kurniadewi, Fera Maulana, Aga Miska Zidna Ilmana Moersilah, Moersilah Muhamad Fazar Nurhadi MUHAMMAD ILHAM Mukhlisuddin Ilyas Muktiningsih Nurjayadi N, Muktiningsih Nailah Fauziyyah Nainggolan, Icha Ananda Nanda, Elsa Vera Nathasya Jofita Neli Nilawati Noviantika Nurul Nabillah Nur Azizah Putri Hasibuan Nurasiah Nurasiah Nurbaity Nurbaity Nurhaliza, Arinda Putri Prabowo, Norbertus Krisnu Prita Atria Karyadi Puspa Rini, Eka Puteri, Hana Alya Putri Hasibuan, Nur Azizah Putri, Andini Lestari Syah Rahma, Wanda Amelia Rahma, Wanda Amelia Ratna Choiryana Rinaldi Idroes Risky, Elsyafahriza Sadam, Padya Adisty Putri Sasmita, Novi Reandy Satrio, Justinus Setia Budi Setia Budi Setiawan, Wiwik Setyorini, Triyana Wahyu SRI WARDANI Stephanie, Mian Maria Sudarmin Suhartono Suhartono Suhendra, Rivansyah Sulistyowati Nur Astuti Sutrisno, Mega Suwirman Nuryadin SUWIRMAN NURYADIN, SUWIRMAN Syafei, Devi Syifa Nur Muttaqin Teuku Rizky Noviandy Tri Retnosari Ucu Cahyana Umayah, Anisa Uwes Anies Chaeruman Virgiawan, Deby Wiwik Setiawan Yuli Rahmawati Yusmaniar Yusmaniar Yusmaniar Yusmaniar Zahriah, Zahriah Zulhipri Zulhipri Zulhipri, Zulhipri Zulmanelis Zulmanelis