Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Indonesian Journal of Chemistry Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) Journal of Educational Management and Learning
Isa, Illyas Md
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Chemistry Education Languange, Linguistic, Communication & Media Mathematics Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Development of Voltammetry Analysis Method of Iron Metal Ions by Solid-State Membrane with Carbon Nanotube Suyanta, Suyanta; Sunarto, Sunarto; Padmaningrum, Regina Tutik; Karlinda, Karlinda; Isa, Illyas Md; Zainul, Rahadian; Fardiyah, Qonitah; Kurniawan, Fredy
Indonesian Journal of Chemistry Vol 24, No 3 (2024)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijc.81771

Abstract

This work developed a selectively modified electrode for measuring the Fe(II) ions in continuous integration using voltammetry techniques. The study assessed various aspects, such as linearity, scan rate, repeatability, and real sample analysis. The experiment is performed using differential pulse voltammetry (DPV). The findings of the study indicated that the voltammetry method exhibited a regression line of y = 36.507 ln(x) + 990.73, with a correlation value of 0.9627, with an optimum scan rate of 20 mV/s and good repeatability over five times measurement. On the other hand, when comparing the results using the UV-Vis spectrophotometric technique, the regression equation was found to be y = 0.20438x − 0.06987, with a correlation value of 0.99583. Notably, the voltammetry measurement outperformed the UV-Vis method since it allowed analysis of Fe(II) at concentrations up to 6.35 × 10−4 ppm (or 1.00 × 10−11 M), while the UV-vis measurement could only analyze up to 1.5 ppm (or 2.36 × 10−5 M). Consequently, the developed technique proves to be superior to the other methods for the analysis of Fe(II).
Exploring The Role of ChatGPT in Chemistry Learning: A Systematic Literature Review Priyadi*, Danang; Suyanta, Suyanta; Suwardi, Suwardi; Isa, Illyas Md
Jurnal Pendidikan Sains Indonesia Vol 13, No 3 (2025): JULY 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jpsi.v13i3.45118

Abstract

This study used the systematic literature review method to analyze the contribution of chatGPT in chemistry education by reviewing 30 related articles. The results showed that chatGPT assists in various learning tasks, such as preparing lesson plans, assessing students' critical thinking and self-regulation skills, guiding practical sessions, and supporting scientific report writing. In addition, chatGPT plays a role in the development of technology-based teaching materials and explaining complex chemical concepts. However, there are challenges in its implementation, such as the potential for students to rely on AI and the need for information verification to ensure the accuracy of scientific concepts. Therefore, in order for chatGPT to remain a useful tool in chemistry learning, it must be accompanied by the right strategy. With appropriate guidance, this technology can increase student interaction and engagement, thereby contributing to improving the quality of chemistry teaching in the digital era
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 Fredy Kurniawan Karlinda Karlinda Maria Paristiowati Priyadi*, Danang Qonitah Fardiyah Rahadian Zainul Regina Tutik Padmaningrum Rinaldi Idroes Sunarto Sunarto Suwardi Suwardi Suyanta Suyanta Teuku Rizky Noviandy