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All Journal Jurnal Multidisiplin Indonesia Journal of Engineering Technology and Science Integration (JETSI)
Muhammad Gibran Alif Prasetya
Universitas Negeri Semarang, Indonesia
Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Veterinary

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Pengaruh Dukungan AI terhadap Motivasi Belajar dan Pemahaman Konsep Siswa dalam Penggunaan Modul Ajar Sains Islam Terintegrasi Ayat Qur’an Muhammad Gibran Alif Prasetya
Jurnal Multidisiplin Indonesia Vol. 5 No. 5 (2026): Jurnal Multidisiplin Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jmi.v5i5.2711

Abstract

Perkembangan Artificial Intelligence (AI) dalam pendidikan mendorong terciptanya pembelajaran yang lebih adaptif dan interaktif, termasuk dalam pembelajaran Sains Islam berbasis ayat Al-Qur’an. Namun, kajian mengenai pengaruh dukungan AI terhadap motivasi belajar dan pemahaman konsep siswa masih terbatas. Penelitian ini bertujuan untuk menganalisis pengaruh dukungan AI terhadap motivasi belajar dan pemahaman konsep siswa dalam penggunaan modul ajar Sains Islam terintegrasi ayat Al-Qur’an. Penelitian menggunakan pendekatan kuantitatif dengan desain eksplanatori melalui metode Structural Equation Modeling berbasis Partial Least Squares (SEM-PLS). Sampel penelitian terdiri atas 100 siswa SMA di Kabupaten Pati yang dipilih menggunakan teknik simple random sampling. Data dikumpulkan melalui kuesioner skala Likert yang mengukur dukungan AI, motivasi belajar, dan pemahaman konsep siswa. Hasil penelitian menunjukkan bahwa dukungan AI berpengaruh positif dan signifikan terhadap motivasi belajar serta pemahaman konsep siswa. Pembahasan menunjukkan bahwa fitur AI seperti personalisasi materi, umpan balik otomatis, dan rekomendasi belajar mampu meningkatkan keterlibatan belajar siswa dan membantu pemahaman konsep biologi yang terintegrasi dengan nilai Qur’ani. Kesimpulan penelitian menegaskan bahwa integrasi AI dalam modul ajar Sains Islam menjadi strategi pembelajaran yang efektif dan relevan dalam mendukung pembelajaran sains berbasis nilai keislaman di era digital.
Data-Driven Assessment Engineering Model Based on Log Automation and Predictive Analytics for the Transformation of Science Evaluation Systems Muhammad Gibran Alif Prasetya; Muhammad Rizqi Muflihuddin
Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration
Publisher : PT. Nordeo Scholar Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68627/jetsi.v1i1.9

Abstract

The transformation of science evaluation systems toward intelligent assessment is a critical urgency in the realm of digital technology, utilizing digital exhaust as a massive source of behavioral data. However, conventional monolithic Learning Management Systems (LMS) lack the architectural capacity to process real-time time-series logs, functioning merely as static score repositories rather than proactive diagnostic tools. This study aims to develop and validate a Data-Driven Assessment Engineering (DDAE) model to address this infrastructural limitation. Utilizing the Design Science Research Methodology (DSRM), the computational architecture was engineered using a distributed microservices approach integrating Apache Kafka, Polyglot Persistence (PostgreSQL, MongoDB, InfluxDB), and Machine Learning algorithms (XGBoost and DBSCAN). The prototype was deployed for junior high school science evaluations involving 312 eighth-grade students across four public schools (936 examination sessions; 487,592 raw log events), and user acceptance was quantitatively assessed using the Technology Acceptance Model (TAM) via Multiple Linear Regression. Load testing demonstrated the DDAE architecture's stability, maintaining an optimal latency of 42.1 ms under a peak load of 1,000 concurrent users. Furthermore, the XGBoost predictive model, optimized via grid-search hyperparameter tuning with stratified 5-fold cross-validation, achieved an outstanding recall of 95.1% in classifying learning mastery, while DBSCAN precisely isolated rapid-guessing anomalies as spatial noise. Consequently, the automated processing of exam logs successfully converts passive raw data into actionable intelligence. This shifts the science evaluation paradigm into a proactive Early Warning System (EWS), heavily driven by its perceived usefulness among educational stakeholders.
Co-Authors Muhammad Rizqi Muflihuddin