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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics JURNAL PENDIDIKAN TAMBUSAI Indonesian Journal of Applied Informatics EDUKATIF : JURNAL ILMU PENDIDIKAN Jurnal Paedagogy Journal of English Language and Education Brilliance: Research of Artificial Intelligence Jurnal Pendidikan Sains dan Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Digital Transformation Technology (Digitech) Nuansa Informatika JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer Journal of Computers for Society Cadika Journal.
Rani Megasari
Universitas Pendidikan Indonesia
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 Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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NLP-Based Quranic Verse Retrieval In Islamic Education: A Development Model And Preliminary Evaluation Of Computational Thinking Irsyad Fauzan Nurdin; Lala Septem Riza; Rani Megasari
Jurnal Paedagogy Vol. 13 No. 3 (2026): July
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jp.v13i3.20755

Abstract

This study aims to develop a Natural Language Processing (NLP)-based Quranic verse retrieval model to support Quranic verse exploration and Computational Thinking (CT)-oriented learning in Islamic Education. The study employed the ADDIE development framework, and the model was implemented with Grade XI students and Islamic Education teachers at a private high school. Data were collected using a mixed-methods approach, including pre- and post-tests, perception questionnaires, expert validation, and teacher interviews. Quantitative data were analyzed using descriptive statistics, the Shapiro–Wilk test, the Wilcoxon signed-rank test, N-gain analysis, and descriptive percentage analysis of questionnaire responses, while qualitative data were analyzed through thematic analysis. The results demonstrated a significant improvement in students’ mean scores, increasing from 74.40 to 96.40 (*p* < 0.001), with a high average N-gain of 0.882. Student and teacher responses reached 90.19% and 77.50%, respectively, indicating positive acceptance of the proposed model. Although the one-group pretest–posttest design limits causal inference and CT was operationalized through learning-process indicators and user perceptions rather than comprehensive performance-based assessment, the findings suggest that the model has considerable potential as a technology-enhanced medium for exploratory learning in Islamic Education. Future research should focus on expanding the Quranic corpus, integrating tafsir resources, validating retrieval performance using standard information retrieval metrics, and incorporating performance-based assessments to measure Computational Thinking more comprehensively.
Peringkasan Teks Berita Berbahasa Indonesia Menggunakan LSTM dan Transformer Christina Prilla Rosaria Ardyanti; Yudi Wibisono; Rani Megasari
IJAI (Indonesian Journal of Applied Informatics) Vol 8, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v8i2.78856

Abstract

Abstrak Pertumbuhan informasi di internet membuat volume data tekstual semakin besar. Hal ini membuat manusia kesulitan dalam mengolah informasi dengan cepat. Peringkasan teks dapat membantu manusia untuk memahami informasi dalam jumlah yang banyak dengan cepat. Pada penelitian ini, arsitektur encoder-decoder akan diimplementasikan pada dataset Indosum menggunakan Long Short Term Memory (LSTM) dengan tambahan  mekanisme atensi dan Transformer. Ujicoba juga dilakukan menggunakan  fine-tuning pada pre-trained model T5-Small dan BART-Small. Eksperimen juga dilakukan dengan membandingkan dataset yang menggunakan praproses dan tanpa praproses. Berdasarkan eksperimen, model LSTM-Atensi memiliki kinerja rendah  dengan nilai ROUGE-L sebesar 13.0 pada dataset yang menggunakan praproses. Sedangkan nilai ROUGE-tertinggi didapatkan dari hasil fine-tuning T5-Small dengan nilai sebesar 66.2.===================================================AbstractThe proliferation of information on the internet has led to an increasing volume of textual data. This presents a challenge for humans in processing information rapidly. Text summarization can aid humans in quickly comprehending large amounts of information. In this research, an encoder-decoder architecture will be implemented on the Indosum dataset using Long Short-Term Memory (LSTM) along with attention mechanisms and Transformer. Experiments will also involve fine-tuning pre-trained models T5-Small and BART-Small. The influence of preprocessing will also be studied through experiments. Based on the experiments, the LSTM-Attention model demonstrates poor performance with an ROUGE-L score of 13.0 on the preprocessed dataset. Conversely, the highest ROUGE score was achieved through fine-tuning T5-Small, scoring 66.2.
Pengembangan Sistem Simulasi Prediksi Struktur Penduduk Menggunakan Model Cohort-Component: Studi Kasus Populasi Jepang Arzaniel Ethan Martosiswoyo; Muhammad Nursalman; Rani Megasari
Digital Transformation Technology Vol. 6 No. 1 (2026): Periode Maret 2026
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v6i1.9029

Abstract

Penuaan penduduk adalah masalah yang kompleks, tetapi peneliti dapat memahaminya lebih baik dengan menggunakan model matematika dan alat visual. Penelitian ini berfokus pada pembuatan dasbor untuk mensimulasikan kemungkinan yang terjadi pada populasi Jepang di masa depan. Penulis menggunakan metode cohort component untuk ini. Model ini melacak orang berdasarkan usia dan jenis kelamin setiap lima tahun. Sistem ini memperbarui angka berdasarkan tingkat kelahiran, tingkat kelangsungan hidup, dan migrasi. Penulis menulis kode dalam Python di Google Colab dan membuat antarmuka web menggunakan Streamlit. Dashboard memungkinkan pengguna untuk mengubah skenario, melihat piramida penduduk, dan mengunduh tabel data. Hasil uji dasar penulis menunjukkan populasi Jepang menurun dari 123,802 juta pada tahun 2024 menjadi sepenelitir 67,288 juta pada tahun 2074. Rasio ketergantungan juga melonjak dari 67,92 menjadi 107,69. Jika peneliti meningkatkan tingkat kelahiran dalam simulasi, penurunan populasi sedikit melambat, berakhir pada 74,892 juta. Namun, peningkatan migrasi penduduk usia kerja menghasilkan hasil terbaik. Hal ini mempertahankan jumlah penduduk sepenelitir 78,442 juta dan menurunkan rasio ketergantungan menjadi 94,98. Singkatnya, dashboard ini merupakan alat yang ampuh untuk mengeksplorasi perubahan demografis, meskipun hanya simulasi dan bukan prediksi resmi pemerintah.
Fine-Grained Classroom Activity Recognition via Detection, Head-Pose, and Appearance Fusion Yaya Wihardi; Raffi Ardhi Naufal; Meutia Jasmine Annisa Herawan; Rani Megasari
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9453

Abstract

Automatic analysis of student behavior in classroom environments is an important prerequisite for smart learning systems that move beyond coarse engagement indicators toward behavior-specific and pedagogically interpretable feedback. Fine-grained classroom activities, however, remain difficult to recognize because several target behaviors exhibit similar visual patterns, are partially occluded, or involve subtle head and upper-body motion. This study addresses five classroom activities commonly observed in authentic instructional settings: nodding, hand-raising, smartphone use, head-supporting, and looking downward. We propose a student-centered framework that first constructs person-consistent 16-frame tracklets and then integrates three complementary sources of evidence: RGB appearance from person crops, head-pose estimation, and an explicit smartphone-related cue. This design addresses two major ambiguity clusters in classroom scenes, namely downward-looking versus smartphone use and downward-looking versus head-supporting, while preserving temporal sensitivity for nodding. The dataset comprises labeled student-centered clips extracted from multi-view classroom recordings. Evaluation follows a subject-separated and session-separated protocol using different acquisition sessions, classroom settings, and participant groups. Under this protocol, the proposed framework achieves 91.13% accuracy and 91.14% macro F1. Compared with a visual-only baseline, the full fusion model improves macro F1 by 6.21 percentage points, while outperforming the strongest non-fusion baseline by 2.83 percentage points. The confusion analysis further indicates that head-pose information and explicit smartphone cues effectively separate visually adjacent downward-oriented behaviors. These findings support multi-cue fusion as an effective strategy for fine-grained classroom activity recognition and classroom analytics under the present held-out evaluation protocol, while broader deployment and cross-session generalization should remain bounded by the current evidence.
Co-Authors Ali Nurdin, Enjang Andriansyah, Rasyid Arzaniel Ethan Martosiswoyo Christina Prilla Rosaria Ardyanti Destian, Rangga Dicki Fathurohman Dwi Cahyono, Muhamad Yasirwan Dwi Hendratmo Widyantoro Eddy Prasetyo Nugroho Eka Fitrajaya Rahman Emir Mauludi Husni Fuadillah, Erry Hazmi Ramadhan Adli Irsyad Fauzan Nurdin ISKANDAR, AYSHA ALIA Jullfikar, Raisyad KUSPRIYANTO Lala Septem Riza M. Munir Marantika, Maya Meutia Jasmine Annisa Herawan Mia Karisma Haq Misbah, Indra Mohamad Asyqari Anugrah Muhammad Nursalman Muhammad Rayhan Nur Muldani, Henhen Imam Munir Munir Munir Munir, Munir Nazir, Shah Nursalman, Muhammad Raffi Ardhi Naufal Ranggana, Alfaza Rasim, Rasim Resky Ramadhandi Santoso Rosa Ariani Sukamto Vani Aprianto Wahyudin , Wahyudin Wahyudin Wahyudin Wibisono, Yudi Wihardi, Yaya Yudi Ahmad Hambali