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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Penelitian dan Pendidikan Agama Islam
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Implementasi Media Pembelajaran Wordwall Dalam Meningkatkan Motivasi Belajar Siswa Pada Mata Pelajaran Pendidikan Agama Islam Di SD Islam Al Muttaqin Sawahlunto Melvi; Kustati, Martin; Amelia, Rezki; Gusmirawati
At-Tarbiyah: Journal of Islamic Religious Research and Education Vol. 2 No. 1 (2024): At-Tarbiyah: Journal of Islamic Religious Research and Education
Publisher : STAI Tebing Tinggi Deli

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Abstract

This study aims to analyze the implementation of Wordwall-based learning media to increase student motivation in the Islamic Religious Education (PAI) subject at SD Islam Al Muttaqin, Sawahlunto. Wordwall, as an interactive learning medium, offers various forms of quizzes, games, and engaging learning activities easily accessible to students, which are expected to enhance their interest and enthusiasm for learning. The research method used is classroom action research (CAR) with a qualitative approach. Data collection was conducted through observation, interviews, and questionnaires, involving PAI teachers and students as research subjects. The data obtained were analyzed using qualitative descriptive analysis techniques to understand the impact of implementing Wordwall media on students' learning motivation. The study results indicate that using Wordwall significantly increased students' learning motivation. Students appeared more enthusiastic and actively engaged in learning and showed improved comprehension of PAI material. Wordwall's use also facilitated teachers in delivering material in a varied and interactive way, creating a fun and effective learning environment. These findings suggest that Wordwall can be an innovative solution in PAI learning at the elementary school level, especially in attracting students' interest in learning. In conclusion, implementing Wordwall as a learning medium can increase students' motivation to learn and positively impact Islamic Religious Education learning at SD Islam Al Muttaqin, Sawahlunto.
Analisis Performa Ekstraksi Konten GPT-3 Dengan Matrik Bertscore Dan Rouge Yuniati, Yetti; Fitria, Kaira Milani; Melvi; Purwiyanti, Sri; Nasrullah, Emir; A Muhammad, Meizano
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 6: Desember 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1168088

Abstract

Integrasi model bahasa canggih dalam tugas-tugas pembangkitan teks telah menampilkan beberapa aplikasi yang luas di berbagai bidang, termasuk ekstraksi konten. Penelitian ini memanfaatkan model bahasa OpenAI GPT-3 untuk mengembangkan aplikasi yang membantu dalam proses persiapan konten penulisan kreatif dengan menerapkan fitur ekstraksi konten. Fitur-fitur ini mencakup ekstraksi informasi, meringkas paragraf, mengidentifikasi topik utama, dan menafsirkan teks untuk presentasi terminologi yang optimal. Penelitian ini menggunakan pendekatan 'few-shot learning' yang melekat pada model GPT-3. Kinerja aplikasi ini dievaluasi secara ketat melalui uji coba, membandingkan efektivitasnya dengan mesin pembangkitan teks komersial yang banyak digunakan saat ini. Tujuannya adalah menganalisis tingkat kelayakan sistem yang telah kami bangun terhadap aplikasi lain yang populer. Metrik evaluasi termasuk BERTscore dan ROUGE digunakan sebagai pengujian. Aplikasi ini mencapai BERTscore sebesar 86% untuk precision, 88% untuk recall, dan 87% untuk F1-Score. Selain itu, evaluasi ROUGE menghasilkan skor ROUGE-L sebesar 55% pada precision, 60% pada recall, dan 57% pada F1-Score, hasil tersebut menunjukkan kekuatan model dalam tugas ekstraksi konten. Hasil ini memberikan gambaran bahwa model GPT-3 berpotensi baik dalam meningkatkan efisiensi dan akurasi untuk tugas persiapan konten tulisan dalam industri penulisan kreatif.   Abstract The integration of advanced language models in text generation tasks has featured some extensive applications in various fields, including content extraction. This research utilises the OpenAI GPT-3 language model to develop an application that assists in the content preparation process of creative writing by implementing content extraction features. These features include information extraction, summarising paragraphs, identifying main topics, and interpreting text for optimal terminology presentation. This research utilises the ‘few-shot learning’ approach inherent to the GPT-3 model. The performance of this application was rigorously evaluated through trials, comparing its effectiveness with commercial text generation engines widely used today. The aim is to analyse the feasibility of the system we have built against other popular applications. Evaluation metrics including BERTscore and ROUGE were used as tests. The application achieved a BERTscore of 86% for precision, 88% for recall, and 87% for F1-Score. In addition, the ROUGE evaluation resulted in ROUGE-L scores of 55% in precision, 60% in recall, and 57% in F1-Score, these results show the strength of the model in the content extraction task. These results illustrate that the GPT-3 model has good potential in improving efficiency and accuracy for the task of writing content preparation in the creative writing industry.
Co-Authors A Muhammad, Meizano emir nasrullah Fitria, Kaira Milani Gusmirawati Martin Kustati Rezki Amelia Sri Purwiyanti Yetti Yuniati