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All Journal Journal of Dinda : Data Science, Information Technology, and Data Analytics Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
MARNA MARNA
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Computer Science & IT

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Pemanfaatan Kahoot dan Quizizz Sebagai Alat Media Pembelajaran Interaktif untuk Meningkatkan Kualitas Belajar di SMAN 1 Parittiga Fithriawan Nugroho; Aditya Ahmad Fauzi; Faishal Farras; Muhammad Iqbal Hanafi; Siti Kamilah; Marna Marna
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 1 (2025): Februari : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i1.725

Abstract

The use of technology in education has become an essential need in modern learning. This study aims to analyze the utilization of Kahoot and Quizizz as interactive learning tools to enhance student learning quality at SMAN 1 Parittiga. The research employs a qualitative approach with observation and interview techniques to explore student and teacher experiences in using these platforms. The findings indicate that Kahoot and Quizizz significantly improve students' motivation, engagement, and comprehension of learning materials. Kahoot is more effective in creating a competitive learning atmosphere, while Quizizz provides flexibility for students to learn at their own pace. However, some challenges arise, such as reliance on a stable internet connection and the need for teacher training to optimize platform features. Therefore, it is recommended that schools improve digital infrastructure and provide training for teachers to support the optimal implementation of technology-based learning.
Document Similarity Using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity Adi Widianto; Eka Pebriyanto; Fitriyanti Fitriyanti; Marna Marna
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1589

Abstract

Document similarity is a fundamental task in natural language processing and information retrieval, with applications ranging from plagiarism detection to recommendation systems. In this study, we leverage the term frequency-inverse document frequency (TF-IDF) to represent documents in a high-dimensional vector space, capturing their unique content while mitigating the influence of common terms. Subsequently, we employ the cosine similarity metric to measure the similarity between pairs of documents, which assesses the angle between their respective TF-IDF vectors. To evaluate the effectiveness of our approach, we conducted experiments on the Document Similarity Triplets Dataset, a benchmark dataset specifically designed for assessing document similarity techniques. Our experimental results demonstrate a significant performance with an accuracy score of 93.6% using bigram-only representation. However, we observed instances where false predictions occurred due to paired documents having similar terms but differing semantics, revealing a weakness in the TF-IDF approach. To address this limitation, future research could focus on augmenting document representations with semantic features. Incorporating semantic information, such as word embeddings or contextual embeddings, could enhance the model's ability to capture nuanced semantic relationships between documents, thereby improving accuracy in scenarios where term overlap does not adequately signify similarity.
Co-Authors Adi Widianto Aditya Ahmad Fauzi Eka Pebriyanto Faishal Farras Fithriawan Nugroho fitriyanti fitriyanti Muhammad Iqbal Hanafi Siti Kamilah