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Jurnal Media Computer Science
Published by Universitas Dehasen Bengkulu
ISSN : -     EISSN : 28280490     DOI : https://doi.org/10.37676/jmcs
Core Subject : Science,
Computer Science & IT
Jurnal Media Computer Science merupakan jurnal nasional yang diterbitkan oleh Universitas Dehasen Bengkulu sejak tahun 2022. Jurnal Media Computer Science memuat artikel hasil-hasil penelitian di bidang Komputer, Sistem Informasi dan Teknologi. Jurnal Media Computer Science berkomitmen untuk menjadi jurnal nasional terbaik dengan mempublikasikan artikel berbahasa Indonesia yang berkualitas dan menjadi rujukan utama para peneliti.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 142 Documents
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Development Of An Artificial Intelligence-Based Essay Exam Assessment System Putra, Andika Fajar; Sukirman, Sukirman
Jurnal Media Computer Science Vol 5 No 2 (2026): April
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i2.11179

Abstract

This study aims to develop an Artificial Intelligence (AI)-based digital platform for automatic essay assessment of students at MTsN Filial Kartasura. The background of this research is the problem of traditional manual essay grading, which is time-consuming, less efficient, and potentially subjective. The research method used is Research and Development (R&D) with a 4D development model consisting of Define, Design, Develop, and Disseminate stages. The system was developed using PHP, HTML, CSS, and MySQL programming languages, and integrated with AI to analyze and assess student essays based on teacher-defined criteria. The results show that the AESAI system is capable of automatically scoring essays based on keyword relevance and answer quality, while also providing instant feedback. The Blackbox testing results indicate that all system features function properly. The material expert validation resulted in a score of 92.24%, categorized as very feasible, while the media expert evaluation obtained 93.21%, also categorized as very feasible. Furthermore, user testing involving 32 teachers produced an average score of 89.72%, categorized as very good. These results indicate that the developed system is well accepted by users. Therefore, the AESAI platform is proven to be feasible as an innovative solution for essay assessment. The system not only improves teachers’ efficiency in grading but also provides more objective, consistent, and transparent evaluation in digital learning environments.
Sentiment Analysis and Characteristics of Youtube User Opinions Toward Samsung and Iphone Brands Using TF-IDF With Naive Bayes and KNN Comparison and Mcnemar Test Nurpadhilah, Naurah Atikah; Saputera, Surya Ade
Jurnal Media Computer Science Vol 5 No 2 (2026): April
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i2.11311

Abstract

The development of social media, particularly YouTube, has generated a large amount of public opinion data that can be utilized to understand user perceptions of products. Samsung and iPhone are two smartphone brands with intense market competition and are frequently discussed in YouTube comment sections. This study aims to compare the performance of the Naive Bayes and K-Nearest Neighbor (KNN) algorithms in sentiment analysis of YouTube comments related to these two brands. The research data were collected through a YouTube comment scraping process using the youtube-comment-downloader library. The research stages included data collection, text pre-processing consisting of case folding, punctuation removal, number removal, stopword removal, and stemming using the Sastrawi library. Furthermore, the text data were transformed into numerical representations using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The classification process was carried out using the Naive Bayes and KNN algorithms and evaluated using accuracy, classification reports, confusion matrices, and the McNemar test to determine the significance of performance differences between the models. In addition, this study also analyzed word distribution based on sentiment and brand using WordCloud visualization. The results indicate that both algorithms are capable of classifying comment sentiments effectively and provide insights into user opinion characteristics toward Samsung and iPhone based on YouTube comments.

Page 15 of 15 | Total Record : 142

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