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All Journal Jurnal Dinamika Informatika (JDI) Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK)
Muhammad Irvan Maulana
Universitas Negeri Surabaya
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering

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Komparasi Algoritma Naive Bayes dan Support Vector Machine pada Analisis Sentimen Komentar Instagram Laga El Clásico Barcelona vs Real Madrid Muhammad Irvan Maulana; Savana Putra Aditama; Harun Al Rosyid
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.432

Abstract

The rapid development of information and communication technology has driven social media to become a primary platform for users to express opinions on various events, including prestigious football matches such as El Clásico between Barcelona and Real Madrid. The high level of interaction among Instagram users generates a large volume of comments with unstructured text characteristics and diverse sentiments, making automatic sentiment analysis necessary to understand public opinion trends. This study aims to analyze the sentiment of Instagram user comments related to the El Clásico match by comparing the Naive Bayes and Support Vector Machine (SVM) algorithms. The dataset consists of 1,526 comments with an imbalanced sentiment class distribution. The research stages include text preprocessing, term weighting using Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification. The experimental results show that the SVM algorithm outperforms Naive Bayes, achieving an accuracy of 62.88% and a weighted F1-score of 0.62, while Naive Bayes achieves an accuracy of 59.53% and a weighted F1-score of 0.52. These results indicate that SVM is more effective in handling high-dimensional data and imbalanced class distributions in social media sentiment analysis.
Pengembangan Media Pembelajaran Berbasis Web “Levietech” Dengan Model Pbl Untuk Meningkatkan Hasil Belajar Siswa Pada Elemen Sistem Komputer Muhammad Irvan Maulana; Rindu Puspita Wibawa
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 5 (2026): IDENTIK - September
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i5.1820

Abstract

Computer System learning in Vocational High Schools (SMK), which is often abstract, motivated this research to develop an interactive website-based learning media named "Levietech". This media is integrated with the Problem Based Learning (PBL) model to improve the cognitive learning outcomes of 10th-grade Computer and Network Engineering (TKJ) students at SMKN 1 Sarirejo. This study applied a Research and Development (R&D) approach using the ADDIE method and a One-Group Pretest-Posttest design involving 22 students. The expert validation results showed that the platform was categorized as "Highly Feasible" with an average validation score of 91.70%. In the implementation phase, a significant improvement in cognitive achievement was proven by the surge in the average pre-test score from 50.68 to 83.41 in the post-test, supported by the Paired Sample T-Test results (p < 0.001) and an N-Gain index of 0.66 (Medium category). Furthermore, the practicality evaluation using the User Experience Questionnaire (UEQ) demonstrated a highly "Positive" student perception across all measurement dimensions (scores > 0.8). Students were specifically enthusiastic about the Live Quiz feature within the website, which successfully made the evaluation process much more enjoyable and engaging. Thus, the Levietech learning media is proven to be valid, highly practical, and effective in significantly boosting students' understanding and learning enthusiasm.
Co-Authors Harun Al Rosyid Rindu Puspita Wibawa Savana Putra Aditama