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All Journal Jurnal Teknik Informatika (JUTIF) Indonesian Research Journal on Education
Pambudi, Satrio
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Sentiment Analysis of Fizzo Novel Application Using Support Vector Machine and Naïve Bayes Algorithm with SEMMA Framework Pambudi, Satrio; Setiaji, Pratomo; Triyanto, Wiwit Agus
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4875

Abstract

The increasing popularity of digital reading platforms in Indonesia, such as Fizzo Novel, has generated many user reviews that can be analyzed to understand their satisfaction. This study analyzes user sentiment toward Fizzo Novel using the SEMMA (Sample, Explore, Modify, Model, Assess) framework, and compares the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms. A total of 139,759 reviews were collected from the Google Play Store through web scraping. The data was then processed through normalization, tokenization, lexicon-based sentiment labeling, and feature extraction using TF-IDF. To address class imbalance, the SMOTE technique was applied. The results showed that SVM achieved the highest accuracy, exceeding 96%, with a consistent F1-score across all sentiment classes. In contrast, Naïve Bayes recorded lower accuracy (75.82% before SMOTE and 73.63% after SMOTE), along with a decline in performance for the neutral class. SVM proved more reliable in handling large and imbalanced text data. Practically, the results of this study can help application developers such as Fizzo Novel in automatically understanding user opinions. With an accurate sentiment classification model, developers can monitor reviews in real-time, identify issues such as excessive advertising or an unpopular chapter division system, and design feature improvements based on real user needs. This research also provides a foundation for algorithm selection in future large-scale sentiment analysis projects and recommends SVM as the more appropriate choice in this context.
Pelatihan Pemrograman Dasar Python:: Meningkatkan Literasi Teknologi Siswa melalui Pembuatan Game Tebak Angka di SMAN 1 Pamotan Pambudi, Satrio; Setiawan, Arif
Indonesian Research Journal on Education Vol. 5 No. 1 (2025): Irje 2025
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/irje.v5i1.1851

Abstract

Perkembangan teknologi di era digital menuntut generasi muda untuk memiliki keterampilan teknologi guna menghadapi tantangan masa depan. Pelatihan Dasar Python Membuat Game Tebak Angka di SMA N 1 Pamotan bertujuan untuk meningkatkan literasi teknologi siswa melalui penguasaan dasar pemrograman Python. Program berbasis proyek ini mengajarkan konsep logika pemrograman seperti perulangan (loop), pengambilan keputusan (if-else), dan penggunaan fungsi input/output dengan membuat game "Tebak Angka." Diikuti oleh 30 siswa kelas X dan XI, pelatihan ini menunjukkan antusiasme tinggi, dengan siswa berhasil memahami materi dan mengembangkan fitur kreatif dalam game. Evaluasi menunjukkan skor rata-rata pemahaman siswa terhadap konsep perulangan, pengambilan keputusan, dan fungsi input mencapai kategori "Baik" hingga "Sangat Baik". Program ini mengembangkan kemampuan berpikir logis, analitis, dan kreatif siswa, mendukung implementasi Kurikulum Merdeka, serta mempersiapkan siswa menghadapi tantangan dunia kerja di era digital.
Co-Authors Arif Setiawan Pratomo Setiaji Wiwit Agus Triyanto