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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics JIKA (Jurnal Informatika) Intechno Journal : Information Technology Journal Scientific Journal of Informatics
arif nur rohman
UNIVERSITAS SEBELAS MARET
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Comparative Performance of SVM and Multinomial Naïve Bayes in Sentiment Analysis of the Film 'Dirty Vote' Iedwan, Aisha Shakila; Mauliza, Nia; Pristyanto, Yoga; Hartanto, Anggit Dwi; Rohman, Arif Nur
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.10290

Abstract

Purpose: The purpose of this research is to analyze and compare the performance of two machine learning models, Support Vector Machine (SVM) and Multinomial Naive Bayes, in conducting sentiment analysis on YouTube comments related to the film "Dirty Vote." Methods: The study involved collecting YouTube comments and preprocessing the data through cleaning, labeling, and feature extraction using TF-IDF. The dataset was then divided into training and testing sets in an 80:20 ratio. Both the SVM and Multinomial Naive Bayes models were trained and tested, with their performance evaluated using accuracy, precision, recall, and F1-score metrics. Result: The results revealed that both models performed well in classifying sentiments, with SVM slightly outperforming Multinomial Naive Bayes in terms of accuracy and precision. Particularly, SVM showed superior performance in detecting positive comments, making it a more reliable model for this specific sentiment analysis task. Novelty: This study contributes to the field of sentiment analysis by providing a detailed comparative analysis of SVM and Multinomial Naive Bayes models on YouTube comments in the context of an Indonesian film. The findings highlight the strengths and weaknesses of each model, offering insights into their applicability for sentiment analysis tasks, particularly in analyzing social media content. This research also suggests potential future directions, including the exploration of advanced NLP techniques and different models to enhance sentiment analysis performance.
Recommendation System Yogyakarta Tourism Using TF-IDF and Cosine Similarity Methods with Word Normalizer Ulul Albab, Jauhar Fauzi; Rohman, Arif Nur
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11751

Abstract

The abundance of tourism information in Yogyakarta often overwhelms tourists due to non-standard text data. This research develops a tourism recommendation system using Content-Based Filtering by integrating TF-IDF and Cosine Similarity algorithms, enhanced with a Word Normalizer stage. The research method involves data preprocessing including case folding, filtering, stopword removal, and stemming combined with word normalization to standardize irregular spellings. Text feature representation is calculated using TF-IDF weighting, followed by measuring similarity between destinations through vector-based Cosine Similarity. The query testing of Pantai Parangtritis against Pantai Ngandong yielded the highest similarity score of 0.9397. System performance evaluation showed a Precision@5 of 0.84, Recall@5 of 0.10, and Mean Average Precision (MAP) of 0.81. In conclusion, strengthening the method with a Word Normalizer significantly improves the validity of top-ranked recommendations, enabling tourists to accurately find relevant attractions according to their preferences.
Co-Authors Anggit Dwi Hartanto, Anggit Dwi Christyawan, Fajar Fajri, Ika Nur Fauzy, Marwan Noor Fiardin, Tsintani Tisaga Haz, Anando Muhammad Rahul Iedwan, Aisha Shakila Maulana, Handika Attha Mauliza, Nia Pratama, Akbar Rayhan, Radhita Romadhon, Ibrahim Aji Fajar Sa'di, Ahmad Siska Siska Ulul Albab, Jauhar Fauzi Wicaksono, Anggito Whiku Yoga Pristyanto