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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Eksplora Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics JIKA (Jurnal Informatika) Information System Journal (INFOS) 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.
PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN TEMPAT KOS DI CONDONG CATUR YOGYAKARTA Ramadhan, Ridhwan Shodiq; Rohman, Arif Nur; Rahmi, Alfie Nur
Information System Journal Vol. 8 No. 02 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i02.2429

Abstract

Pemilihan tempat kos yang ideal merupakan tantangan besar bagi mahasiswa yang ingin melanjutkan pendidikannya keluar kota. Namun, proses pencarian proses penilaian fasilitas tempat kos sering kali masih harus dilakukan secara langsung, yang menghabiskan waktu dan biaya. Oleh karena itu dibangunlah sistem pendukung keputusan menggunakan metode Simple Additive Weighting (SAW) berbasis website yang dapat membantu mahasiswa dalam menentukan tempat kos sesuai dengan kriteria yang diinginkan. Metode penelitian dilakukan melalui beberapa tahap: pengumpulan data dari sumber terkait, analisis sistem untuk menyusun alur kerja, perancangan sistem rekomendasi, dan implementasi sistem. Berdasarkan hasil perhitungan perangkingan, Residence Permai terpilih sebagai rekomendasi terbaik dengan nilai akhir 0.8500, diikuti oleh Griya Seturan dengan nilai 0,8400, dan Kos Candi Gebang dengan nilai 0,8000. Hasil pengujian menunjukkan bahwa sistem ini berjalan sesuai dengan yang diharapkan dan mampu memberikan solusi yang lebih komprehensif dan praktis bagi mahasiswa yang mencari tempat kos di Condong Catur, Sleman, Yogyakarta
Implementation of a Hybrid Filtering Approach in a Website-based Football News Recommendation System Putra, Krissna Haridarma; Rohman, Arif Nur; hikmah, Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5854

Abstract

The rapid growth of football news on digital portals has made it increasingly difficult for users to find information that matches their interests. This study develops a web-based news recommendation system by combining Content-Based Filtering and Collaborative Filtering through a feature-level Hybrid Filtering approach. The proposed hybrid approach constitutes the main novelty of this research, as it does not rely on score aggregation methods commonly used in previous studies, making it lighter, simpler, and more suitable for small datasets and limited user interactions. The system employs Term Overlap Matching to measure the similarity between news titles and Cosine Similarity to assess user preference similarity based on bookmark data. The evaluation results show that Content-Based Filtering achieves the best performance, with a Precision of 0.60, Recall of 0.75, and an F1-score of 0.67, while Collaborative Filtering performs poorly due to data sparsity in user interactions, resulting in a Precision of 0, Recall of 0, and an F1-score of 0. Overall, the feature-based hybrid approach is able to provide relevant recommendations from both content and preference perspectives, although system accuracy is still predominantly driven by Content-Based Filtering. These findings indicate that the proposed simple hybrid model can serve as an effective solution for small-scale sports news platforms and has the potential to be further improved through increased data availability, enhanced user interaction, and the adoption of more advanced NLP techniques.
Implementation of the Apriori Algorithm for Clothing Store Product Recommendations based on Sales Transaction History Saputro, M Ilham; Rohman, Arif Nur
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5648

Abstract

This study is motivated by the limitations faced by small-scale clothing stores, which generally do not have customer ratings or reviews that can be used as a basis for product recommendations. This condition necessitates an alternative method capable of utilizing available sales transaction data. The objective of this study is to generate product recommendations by identifying consumer purchasing patterns through the application of the Apriori Algorithm. The methodology involves processing sales transaction data consisting of transaction codes, lists of purchased products, and transaction timestamps. Support, confidence, and lift ratio values are calculated to generate and validate association rules among products. The analyzed data are derived from the transaction history of a clothing store and are processed using a web-based system developed with PHP and MySQL. The experimental results indicate that several product combinations achieve confidence values of 50% and lift ratios greater than or equal to 1, suggesting that these patterns can be used as a basis for product recommendations. These findings demonstrate a strong association among items that are frequently purchased together. Based on the results, this study concludes that the Apriori Algorithm is effective in identifying meaningful purchasing patterns that can support product arrangement strategies and inventory management in small-scale clothing stores.
Co-Authors Anggit Dwi Hartanto, Anggit Dwi Christyawan, Fajar Fajri, Ika Nur Fauzy, Marwan Noor Fiardin, Tsintani Tisaga Haz, Anando Muhammad Rahul hikmah, Norhikmah Iedwan, Aisha Shakila Maulana, Handika Attha Mauliza, Nia Pratama, Akbar Putra, Krissna Haridarma Rahmi, Alfie Nur Ramadhan, Ridhwan Shodiq Rayhan, Radhita Romadhon, Ibrahim Aji Fajar Sa'di, Ahmad Saputro, M Ilham Siska Siska Wicaksono, Anggito Whiku Yoga Pristyanto