Article Per Year (5 Year)
p-Index From 2020 - 2025
1.99
P-Index
This Author published in this journals
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

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
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) - In Process
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
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 Rahmi, Alfie Nur Ramadhan, Ridhwan Shodiq Rayhan, Radhita Romadhon, Ibrahim Aji Fajar Sa'di, Ahmad Siska Siska Wicaksono, Anggito Whiku Yoga Pristyanto