Article Per Year (5 Year)
p-Index From 2020 - 2025
1.873
P-Index
This Author published in this journals
All Journal JIEET (Journal of Information Engineering and Educational Technology) KOMPUTEK Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika Terpadu Journal of Artificial Intelligence and Digital Business SCIENTIA JOURNAL : Jurnal Ilmiah Mahasiswa
Rudiman, Rudiman
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mechanical Engineering

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

Found 12 Documents
Search

 1   2 
Analisis Model Klasifikasi Sentimen Publik Terhadap Kebijakan Keberlanjutan IKN Menggunakan BERT Sebagai Feature Extractor dan K-Nearest Neighbor (KNN) Fiqri, Mohammad Hiqmal; Rudiman, Rudiman; Verdikha, Naufal Azmi
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8168

Abstract

This study aims to evaluate the performance of sentiment classification models for public opinions regarding the relocation of Indonesia’s new capital (IKN) using a combination of IndoBERT as a feature extractor and K-Nearest Neighbor (KNN) as a classifier. The dataset consisted of 1,274 YouTube comments related to IKN, which were annotated by an expert in sociology and text analysis. The preprocessing stage involved cleaning numbers, URLs, emojis, and punctuation, as well as removing stopwords using the Sastrawi library. IndoBERT produced 768-dimensional vector representations, which were then classified using KNN with k=5 and Euclidean distance. Evaluation with 5-fold cross validation achieved an accuracy of 73.31%. However, the recall for the positive class was relatively low (0.49), indicating challenges in detecting positive comments due to class imbalance (831 negative, 294 positive, 149 neutral). These findings suggest that the IndoBERT+KNN model performs well on majority classes but struggles with minority classes. The contribution of this research is to provide a critical analysis of the limitations of IndoBERT-based models in Indonesian sentiment classification and to recommend future directions, including data balancing and fine-tuning approaches.
Analisis Sentimen Opini Publik Terhadap Peristiwa Bitcoin Halving Pada Data Teks Twitter Menggunakan Metode Naïve Bayes Dan Pembobotan Fitur TF-IDF Halim, Andi Nur; Rudiman, Rudiman; Verdikha, Nauval Azmi
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2291

Abstract

Penelitian ini bertujuan menganalisis sentimen opini publik terhadap peristiwa Bitcoin Halving pada data teks Twitter menggunakan Naïve Bayes Classifier dan pembobotan fitur TF-IDF. Latar belakang penelitian ini adalah pesatnya pertumbuhan media sosial sebagai sumber data opini publik yang dinamis, khususnya terkait peristiwa finansial seperti Bitcoin Halving. Pendekatan kuantitatif dengan metode deskriptif analitis digunakan untuk mengklasifikasikan sentimen. Populasi penelitian adalah seluruh tweet yang berkaitan dengan topik tersebut, dan sampelnya berjumlah 538 tweet setelah melalui proses crawling dan preprocessing. Instrumen yang digunakan adalah bahasa pemrograman Python dan library tweet-harvest. Hasil penelitian menunjukkan bahwa model Naïve Bayes efektif, dengan akurasi tertinggi sebesar 74% pada rasio pembagian data 80:20. Kesimpulan dari penelitian ini adalah bahwa kombinasi metode tersebut mampu memberikan wawasan berharga mengenai sentimen pasar secara real-time.
Co-Authors Abror, Irfan Fiqry Ali, Sultan Anggi, Saputra Elfansyah, Muhammad Rayhan Fiqri, Mohammad Hiqmal Halim, Andi Nur Hidayat, Muhammad Rahman Ihram Nabawi, Ikhsan Nuttakwa Takbirata Munawaroh, Al Naufal Azmi Verdikha Nursyarif, Muhammad Khumaidi Ompusunggu, Hermaya Perdana, Muhammad Reifin Poernamawan, Ahmad Nugraha Pranata, Aditya Rahman, Febrian Nor Ridhoi, Reno Sidiq, Rezki Subhan Insani Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tirta, Muhamad Wahyu Verdikha, Nauval Azmi Widyastuti, Dessy