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All Journal Jurnal Informatika dan Teknik Elektro Terapan JIEET (Journal of Information Engineering and Educational Technology) KOMPUTEK Building of Informatics, Technology and Science Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika Terpadu Journal of Artificial Intelligence and Digital Business Innovative: Journal Of Social Science Research SCIENTIA JOURNAL : Jurnal Ilmiah Mahasiswa JSE Journal of Science and Engineering
Rudiman, Rudiman
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Transportation Other

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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.
METODE PEMBOBOTAN TF-IDF UNTUK KLASIFIKASI TEKS QUICK COUNT PEMILIHAN WAKIL PRESIDEN INDONESIA 2024 PADA X TWITTER DENGAN METODE SVM Pranata, Ricky Albin; Rudiman, Rudiman; Azmi Verdikha, Naufal
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14934

Abstract

The 2024 Indonesian Vice Presidential Election Quick Count sparked diverse public reactions on X Twitter. The sheer volume and variety of expressed opinions complicate accurate sentiment identification and classification. This study aims to develop a text classification model using Support Vector Machine (SVM) to identify sentiment in election Quick Count-related tweets. Data was acquired through tweet collection, followed by pre-processing, word weighting using TF-IDF, and data splitting for model training and testing. Results indicated that the developed SVM model achieved 77.30% accuracy in tweet sentiment classification. The model's implementation is expected to aid in more effective information filtering and assist stakeholders in understanding public opinion more accurately.
PERBANDINGAN METODE K–NEAREST NEIGHBOR (KNN) DAN NAIVE BAYES TERHADAP ANALISIS SENTIMEN PADA PENGGUNA E-WALLET APLIKASI DANA MENGGUNAKAN FITUR EKSTRAKSI TF-IDF Rayhan, Muhammad Rayhan Elfansyah; Rudiman, Rudiman; Fendy, Fendy Yulianto
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.15009

Abstract

This research compares the accuracy of the K-Nearest Neighbor (KNN) and Naive Bayes methods in classifying user sentiment towards the DANA e-wallet application using Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. User review data was collected through web scraping techniques and labeled by linguists and lexicon models. After undergoing pre-processing steps such as case folding, cleaning, tokenizing, stopword removal, and stemming, the data was classified using the KNN and Naive Bayes methods. The research results indicate that data labeling by linguists significantly improves the accuracy of both classification methods. Additionally, using TF-IDF as a word weighting method proves effective in enhancing the performance of sentiment classification models. Sentiment analysis of user reviews of the DANA application reveals various complaints and issues faced by users, providing information that can be used to improve the features and services offered, thereby increasing user satisfaction. This research also provides a comparison between the KNN and Naive Bayes methods, which can serve as a reference for other researchers in selecting appropriate methods for sentiment analysis on similar datasets.
ANALISIS SENTIMEN PADA ULASAN APLIKASI GOOGLE MAPS TERHADAP PELAYANAN BADAN PENYELENGGARA JAMINAN SOSIAL (BPJS) KESEHATAN SAMARINDA MENGGUNAKAN METODE K-NEAREST NEIGHBOR DENGAN FITUR EKSTRAKSI TF-IDF Ikhsan, Ikhsan Nuttakwa Takbirata Ihram Nabawi; Rudiman, Rudiman; Fendy, Fendy Yulianto
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.15010

Abstract

This study aims to analyze public sentiment towards the services of BPJS Kesehatan Samarinda based on reviews on the Google Maps application. The method used in this research is K-Nearest Neighbor (KNN) with TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction. The data used consists of 500 Indonesian-language reviews collected through web scraping techniques. After the data collection process, the data was labeled by an expert, and then a pre-processing stage was carried out, including case folding, cleaning, tokenizing, stop word removal, and stemming. The data was then weighted using the TF-IDF method to identify important words. The testing was conducted using a training and testing data ratio of 70:30 and a k value of 5. The results showed that the KNN method was able to classify positive and negative sentiments with an accuracy rate of 93.3%. This analysis provides an overview of the service quality of BPJS Kesehatan in Samarinda and can be used as a basis for service improvements. Additionally, this research contributes to the use of KNN and TF-IDF for sentiment analysis, opening opportunities for further research in this field.
KLASIFIKASI SENTIMEN X-TWITTER PERIHAL PEMINDAHAN IBU KOTA INDONESIA MENGGUNAKAN EKSTRAKSI FITUR TF-IDF DAN METODE SUPPORT VECTOR MACHINE (SVM) Wahyudi, Tri; Rudiman, Rudiman; Verdikha, Naufal Azmi
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.15015

Abstract

The classification model has reached the realm of sentiment classification to analyze user sentiment in providing comments. this research aims to classify sentiment regarding the topic of moving the capital city of Indonesia using the Support Vector Machine (SVM) method with TF-IDF weighting. SVM has its own advantages, namely to overcome complex problems in SVM classification using the kernel function. the kernel functions to transform input data into a high dimensional feature space, allowing linear separation of data more easily. there are 3 sentiment categories in this study, namely Negative, Neutral and Positive sentiment. to determine these 3 categories, researchers used expert labelling services. the purpose of this study using the SVM method and TF-IDF feature extraction is to find out and analyze the accuracy results obtained in processing sentiment data regarding the transfer of the capital city of Indonesia. The accuracy results obtained are 64%, this shows that the SVM method with TF-IDF weighting is able to classify sentiment data with fairly good results.
Penerapan Fitur Ekstraksi TF-IDF untuk Analisis Sentimen Ulasan Game Bus Simulator Indonesia dengan Algoritma Naive Bayes Alfawas, Thoriq Ikhwan; Rahim, Abdul; Rudiman, Rudiman
Innovative: Journal Of Social Science Research Vol. 4 No. 5 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i5.13975

Abstract

Di era digital saat ini, permainan video khususnya game simulasi telah menjadi hiburan yang sangat populer di berbagai kalangan. Bus Simulator Indonesia juga menawarkan pengalaman bermain yang autentik dengan mempersembahkan detail-detail khas Indonesia, mulai dari lingkungan sekitar hingga perilaku lalu lintas yang serupa dengan kondisi di negara ini. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna terhadap BUSSID dengan menggunakan ekstraksi fitur TF-IDF dan algoritma Naive Bayes. Kesulitan dalam melakukan pembobotan dengan TF-IDF Vectorizer termasuk memahami konsep TF dan IDF serta menangani teks yang tidak biasa seperti Bahasa non-standar atau singkatan. Analisis dilakukan dengan menguji lima variasi pembagian data yang berbeda untuk menentukan akurasi tertinggi dan terendah dari model Multinomial Naive Bayes setelah menggunakan fitur TF-IDF. Berdasarkan hasil pengujian manual dengan 5 variasi data latih dan uji menunjukkan bahwa model Multinomial Naïve Bayes memberikan akurasi tertinggi sebesar 85% pada variasi 90%:10%, dengan nilai precision, recall, dan F1-score yang konsisten. Variasi lainnya menunjukkan akurasi sedikit lebih rendah, antara 82% hingga 84%, namun tetap menunjukkan kinerja model yang baik. Hasil ini memberikan gambaran yang jelas tentang efektivitas model Naive Bayes dalam menganalisis sentimen ulasan pengguna setelah penerapan pembobotan TF-IDF.
ANALISIS SENTIMEN ULASAN “OJOL THE GAME” DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES DAN MODEL EKSTRAKSI FITUR TF-IDF UNTUK MENINGKATKAN KUALITAS GAME Rahmadani, Rafi; Rahim, Abdul; Rudiman, Rudiman
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4988

Abstract

Abstrak. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna terhadap game "OJOL THE GAME" di Google Play Store memanfaatakan algoritma Naive Bayes dan model ekstraksi fitur TF-IDF. Data ulasan dikumpulkan melalui teknik web scraping menggunakan Python, kemudian diproses dengan tahapan preprocessing meliputi pembersihan data, case folding, stop word removal, tokenizing, dan stemming. Data yang telah diproses kemudian dianalisis menggunakan algoritma Metode Naive Bayes digunakan untuk mengklasifikasikan sentimen positif dan negatif. Hasil penelitian mengindikasikan bahwa kombinasi antara algoritma Naive Bayes dan TF-IDF memberikan akurasi sebesar94,12%, menunjukkan efektivitas tinggi dalam mengidentifikasi sentimen pengguna. Temuan ini memberikan wawasan berharga  dalam memahami opini pengguna, meningkatkan kualitas game.Abstract. This study aims to analyze user sentiment towards the game "OJOL THE GAME" on Google Play Store using the Naive Bayes algorithm and the TF-IDF feature extraction model. User review data was collected through web scraping techniques using Python, then processed through preprocessing stages including data cleaning, case folding, stop word removal, tokenizing, and stemming. The processed data was then analyzed using the Naive Bayes algorithm to classify positive and negative sentiments. The results of the study show that the combination of the Naive Bayes algorithm and TF-IDF yielded an accuracy of 94.12%, demonstrating high effectiveness in identifying user sentiment. These findings provide valuable insights into understanding user opinions and improving the quality of the game.
Latent Dirichlet Allocation Utilization as a Text Mining Method to Elaborate Learning Effectiveness Rahmi, Netri Alia; Rudiman, Rudiman
JSE Journal of Science and Engineering Vol. 2 No. 1 (2023): Journal of Science and Engineering
Publisher : LPPI Universitas Muhammadiyah Kalimantan Timur (UMKT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30650/jse.v1i1.3680

Abstract

Learning method is a way to explain the lesson materials to students so that the learning process can occur in students as an effort to achieve the goals. Learning methods can be said to be a success if students are active, both physically, mentally, and socially in the learning process, in addition to showing high enthusiasm for learning and having self-confidence. The purpose of this study is to classify the opinions of Indonesian students regarding the existing learning methods and what learning methods they expected. In order to evaluate existing learning methods using the latent dirichlet allocation method. The data used comes from tweets of Twitter users within the range of January to March 2022. The data is taken using the scrapping method through the help of the python twisel library and totaled to 3778 data, then preprocessed through the nltk and Sastrawi libraries. The results of this analysis stated that student opinions can be classified into 3 major topics which state students' opinions regarding effective learning methods, student difficulties in applicable learning methods, and high cross-departmental interest.
Sentiment Analysis of the Public on the Deployment of Smart Robots in Indonesia Using the Naïve Bayes Method Muthmainnah, Muthmainnah; Rudiman, Rudiman; Yulianto, Fendy
JSE Journal of Science and Engineering Vol. 3 No. 2 (2025): Journal of Science and Engineering
Publisher : LPPI Universitas Muhammadiyah Kalimantan Timur (UMKT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30650/jse.v3i2.3887

Abstract

In this digital era, social media platforms have become the primary means for the public to express opinions on current issues, including discussions about the roles of robots and artificial intelligence in replacing human jobs. The focus of this community service is to investigate public sentiment regarding the implementation of smart robots in Indonesia, utilizing text-based sentiment analysis. The Naïve Bayes method is chosen as the approach to classify sentiments, overcoming challenges such as language and cultural variations. Through data testing and training, this research successfully achieved an accuracy rate of 98%, with high Precision, Recall, and F1 Score. The results provide valuable insights for companies and organizations that need to understand public perspectives on technological advancements and their impact on human employment.
Co-Authors Abdul Rahim Abror, Irfan Fiqry Alfawas, Thoriq Ikhwan Ali, Sultan Anggi, Saputra Elfansyah, Muhammad Rayhan Fendy Yulianto Fendy, Fendy Yulianto Fiqri, Mohammad Hiqmal Halim, Andi Nur Hallim, Abd Hidayat, Muhammad Rahman Ihram Nabawi, Ikhsan Nuttakwa Takbirata Ikhsan, Ikhsan Nuttakwa Takbirata Ihram Nabawi Munawaroh, Al Muthmainnah Muthmainnah Naufal Azmi Verdikha Nurlita, Nurlita Nursyarif, Muhammad Khumaidi Ompusunggu, Hermaya Perdana, Muhammad Reifin Poernamawan, Ahmad Nugraha Pranata, Aditya Pranata, Ricky Albin Rahmadani, Rafi Rahman, Febrian Nor Rahmi, Netri Alia Rayhan, Muhammad Rayhan Elfansyah Ridhoi, Reno Sidiq, Rezki Subhan Insani Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tirta, Muhamad Wahyu Tri Wahyudi Verdikha, Nauval Azmi Widyastuti, Dessy