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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Bulletin of Electrical Engineering and Informatics Journal of Information Systems Engineering and Business Intelligence Annual Research Seminar Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Jurnal Teknoinfo JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mnemonic Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi dan Sistem Informasi Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer EXPLORER Jurnal Ekonomika Dan Bisnis Paradigma Malcom: Indonesian Journal of Machine Learning and Computer Science Eduvest - Journal of Universal Studies Jurnal Ilmiah Informatika dan Ilmu Komputer The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Ryan Randy Suryono
Universitas Teknokrat Indonesia
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Perbandingan Kinerja Algoritma Random Forest, KNN, dan SVM dalam Analisis Sentimen Cryptocurrency AndaruJaya, Rinaldi Sukma; Suryono, Ryan Randy
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6572

Abstract

Cryptocurrency is a digital money based on blockchain technology that offers security and transparency in transactions, so it has increasingly attracted the attention of the public, including in Indonesia. With the number of investors surpassing 20 million, cryptocurrencies have generated a variety of opinions on social media. Some see it as a promising modern investment opportunity, while others highlight the risks of price fluctuations, security, and unclear regulations. To understand public sentiment towards cryptocurrencies, machine learning-based sentiment analysis is a relevant solution. This research compares the performance of three popular algorithms, namely Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in sentiment analysis of public opinion. These three algorithms have different advantages and disadvantages, depending on the characteristics of the data and the purpose of the analysis. Random Forest is known to be stable but requires high computation, KNN is easy to apply but less reliable on high-dimensional data, and SVM excels at separating complex data but requires careful parameter tuning. Previous research has shown differences in the accuracy of these three algorithms on various datasets, so further evaluation is needed to determine the most effective algorithm. The results of this study are expected to provide guidance in choosing the right algorithm for sentiment analysis, especially on cryptocurrency-related opinion data, as well as expand the understanding of the application of algorithms on dynamic and complex data.
Analisis Sentimen Acara Clash of Champions dengan Algoritma Naïve Bayes dan Support Vector Machine Purnama, Putri Intan; Suryono, Ryan Randy
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6575

Abstract

With the advancement of information and communication technology, it has become easier for people to exchange information and access educational content, including through online learning platforms such as Ruangguru. One of Ruangguru's flagship programs is Clash of Champions, which attracts public attention and generates various sentiments on social media. However, analyzing public sentiment towards this program faces challenges, especially due to the imbalance in the amount of data between majority and minority sentiments, which may affect the accuracy of sentiment analysis models. This study aims to compare the performance of two algorithms, namely Naïve Bayes and Support Vector Machine (SVM), in analyzing public sentiment towards this program. Using 5,226 tweets from social media X, the data was balanced using the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem. After the data was divided into 80% for training and 20% for testing, the results showed that before using SMOTE, Naïve Bayes had an accuracy of 78%, while SVM reached 82%. After SMOTE was applied, Naïve Bayes' accuracy increased to 79%, while SVM rose to 84%. In addition to accuracy, significant improvements were also seen in precision, recall, and f1-score, especially for positive sentiments. The results show that SVM is superior to Naïve Bayes, both in accuracy and other evaluation metrics. This research provides an in-depth understanding of the effectiveness of algorithms in sentiment analysis on entertainment-based educational programs and is expected to be a reference for the development of similar models in the future.
Perbandingan Algoritma Random Forest, KNN, SVM Untuk Analisis Sentimen Pengalaman Belanja Thrift Di X Raihandika, M Rafi; Suryono, Ryan Randy
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6797

Abstract

The thrifting phenomenon is gaining traction, especially among millennials and Generation Z. Along with the increasing interest in thrifting, X social media has emerged as one of the main platforms for people to share experiences and opinions related to thrift shopping. This research aims to analyze people's sentiments about thrift shopping experiences by comparing the performance of Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. The dataset used in this study was obtained from Twitter as many as 6,390 tweets collected through crawling techniques with a time span of August 2, 2024 to September 4, 2024. The dataset is then processed to produce clean data. After the cleaning process, the data is divided 80:20 for training and testing. In testing the three algorithms, an accuracy level is obtained that shows how well the model makes predictions. This accuracy measures the extent to which the model successfully predicts the sentiment of the thrifting shopping experience based on the Twitter dataset. The results show that the Random Forest algorithm has the highest accuracy with 95%, precision 97%, recall 78%, and f1-score 85%. SVM achieved 93% accuracy, 93% precision, 72% recall, and 78% f1-score. KNN obtained 89% accuracy, 72% precision, 59% recall, and 61% f1-score. From the results obtained, the Random Forest algorithm shows the best accuracy for sentiment analysis of thrifting experiences on Twitter Indonesia. Its advantage lies in its stable ensemble learning approach, where multiple decision trees are combined to produce more accurate predictions. This ability makes Random Forest effective in handling varied and complex Twitter text data, making it the most reliable algorithm in this context.
Komparasi Berbagai Metode Klasifikasi Teks Untuk Sentimen Pengguna Gawai Di Usia Dini Meliana, Yovi; Suryono, Ryan Randy
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4439

Abstract

In the context of rapid digital development, the use of gadgets among Indonesian children has become a very important topic to study. This study aims to analyze sentiments related to gadget use by applying classification methods such as Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. To overcome data imbalance, After applying the SMOTE technique, the results of the study revealed that SVM obtained the highest accuracy of 99% with SMOTE, followed by Decision Tree which reached 98% and Naïve Bayes which obtained 94% when SMOTE was applied. In addition, the application of preprocessing techniques such as tokenization, stemming, and filtering contributed to improving data quality. These findings emphasize the importance of choosing the right method in sentiment analysis to understand the impact of gadget use on children's development. This study provides meaningful insights for the development of better policies and practices related to children's digital device use
Perbandingan Berbagai Metode Klasifikasi Teks Untuk Sentimen Kebijakan Makan Gratis Di Indonesia Yuspita, Emi; Suryono, Ryan Randy
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4440

Abstract

The free meal policy is an important initiative to improve the nutrition of children under five and pregnant women and reduce social inequality. This policy supports low-income families by providing free food and milk in schools and Islamic boarding schools. On social media, especially platform X (Twitter), this policy sparked public discussion. This research aims to analyze sentiment regarding the free meal policy using Naive Bayes, SVM, and Decision Tree methods, as well as providing the effectiveness of classification algorithms in understanding public opinion. Of the 5,205 tweets analyzed, there were 4,735 positive tweets and 470 negative tweets. Applying Smote to this analysis provides significant results. SVM achieved 99% accuracy, Decision Tree also showed good performance with 98% accuracy. Meanwhile, Naive Bayes experienced an increase in accuracy of up to 91%, although it was still less than optimal in detecting negative sentiment compared to SVM and Decision Tree.
Perbandingan Algoritma Naive Bayes dan SVM dalam Analisis Sentimen Pengguna AI di Platform X Firdaus, Noval Dinda; Suryono, Ryan Randy
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7081

Abstract

The rapid development of artificial intelligence (AI) has also had a significant impact on various aspects of life, including interactions on social media platforms such as Platform X. On this platform, users actively discuss various topics related to AI, from the benefits to the challenges it poses. Understanding how the public responds to AI technology is important for developers, researchers, and policy makers in order to design strategies that are more in line with the needs and expectations of the community. This study aims to evaluate and compare the performance of two algorithms commonly used in sentiment analysis, namely Naïve Bayes and Support Vector Machine (SVM). Data were collected through crawling techniques using Google Colab, which resulted in 9,183 entries. Before the analysis was carried out, the data went through a series of initial processing stages, including text cleaning, letter normalization, tokenization, removing frequently used words (stopword removal), and stemming to simplify words. The results of the analysis show that SVM has advantages in terms of accuracy and capability, namely 96% accuracy in handling complex data, while Naïve Bayes is faster in the computational process and efficient for large datasets, resulting in an accuracy of 84% smaller than SVM accuracy. The assessment is carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix.
Public Sentiment Analysis on Dirty Vote Movie on YouTube using Random Forest and Naïve Bayes Christ Mario; Ryan Randy Suryono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ev9j2g33

Abstract

In early 2024, the film Dirty Vote attracted public attention, sparking discussions on YouTube. Understanding public sentiment towards this film is important for evaluating the reception of the work and its impact on public opinion. This study analyses 4,551 YouTube comments using the Random Forest and Naïve Bayes algorithms. The data was collected using the Apify platform, which allows the extraction of comment data based on video links and the desired amount of data. The analysis results show that the film received more negative comments than positive, reflecting the public's reception of the socio-political issues raised in the film. This dominance of negative sentiment is important for understanding how the film's message is received, which could influence marketing strategies and the film's reception in the digital media industry. This study also compares the effectiveness of both algorithms in sentiment analysis, with Random Forest being more effective at identifying positive sentiment, while Naïve Bayes is more efficient, though less accurate at capturing positive sentiment. These findings provide insights for developers and analysts in selecting the appropriate algorithm for sentiment analysis applications on social media.
Comparison of Naïve Bayes, Random Forest, and Logistic Regression Algorithms for Sentiment Analysis Online Gambling Dwi Nanda Agustia; Ryan Randy Suryono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/prk93630

Abstract

This study aims to compare the performance of Naïve Bayes, Random Forest, and Logistic Regression algorithms for sentiment analysis on the topic of online gambling. The dataset consisted of 4592 entries after preprocessing and applying the SMOTE technique to address class imbalance. The evaluation results show that Random Forest achieved the best performance with an accuracy of 78%, followed by Naïve Bayes and Logistic Regression, both achieving 77%. Random Forest excelled in classifying positive and negative sentiments, while Naïve Bayes demonstrated a significant improvement in recall for neutral sentiment, increasing from 0.45 to 0.82 after the SMOTE application. Logistic Regression showed less optimal performance, particularly for neutral sentiment. This study provides essential guidance for selecting the best algorithms for sentiment analysis in specific domains such as online gambling and highlights the importance of SMOTE in handling imbalanced datasets. The findings of this study can be used by practitioners and policymakers to make more informed decisions in regulating online gambling.
Sentiment Analysis of the Influence of the Korean Wave in Indonesia using the Naive Bayes Method and Support Vector Machine Natasha; Ryan Randy Suryono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/85x4wd90

Abstract

This study analyzes public sentiment towards the influence of the Korean wave in Indonesia using the Naive Bayes and Support Vector Machine (SVM) methods. The Korean wave, as a popular cultural phenomenon from South Korea, has had a significant influence on various aspects of Indonesian society. The dataset consists of 6,237 tweets obtained through a crawling process on social media X, with 80% data divided for training and 20% for testing. The pre-processing process includes cleaning, case folding, tokenizing, stopwords, and stemming. Data imbalance in sentiment distribution is overcome by the SMOTE technique. The test results show that the SVM model has the highest accuracy of 88%, outperforming the Naive Bayes model with an accuracy of 81%. Performance evaluation using precision, recall, and F1-score shows that SVM is more consistent in classifying positive and negative sentiments. Data visualization is done using bar charts and word clouds to illustrate the main patterns and themes in discussions related to the Korean wave in Indonesia. However, this study has limitations, such as data is only taken from one social media platform, so the results are less representative of public opinion as a whole. Nevertheless, this study provides new insights into how Indonesian society responds to popular culture phenomena online. These findings can also be utilized by policy makers to support the development of creative industries based on popular culture.
Peran Big Data dalam Inovasi Bisnis Digital: Pendekatan Tinjauan Literatur Sistematis Fadli, Muhammad; Prasetio, Mugi; Sanjaya, Ival; Surono, Muhammad; Dewantoro, Mahendra; Suryono, Ryan Randy
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 4 No. 1 (2025): Volume 4 Nomor 1 March 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v4i1.48

Abstract

Penelitian ini meninjau bagaimana Big Data berperan dalam inovasi bisnis digital, terutama dalam membantu pengambilan keputusan strategis, memperbaiki efisiensi operasional, serta menciptakan Produk dan layanan digital yang benar-benar pas dengan kebutuhan pengguna. Dengan menggunakan pendekatan tinjauan literatur sistematis, penelitian ini mengidentifikasi manfaat signifikan Big Data, termasuk kemampuannya untuk menyediakan analisis mendalam, memprediksi tren pasar, dan personalisasi layanan pelanggan. Namun, penelitian ini juga mengungkap berbagai tantangan dan kendala dalam implementasi Big Data, seperti keterbatasan infrastruktur teknologi, kualitas data yang rendah, serta isu privasi dan keamanan data. Hasil penelitian menunjukkan bahwa pemanfaatan Big Data yang optimal dapat meningkatkan daya saing bisnis digital, tetapi membutuhkan dukungan infrastruktur yang memadai dan kepatuhan terhadap regulasi yang berlaku. Studi ini berkontribusi pada pengembangan pemahaman tentang bagaimana Big Data dapat diintegrasikan ke dalam strategi inovasi bisnis digital untuk mendorong pertumbuhan dan keberlanjutan bisnis di era digital.
Co-Authors ., Bagastian Achmad Nizar Hidayanto Ade Dwi Putra Aditia Yudhistira Agresia, Vania Ahmad Ari Aldino Ajie Tri Hutama Al Afif, Satria Anadas, Sylvi Ananda, Dhea AndaruJaya, Rinaldi Sukma Ansyah, Ferdi Ariany, Fenty Arshad, Muhammad Waqas Bagus Reynaldi, Dimas Bakti, Da'i Rahman Bhatara, Dimas Wahyu Budi Santosa Budi Santosa Budiawan, Aditia Budiman, Ega Christ Mario Cynthia Deborah Nababan Dana Indra Sensuse Dana Indra Sensuse Darmini Darmini DAVID KURNIAWAN Dede Krisna Friansyah Dedi Darwis Desi Fitria Dewantoro, Mahendra Dinda Septia Ningsih Dwi Nanda Agustia Dyah Ayu Megawaty Eko Putro, Dimas Eskiyaturrofikoh, Eskiyaturrofikoh Firdaus, Noval Dinda Firmanda, Fabian Fudholi, Muhammad Fahmi Gunawan, Rakhmat Dedi Handini, Meitry Ayu Hasiholan Simamora, Alfred Heni Sulistiani Hermana, BP Putra Ignatius Adrian Mastan Indra Budi INDRIANI, YULIA Isnain, Auliya Rahman Iwan Purwanto Iwan Purwanto Juarsa, Doris Junita, Elvika Alya Kamrozi Karimah Sofa Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Krishna Yudhakusuma P.M. Laksono, Urip Hadi Megawaty, Dyah Ayu Meliana, Yovi Mesran, Mesran Miranda, Khyntia Muh. Alviazra Virgananda Muhamad Adhytia Wana Putra Rahmadhan Muhammad Fadli Muhammad Ridwan Muhammad Waqas Arshad Mustaqim, Ilham Zharif Natasha Panca Hadi Putra Prasetio, Mugi Pratama, Rangga Rizky Pratiwi, Adelia Purnama, Putri Intan Purwanti, Dian Sri Rachmad Nugroho Rachmi Azanisa Putri Rahmat Dedi Gunawan Raihandika, M Rafi Ramadhani, Bagus Reifco Harry Farrizqy Rias Kumalasari Devi Riyama Ambarwati Sanjaya, Ival Sanriomi Sintaro Saputra, Melian Jefri Saputra, Rizky Herdian Sari, Kevinda Sari, Putri Kumala Sarumpaet, Lisyo Hileria Setiawan, Andra Setiawansyah Setiawansyah Setyani, Tria Simarmata, Yohanes Sobirin, Muhammad Hamdan Sulistiyo, Raka Sumanto, Sumanto Surono, Muhammad Surya Indra Gunawan Tri Widodo Ulum, Faruk Wahyudi, Agung Deni Wang, Junhai Waqas Arshad, Muhammad Yeni Agus Nurhuda Yeni Agus Nurhuda Yuri Rahmanto Yuspita, Emi