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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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Analisis Sentimen Publik Terhadap Deepfake AI Menggunakan Aplikasi X Dengan Metode Support Vector Machine dan Naive Bayes Classifier Al Afif, Satria; Suryono, Ryan Randy
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.v7i2.8303

Abstract

The rapid development of artificial intelligence (AI) technology has driven increased public interaction with AI-based platforms, including Deepfake AI. One of the main challenges that arises is how to objectively assess public opinion, particularly on social media, which serves as a primary medium for expressing opinions. This study aims to compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB), in analyzing public sentiment toward Deepfake AI on the X social media platform. The research dataset consists of 7,774 tweets collected between October and November 2024. After preprocessing, 5,559 tweets were used, categorized into three sentiment classes: positive, negative, and neutral. Data imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), with 80% of the data allocated for training and 20% for testing. The results show that before applying SMOTE, the SVM algorithm achieved the highest accuracy at 71%, while Naïve Bayes only reached 62%. After the application of SMOTE, the performance of both algorithms improved, with SVM achieving 77% accuracy and Naïve Bayes reaching 68%. Thus, SVM proved to be the best-performing algorithm in this study, both before and after SMOTE application, delivering more balanced results across sentiment classes. This research demonstrates that sentiment analysis based on machine learning can be utilized to understand public opinion toward AI platforms, while also providing valuable insights for developers to improve service quality and strengthen public trust.
Komparasi Metode Naïve Bayes, Random Forest dan KNN untuk Analisis Sentimen Penambangan Nikel Setiyana, Beta Agus; Suryono, Ryan Randy
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.v7i2.8263

Abstract

The phenomenon of increasing natural resource exploitation in Indonesia’s conservation areas has raised significant public concern, one of which involves the planned nickel mining project in Raja Ampat, a region renowned for its extraordinary marine biodiversity. This plan has sparked debates between economic interests, environmental preservation, and the sociocultural values of local communities. Amid the growing public discourse, social media has become a major platform for people to express their opinions, support, or opposition toward mining activities. This study aims to map public sentiment regarding the nickel mining issue in Raja Ampat by analyzing 5,556 Indonesian-language tweets collected from the social media platform X using the keyword “save raja ampat” between January- June 2025. The data underwent several preprocessing stages, including cleaning, case folding, tokenizing, stopword removal, and normalization, and were then represented using the TF-IDF method. Sentiment labeling was performed semi automatically using a lexicon based approach into three categories: positive, neutral, and negative. The sentiment distribution showed dominance of neutral (72.9%), followed by negative (24.3%) and positive (2.8%), indicating class imbalance. To address this issue, the SMOTE technique was applied to the training data. Three classical algorithms K-Nearest Neighbor (KNN), Complement Naïve Bayes (CNB), and Random Forest (RF) were compared using cross-validation and holdout testing with accuracy, precision, recall, and F1-score as evaluation metrics. The results show that CNB performed most stably before SMOTE, while after SMOTE, KNN demonstrated significant improvement, especially in recall and macro F1-score. These findings confirm that the combination of data balancing techniques and classical algorithms remains relevant and efficient as a methodological baseline for public sentiment analysis on complex environmental issues such as nickel mining in Raja Ampat.
Combination of Objective Weighting Method using MEREC and A New Additive Ratio Assessment in Coffee Barista Admissions Arshad, Muhammad Waqas; Suryono, Ryan Randy; Rahmanto, Yuri; Sumanto, Sumanto; Sintaro, Sanriomi; Setiawansyah, Setiawansyah
TIN: Terapan Informatika Nusantara Vol 5 No 3 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i3.5771

Abstract

A coffee barista is a professional who is skilled in the art of brewing and serving coffee in an attractive and high-quality way. The role of a barista is not only limited to operating an espresso machine and grinding coffee beans, but also includes in-depth knowledge of different types of coffee beans, manufacturing techniques, and the resulting flavors. The main problem in the acceptance of coffee baristas often has to do with the gap between industry expectations and the skills possessed by prospective workers. Many candidates may lack formal training or practical experience in brewing coffee, so they do not meet the standards expected by cafes or restaurants. The purpose of the research on the Combination of Objective Weighting Methods using MEREC and ARAS in Coffee Barista Admission is to develop and apply a more systematic and objective approach in the selection process of prospective baristas. The combination of objective weighting methods and the new additive ratio assessment (ARAS) approach offers a sophisticated framework for evaluating candidates in coffee barista admissions. The objective weighting method ensures that evaluation criteria are prioritized based on their intrinsic importance, thereby minimizing subjective preference. When combined with the ARAS method, which ranks alternatives based on their performance ratio to the ideal solution, this approach provides a balanced and comprehensive assessment for each candidate. Based on the results of the evaluation of the barista admission selection, Clara Dewi ranked first with the highest final score of 0.98553, followed by Hanafi Lestari with a score of 0.95921 and Erika Santosa with a score of 0.95726 who ranked second and third.
COMPARISON OF NAÏVE BAYES AND INFORMATION GAIN ALGORITHMS IN CYBERBULLYING SENTIMENT ANALYSIS ON TWITTER Dinda Septia Ningsih; Suryono, Ryan Randy
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1908

Abstract

In the current digital era, cyberbullying is very easy to do because access to various social media platforms is very easy to obtain. Generation Z is a generation born in the era of digital technology advancement, being one of the parties that plays a role in the increasing cases of cyberbullying. The twitter social media platform is one of the platforms that is often used as a place for cyberbullying in Indonesia. With the alarming impact, this research aims to analyze cyberbullying cases on twitter. By comparing Naïve Bayes and Information Gain algorithms, this research will provide accuracy results from tweet data containing cyberbullying content. The dataset used comes from twitter with the time span of collecting the dataset is from January 05, 2024 to January 25, 2024. The dataset is then processed to produce a clean dataset that is ready to be tested using both algorithms. In this study, testing the two algorithms using the K-fold Cross Validation technique resulted in variations in each test. In testing both algorithms, an accuracy level is obtained that indicates how successful the model is in making predictions. In simple terms, this accuracy assesses how effective the model is in predicting cyberbullying sentiment in datasets from Indonesian twitter. Testing the Naïve Bayes algorithm obtained an accuracy of 92.3%. Testing the Information Gain algorithm has an accuracy of 97.8%. From the results obtained, it can be concluded that the Information Gain algorithm gets higher accuracy than the Naïve Bayes algorithm for cyberbullying sentiment analysis on Indonesian twitter.
Tourists’ Acceptance Analysis Of Tourism Village Website Towards The Motivation To Visit Nababan, Cynthia Deborah; Sensuse, Dana Indra; Suryono, Ryan Randy; Kautsarina, Kautsarina
Eduvest - Journal of Universal Studies Vol. 4 No. 7 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i7.1226

Abstract

The development of tourism village websites aims to promote the potential of tourism villages in Indonesia. However, the increasing use of social media has reduced the intensity of websites being used. This research aims to determine whether the tourism village websites still influence tourists to visit the tourism village and whether it is relevant to the government's goals. Data was collected by distributing questionnaires and producing a valid sample of 242 respondents who actively use the Internet and have the potential to travel. The research model design was created by combining the DeLone & McLean IS success model with the Technology Acceptance Model (TAM). Data were analysed using the Structural Equation Modeling Partial Least Square (SEM PLS). The results show that the variables of information quality, service, and design positively affect trust, usability, ease of use, and enjoyment, positively affecting the intention to use the website. However, trust does not significantly influence the intention to use the website on the acceptance model. The frequent use of the tourism village website positively affects the tourist's motivation for the actual visit. Therefore, deeper analysis is needed to determine the variables that affect tourist confidence in developing acceptance models for further research. In addition, this research has practical implications for the government in making decisions regarding developing tourism village websites in terms of interface, user experience, information, and features.
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method Rahmanto, Yuri; Wang, Junhai; Setiawansyah, Setiawansyah; Yudhistira, Aditia; Darwis, Dedi; Suryono, Ryan Randy
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.8224

Abstract

An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
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): March
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): March
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; Suryono, Ryan Randy
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
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.
Komparasi Algoritma Support Vector Machine dan Decision Tree Dalam Analisis Sentimen Publik Terhadap Penerapan PPN 12% Putra, Djalu Bintang; Suryono, Ryan Randy
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7147

Abstract

The implementation of the 12% Value-Added Tax (VAT) policy in Indonesia has generated various reactions from the public, both positive and negative. To understand public perception, researchers compared the performance of two algorithms, namely Support Vector Machine (SVM) and Decision Tree, in analyzing sentiment on social media. A total of 7,965 tweets were collected from the X (Twitter) platform using web scraping techniques and processed through several stages, including text cleaning, tokenization, stopword removal, stemming, and data balancing using the SMOTE method to improve model accuracy. The evaluation results showed that SVM achieved 80% accuracy, higher than Decision Tree, which only reached 68%. Based on these findings, it can be concluded that SVM is more effective in analyzing public sentiment regarding the 12% VAT policy. These findings can serve as a reference for the government and relevant stakeholders to better understand public opinion and design more suitable policies. This study also provides opportunities for further development by exploring other algorithms or more advanced data processing techniques to enhance the accuracy and effectiveness of sentiment analysis in the future.
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 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 Fadli, Muhammad 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 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 Mustaqim, Ilham Zharif Nababan, Cynthia Deborah Natasha Panca Hadi Putra Prasetio, Mugi Pratama, Rangga Rizky Pratiwi, Adelia Purnama, Putri Intan Purwanti, Dian Sri Putra, Djalu Bintang 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, Cici Nurita Kumala Sari, Kevinda Sari, Putri Kumala Sarumpaet, Lisyo Hileria Setiawan, Andra Setiawansyah Setiawansyah Setiyana, Beta Agus 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 Yulia Indriani Yuri Rahmanto Yuspita, Emi