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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
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF NAÏVE BAYES AND INFORMATION GAIN ALGORITHMS IN CYBERBULLYING SENTIMENT ANALYSIS ON TWITTER Dinda Septia Ningsih; Ryan Randy Suryono
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.
CLASSIFICATION OF PUBLIC SENTIMENT TOWARDS STUNTING PREVENTION PROGRAM USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE ON X APPLICATION Fitria, Desi; Suryono, Ryan Randy
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3998

Abstract

Indonesia has serious health problems, one of which is stunting among children. Stunting is caused by chronic malnutrition that affects a child's physical and cognitive growth. To address its impact, the government launched a prevention program that focuses on improving nutrition, improving sanitation, and health education. Public response to these programs has varied, with some supportive and others skeptical. In the digital age, public opinion is expressed through social media, making sentiment analysis important to understand public perception. This research aims to classify public sentiment towards the stunting prevention program using Naive Bayes and Support Vector Machine (SVM) methods. Data preprocessing includes cleaning, case folding, tokenizing, stopwords, and stemming, ensuring the text data is ready for analysis. The dataset consists of 5907 tweets divided by a ratio of 80:20, resulting in 4725 tweets for training data and 1182 tweets for testing data. The analysis results show that the Naive Bayes model achieved an accuracy of 95.34% for training data and 84.52% for testing data, while SVM achieved an accuracy of 95.43% for training data and 96.74% for testing data, indicating the performance of the SVM model is better than the Naïve Bayes model. The important impact of this research is to assist policymakers in understanding the public's perception of government programs so that they can design communication strategies.
SENTIMENT ANALYSIS OF ONLINE DATING APPS USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS Laksono, Urip Hadi; Suryono, Ryan Randy
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.2105

Abstract

In daily life, the use of digital applications is increasingly widespread, making dating apps increasingly popular and an important part of modern social interaction. This research aims to analyze user sentiment towards online dating apps, specifically Tinder, using Support Vector Machine (SVM) and Naïve Bayes algorithms. The problem underlying the importance of this research is the lack of balance between positive and negative sentiments in Tinder app users, which can affect user experience and the quality of service provided by Tinder. Utilizing the CRISP-DM framework, this research involves six stages, from data collection to evaluation. The results showed a significant imbalance between the number of positive and negative sentiments before optimization, but after the application of the SMOTE technique, there was a balancing between the two sentiment categories. SVM achieved 85% accuracy, while Naïve Bayes achieved 84%, with similar performance in identifying positive and negative sentiments. While both models performed satisfactorily, SVM appeared more stable in recognizing both positive and negative sentiments, suggesting the potential to be a superior choice in the context of dating apps. As such, this research makes an important contribution to the understanding of users' views on Timder apps and provides a basis for further development.
COMPARISON OF ACCURACY OF VARIOUS TEXT CLASSIFICATION METHODS IN SENTIMENT ANALYSIS OF E-STAMPS AT X Bagus Reynaldi, Dimas; Suryono, Ryan Randy
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3999

Abstract

In the rapidly evolving digital era, technological innovations are applied in various fields, including law and administration, to improve the efficiency and effectiveness of processes. One of the latest innovations in Indonesia is the implementation of e-metals, which is designed to facilitate legal and secure electronic transactions, and meet the needs of a society that is increasingly dependent on digital technology. Although e-stamps aim to improve efficiency and security in transactions, there are still various perceptions from the public that reflect their views and experiences regarding the implementation of this technology. In this case, sentiment analysis is an effective method to evaluate public opinion generated from text data, such as user reviews and comments on social media. This research aims to analyze the sentiment towards e-metallocations in X app, using text classification methods to separate positive and negative sentiments. After collecting 3282 datasets and performing preprocessing that includes case folding, data cleaning, tokenizing, and stemming, the evaluation results show that the Naive Bayes (GNB) model achieves 96.65% accuracy on training data and 95.28% on testing data. On the other hand, the Support Vector Machine (SVM) model recorded an accuracy of 98.32% on training data and 96.80% on testing data. Meanwhile, the Random Forest model showed a perfect accuracy of 100% on training data and 99.09% on testing data, making it the highest performing model among the three methods tested.
SENTIMENT ANALYSIS OF POST-COVID ONLINE EDUCATION AMONG GEN Z WITH VARIOUS CLASSIFICATION METHODS Bakti, Da'i Rahman; Suryono, Ryan Randy
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4003

Abstract

The COVID-19 pandemic has significantly changed the education sector, shifting from traditional learning to online learning. Generation Z's perception of online education is influenced by their experience as “Digital Natives” who have been familiar with technology since childhood. However, this sudden transition brings new challenges, such as screen fatigue, lack of social interaction, and difficulty in maintaining learning motivation. Sentiment analysis is an important tool to evaluate their experiences and views on online learning. This study aims to investigate Generation Z's views on online education after the pandemic, utilizing various classification methods. Data was collected from Twitter through scraping technique with specific keywords, resulting in a total of 4,986 data obtained using the Tweet Harvest library in Python programming language. The dataset then went through a preprocessing stage, including data cleaning, case folding, tokenizing, stopword removal, and stemming. Before applying Random Forest, SVM, and Naïve Bayes methods, the data is divided into two parts, namely, 3988 training data and 998 testing data with a ratio of 80:20. The accuracy results show that Naïve Bayes achieved 95.49% on training data and 76.05% on testing data, SVM recorded 94.77% accuracy on training data and 87.33% on testing data, and Random Forest obtained 99.97% accuracy on training data and 92.21% on testing data. This research provides important insights into Generation Z's perceptions of post-COVID-19 online education and learning platforms to improve the effectiveness of online learning and identify student challenges in the digital era.
PERFORMANCE COMPARISON OF NAIVE BAYES, SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR APPLE VISION PRO SENTIMENT ANALYSIS Pratama, Rangga Rizky; Suryono, Ryan Randy
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4035

Abstract

With the development of spatial computing devices, there arises a need to analyze consumer opinions about products such as the Apple Vision Pro (AVP), a technology that combines augmented reality (AR) and virtual reality (VR). This study aims to analyze consumer opinions on the Apple Vision Pro by utilizing data from the social media platform X. Three algorithms—Random Forest, Support Vector Machine (SVM), and Naïve Bayes—are used in text categorization to identify sentiment trends. Data was collected through a crawling process, resulting in 3,753 entries. After preprocessing and labeling, 2,609 clean data points were obtained, with 1,618 classified as negative and 991 as positive. In sentiment analysis, Random Forest delivered the best performance with an accuracy of 83%, followed by SVM at 80%, and Naïve Bayes at 75%. These results indicate that the Random Forest algorithm is more effective in sentiment categorization related to Apple Vision Pro. This study provides significant contributions to companies in understanding public perceptions and crafting more precise data-driven marketing strategies.
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