Article Per Year (5 Year)
p-Index From 2021 - 2026
9.873
P-Index
This Author published in this journals
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) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) 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 Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika 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 SENTRI: Jurnal Riset Ilmiah 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 Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Jurnal Komputasi
Ryan Randy Suryono
Universitas Teknokrat Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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

Published : 90 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 90 Documents
Search

 2   3   4   5   6 
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.
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.
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.
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
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.
Co-Authors ., Bagastian Achmad Nizar Hidayanto Ade Dwi Putra Adelia Pratiwi Aditia Yudhistira Agresia, Vania Agus Wantoro AGUSTIAN, VENDRI RAMA Ahmad Ari Aldino Ajie Tri Hutama Al Afif, Satria Amarudin Amri Reza Wahyudin Anadas, Sylvi Ananda, Dhea AndaruJaya, Rinaldi Sukma Ansyah, Ferdi Ariany, Fenty Arshad, Muhammad Waqas Aryuda Aryuda Bagastian Bagastian Bagastian Bagus Reynaldi, Dimas Bakti, Da'i Rahman Budi Santosa Budi Santosa Budi Santosa Budiawan, Aditia Budiman, Ega Christ Mario Christ Mario Cynthia Deborah Nababan Dana Indra Sensuse Dana Indra Sensuse Darmini Darmini DAVID KURNIAWAN Dede Krisna Friansyah Dedi Darwis Desi Fitria Dewantoro, Mahendra Dimas Eko Putro Dimas Wahyu Bhatara Dinda Septia Ningsih Dwi Nanda Agustia Dwi Nanda Agustia Dyah Ayu Megawaty Ega Budiman Eko Putro, Dimas Elin Mayoana Fitri Elvika Alya Junita Eskiyaturrofikoh, Eskiyaturrofikoh Fadli, Muhammad Feri Cahya Setiawan Firdaus, Noval Dinda Firmanda, Fabian Fudholi, Muhammad Fahmi Gunawan, Rakhmat Dedi Handini, Meitry Ayu Hasiholan Simamora, Alfred Helma Nopijani Heidy Heni Sulistiani Hermana, BP Putra Ignatius Adrian Mastan Indra Budi Isnain, Auliya Rahman Ival Sanjaya Iwan Purwanto Iwan Purwanto Jelna Anggreni Juan Adi Putra Juarsa, Doris Junhai Wang Kamrozi Kardita Magda Karimah Sofa Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kevinda Sari Krishna Yudhakusuma P.M. Laksono, Urip Hadi M Sahyudi Mahendra Dewantoro Maylanda, Putri Oktaria Megawaty, Dyah Ayu Mesran, Mesran Miranda, Khyntia Mugi Prasetio Muh. Alviazra Virgananda Muhamad Adhytia Wana Putra Rahmadhan Muhammad Fadli Muhammad Fahmi Fudholi Muhammad Ridwan Muhammad Sahyudi Muhammad Surono Muhammad Waqas Arshad Mustaqim, Ilham Zharif Natasha Natasha Panca Hadi Putra Pratama, Rangga Rizky Purnama, Putri Intan Purwanti, Dian Sri Putra, Djalu Bintang Putra, Satya Setiawan Putri Oktaria Maylanda Rachmad Nugroho Rachmi Azanisa Putri Rahmat Dedi Gunawan Raihandika, M Rafi Raka Sulistiyo Ramadhani, Bagus Reifco Harry Farrizqy Rias Kumalasari Devi Riyama Ambarwati Sampurna Dadi Riskiono Sanriomi Sintaro Saputra, Melian Jefri Saputra, Rizky Herdian Sari, Cici Nurita Kumala Sari, Putri Kumala Sarumpaet, Lisyo Hileria septiana Rahayu Septiana Rahayu Setiawan, Andra Setiawansyah Setiawansyah Setiyana, Beta Agus Simarmata, Yohanes Sobirin, Muhammad Hamdan Sri Murdiawati SUDIARTE, PUTU Sumanto Sumanto Surono, Muhammad Surya Indra Gunawan TAHARA, ANGGIT PRANA Tri Widodo Tria Setyani Turlia Indah Sapitri Ulum, Faruk Vania Agresia Wahyudi, Agung Deni Wang, Junhai Waqas Arshad, Muhammad Yeni Agus Nurhuda Yeni Agus Nurhuda Yovi Meliana Yulia Indriani Yuri Rahmanto Yuspita, Emi