Article Per Year (5 Year)
p-Index From 2021 - 2026
8.407
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) 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 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)
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 : 77 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 77 Documents
Search

 3   4   5   6   7 
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.
Comparison of SVM, Naïve Bayes, and Logistic Regression Algorithms for Sentiment Analysis of Fraud and Bots in Purcashing Concert Ticket Agresia, Vania; Suryono, Ryan Randy
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/npyfdh47

Abstract

Music concerts are highly anticipated entertainment events, but they are often subject to fraud and the use of bots in online ticket purchases, to the detriment of fans and organisers. Fans may lose confidence in the ticket system and reduce interest in the event. For organizers, it can reduce the event's reputation and finances. This research aims to analyse public sentiment regarding this issue by comparing three classification algorithms: Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression. Data taken from Twitter which contains comments related to fraud and bots. The methods used include data crawling, preprocessing, sentiment labelling, and model evaluation. Preprocessing includes data cleaning, case folding, tokenising, stopwords, and stemming. Sentiment labelling is done manually or by human annotators. The results showed that SVM had the best accuracy of 91.27%, followed by Logistic Regression (90.03%) and Naïve Bayes (77.70%). Applying SMOTE to overcome class imbalance and improve the performance of negative sentiment models. This research emphasizes the importance of choosing the right algorithm and using SMOTE to improve the accuracy of sentiment analysis regarding fraud and bots in concert ticket purchases. The research results can be applied to improve bot usage detection systems and provide insight for organizers.
Audit Tata Kelola Teknologi Informasi untuk Website Pelayanan Terpadu Satu Pintu (PTSP) menggunakan Framework Cobit 5 Sobirin, Muhammad Hamdan; Suryono, Ryan Randy
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10138

Abstract

Website Pelayanan Terpadu Satu Pintu (PTSP) merupakan wujud digitalisasi layanan publik yang membutuhkan tata kelola Teknologi Informasi (TI) yang efektif. Penelitian ini bertujuan untuk mengevaluasi tingkat kematangan tata kelola TI menggunakan framework COBIT 5 serta menilai keamanan dan performa sistem. Metode yang digunakan adalah deskriptif kuantitatif dengan audit pada 13 proses dari lima domain COBIT 5. Pengujian keamanan dilakukan menggunakan Nessus Scanner, sedangkan Apache JMeter digunakan untuk mengukur performa sistem. Hasil evaluasi menunjukkan rata-rata tingkat kematangan sebesar 3,6 dengan selisih (gap) 1,4 dari target level 5 (Optimized). Domain MEA02 memiliki gap tertinggi sebesar 2,1, menunjukkan kurangnya pengawasan internal. Pemindaian keamanan tidak menemukan celah dengan risiko tinggi, namun semua temuan berada pada kategori Informational. Dari sisi performa, halaman Home memiliki waktu respons tertinggi sebesar 22.683 ms dan throughput hanya 2,1 permintaan/detik, jauh di bawah standar pembanding. Hasil ini menunjukkan bahwa meskipun sistem telah berjalan cukup baik, masih diperlukan perbaikan dalam aspek pengawasan, keamanan, dan performa agar layanan PTSP dapat berjalan lebih optimal dan stabil.
Systematic Literature Review: Fintech dan Program Pemerintah dalam Permodalan UMKM: Inklusi atau Ilusi Santosa, Budi; Budiman, Ega; Simarmata, Yohanes; Kurniawan, David; Indriani, Yulia; Suryono, Ryan Randy
Jurnal Ekonomika Dan Bisnis (JEBS) Vol. 5 No. 1 (2025): Januari - Februari
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jebs.v5i1.2579

Abstract

Penelitian ini bertujuan untuk menganalisis peran teknologi keuangan (fintech) dan sumber permodalan lainnya dalam mendukung inklusi keuangan serta keberlanjutan usaha mikro, kecil, dan menengah (UMKM) di Indonesia. Dengan pendekatan systematic literature review (SLR), 10 studi primer dianalisis untuk mengevaluasi inklusivitas, efisiensi, dan keberlanjutan solusi pendanaan berbasis fintech. Hasil penelitian menunjukkan bahwa fintech secara signifikan meningkatkan inklusi keuangan UMKM dengan menyederhanakan akses pendanaan dan mengurangi hambatan prosedural. Namun, tantangan seperti rendahnya literasi keuangan dan adopsi teknologi masih menjadi kendala. Rekomendasi diberikan untuk meningkatkan akses UMKM terhadap pendanaan dan memperkuat literasi keuangan guna mendukung pertumbuhan yang berkelanjutan.
Pengaruh Keterampilan Teknologi dan Inovasi terhadap Keberhasilan Teknopreneurship di Sektor Digital Eko Putro, Dimas; ., Bagastian; Fudholi, Muhammad Fahmi; Hermana, BP Putra; Juarsa, Doris; Suryono, Ryan Randy
Jurnal Ekonomika Dan Bisnis (JEBS) Vol. 5 No. 1 (2025): Januari - Februari
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jebs.v5i1.2595

Abstract

Penelitian ini mengkaji pengaruh keterampilan teknologi dan inovasi terhadap keberhasilan teknopreneurship di sektor digital. Dengan menggunakan metode kaji literatur, penelitian ini menganalisis berbagai studi relevan untuk mengeksplorasi hubungan antara keterampilan teknologi, inovasi, dan daya saing teknopreneur. Hasil penelitian menunjukkan bahwa keterampilan teknologi, seperti penguasaan perangkat lunak, big data, dan teknologi canggih, memberikan keunggulan kompetitif bagi teknopreneur dalam menciptakan solusi efisien dan adaptif terhadap perubahan pasar. Inovasi dalam produk, layanan, dan model bisnis, seperti subscription dan teknologi berbasis cloud computing, memainkan peran signifikan dalam meningkatkan nilai tambah, loyalitas pelanggan, dan keberhasilan usaha. Sinergi antara keterampilan teknologi dan inovasi menghasilkan produk dan layanan yang relevan dengan kebutuhan pasar sekaligus mendorong keberhasilan teknopreneur. Penelitian ini juga menyoroti pentingnya faktor pendukung, seperti akses ke sumber daya keuangan dan kemitraan strategis, dalam mempercepat pengembangan produk dan memperluas pasar. Studi ini memberikan wawasan bagi pengembangan teori dan praktik teknopreneurship di era digital.
A Systematic Literature Review of Topic Modeling Techniques in User Reviews Mustaqim, Ilham Zharif; Suryono, Ryan Randy
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.238-253

Abstract

Background: The escalating volume of user review data is necessitating automated methods for extracting valuable insights. Topic modeling was a vital method for understanding key discussions and user opinions. However, there was no comprehensive analysis of the scientific work specifically on topic modeling applied to user review datasets, including its main applications and a comparative analysis of the strengths and limitations of identified methods. This study addressed the gap by characterizing the scientific discussion, identifying potential directions, and exploring currently underutilized application areas within the context of user review analysis.  Objective: This study aimed to recognize the implementation trend of topic modeling in various areas and to comprehend the methodology that could be applied to the user review dataset.  Methods: A systematic literature review (SLR) was adopted by implementing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines within six-year spans, narrowing 1746 to 28 selected primary studies.  Results: The underlying insight was that user reviews had been critical as the primary data for topic modeling in analyzing various applications. Digital banking and transportation applications were the sectors that received the greatest attention. In this context, Latent Dirichlet Allocation (LDA) was the most extensively used method, with a focus on overcoming its limitations by incorporating additional strategies into LDA-based models.  Conclusion: The bibliometric analysis and mapping study practically contributed as a reference when assessing the dominant topic in similar app categories and topic modeling algorithms. Furthermore, this study comprehensively analyzed various topic modeling algorithms, presenting both the strengths and weaknesses of informed selection in relevant applications. Considering the keywords cluster analysis, service quality could be adopted based on the output of the topic modeling.  Keywords: Topic modeling, User review, Systematic literature review, Bibliometric analysis
Analisis Sentimen Publik Program PPPK di Media Sosial X menggunakan Naïve Bayes dan SVM Sarumpaet, Lisyo Hileria; Suryono, Ryan Randy
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30065

Abstract

Sentiment analysis of the Government Employee Program with Work Agreement (PPPK) is important to understand public perception and as a basis for policy evaluation. This study aims to analyze public sentiment towards the PPPK policy and evaluate the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying public opinion on social media X. This study is a quantitative study with a data mining approach. The stages begin with collecting data collection of 7,508 tweets and processed through the stages of preprocessing, labeling, feature extraction using TF-IDF, and classification with SVM and Naïve Bayes. Data balancing is done using the Synthetic Minority Oversampling Technique (SMOTE). Our findings show that SVM produces the highest accuracy of 95%, while Naïve Bayes reaches 87%. The application of SMOTE has been shown to improve the performance of both models, especially in recognizing negative sentiment. The advantage of SVM lies in its ability to optimally separate classes through maximum margin, which is effective for high-dimensional text data. Meanwhile, SMOTE plays an important role in balancing class distribution, thereby increasing accuracy, precision, and recall. These findings provide an important basis for policy makers to respond to public opinion more appropriately based on valid and representative data.
Analisis Sentimen Pinjaman Online: Studi Komparatif Algoritma Naïve Bayes, Decision Tree, dan KNN Miranda, Khyntia; Suryono, Ryan Randy
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30142

Abstract

The development of online lending services in Indonesia has led to various responses from the public on social media, including complaints about billing methods and concerns about high interest rates. This study aims to compare the performance of Naive Bayes, Decision Tree, and K-Nearest Neighbors (KNN) algorithms. This type of research is quantitative, and the data used is 5,941 tweets through crawling techniques from X social media, followed by preprocessing, data labeling with a lexicon-based feature extraction using TF-IDF, and sentiment classification using the three algorithms. The evaluation stage uses a confusion matrix, which can calculate accuracy, precision, recall, and the F-1 score. The results show that the decision tree provides the most consistent performance with 69% accuracy due to its ability to recognize complex data patterns and understand relationships between features. Naive Bayes excels in negative sentiment classification with 68% accuracy, while KNN shows the lowest performance with 44% accuracy because it is not effective in handling high-dimensional text data. These results can be utilized by online loan service providers and regulators to build an accurate public opinion monitoring system in order to respond to issues of public concern and improve service quality on an ongoing basis.
Co-Authors ., Bagastian Achmad Nizar Hidayanto Ade Dwi Putra Adelia Pratiwi 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 Bagastian, Bagastian 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 Ival Sanjaya Iwan Purwanto Iwan Purwanto Juarsa, Doris Junita, Elvika Alya Kamrozi Karimah Sofa Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kevinda Sari Krishna Yudhakusuma P.M. Laksono, Urip Hadi M Sahyudi Magda, Kardita Mahendra Dewantoro Maylanda, Putri Oktaria Megawaty, Dyah Ayu Meliana, Yovi Mesran, Mesran Miranda, Khyntia Mugi Prasetio Muh. Alviazra Virgananda Muhamad Adhytia Wana Putra Rahmadhan Muhammad Fadli Muhammad Ridwan Muhammad Sahyudi Muhammad Surono Mustaqim, Ilham Zharif Nababan, Cynthia Deborah Natasha Panca Hadi Putra Pratama, Rangga Rizky Purnama, Putri Intan Purwanti, Dian Sri Putra, Djalu Bintang Putra, Satya Setiawan Putri Oktaria Maylanda Putro, Dimas Eko 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 Sumanto, Sumanto Surono, Muhammad Surya Indra Gunawan Tri Widodo Tria Setyani Ulum, Faruk Wahyudi, Agung Deni Wang, Junhai Waqas Arshad, Muhammad Yeni Agus Nurhuda Yeni Agus Nurhuda Yulia Indriani Yuri Rahmanto Yuspita, Emi