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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) 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
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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.
Sentiment Classification of Indonesian-Language Roblox Reviews Using IndoBERT with SMOTE Optimization Ansyah, Ferdi; Suryono, Ryan Randy
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10155

Abstract

Roblox is a community-based gaming platform that is extremely popular among users of various age groups. Millions of user reviews available on the platform contain valuable information regarding user satisfaction, expectations, and criticisms of the gameplay experience. To extract insights from these reviews, a reliable natural language processing (NLP) approach tailored to the local language context is essential. This study aims to classify sentiments in Indonesian-language user reviews of Roblox into three categories: positive, negative, and neutral. The model used is IndoBERT, a transformer-based model specifically trained to understand the structure and vocabulary of the Indonesian language. One of the main challenges in this study is the imbalance in the number of data points across sentiment classes. To address this, the SMOTE (Synthetic Minority Over-sampling Technique) method is applied to strengthen the representation of minority classes. The dataset consists of thousands of reviews that have been manually labeled by annotators. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the combination of IndoBERT and SMOTE provides significant improvements compared to the baseline approach without oversampling. This research contributes to the development of automated sentiment analysis systems in the Indonesian language, which can be applied across various digital platforms. The implementation of this model can assist game developers and product analysts in efficiently understanding user opinions, thereby driving improvements in service quality and user satisfaction in the future.
Analisis Sentimen Publik Terhadap Program Lapor Mas Wapres Periode 2024 di Media Sosial X Menggunakan Metode Naive Bayes dan Support Vector Machine (SVM) Anadas, Sylvi; Suryono, Ryan Randy
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 8 (2025): JPTI - Agustus 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.934

Abstract

Program Lapor Mas Wapres merupakan platform digital yang difasilitasi oleh pemerintah guna menyalurkan aspirasi, keluhan, dan masukan masyarakat diajukan langsung kepada Wakil Presiden Republik Indonesia. Penelitian ini bertujuan untuk mengevaluasi opini masyarakat terhadap program tersebut melalui platform media sosial X dengan menerapkan algoritma klasifikasi Naïve Bayes dan Support Vector Machine (SVM). Dari total 8.108 tweet yang berhasil dikumpulkan, sebanyak 6.967 data digunakan setelah melalui tahap pembersihan dan pra-pemrosesan. Untuk mengatasi ketidakseimbangan kelas sentimen, diterapkan teknik Synthetic Minority Over-sampling Technique (SMOTE), dengan pembagian data 70% untuk pelatihan dan 30% untuk pengujian. Berdasarkan hasil analisis, algoritma Naïve Bayes menunjukkan akurasi awal sebesar 63% sebelum penerapan SMOTE, yang kemudian meningkat menjadi 78% setelah penerapan SMOTE. Sementara itu, algoritma SVM menunjukkan kinerja lebih unggul dengan akurasi sebesar 80% sebelum SMOTE dan meningkat menjadi 91% setelah SMOTE. Sentimen positif publik umumnya dipengaruhi oleh persepsi terhadap keterbukaan informasi dan kemudahan akses program, sedangkan sentimen negatif muncul akibat respons pemerintah yang dianggap lambat atau tidak memadai. Penerapan SMOTE terbukti efektif dalam meningkatkan kinerja model klasifikasi, terutama pada SVM. Temuan ini memberikan wawasan bagi pemerintah untuk meningkatkan efektifitas layanan pengaduan digital dan menyusun strategi komunikasi publik yang lebih responsif.
DIAGNOSIS CEREBROVASCULAR ACCIDENTS MENGGUNAKAN TEKNIK SMOTEEN DENGAN MEMBANDINGKAN METODE KLASIFIKASI DECISION TREE DAN XGBOOST Fadli, Muhammad; Purwanti, Dian Sri; Surono, Muhammad; Dewantoro, Mahendra; Suryono, Ryan Randy
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2025

Abstract

Cerebrovascular Accident (stroke) is a critical health issue in Indonesia, often leading to high mortality and long-term disability. Early detection through machine learning has emerged as a promising approach to improve diagnosis and treatment outcomes. This study aims to compare the performance of two classification algorithms, Decision Tree and Extreme Gradient Boosting (XGBoost), in diagnosing stroke using the SMOTEENN (Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor) technique to address data imbalance. The dataset used contains 5110 samples with 11 independent variables and one dependent variable (stroke status), obtained from a public repository. After preprocessing and data balancing, both models were trained and evaluated based on accuracy, precision, recall, and F1-score. The results show that XGBoost outperforms Decision Tree in all evaluation metrics, achieving an accuracy of 96.48%, precision of 94.75%, recall of 99.03%, and F1-score of 96.85%, compared to Decision Tree’s accuracy of 91.55%, precision of 89.82%, recall of 95.32%, and F1-score of 92.49%. These findings confirm that the combination of XGBoost and SMOTEENN provides a more effective and reliable classification model for early stroke diagnosis. Future research is encouraged to explore deep learning techniques to further enhance diagnostic accuracy.
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