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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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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.
ANALISIS SENTIMEN PUBLIK TERHADAP REVISI UNDANG-UNDANG TENTARA NASIONAL INDONESIA DI TWITTER MENGGUNAKAN NAIVE BAYES DAN RANDOM FOREST Hasiholan Simamora, Alfred; Ryan Randy Suryono
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1044

Abstract

Media sosial X (sebelumnya Twitter) menjadi ruang diskusi intens terkait rencana Revisi Undang-Undang Tentara Nasional Indonesia (RUU TNI), yang memunculkan berbagai opini publik. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap RUU TNI dengan mengelompokkan opini pengguna ke dalam kategori positif dan negatif. Data sebanyak 5.646 unggahan dikumpulkan melalui teknik crawling berbasis kata kunci, lalu diproses melalui tahapan pembersihan, tokenisasi, stopword removal, dan stemming. Fitur diekstraksi menggunakan pendekatan lexicon-based Bahasa Indonesia dan TF-IDF, serta penanganan ketidakseimbangan data dilakukan menggunakan class weight. Dua algoritma machine learning, Naive Bayes dan Random Forest, digunakan untuk membandingkan performa klasifikasi. Hasil menunjukkan bahwa Random Forest memberikan akurasi tertinggi sebesar 84,51%, dengan f1-score 91% pada kelas negatif. Naive Bayes mencatat akurasi 83,63%, dengan f1-score 90% untuk kelas negatif. Kedua model mengalami kesulitan dalam mendeteksi sentimen positif, namun Random Forest terbukti lebih andal secara keseluruhan. Temuan menunjukkan dominasi sentimen negatif, mencerminkan kekhawatiran masyarakat terhadap implikasi revisi undang-undang. Penelitian ini memberikan kontribusi akademik dalam pemanfaatan analisis sentimen untuk memahami persepsi publik terhadap isu kebijakan nasional secara cepat dan terukur.
Modification of Additive Ratio Assessment Method through Distance-Based Weighting Approach for Optimizing Assessment Accuracy Gunawan, Rakhmat Dedi; Arshad, Muhammad Waqas; Wahyudi, Agung Deni; Suryono, Ryan Randy; Widodo, Tri; Ulum, Faruk
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.8810

Abstract

The Additive Ratio Assessment (ARAS) method is one of the approaches in multi-criteria decision making (MCDM) used to determine the best alternative based on a number of predetermined criteria. The drawback of this method is its heavy reliance on the accuracy of the criterion weighting determination; non-objective weights can lead to biased results. This study aims to improve the accuracy of ranking in multicriteria decision-making through the modification of the ARAS method with a distance-based weighting approach called ARAS-D. The ARAS method, known for its simplicity in calculation, was modified to be more responsive to the distribution of alternative data on each criterion. This distance-based weighting approach objectively determines the weight of the criteria based on variations in data performance, thereby reducing subjectivity in the weighting process. A case study was conducted on the selection of a new store location with six main criteria: rental cost, building area, accessibility, consumer traffic, parking availability, and infrastructure. The results of the evaluation show that the ARAS-D method is able to produce more precise ratings than the standard approach. Store locations with the highest utility value are recommended as the best choice, proving the effectiveness of the method in supporting strategic decisions. The results of the New Store Location 5 alternative rating obtained the highest score with a value of 0.9083, indicating that this location is the most optimal choice overall. This is followed by New Store Location 3 with a value of 0.8617 and New Store Location 1 with a value of 0.8415, which also shows excellent performance against the criteria that have been set. This research contributes to the development of more adaptive and data-based decision-making methods.
ANALISIS SENTIMEN PRODUK APPLE VISION PRO MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE Handini, Meitry Ayu; Suryono, Ryan Randy
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6495

Abstract

Dalam era digital, pandangan pengguna kini memengaruhi keputusan konsumen, terutama dalam industri teknologi. Analisis sentimen terhadap produk, seperti yang dilakukan dalam penelitian ini terhadap Apple Vision Pro, menjadi krusial dalam memahami respons pengguna terhadap inovasi teknologi. Sebelum memasuki konteks produk Apple Vision Pro, fenomena ini mencerminkan bagaimana pandangan pengguna secara umum dapat menjadi penentu penting dalam kesuksesan suatu produk teknologi. Penelitian ini menerapkan Naïve Bayes dan Support Vector Machine (SVM) untuk menganalisis sentimen ulasan produk Apple Vision Pro. Model Naïve Bayes menunjukkan konsistensi dengan precision 79% (positif) dan 78% (negatif), recall 78%, dan F1-score 78% untuk kedua kelas. Setelah optimasi dengan SMOTE, model SVM menunjukkan peningkatan dengan precision 81% (positif) dan 76% (negatif), recall 75% (positif) dan 82% (negatif), serta F1-score 78% (positif) dan 79% (negatif). Namun, dengan pertimbangan konsistensi, Naïve Bayes lebih diutamakan karena stabilitasnya dalam mengklasifikasikan sentimen. Penelitian ini menyajikan perspektif yang mendalam mengenai penggunaan analisis sentimen untuk memahami tanggapan pengguna terhadap teknologi, terutama dalam konteks produk Apple Vision Pro.
Perbandingan Algoritma SVM, Random Forest, dan Naive Bayes Terhadap Kasus Scam di Media Sosial Twitter Saputra, Rizky Herdian; 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.v7i2.7236

Abstract

The rapid growth of information and communication technology has a significant impact on the level of cybercrime. The internet, which was originally used to expedite the exchange of information, is also misused by irresponsible parties. One of the prevalent forms of crime is scams, which are fraudulent activities aimed at gaining unlawful profits by exploiting victims through various tactics. The purpose of this research is to evaluate and compare the performance of three algorithms: Support Vector Machine (SVM), Random Forest, and Naive Bayes in analyzing public sentiment regarding scam cases on social media Twitter. The dataset consists of 9,132 tweets, which undergo preprocessing stages such as cleaning, case folding, and word normalization, leaving 8,879 tweets for analysis. Then, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, with the dataset divided into 80% for training and 20% for testing. The results show that before applying SMOTE, the SVM algorithm achieved the highest accuracy at 82%, followed by Random Forest at 79%, and Naive Bayes at 74%. After applying SMOTE, accuracy significantly increased, with SVM reaching 88%, Random Forest at 84%, and Naive Bayes at 76%. This demonstrates that in sentiment analysis of scam cases, the SVM method achieves higher accuracy than both Random Forest and Naive Bayes.
Analisis Sentimen Publik Terhadap Danantara di Media Sosial X Menggunakan Naïve Bayes dan Support Vector Machine Firmanda, Fabian; 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.v7i2.7250

Abstract

Danantara a state-owned investment management institution, has become a topic of widespread public discussion, particularly on social media platform X, where diverse public opinions are expressed. This study aims to evaluate public sentiment toward Danantara through sentiment analysis using machine learning techniques. The dataset consists of 10,108 tweets, of which 9,790 tweets remained after the preprocessing stage and were ready for analysis. The methodology involves word weighting using Term Frequency-Inverse Document Frequency (TF-IDF) and the implementation of two classification algorithms: Naïve Bayes and Support Vector Machine (SVM). To address the class imbalance in sentiment data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Initial results show that before applying SMOTE, the Naïve Bayes algorithm achieved an accuracy of 64%, while SVM performed better with an accuracy of 80%. After applying SMOTE, Naïve Bayes accuracy improved to 72%, and SVM increased significantly to 89%. These results indicate that SMOTE is effective in handling data imbalance and enhancing classification performance. Overall, this study provides a clearer picture of public opinion toward Danantara and demonstrates that the combination of preprocessing, TF-IDF, machine learning algorithms, and data balancing techniques can produce more accurate sentiment analysis.
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