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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Informatics and Computation Bima Abdi: Jurnal Pengabdian Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology International Journal of Informatics Engineering and Computing Jurnal Informatika
Selamet Riadi
Universitas Teknologi Mataram
Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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Comparison of NB and SVM in Sentiment Analysis of Cyberbullying using Feature Selection Riadi, Selamet; Utami, Ema; Yaqin, Ainul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12629

Abstract

In the past few decades, the internet has become an inseparable part of human life. It provides ease of access and permeates almost every aspect of human existence. One of the internet platforms that is widely used by people around the world is social media. Apart from being spoiled with the convenience and efficiency offered by social media to support daily life, it has gained popularity among a wide audience. This has positive implications when utilized effectively, but it cannot be denied that there are negative consequences if not utilized properly. One such consequence is the prevalence of cyberbullying activities on social media. Cyberbullying has become a major concern for the public and social media users, prompting researchers to leverage information technology in developing technologies that can identify the elements of cyberbullying, particularly on social media platforms. Sentiment analysis has been employed by researchers to identify the components of cyberbullying in online platforms. Sentiment analysis involves the application of natural language processing techniques and text analysis to identify and extract subjective information from text. This study aims to compare the Naive Bayes algorithm and the Support Vector Machine algorithm, while utilizing feature selection, specifically chi-square, to enhance the accuracy of both algorithms in classifying Instagram comments. The experimental results indicate that the Multinomial Naive Bayes (MNB) algorithm outperforms the Support Vector Machine (SVM) algorithm, achieving an accuracy of 83.85% without feature selection and 90.77% with feature selection. Meanwhile, SVM achieves an accuracy of 82.31% without feature selection and 90% with feature selection. Evaluation through the confusion matrix and classification report reveals that MNB exhibits better precision and recall rates compared to SVM in identifying bullying and non-bullying classes. The use of feature selection enhances the performance of both algorithms in classifying Instagram comments related to cyberbullying.
Sentiment Analysis of a 271 Trillion Rupiahs Corruption Case Using LSTM Selamet Riadi; Rudi Muslim; Emi Suryadi; Karina Nurwijayanti; M. Zulpahmi; Muhamad Masjun Efendi; Bahtiar Imran
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.104

Abstract

Corruption is one of the most pressing issues in Indonesia, significantly affecting public trust in governance and the nation’s development. Among the many corruption cases that have surfaced, the recent 271 trillion rupiah corruption case has drawn widespread attention and public discourse. Understanding the public's perception and sentiment regarding such cases can provide valuable insights into how these issues impact society. Researchers identified an opportunity to leverage sentiment analysis as a method to capture and analyze public sentiment in this context. The dataset for this study was collected from the social media platform Twitter (X) using a data crawling technique. Prior to analysis, preprocessing was performed to clean and prepare the data. After preprocessing, the data was categorized into three sentiment labels: negative, positive, and neutral. To perform sentiment classification, this study utilized the LSTM (Long Short-Term Memory) algorithm, a deep learning method particularly suited for sequential data analysis. The model was trained over a total of 10 epochs. The classification results demonstrated that the LSTM algorithm achieved an accuracy of 0.9365 at the 10th epoch, showcasing its effectiveness in analyzing public sentiment regarding 271 trillion rupiah corruption issues.
FAKE REVIEW DETECTION ON DIGITAL PLATFORMS USING THE ROBERTA MODEL: A DEEP LEARNING AND NLP APPROACH Hadi, Zulpan; Nurkholis, Lalu Moh.; Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi
Journal Computer and Technology Vol. 3 No. 1 (2025): Juli 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i1.355

Abstract

Fake reviews have emerged as a serious threat to the integrity of digital platforms, particularly in e-commerce and online review sites. This study explores the application of RoBERTa (Robustly Optimized BERT Approach), a transformer-based architecture optimized for natural language processing (NLP), in automatically detecting fake reviews. The methodology includes data collection from online platforms, contextual feature extraction using RoBERTa embeddings, model training through supervised learning, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The training results indicate a significant convergence trend in the training loss, while the validation loss remains relatively unstable, reflecting challenges in model generalization. Nevertheless, experimental results demonstrate that RoBERTa outperforms other approaches such as Logistic Regression PU, K-NN with EM, and LDA-BPTextCNN, achieving an accuracy of 86.25%. These findings highlight RoBERTa's strong potential in detecting manipulative content and underscore its value as an essential tool in building a transparent and trustworthy digital ecosystem.
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Bahtiar Imran; Selamet Riadi; Emi Suryadi; M. Zulpahmi; Zaeniah Zaeniah; Erfan Wahyudi
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Bug detection in Python programming is a crucial aspect of software development. This study develops an automated bug detection system using feature extraction based on Abstract Syntax Tree (AST) and a Random Forest Classifier model. The dataset consists of 100 manually classified bugged files and 100 non-bugged files. The model is trained using structural code features such as the number of functions, classes, variables, conditions, and exception handling. Evaluation results indicate an accuracy of 86.67%, with balanced precision and recall across both classes. Confusion matrix analysis identifies the presence of false positives and false negatives, albeit in relatively low numbers. The accuracy curve suggests a potential overfitting issue, as training accuracy is higher than testing accuracy. This study demonstrates that the combination of AST-based feature extraction and Random Forest can be an effective approach for automated bug detection, with potential improvements through model optimization and a larger dataset.
Peningkatan Keterampilan Peserta Didik Mengkonfigurasi Jaringan LAN melalui Program Uji Kompetensi Keahlian (UKK) di SMK Qamarul Huda Emi Suryadi; Ahmad Yani; San Sudirman; Selamet Riadi
Bima Abdi: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2026): Bima Abdi: Jurnal Pengabdian Masyarakat
Publisher : Yayasan Pendidikan Bima Berilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53299/ba-jpm.v6i2.4525

Abstract

Uji Kompetensi Keahlian (UKK) memastikan penguasaan keterampilan dan pengetahuan pada bidang jaringan LAN. Selain sebagai syarat kelulusan, proses UKK dapat menilai kesiapan dalam memasuki dunia kerja atau melanjutkan pendidikan lebih tinggi. Kegiatan UKK kelas XII jurusan TKJ berlangsung selama 5 hari yang bertempat di Laboratorium Komputer SMK Qamarul Huda. Evaluasi peserta didik dilakukan melalui uji kompetensi untuk mengukur keterampilan dalam konfigurasi jaringan LAN. Pelaksanaan  UKK melibatkan penguji internal yaitu guru produktif dan asesor eksternal sebagai penguji. Instrumen yang digunakan meliputi lembar tugas UKK, rubrik penilain, lembar observasi, serta perangkat peraktek yang mendukung pelaksanaan UKK. Proses evaluasi kompetensi peserta didik SMK Qamarul Huda memiliki tiga tahapan yaitu tahapan pertama persiapan, mempersiapkan perlengkapan dan bahan yang akan digunakan serta mengundang asesor eksternal dari perguruan tinggi. Tahapan kedua yaitu disini peserta didik akan melakukan praktek dengan mengkonfigurasi jaringan komputer LAN agar dapat terkoneksi antara server dan client. Tahapan terakhir yaitu evaluasi, pada tahapan ini asesor melakukan penilaian dengan melihat hasil kerja peserta didik selama mengikuti proses ujian. Peserta didik yang mengikuti UKK berjumlah 90 orang dari jurusan TKJ SMK Qamarul Huda. Hasil evaluasi konfigurasi jaringan LAN bahwa telah diperoleh sebanyak 98% peserta didik dinyatakan sangat kompeten dan 2% dinyatakan kompeten. Kegiatan ini dapat meningkatkan keterampilan praktek serta kesiapan peserta didik dalam menekuni bidang jaringan komputer. Program UKK ini dapat membantu sekolah memastikan ketercapaian kompetensi lulusan peserta didik dalam meghadapi dunia kerja.
A Dual-Pipeline Imbalance-Robust Framework for SMS Spam Detection: Achieving Flawless Precision via SMOTE-Augmented Ensembles with Rigorous Statistical Validation Zulpan Hadi; Selamet Riadi; Supardianto; Aulia Riswanti Naya; Liana Trihardianingsih
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.510

Abstract

The rapid proliferation of digital communication has exponentially increased the volume of Short Message Service (SMS) spam, exposing mobile users to systemic convenience disruptions, productivity drops, and severe financial losses through sophisticated fraudulent schemes. To construct a highly dependable filtering mechanism, this study presents a rigorous dual-pipeline machine learning framework that systematically addresses the challenges of class imbalance in statistical text mining. Utilizing a verified dataset of 5,572 Indonesian-context short messages, the raw textual corpus is subjected to uniform case normalization, structural URL extraction, and character filtering before feature projection via Term Frequency–Inverse Document Frequency (TF-IDF) vectorization. To overcome the inherent accuracy paradox of skewed class distributions, the experimental design evaluates a baseline pipeline (imbalanced data) against a synthetic data augmentation pipeline leveraging the Synthetic Minority Oversampling Technique (SMOTE) across four distinct classifiers: Logistic Regression, Naive Bayes, Linear Support Vector Machine (Linear SVM), and Random Forest. Empirical results demonstrate that while the baseline Linear SVM serves as the optimal standalone model for overall balance, achieving a peak accuracy of 98.11% and a dominant F1-Score of 92.83%, the SMOTE-augmented Random Forest configuration yields an exceptional high-security alternative by securing a flawless 100.00% precision envelope alongside an 83.89% recall rate. Advanced post-hoc evaluations including McNemar's statistical significance tests (,  for Random Forest), qualitative error analyses of semantic edge cases, and runtime profiling confirm that the developed architecture establishes a highly scalable, mathematically verified, and low-latency solution suitable for integration into real-time telecom filtering gateways.
Intrusion Detection System in Network Security Using Naive Bayes and Support Vector Machine Selamet Riadi; Mohammad Nur Fawaiq
International Journal of Informatics Engineering and Computing Vol. 1 No. 1 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i1.24

Abstract

An Intrusion Detection System (IDS) is designed to detect suspicious activities or security threats within a network, necessitating continuous advancements in the field. Both implementation techniques and algorithmic research play pivotal roles in enhancing IDS capabilities. This study addresses this need by focusing on the implementation and comparison of two prominent classification models: Naive Bayes and Support Vector Machine (SVM). The study is centered within the domain of Intrusion Detection System (IDS) tailored for network security. In the course of this research, a relevant dataset sourced from Kaggle serves as the foundation for training and testing both classification models. The findings of this study underscore the models' efficacy in intrusion detection. The SVM model, in particular, emerges as a standout performer, showcasing an accuracy rate that approaches 100%, thus exemplifying its potential in real-world scenarios. Meanwhile, the Naive Bayes model delivers commendable accuracy, surpassing 88%. This investigation not only contributes to the advancement of intrusion detection methodologies but also highlights the viability of these classification models for bolstering network security against the ever-evolving threat landscape.
Enhancing Rainfall Prediction Using LSTM Algorithm Selamet Riadi; Trisna Jamil
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.86

Abstract

Rainfall is an important factor that influences various aspects of human life, including agriculture, transportation, and urban planning. With climate change, the need for accurate rainfall prediction systems is becoming increasingly urgent. Traditional methods, such as statistical or physical models, often struggle to deal with the complex and nonlinear nature of weather data. This research proposes the use of Long Short-Term Memory (LSTM), a deep learning model capable of processing sequential data, to predict rainfall based on historical data. The model can capture long-term dependencies, making it suitable for analyzing meteorological data such as temperature, humidity, wind speed and rainfall intensity. This paper investigates the performance of an LSTM-based rainfall prediction system, and compares it with traditional forecasting methods. Evaluation metrics such as Root Mean Square Error (RMSE) are used to assess the accuracy of predictions. These findings indicate that LSTM-based models provide a more reliable solution for rainfall prediction, especially in detecting extreme weather events early.
Interpreting Text-Enriched Dual-Head Multitask Learning for Indonesian Hateful Meme Detection Using Explainable AI Selamet Riadi; Emi Suryadi; Muhamad Masjun Efendi; Bahtiar Imran; Muhammad Zamroni Uska
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.576

Abstract

Internet memes in Indonesia are frequently weaponized to disseminate implicithate speech through sarcasm and cultural nuances. Automatically detectingsuch content is computationally challenging, and existing deep learningframeworks predominantly operate as opaque black boxes, lacking decisiontransparency. This study implements and optimizes a text-enriched dual-headmultitask learning architecture utilizing IndoBERTweet to concurrently classifyhatefulness and appropriateness within the INDOMEME dataset. Ratherthan processing raw image pixels, we employ a text-enrichment strategy wherevisual semantics are transcribed into textual descriptors via Optical CharacterRecognition and vision-language captioning. To bridge the interpretability gap,we deploy Local Interpretable Model-agnostic Explanations (LIME) to decodethe internal feature attributions of the architecture. Furthermore, advancedtraining optimizations, encompassing cosine annealing, gradient accumulation,class-weighted loss, and dynamic threshold calibration, were engineered toenhance model generalization. Experimental evaluations demonstrate thatthe optimized model achieves a Macro-F1 score of 0.812 for hatefulness and0.820 for appropriateness, surpassing the established baseline. Crucially, theLIME analysis unveils a pivotal finding: despite sharing an identical textualbackbone, the hate-specific head predominantly focuses on lexicons carryingsocial agitation, whereas the appropriateness head prioritizes general normviolations. These empirical findings substantiate that multitask learning enrichessemantic representation quality, offering a transparent framework fortrustworthy content moderation.
Co-Authors AA Sudharmawan, AA Ahmad Yani Aulia Riswanti Naya Efendi, Muhamad Masjun Ema Utami Erfan Wahyudi Imran, Bahtiar Karina Nurwijayanti Liana Trihardianingsih M. Zulpahmi Mohammad Nur Fawaiq Nurkholis, Lalu Moh. Rudi Muslim San Sudirman Supardianto Suryadi, Emi Trisna Jamil Uska, Muhammad Zamroni Zaeniah Zaeniah Zulpan Hadi