Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Computation Bima Abdi: Jurnal Pengabdian Masyarakat Jurnal Computer and Technology Jurnal Informatika
Selamet Riadi
Universitas Teknologi Mataram
Computer Science & IT Education Languange, Linguistic, Communication & Media Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
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.
Co-Authors Ahmad Yani Aulia Riswanti Naya Efendi, Muhamad Masjun Erfan Wahyudi Imran, Bahtiar Karina Nurwijayanti Liana Trihardianingsih M. Zulpahmi Rudi Muslim San Sudirman Supardianto Suryadi, Emi Zaeniah Zaeniah Zulpan Hadi