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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH Informatika : Jurnal Komputer dan Informatika PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ipteks Terapan The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Informatika Dan Tekonologi Komputer (JITEK) pendidikan, science, teknologi, dan ekonomi Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik Jurnal Informatika Dan Tekonologi Komputer
Kurniabudi, Kurniabudi
Universitas Dinamika Bangsa
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health Other

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Model Deteksi Mahasiswa Dropout Menggunakan Artificial Neural Network Denia Igesti Nur Mellyati; Kurniabudi Kurniabudi; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.140

Abstract

Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.
Analisis Sentimen Ulasan Aplikasi CapCut Berbasis IndoBERT dengan Validasi Silang Tasya Nurdin; Dodo Zaenal Abidin; Kurniabudi Kurniabudi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.157

Abstract

This study conducts sentiment analysis of Indonesian user reviews of the CapCut application using IndoBERT and compares two evaluation schemes: a single 80/20 train–test split and stratified 5-fold cross-validation (k=5). A total of 1,048,575 reviews were collected from the Google Play Store through web scraping and labeled into three sentiment classes based on rating: negative (1–2), neutral (3), and positive (4–5). After preprocessing—cleaning, case folding, banned-word removal, normalization—and duplicate removal, 517,962 reviews were retained. IndoBERT Base P1 was fine-tuned using fixed hyperparameters (batch size 32, learning rate 2e-5, up to 4 epochs, early stopping patience 2), while undersampling was applied to the training set to address class imbalance. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, supported by confusion matrix and ROC-curve visualizations. The single split achieved an accuracy of 0.756, whereas cross-validation produced a mean accuracy of 0.740. Across both schemes, the positive class achieved the best performance (F1-score 0.850; ROC-AUC 0.918–0.919), while the neutral class remained the most challenging (precision 0.198–0.206; F1-score 0.280–0.283). Overall, cross-validation is recommended for reporting because it reduces dependence on a single partition and provides a more representative estimate across multiple splits.
Prediction of Indonesian Banking Stock Prices Using a Hybrid LSTM and XGBoost Model with Optuna Based Hyperparameter Optimization Admaja, Admaja; Kurniabudi, Kurniabudi; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5715

Abstract

Stock price prediction is a critical task in investment decision-making, particularly in highly volatile financial markets such as the Indonesian banking sector. While Long Short-Term Memory (LSTM) networks are effective in modeling temporal dependencies, they often fail to capture nonlinear residual patterns in financial time-series data, and their performance is highly sensitive to hyperparameter selection. To address these limitations, this study proposes a residual learning–based hybrid LSTM–XGBoost framework optimized using Optuna for predicting stock prices of major Indonesian banking stocks, namely BBCA, BBNI, BBRI, and BMRI. LSTM is employed as the base learner to model log-return sequences, while XGBoost is used to learn nonlinear residual structures from LSTM predictions. Latent embeddings extracted from the LSTM are further refined using Principal Component Analysis (PCA) to reduce redundancy and improve generalization. Hyperparameters of the LSTM, PCA, XGBoost, and calibration components are jointly optimized using Optuna with a Tree-structured Parzen Estimator (TPE) strategy. Experimental results demonstrate that the Optuna-optimized hybrid model consistently outperforms the baseline hybrid model across all datasets, achieving lower Mean Absolute Percentage Error (MAPE) values of 1.196% for BBCA, 1.67% for BBNI, 1.53% for BBRI, and 1.70% for BMRI. Additional stability analyses confirm that the proposed framework delivers stable and reliable predictions on unseen data. These findings provide a scalable hybrid forecasting framework that contributes to the development of intelligent financial decision-support systems and demonstrates the effectiveness of adaptive hybrid deep learning optimization techniques in real-world time-series prediction problems within the field of informatics.
Information Gain-Based Feature Selection and Machine Learning Classification for DDoS Attack Variant Detection in Cloud Computing Environment Winanto, Eko Arip; Kurniabudi, Kurniabudi; Sharipuddin, Sharipuddin; Mellyati, Denia Igesti Nur
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5752

Abstract

Cloud computing environments face significant security vulnerabilities from Distributed Denial of Service (DDoS) attacks, which can cause system failures and service disruptions. Despite various existing detection methods, challenges remain regarding high computational overhead and suboptimal accuracy due to redundant features in complex datasets. This study aims to identify the optimal feature subset and evaluate its impact on detection performance across multiple machine learning algorithms for multi-class DDoS variants. The research methodology employs a two-stage approach: feature selection using Information Gain (IG) to reduce 47 original features into subsets of 8, 10, 15, and 20, followed by classification using Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB) on the CICIoT2023 dataset. Experimental results demonstrate that the Decision Tree model with an optimized subset of only 8 features, primarily Inter-Arrival Time (IAT), Header_Length, and Tot_size, achieves a superior accuracy of 99.97%. While Naïve Bayes performs well in binary classification, its accuracy drops significantly to approximately 30% in multiclass settings. This study concludes that IG-based feature selection reduces computational complexity by 30-40% while maintaining high performance across 12 DDoS variants. These findings provide a practical framework for scalable and efficient intrusion detection systems suitable for real-time deployment in resource-constrained IoT-cloud environments.
Co-Authors Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Rahim Abdul Rahim Admaja, Admaja Ahmad Heryanto Albertus Edward Mintaria Albertus Edward Mintaria Ammar panji Pratama Bedine Kerim Bedine Kerim Candra Adi Rahmat Chindra Saputra Darmawijoyo, Darmawijoyo Dede Andri Wahyudin Denia Igesti Nur Mellyati Deris Stiawan Dodi Sandra Dodi Sandra Dodo Zaenal Abidin Dr. Hendri, S.Kom., S.H., M.S.I., M.H Eko Arip Winanto Eko Arip Winanto Elvi Yanti Elvi Yanti Elvira Rosanda Erick Fernando Erick Fernando Erick Fernando B311087192 Fachruddin Febriyan Nurmansyah Harid, Harid Harris, Abdul Hendri Hendri Hendri Hendri Hendy Saryanto Herry Mulyono Ibnu Sani Wijaya Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Rofi’i Imam Rofi’i Irawan, Beni Irfan, Fadhel Muhammad Jasmir, Jasmir Kurniabudi Kurnianto Basuki Lola Yorita Astri, Lola Yorita Mellyati, Denia Igesti Nur Minal Juadli Mintaria, Albertus Edward Mohd Yazid bin Idris Mohd Yazid Bin Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Arief Maulana Muhammad Rafly Ramadhan Muhammad Riza Pahlevi Nabila Kamila Hasna Nabila Kamila Hasna Nurhadi Pandapotan Siagian Pareza Alam Jusia, Pareza Alam Purnama, Benni Putri Nawang Wulan Putri Nawang Wulan Rahman saibi Rahmat Budiarto Rahmat Budiarto Realensi Realensi Rilis Pebriyanti Siringo Ringo Risky Radison Nasution Ryan Sihopong Parlindungan Siregar Samsuryadi Samsuryadi Setiawan Assegaf Sharipuddin, Sharipuddin Sharipuddin, Sharipuddin Shelby Amalia Sandi Siagian, Pandapotan Suwaldo Aris Ferry Hutabarat Syamsul Arifin Syifqi, Achmad Tasya Nurdin Valensia, Vally Veronica Veronica VERONICA VERONICA WILLY RIYADI Winarno Wirmaini, Wirmaini Yudi Novianto Yudi Novianto Yundari, Yundari Zulwaqar Zain Mohtar