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All Journal Jurnal Teknologi Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Informatik : Jurnal Ilmu Komputer JURIKOM (Jurnal Riset Komputer) JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Majalah Ilmiah Bahari Jogja Edsence: Jurnal Pendidikan Multimedia Gema Wiralodra Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Teknimedia: Teknologi Informasi dan Multimedia Teknika Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Indonesian Journal Computer Science (ijcs) Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia SmartComp semanTIK
Hanafi Hanafi
Universitas Amikom Yogyakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Transportation Other

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Towards Interpretable Intrusion Detection: A Double-Layer GRU with Feature Fusion Explained by SHAP and LIME Wijaya, Mochamad Rozikul; M. Hanafi
Informatik : Jurnal Ilmu Komputer Vol 21 No 3 (2025): Desember 2025
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v21i3.12187

Abstract

Computer network security has become increasingly important with the growing complexity of cyberattacks. Deep learning-based Intrusion Detection Systems (IDS) represent a potential solution due to their capability to capture sequential patterns in network traffic. This study proposes a Double-Layer GRU-based IDS with Feature Fusion to enhance the representation of both numerical and categorical data in the NSL-KDD dataset. The training process employs systematic preprocessing techniques, including normalization and one-hot encoding. Experimental results demonstrate high accuracy and generalization with stable performance on both training and testing data, as well as competitive macro F1-scores for multi-class attack detection. Furthermore, interpretability aspects are explored through Explainable Artificial Intelligence (XAI) methods using SHAP and LIME. SHAP provides global insights into the contributions of important features, while LIME explains the influence of features at the local level for individual predictions. The integration of both methods not only enhances transparency and trust in the IDS but also offers deeper insights into dominant attributes in detecting attack patterns. Accordingly, this study contributes to the development of IDS that are accurate, interpretable, and applicable to modern network security.
An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning Hanafi, Hanafi; Muhammad, Alva Hendi; Verawati, Ike; Hardi, Richki
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.990

Abstract

In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection
PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES DAN C4.5 DALAM PREDIKSI PENYAKIT JANTUNG Sri Wulandari; Kusrini Kusrini; Hanafi Hanafi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i2.284

Abstract

Information Technology is a data processing technology and is a variety of ways to produce high-quality information accurately and quickly, relevant to the needs of individuals and businesses. Strategic information about decision making. The development of information technology is one of the most important factors for the progress of time. There are several fields that are important for technological progress and affect the progress of the country, such as the education sector, the economic sector, the health sector, the government sector, and the socio-cultural sector. Basically, technology is developed to promote human work. Currently, technology is a great need for humanity. In fact, technology is used in all aspects of human life. Predicting heart disease accurately is essential to treat heart patients efficiently before a heart attack occurs. This goal can be achieved by using an optimal machine learning model with complete heart disease health data. Therefore, a comparison of the performance of the Naive Bayes algorithm and the C4.5 algorithm in predicting heart disease requires calculation so that the results obtained are more accurate. Before doing the calculation, it is necessary to check the feasibility of the data to be used, then the division of training and testing data. In the study, there were several scenarios for dividing training and testing data using a confusion matrix. This study resulted in a performance comparison of the Naïve Bayes and C4.5 algorithms in predicting heart disease, 6 experimental scenarios were carried out, each algorithm had 3 experiments with varying amounts of training data and testing data. The C4.5 algorithm performed 3 experimental scenarios, in the first experiment the Naïve Bayes algorithm, the first experiment 70:30 produced an accuracy of 83%. In the second experiment 80:20 produced an accuracy of 83%. In the third experiment 90:10 produced an accuracy of 85%. Then the C4.5 algorithm performed 3 experimental scenarios, in the first experiment 70:30 produced an accuracy of 98%. In the second experiment 80:20 produced an accuracy of 100% In the third experiment 90:10 produced an accuracy of 100%.
Optimasi Hyperparameter Optuna Pada Model mT5 Untuk Penerjemahan Angkola-Indonesia Harahap, Awal Ridho; Hanafi, Hanafi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9465

Abstract

This research aims to address the challenges of preserving the Angkola language in the digital era, which are exacerbated by the lack of an adequate digital data corpus, by developing an accurate and efficient automatic Angkola-to-Indonesian machine translation system. The proposed method focuses on a fine-tuning approach for the Multilingual Text-to-Text Transfer Transformer (mT5-base) model using an Angkola-Indonesian text data corpus.The initial dataset, consisting of Angkola-Indonesian sentence pairs, was cleaned, resulting in 28,775 sentence pairs used for training. The data was subsequently split into 70% training data (20,142 lines), 15% validation data (4,316 lines), and 15% test data (4,317 lines). Intelligent model performance optimization was conducted using Optuna Hyperparameter Tuning to find the best hyperparameter combination. Optuna's objective function was designed to maximize a composite score based on the BLEU and chrF metrics from the validation evaluation results. The optimization process yielded the best Trial (Trial 50) with key hyperparameters: learning rate = 0.0004316 and num beams = 4. The best model obtained from the fine-tuning process was then evaluated on a separate Test dataset. The final evaluation on the test data using standard translation metrics demonstrated excellent performance, achieving a BLEU score of 73.84 and a chrF score of 83.34. Overall, this research successfully implemented hyperparameter optimization using Optuna for the mT5 model, resulting in an Angkola-to-Indonesian translation model that exhibits high accuracy and more efficient performance. These results provide a tangible contribution to the preservation of the Angkola language by offering a modern and accurate translation tool.
Optimasi IndoBERT untuk Pengenalan Entitas Bernama Bahasa Indonesia pada Data Media Sosial dengan Penalaan Hiperparameter Optuna Siswanto, Bambang; M. Hanafi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9545

Abstract

Named Entity Recognition (NER) merupakan salah satu tugas fundamental dalam pemrosesan bahasa alami yang berperan penting dalam ekstraksi informasi terstruktur dari teks tidak terstruktur. Pada Bahasa Indonesia, kinerja model NER berbasis pre-trained BERT sangat dipengaruhi oleh konfigurasi hiperparameter pada tahap fine-tuning. Namun, banyak penelitian masih menggunakan konfigurasi bawaan atau penyesuaian terbatas, sehingga potensi peningkatan kinerja dan stabilitas model belum sepenuhnya dimanfaatkan. Penelitian ini bertujuan untuk mengevaluasi dampak optimasi hiperparameter berbasis Optuna terhadap kinerja dan stabilitas pelatihan model pre-trained BERT untuk tugas NER Bahasa Indonesia. Model yang digunakan adalah IndoBERT (indobenchmark/indobert-base-p1) yang difine-tune untuk mengenali entitas Person (PER), Organization (ORG), dan Location (LOC) dengan skema pelabelan BIO. Metode optimasi hiperparameter dilakukan menggunakan pendekatan Bayesian berbasis Named Entity Recognition (NER) is a fundamental task in natural language processing for extracting structured information from unstructured text. In Indonesian, particularly for informal and diverse social media text, the performance of NER models based on Bidirectional Encoder Representations from Transformers (BERT) is strongly influenced by hyperparameter configurations during fine-tuning. However, many studies still rely on default settings or limited adjustments, so the potential improvements in performance and training stability have not been fully exploited. This study evaluates the impact of hyperparameter tuning using Optuna with a Tree-structured Parzen Estimator (TPE) on the performance and training stability of IndoBERT (indobenchmark/indobert-base-p1) on Twitter/X data. The main contribution of this work is an empirical evaluation of how hyperparameter tuning improves IndoBERT’s performance and training stability, and the resulting recommendations of reliable configurations for reproducible experiments and practical deployment of Indonesian NER. The dataset is annotated using the Begin–Inside–Outside (BIO) labeling scheme for three entity types: person (PER), organization (ORG), and location (LOC). The optimization objective is defined as the F1-score on the validation set. The results show that the Optuna configuration achieves a precision of 0.9338, recall of 0.9312, F1-score of 0.9325, and accuracy of 0.9854 on the test set, outperforming the baseline with an F1-score of 0.9253 and accuracy of 0.9837. Multi-seed evaluation indicates consistent improvements, with an average F1 of 0.9302 ± 0.0016 compared to 0.9238 ± 0.0009 for the baseline. These findings confirm that Optuna-based hyperparameter tuning improves both the performance and reliability of IndoBERT for Indonesian NER on social media text.
Co-Authors - Jaeni Abdul Malik Zuhdi Abdul Samad Hasan Basari Abidullah S. Salim Aflahah Apriliyani Ahmad Febri Diansyah Ahmad Jaisy Rahman Alva Hendi Muhammad Amir, Fail Andi Sunyoto Andri Pranolo Anggit Dwi Hartanto Anik Sri Widowati Asfa Dhevi Azzumzumi Asy'ari, Ahmad Hasyim B, Arijal Bambang Siswanto Cahyarini Maulida Tri Yunanda Catur Riyono Heri Wibowo D. Diffran Nur Cahyo Djatmiko, Widdi Ema Utami Fahmi Rusdi Al Islami Fendy Prasetyo Nugroho Fidya Farasalsabila Harahap, Awal Ridho Jack Febrian Rusdi Jatmiko, Burham Dwi KUSRINI Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Leonel Hernandez Lopes, David Dos Santos Pinto Marwan Noor Fauzy Moch Mashuri Muh Adha Muhammad Akbar Maulana Muhammad Fajrian Noor Muhammad Fatahillah Muhammad Ilham Fadila Muhammad Rusdi Rahman Muqorobin Muqorobin Nanang Fitriana Kurniawan Nanna Suryana Nugraha, Icha Nura Nur Avin Maulana Reflan Nuari Richki Hardi Rizky . Robi Wariyanto Abdullah Rona Guines Purnasiwi Sentoso, Thedjo Sri Ngudi Wahyuni, Sri Ngudi Sri Wulandari Sri Wulandari Taqwa Hariguna Tutik Lestari Verawati, Ike Verra Budhi Lestari Widowati, Anik Sri Wijaya, Mochamad Rozikul Windha Mega Pradnya Dhuhita Windha Mega Pradnya Dhuhita Yingchi Mao Yoga Dwi Pambudi Yudhi Setiyantara Yulita Fatma Andriani Zubaedi, Umam Faqih Zul Hisyam