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All Journal Pro Bisnis Telematika International Journal of Advances in Intelligent Informatics Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT JITK (Jurnal Ilmu Pengetahuan dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Infotekmesin Journal of Information Systems and Informatics Journal of Innovation Information Technology and Application (JINITA) Journal of Computer Science and Engineering (JCSE) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Minfo Polgan (JMP)
Azhari Shouni Barkah
Universitas AMIKOM Purwokerto
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Mechanical Engineering Social Sciences

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ENHANCING HANDWRITTEN DIGIT RECOGNITION ACCURACY ON THE MNIST DATASET USING A HYBRID CNN-BILSTM MODEL WITH DATA AUGMENTATION Yugi, Muhtyas; Latif, Ahmad; Utomo, Fandy Setyo; Barkah, Azhari Shouni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7758

Abstract

Handwritten digit recognition is a classic challenge in the field of computer vision and machine learning, and continues to be developed to achieve higher accuracy. This study proposes a hybrid method that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance performance in handwritten digit classification using the MNIST dataset. CNNs are em-ployed to extract spatial features from digit images, while BiLSTMs are used to capture the temporal patterns and sequential context from the extracted features. To address limitations in data variation and improve the model’s generalization capabilities, the study also applies data augmentation techniques based on image transformations such as rota-tion, translation, scaling, and flipping. Experimental results demonstrate that the hybrid CNN-BiLSTM model with data augmentation signifi-cantly improves classification accuracy compared to baseline ap-proaches without augmentation or without BiLSTM. The model achieved the following accuracy on the MNIST test data: CNN Model Accuracy: Before Augmentation: 98.0%. After Augmentation: 98.5%; CNN-BiLSTM Model Accuracy: Before Augmentation: 98.0%. After Augmentation: 98.7%. These results highlight the effectiveness of the hybrid approach in enhancing handwritten digit recognition perfor-mance. This research contributes to the development of more accurate and robust deep learning models for handwritten image processing
Fine-tuned hyperparameter optimization for phishing website detection: insights into efficiency and performance Rizki Wahyudi; Azhari Shouni Barkah; Siti Rahayu Selamat; Pungkas Subarkah
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.1920

Abstract

The escalation of digital threats has made phishing-site identification a critical aspect of online protection. This study investigates how systematic hyperparameter adjustment through grid search influences both predictive precision and computational efficiency in phishing detection. Nine supervised classifiers from different algorithmic families were analyzed: tree-based models (DT, RF, GB, XGBoost), margin and distance-based learners (SVM, k-NN), probabilistic and neural approaches (NB, MLP), and a linear baseline using logistic regression (LR). Although machine learning (ML) approaches have demonstrated strong predictive capability, their reliability largely depends on precise parameter calibration. Through systematic exploration of parameter combinations, the grid-search approach identifies optimal settings for each model. Using the Kaggle phishing-URL dataset, tuned models achieved noticeable accuracy gains. DT, RF, and k-NN reached 99.1% accuracy with training times of 0.10 s, 1.55 s, and 0.01 s, respectively. MLP yielded 99.0% accuracy but required 2758 s, while SVM and LR achieved 97.8% and 92.9%. NB did the worst (62.7%). The results indicate that careful hyperparameter optimization enhances predictive ability, whereas model complexity heavily impacts runtime. This study’s novelty lies in a balanced assessment of accuracy and efficiency trade-offs, offering guidelines for selecting computationally efficient algorithms in practical phishing-detection systems.
IMPROVING HANDWRITTEN DIGIT RECOGNITION USING CYCLEGAN-AUGMENTED DATA WITH CNN–BILSTM HYBRID MODEL Muhtyas Yugi; Fandy Setyo Utomo; Azhari Shouni Barkah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6982

Abstract

Handwritten digit recognition presents persistent challenges in computer vision due to the high variability in human handwriting styles, which necessitates robust generalization in classification models. This study proposes an advanced data augmentation strategy using Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve recognition accuracy on the MNIST dataset. Two architectures are evaluated: a standard Convolutional Neural Network (CNN) and a hybrid model combining CNN for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for sequential pattern modeling. The CycleGAN-based augmentation generates realistic synthetic images that enrich the training data distribution. Experimental results demonstrate that both models benefit from the augmentation, with the CNN-BiLSTM model achieving the highest accuracy of 99.22%, outperforming the CNN model’s 99.01%. The study’s novelty lies in the integration of CycleGAN-generated data with a CNN–BiLSTM architecture, which has been rarely explored in previous works. These findings contribute to the development of more generalized and accurate deep learning models for handwritten digit classification and similar pattern recognition tasks.
Impact of Stopword Variation on Qur'anic Text Classification using Support Vector Machine and Backpropagation Afit Ajis Solihin; Fandy Setyo Utomo; Azhari Shouni Barkah
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3069

Abstract

This study aims to analyze the impact of varying stopword sets on the performance of Qur'anic text classification models in Indonesian translations, using two machine learning algorithms: Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN). The research involved six stopword variants: Sastrawi, Damian Doyle, Fadillah Z. Tala, Natural Language Toolkit (NLTK) Indonesian, Yudi Wibisono, and a combination of all these lists. The preprocessing steps included cleaning, case folding, tokenization, stopword removal, and stemming, followed by TF-IDF (Term Frequency-Inverse Document Frequency) text representation. Feature selection was performed using the Chi-Square method to select the top 1,000 features. The evaluation results showed that SVM consistently outperformed BPNN across all metrics, including accuracy, precision, recall, and F1-score. The Sastrawi stopword variant delivered the best performance with an F1-score of 0.6697, followed by Fadillah Z. Tala and Damian Doyle. In contrast, BPNN showed lower performance, with the highest F1-score of 0.4607 achieved using the NLTK stopword variant. These findings highlight that selecting relevant, contextually appropriate stopwords is critical to classification Effectiveness. SVMs proved more reliable at handling high-dimensional text data while preserving the semantic meaning of Qur'anic verses.
Otomatisasi Pelabelan Korpus Sarkasme pada Komentar YouTube Berbahasa Indonesia Menggunakan Model Bahasa RoBERTa Rizky Agil Singgih Susanto; Taqwa Hariguna; Azhari Shouni Barkah
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Sarcasm detection in Indonesian YouTube comments remains challenging due to contextual ambiguity, limited labeled data, and class imbalance. This study proposes a fine-tuned RoBERTa-based auto-labeling pipeline to generate high-confidence pseudo-labels for unlabeled comments. The scientific contribution lies in integrating Back-Translation augmentation, confidence-threshold filtering at P ≥ 0.85, and comparative evaluation against baseline models on a YouTube sarcasm corpus. The data were collected from 10 public Indonesian YouTube videos covering public service, political, social, and entertainment topics during January-March 2025. From 1,000 raw comments, 493 clean comments, 200 manually labeled instances, and 536 final instances were obtained after augmentation and pseudo-label filtering. The 5-fold cross-validation results show that RoBERTa achieved an accuracy of 0.89 and a macro F1-Score of 0.87, with class-wise precision/recall of 0.81/0.81 for sarcasm and 0.92/0.92 for non-sarcasm. Compared with TF-IDF + SVM, BiLSTM, and IndoBERT, RoBERTa improved the F1-Score by 24.29%, 12.99%, and 3.57%, respectively. These findings indicate that RoBERTa-based auto-labeling can support a more controlled expansion of sarcasm corpora while reducing reliance on fully manual annotation.
Optimizing Early Network Intrusion Detection: A Comparison of LSTM and LinearSVC with SMOTE on Imbalanced Data Nugroho, Khabib Adi; Hariguna, Taqwa; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4672

Abstract

This study aims to improve network intrusion detection systems (IDS) by addressing class imbalance in the CICIDS 2017 dataset. It compares the effectiveness of Long Short-Term Memory (LSTM) networks and Linear Support Vector Classifier (LinearSVC) in detecting intrusions, with a focus on the impact of Synthetic Minority Over-sampling Technique (SMOTE) for balancing the dataset. The dataset was preprocessed by removing irrelevant features, handling missing values, and applying Min-Max normalization. SMOTE was applied to balance the training dataset. Results showed that LSTM outperformed LinearSVC, especially in recall and F1-score, after applying SMOTE. This research highlights the benefits of combining LSTM with SMOTE to address class imbalance in IDS and emphasizes the importance of temporal sequence models like LSTM for detecting network intrusions. Future work could involve using the full dataset, exploring advanced feature engineering, and implementing more complex architectures to further enhance performance. This research underscores the critical need for improving network security by addressing the challenges of class imbalance in intrusion detection systems, which is vital for ensuring the real-time identification and mitigation of sophisticated cyber threats in the ever-evolving landscape of network security.
User Acceptance Analysis of SINAGA Digital Attendance System Using Integrated UTAUT and SCT Models with PLS-SEM for Civil Servants in Purbalingga Regency Latif, Imam Sofarudin; Saputro, Rujianto Eko; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5584

Abstract

This study combines two main theories, namely the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), to analyze the level of user acceptance of the SINAGA digital attendance system among civil servants in Purbalingga Regency. This study aims to identify factors that influence technology adoption through an integrated UTAUT approach with SCT moderation, particularly self-efficacy. The method used was a survey of 102 respondents, with analysis using Partial Least Squares-Structural Equation Modeling (PLS-SEM) involving testing of outer and inner models through the Slovin approach. The results show that factors such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) significantly influence Behavioral Intention (BI). Self-Efficacy (SE) and Outcome Expectancy (OE) also act as moderating factors that strengthen the relationship between PE and BI, as well as EE and BI. With an R2 value of 78%, this model has a high explanatory power regarding users' behavioral intentions in adopting the system. This study contributes to the development of technology acceptance theory in the public sector, particularly for e-government systems, and suggests improving users' digital competence and optimizing infrastructure to support further technology acceptance with the integration of artificial intelligence (AI) technology in the system for more efficient dynamic monitoring. The main contribution of this research is the development of digital systems within the Indonesian government, in line with the sustainability of technology adoption in the public sector.
ANALISIS POLA PENYEBARAN PENYAKIT MENGGUNAKAN PENDEKATAN CLUSTERING HIERARKIS DAN K-MEANS Dilliana Tugas Setiyawan; Berlilana Berlilana; Azhari Shouni Barkah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7328

Abstract

Penyebaran penyakit, baik yang bersifat menular maupun tidak menular, merupakan isu penting yang harus diidentifikasi secara tepat untuk mendukung upaya pencegahan dan pengendalian kesehatan masyarakat. Identifikasi pola sebaran penyakit menjadi krusial karena setiap penyakit memiliki karakteristik penyebaran yang berbeda, baik berdasarkan faktor lingkungan, demografi, maupun perilaku masyarakat. Penerapan K-Means Cluster Analysis merupakan metode yang digunakan untuk mengelompokkan data menjadi beberapa kelompok (cluster) berdasarkan kesamaan karakteristik. Selain itu, pendekatan Hierarchical Clustering diterapkan untuk memvisualisasikan hubungan antar data secara hierarkis, memungkinkan analisis yang lebih mendalam. Tujuan penelitian ini adalah untuk menganalisis pola penyebaran penyakit menggunakan pendekatan Clustering Hierarkis dan K-Means. Data dari 35 Puskesmas dianalisis berdasarkan jumlah pasien dan prevalensi penyakit, termasuk Tuberkulosis, Diabetes, Hipertensi, dan penyakit menular lainnya. Hasil penelitian menunjukkan bahwa kedua metode memberikan wawasan yang saling melengkapi. K-Means efektif dalam membagi data menjadi cluster yang merata dan efisien, sementara Hierarchical Clustering memungkinkan identifikasi hubungan hierarkis dan distribusi granular antar Puskesmas
Implementasi Vulnerability Scanner Berbasis Python untuk Analisis Kerentanan dan Rekomendasi Mitigasi Keamanan Website Muhamad Saroful Hakim; Azhari Shouni Barkah; Darso
Jurnal Minfo Polgan Vol. 15 No. 3 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i3.16457

Abstract

Perkembangan website yang semakin pesat meningkatkan kebutuhan akan sistem keamanan yang mampu melindungi data dan layanan dari berbagai ancaman siber. Kerentanan seperti konfigurasi keamanan yang lemah, security header yang tidak lengkap, dan keamanan cookie yang kurang optimal dapat meningkatkan risiko serangan pada website. Penelitian ini bertujuan mengimplementasikan vulnerability scanner berbasis Python untuk menganalisis kerentanan dan memberikan rekomendasi mitigasi keamanan secara otomatis. Metode yang digunakan adalah Prototype yang meliputi pengumpulan kebutuhan, pembuatan prototype, evaluasi, implementasi, pengujian, dan sistem final. Sistem yang dikembangkan mampu memeriksa HTTPS, validitas sertifikat SSL/TLS, security header, keamanan cookie, directory listing, robots.txt exposure, dan metode HTTP. Pengujian menggunakan Black Box Testing menunjukkan seluruh fitur berjalan sesuai kebutuhan. Hasil penelitian menunjukkan sistem mampu melakukan pemindaian keamanan website, menghasilkan Security Score, mengelompokkan temuan berdasarkan tingkat risiko, serta menyediakan rekomendasi perbaikan dan laporan dalam format HTML, CSV, dan JSON. Sistem ini membantu proses vulnerability assessment menjadi lebih cepat, efisien, dan konsisten.
Co-Authors Abidin, Uun Abidin, Zaheera Zainal Afit Ajis Solihin Ajis Solihin, Afit Anggun Purnomo Bagaskoro, Galih Berlilana Berlilana Darmono Darso Dilliana Tugas Setiyawan Fandy Setyo Utomo Harimanto, Bambang Harimato, Bambang Heru Supriyanto Imam Tahyudin Kusreynada, Sabrina Uhti Latif, Ahmad Latif, Imam Sofarudin Melia Dianingrum Muhamad Saroful Hakim Muhtyas Yugi Muhtyas Yugi Nanjar, Agi Nugroho, Khabib Adi Pungkas Subarkah Ramadani, Nevita Cahaya Rizki Wahyudi Rizky Agil Singgih Susanto Rujianto Eko Saputro Selamat, Siti Rahayu Setiyawan, Dillyana Tugas Siti Rahayu Selamat Sumiati, Ai Irma Taqwa Hariguna Taqwa Hariguna Yugi, Muhtyas Yuwono Wisudo Pramono