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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 Journal of Information Systems and Informatics Journal of Computer Science and Engineering (JCSE) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia
Azhari Shouni Barkah
Universitas AMIKOM Purwokerto
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering

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IMPROVING HANDWRITTEN DIGIT RECOGNITION USING CYCLEGAN-AUGMENTED DATA WITH CNN–BILSTM HYBRID MODEL Utomo, Fandy Setyo; Barkah, Azhari Shouni; Muhtyas Yugi
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.
Fine-tuned hyperparameter optimization for phishing website detection: insights into efficiency and performance Wahyudi, Rizki; Barkah, Azhari Shouni; Selamat, Siti Rahayu; Subarkah, Pungkas
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.
A Decision Support System for Assessing High School Students' Soft Skills Using the Analytical Hierarchy Process Yuwono Wisudo Pramono; Berlilana Berlilana; Azhari Shouni Barkah
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1420

Abstract

Assessing students' soft skills in educational settings is often challenging due to the subjectivity and inconsistency inherent in evaluating qualitative traits. This study employs the Analytical Hierarchy Process (AHP) as a decision support tool to provide a more systematic, consistent, and objective method for evaluating students' soft skills. The assessment model is based on four key criteria—critical thinking, communication, collaboration, and creativity—each further broken down into measurable subcriteria. The study was conducted at MA Mu’allimin Sruweng Kebumen, where evaluations were carried out by a guidance and counseling teacher acting as an expert evaluator, using a numerical scale ranging from 1 to 100. Pairwise comparison matrices were developed using Saaty’s fundamental scale to determine the weights for both criteria and subcriteria, followed by consistency testing using the Consistency Ratio (CR). The findings reveal that critical thinking and collaboration were assigned the highest priority weights, with all comparison matrices meeting the acceptable consistency threshold. The resulting global preference values offer a more objective, proportional representation of students’ soft skills achievements. This AHP-based model enables fairer, more consistent evaluations and provides quantitative outputs that can be utilized for student ranking and structured feedback in educational decision-making.
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
ANALISIS POLA PENYEBARAN PENYAKIT MENGGUNAKAN PENDEKATAN CLUSTERING HIERARKIS DAN K-MEANS Setiyawan, Dilliana Tugas; Berlilana, Berlilana; Barkah, Azhari Shouni
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
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.
Co-Authors Abidin, Uun Abidin, Zaheera Zainal Ajis Solihin, Afit Anggun Purnomo Bagaskoro, Galih Berlilana Berlilana Darmono Fandy Setyo Utomo Harimanto, Bambang Harimato, Bambang Heru Supriyanto Imam Tahyudin Kusreynada, Sabrina Uhti Latif, Ahmad Latif, Imam Sofarudin Melia Dianingrum Muhtyas Yugi Nanjar, Agi Nugroho, Khabib Adi Pungkas Subarkah Ramadani, Nevita Cahaya Rizki Wahyudi Rujianto Eko Saputro Selamat, Siti Rahayu Setiyawan, Dilliana Tugas Setiyawan, Dillyana Tugas Sumiati, Ai Irma Taqwa Hariguna Yugi, Muhtyas Yuwono Wisudo Pramono