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All Journal Techno.Com: Jurnal Teknologi Informasi International Journal of Advances in Intelligent Informatics JAIS (Journal of Applied Intelligent System) Journal of Computing Theories and Applications
Prajanto Wahyu Adi
Universitas Diponegoro
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Pengembangan Sistem Manajemen Naskah Soal dengan Keamanan Pre-Hash Coding Prajanto Wahyu Adi; Retno Kusumaningrum
Techno.Com Vol 20, No 4 (2021): November 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i4.5271

Abstract

Sistem pengelolahan dokumen secara elektronik sudah menjadi salah satu kebutuhan penting dalam institusi pendidikan khususnya dalam pengelolaan naskah soal. Masalah utama dalam pengelolaan naskah elektronik adalah adanya berbagai format yang digunakan serta kekhawatiran terhadap tingkat keamanan akun. Sistem keamanan akun dengan menggunakan password yang sederhana akan mudah diretas sedangkan penggunaan sistem yang kompleks akan mempersulit pengguna. Departemen Informatika Universitas Diponegoro mengembangkan sistem manajemen naskah soal yang mampu menghasilkan format naskah soal sesuai dengan standar tunggal serta memiliki sistem keamanan yang sederhana namun kuat melalui sistem pre-hash coding dengan nilai unik pengguna melalui dua skema. Pengujian pertama yang dilakukan berhasil membuktikan kemampuan sistem dalam menghasilkan naskah soal sesuai dengan standar tunggal. Percobaan kedua dilakukan untuk menguji tingkat keamanan terhadap nilai hash dari sampel password melalui uji brute-force menggunakan sistem Hashcat. Sistem yang diusulkan mampu menggagalkan peretasan sebesar 40% pada karakter alfanumerik pada skema pertama dengan operator bitwise xor sedangkan pada skema kedua dengan operator penjumlahan mampu menggagalkan seluruh peretasan yang dilakukan. Sistem yang diusulkan mampu memenuhi kebutuhan pengguna terhadap password yang sederhana namun kuat.
New Image Texture Feature for Chest X-Ray Classification Prajanto Wahyu Adi; Fajar Agung Nugroho; Yani Parti Astuti
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5340

Abstract

This study proposes a new feature extraction model to identify CXR images of covid-19 and pneumonia has a high visual resemblance. The feature extraction model starts by using histogram equalization and average filters as lowpass features and high pass features obtained through Laplacian and LoG filters. In the next step, covariance matrix of image along with the entire features are used to produce an eigen vector that will be used as a feature vector in the classification process. The final stage is the process of testing features on the classification algorithms KNN, SVM, LDA, Naïve Bayes, and Decision Tree through a 10-foldcross validation scheme with 0.9 training data and 0.1 test data. The first experiment for the Covid-19 and normal classes shows that the proposed model is able to produce an accuracy of 96% as the comparison model with GLCM texture extraction have an accuracy value of 91%. The second test is conducted for the class Covid-19 and pneumonia and obtained an accuracy value of 89% for the proposed model and 73% for the GLCM texture extraction. Experiments proved that the proposed model successfully outperformed the GLCM texture extraction model in all of classification algorithms used.
Fragile watermarking for image authentication using dyadic walsh ordering Prajanto Wahyu Adi; Adi Wibowo; Guruh Aryotejo; Ferda Ernawan
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1017

Abstract

A digital image is subjected to the most manipulation. This is driven by the easy manipulating process through image editing software which is growing rapidly. These problems can be solved through the watermarking model as an active authentication system for the image. One of the most popular methods is Singular Value Decomposition (SVD) which has good imperceptibility and detection capabilities. Nevertheless, SVD has high complexity and can only utilize one singular matrix S, and ignore two orthogonal matrices. This paper proposes the use of the Walsh matrix with dyadic ordering to generate a new S matrix without the orthogonal matrices. The experimental results showed that the proposed method was able to reduce computational time by 22% and 13% compared to the SVD-based method and similar methods based on the Hadamard matrix respectively. This research can be used as a reference to speed up the computing time of the watermarking methods without compromising the level of imperceptibility and authentication.
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Muh Galuh Surya Putra Kusuma; De Rosal Ignatius Moses Setiadi; Wise Herowati; T. Sutojo; Prajanto Wahyu Adi; Pushan Kumar Dutta; Minh T. Nguyen
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14873

Abstract

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
An Explainable Multimodal Framework for Chest X-Ray Alert Classification Using Radiology Reports and Images Edy Winarno; Indah Manfaati Nur; Abdul Karim; Saeful Amri; Ismi Elya Wirdati; Prajanto Wahyu Adi
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16023

Abstract

Artificial intelligence has the potential to support radiology workflows by assisting in the identification of cases that may require additional clinical attention. However, alert-oriented medical AI systems should provide not only classification outputs but also interpretable evidence that can be reviewed and audited by clinicians. This study develops and evaluates an explainable multimodal framework for binary chest X-ray alert classification using paired radiology reports and chest X-ray images. The text branch employs TF-IDF n-gram features with a class-balanced Logistic Regression classifier, while the image branch fine-tunes a pretrained ResNet18 model. The two branches are integrated through probability-level late fusion using a validation-selected fusion weight. Explainability is implemented in a modality-specific manner: global coefficient analysis is used to identify influential textual cues, while Grad-CAM heatmaps are used to visualize salient image regions. Experiments were conducted on paired samples from the Open-i/IU X-Ray dataset using text-only, image-only, and fusion-based evaluation settings. Additional analyses include case-level complementarity analysis, bootstrap confidence intervals for ROC-AUC, shortcut-feature inspection, and qualitative Grad-CAM auditing. The results indicate that the text modality provides the dominant predictive signal under the current proxy-label setting. Late fusion produced a small descriptive improvement on the test set, increasing accuracy from 0.8533 to 0.8667, F1-score from 0.8817 to 0.8936, and ROC-AUC from 0.8936 to 0.9025 compared with the text-only baseline. However, the observed ROC-AUC improvement was not statistically conclusive based on bootstrap analysis. These findings suggest that the proposed framework is useful as a reproducible and auditable multimodal prototype, while also highlighting important limitations, including proxy-label ambiguity, potential label leakage from radiology reports, limited image-branch contribution, lack of external validation, and the need for stronger explanation and calibration assessment.
Co-Authors Abdul Karim Adi Wibowo Amri, Saeful De Rosal Ignatius Moses Setiadi Edy Winarno Fajar Agung Nugroho Ferda Ernawan Guruh Aryotejo Indah Manfaati Nur Ismi Elya Wirdati Minh T. Nguyen Muh Galuh Surya Putra Kusuma Pushan Kumar Dutta Retno Kusumaningrum T. Sutojo Wise Herowati Yani Parti Astuti