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All Journal Jurnal Sistem Informasi dan Teknologi Informasi Bulletin of Intelligent Machines and Algorithms
Sardjono
Universitas Informatika dan Bisnis Indonesia
Computer Science & IT

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Identification of Distorted Fingerprints Using Wavelet Method and Convolutional Neural Network (CNN) Marwondo; Sardjono; Ruslan Efendi
JUSTINFO | Jurnal Sistem Informasi dan Teknologi Informasi Vol. 1 No. 2 (2023): June 2024
Publisher : LP2M Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/justinfo.vol1.iss2.2023.2074

Abstract

Biometrics offers a valuable tool for disaster victim identification, particularly through fingerprints. However, distorted or damaged fingerprints pose a significant challenge for recognition. This study explores the potential of Wavelet and Convolutional Neural Network (CNN) techniques to enhance the accuracy of distorted fingerprint recognition. Wavelet transform addresses the non-stationary nature of images and reduces detected noise. Convolutional Autoencoder, a CNN component, generates simplified feature representations from input images and attempts to reconstruct them. Utilizing 500 fingerprint samples, the testing results demonstrate accuracy variations ranging from 11% to 59.2%. Image reconstruction achieved 7.16% to 12.47% accuracy, while fingerprint matching attained accuracies between 92.71% and 93.96%. Averaging across all damage levels, the overall accuracy reached 37.65%, with average fingerprint reconstruction at 9.31% and average matching accuracy at 93.03%. The successful reconstruction and matching of distorted fingerprints within a certain range of damage using Wavelet and Convolutional Neural Network highlights the promising potential of these techniques for improved fingerprint identification in forensic and security contexts.
Comparative Analysis of Machine Learning Algorithms for Indonesian Twitter Sentiment Classification on the Jakarta–Bandung High-Speed Rail Project Muhammad Noerhadi; Budiman; Sardjono
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i1.3

Abstract

The rapid growth of social media in Indonesia has opened up new opportunities to gauge public opinion on major national initiatives. One of the most controversial projects is the Jakarta–Bandung High-Speed Railway (KCJB), which has sparked mixed responses due to its financial, environmental, and socio-political implications. To meet the need for systematic analysis, this study applies sentiment analysis to Indonesian Twitter data to evaluate public perspectives on the KCJB project. This research uses a step-by-step methodology, including data collection via the Twitter API, text preprocessing, manual tagging into positive and negative sentiments, and feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Four machine learning algorithms—Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Random Forest—were trained and verified on stratified data splits, with performance evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results show that SVM consistently outperforms other models, achieving up to 73% accuracy with balanced precision and recall, as well as the highest AUC value. These findings confirm the robustness of SVM in handling high-dimensional Indonesian text. In addition to its academic contribution to sentiment analysis in languages with limited resources, this research offers practical implications by providing data-driven insights for policymakers and businesses for real-time monitoring, strategic communication, and informed decision-making.
Co-Authors Budiman Marwondo Muhammad Noerhadi Ruslan Efendi