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All Journal Journix: Journal of Informatics and Computing Indonesian Applied Research Computing and Informatics
Enggar Novianto
Universitas Sebelas Maret
Computer Science & IT Control & Systems Engineering

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Implementation of Post-Quantum Cryptography Algorithms for Financial Applications in Indonesia Sutriawan; Enggar Novianto
Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i2.13

Abstract

The development of quantum computing poses a serious threat to classical cryptographic algorithms that have been used to protect digital data and financial transactions. Algorithms such as RSA and Elliptic Curve Cryptography (ECC) are vulnerable to quantum computer attacks capable of running Shor's algorithm to efficiently solve large number factorization problems. This study aims to explore and analyze the implementation of Post-Quantum Cryptography (PQC) algorithms, specifically Falcon and Dilithium, in the context of digital financial systems in Indonesia. The research approach was conducted through literature studies and case study analysis on the Algorand platform, which has adopted the Falcon algorithm to strengthen digital signature security. The results of the study show that the integration of PQC algorithms can be done without sacrificing system efficiency, while providing a significant increase in security resilience against quantum threats. This research is expected to serve as a reference for financial institutions and national regulators in formulating transition strategies towards a secure digital security infrastructure in the quantum era.
Improving Thesis Title Classification Accuracy Using Ensemble Classifier and Modified Chi-Square Feature Selection Method Ritzkal; Wahyu Tisno Atmojo; Panji Novantara; Sabir Rosidin; Ahmad Dedi Jubaedi; Enggar Novianto
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Text classification of academic documents, particularly thesis titles, poses challenges due to high dimensionality, sparsity, and topic heterogeneity. Conventional feature selection techniques, such as the standard Chi-Square, often fall short in capturing discriminative features effectively. This research aims to enhance classification accuracy by proposing a Modified Chi-Square feature selection method that integrates term frequency and class distribution information. The selected features are then classified using ensemble decision tree algorithms, including Random Forest, Gradient Boosting, and XGBoost. Experiments were conducted on a labeled dataset of thesis titles using TF-IDF for vector representation. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC were used to assess model performance. The results showed that the combination of Modified Chi-Square and XGBoost outperformed other models, achieving the highest accuracy of 93.8% and an AUC of 0.94. These findings demonstrate that the integration of advanced feature selection and ensemble learning techniques can significantly improve academic text classification performance, providing valuable implications for the development of intelligent digital repositories and recommendation systems.
Co-Authors Ahmad Dedi Jubaedi Panji Novantara Ritzkal, Ritzkal Sabir Rosidin Sutriawan Wahyu Tisno Atmojo