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Melina
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Journal : Jurnal Algoritma

 1 
Implementasi Algoritma Rivest Shamir Adleman (RSA) dan Zero-Knowledge Proofs (ZKP) untuk Meningkatkan Keamanan Data Rekam Medis Elektronik Lestari, Abdila; Id Hadiana, Asep; Melina
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2360

Abstract

Perkembangan teknologi komputer dan telekomunikasi meningkatkan efisiensi pengolahan data, namun menimbulkan tantangan keamanan, khususnya pada data rekam medis elektronik (RME) yang bersifat sensitif. Penelitian ini mengimplementasikan metode Zero-Knowledge Proof (ZKP) dan Revest Shamir Adleman (RSA) untuk meningkatkan keamanan dan privasi RME. ZKP memungkinkan pembuktian tanpa mengungkapkan informasi rahasia, sedangkan RSA menjaga kerahasiaan dan integritas data melalui enkripsi-dekripsi. Hasilnya, entropi data meningkat 24,53% (4,8314 menjadi 6,0165 bits/byte) setelah enkripsi RSA 2048-bit dengan padding OAEP berbasis SHA-256. Protokol ZKP metode Schnorr berhasil diimplementasikan tanpa membocorkan rahasia pengguna. Pengujian pada 100 pengguna simultan menunjukkan waktu respons rata-rata 1,8 detik dengan keberhasilan permintaan di atas 94%. Tantangan utama adalah beban komputasi autentikasi ZKP dan efisiensi saat jumlah pengguna bertambah. Integrasi RSA dan ZKP terbukti efektif meningkatkan keamanan, menjaga privasi, dan mempertahankan kinerja sistem RME.
Sistem Data Loss Prevention Untuk Deteksi dan Enkripsi pada Dokumen Menggunakan Regex dan Format Preserving Encryption Rahmawati, A Lusi Fitri; Hadiana, Asep Id; Melina
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2387

Abstract

In today’s digital era, the leakage of sensitive information has become a serious threat for both individuals and organizations, especially when data is not adequately protected. To address this issue, a system is required that not only detects the presence of sensitive data but also protects it effectively. This study develops a Data Loss Prevention (DLP) system that integrates sensitive data pattern detection using regular expressions (regex) with Format-Preserving Encryption (FPE) techniques to safeguard sensitive information in digital documents. The system is designed to identify data patterns such as national ID numbers (NIK), tax identification numbers (NPWP), phone numbers, email addresses, and bank account numbers using regex, and then encrypt the detected data without altering its original format. The test data used in this research consists of synthetic datasets that resemble real-world data. The encryption process employs the FF3 algorithm with a deterministic approach tailored to each data type to maintain system compatibility. The evaluation covers detection effectiveness using precision, recall, and F1-score metrics, as well as encryption efficiency and security through processing time measurements and entropy values. The evaluation results indicate a detection accuracy of 94.1%, precision of 100%, recall of 88.8%, and an F1-score of 94.1%. The average encryption time per document is only 0.15 milliseconds, while the encryption process successfully increases the document entropy by 0.0645 bits. This system demonstrates stable and reliable performance in detecting and protecting sensitive information without disrupting data structure or operational processes.
Klasterisasi Gaya Belajar Mahasiswa Berbasis VARK dengan Algoritma DBSCAN untuk Personalisasi E-Learning Maulana, Iqbal; Witanti, Wina; Melina
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2980

Abstract

The incompatibility between e-learning systems and students' learning styles remains a major challenge in improving the effectiveness of learning in Indonesian universities. This study aims to classify the learning styles of students at Jenderal Achmad Yani University using the VARK (Visual, Auditory, Read/Write, Kinesthetic) model, enriched with the Kano method. Data were collected from 1,000 students through the VARK-Kano questionnaire and analyzed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. The clustering process was carried out by determining the optimal parameters using the k-distance plot, and the validity of the clusters was assessed using the Silhouette Score. The results showed that DBSCAN could form representative clusters of student learning styles and effectively detect data noise. This study contributed to the development of a cluster-based adaptive e-learning framework that could be implemented in Indonesian universities. These findings could serve as a basis for designing adaptive learning strategies that are more suited to student characteristics, thereby increasing the effectiveness of e-learning and learning motivation.
Co-Authors Adam Firmansyah Suhendar apriliyanti mugianto putri, ira dwi Azurah, Nur Baharuddin Patandjengi Bongga Arifwidodo Busthanul, Nurabaya Firdaus, Syahrul Hadiana, Asep Id Iqbal Dwi Nulhakim Iqbal Maulana Jafaruddin Gusti Amri Ginting Julian Evan Chrisnanto Lestari, Abdila Muhammad Azrai, Muhammad Mustika Tuwo, Mustika Nida Ulhasanah Nizar Septi maulana Nur Hardina Rahmawati, A Lusi Fitri Rahmawati, Firda Salsabila, Mira Suharyani, Ine Tutik Kuswinanti Wina Witanti Wina Witanti Yulison Herry Chrisnanto