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All Journal Informatik : Jurnal Ilmu Komputer SEINASI-KESI Jurnal Sistem Cerdas The Indonesian Journal of Computer Science
Falih, Noor
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Model Pengamanan Berkas Menggunakan Kriptografi Asimetris RSA Dan Algoritma Kompresi PPM Pada File Curriculum Vitae (CV) Annisa, Siti; Seta, Henki Bayu; Falih, Noor
Informatik : Jurnal Ilmu Komputer Vol 18 No 2 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v18i2.4669

Abstract

Model pengamanan berkas adalah suatu model yang mengimplementasikan satu atau lebih algoritma keamanan untuk melindungi keamanan berkas. Sudah banyak penelitian yang mengusung model pengamanan berkas. Namun, berdasarkan penelitian sebelumnya, model pengamanan berkas masih dapat dikembangkan. Peneliti mengusung model pengamanan berkas baru menggunakan kriptografi asimetris RSA dan algoritma kompresi PPM. Kriptografi asimetri RSA dipilih karena kuatnya algoritma dalam mengamankan berkas. Algoritma kompresi PPM dipilih sebagai penyokong kelemahan RSA dengan mengurangi besarnya ukuran file ciphertext yang dihasilkan. Penelitian ini bertujuan untuk mengukur performa dari model pengamanan berkas menggunakan kriptografi asimetris RSA dan algoritma kompresi PPM dari segi keamanan, waktu, dan ukuran berkas yang dihasilkan. Dari 5 berkas CV yang digunakan didapat hasil waktu komputasi sebesar 0,37 detik dengan rasio kompresi sebesar 70,7611 %. Dengan demikian hasil dari model yang menggunakan RSA dan PPM memiliki hasil rasio kompresi yang lebih baik dibandingkan model pengamanan berkas terhadulu yang menggunakan Blowfish dan LZW.
The Effect of Lighting Variations on the Accuracy of Formalin Detection in Milkfish Using HSV Color Space and k-Nearest Neighbors (kNN) Algorithm Falih, Noor; Wadu, Ruth Mariana Bunga; Indarso, Andhika Octa; Audytra, Hastie; Dani, Ahmad Ali Hakam
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.564

Abstract

Milkfish (Chanos chanos) is a widely consumed fish commodity in Indonesia, often subject to preservation using formalin, a chemical with serious health risks when misused. This study proposes a non-destructive formalin detection method using HSV (Hue, Saturation, Value) color features extracted from eye images of milkfish, classified via the k-Nearest Neighbor (kNN) algorithm. The research investigates the impact of varying illumination levels low, medium, and high on the consistency of HSV features and the accuracy of kNN classification. Results show that medium lighting conditions yield the highest classification accuracy, suggesting an optimal illumination range for field deployment. The system's simplicity and potential for real-time implementation on mobile or embedded platforms make it suitable for use by non-technical personnel in traditional markets. Challenges such as environmental temperature, image angle, and surface reflectivity are addressed through calibration strategies and operational guidelines. This study contributes practical insights into lighting control and feature stability, enhancing the reliability of image-based formalin detection systems.
Co-Authors Ahmad Ali Hakam Dani Aldino, Niko Andhika Octa Indarso Audytra, Hastie Desta Sandya Prasvita Ermatita - Ernawati, Iin Fetty Fitriyanti Lubis Henki Bayu Seta Irmanda, Helena Nurramdhani Isnainiyah, Ika Nurlaili Kurniawan, Mochamad Aryo Aji Muhammad, Duwen Imantata Pangaribuan, A. B. Prabowo, Okyza Maherdy Ruth Mariana Bunga Wadu Santoni, Mayanda Mega Sarika Afrizal, Sarika Siti Annisa, Siti Suhono Harso Supangkat