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Teknik Penyembunyian Data Menggunakan Kombinasi Kriptografi Rijndael dan Steganografi Least Significant Bit (LSB) Dwi Ely Kurniawan; Narupi Narupi
Jurnal Teknik Informatika dan Sistem Informasi Vol 2 No 3 (2016): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v2i3.630

Abstract

In this study, techniques for Data Hiding performed by combining the Rijndael algorithm and the Least Significant Bit (LSB). At first the sender encrypts the message and inserts the message into the image, and then decipher the hidden message back that can be read by the recipient. System testing is done by sending data from Rijndael encryption and Least Significant Bits to the various media sender. Results from the study indicate that the data is encrypted before and after have the same relative size, but changing the amount of image quality depends on the bit value of the quality of media and messages. The more difference bit value, the greater the changes.
Scenario-Based Association Rule Mining in Veterinary Services Using FP-Growth: Differentiating Clinical and Customer-Driven Patterns Rafi Dio; Aulia Agung Dermawan; Dwila Sempi Yusiani; Rifaldi Herikson; Andikha, Andikha; Dwi Ely Kurniawan; Adyk Marga Raharja
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9698

Abstract

Veterinary clinics routinely generate transactional data that contain valuable information about both operational workflows and customer preferences. This study aims to differentiate between procedural and customer-driven service patterns by applying the FP-Growth association rule mining algorithm to 1,000 anonymized transactions comprising 94 unique items, collected from a veterinary clinic in West Java, Indonesia, during 2023. Two distinct analytical scenarios were constructed: Scenario 1 includes all services (procedural and customer-driven), while Scenario 2 excludes procedural items such as “Vet” and “Visit Dokter” to focus solely on client-initiated behaviors. Data preprocessing involved aggregating transaction items into a market basket format suitable for frequent pattern mining. The FP-Growth algorithm was employed to extract association rules, evaluated using support, confidence, and lift metrics. Results from Scenario 1 revealed rule patterns reflective of standard clinical protocols and operational dependencies, informing bundled service packages and inventory management. In contrast, Scenario 2 uncovered customer-driven associations, highlighting opportunities for personalized promotions and service innovation. The comparative analysis demonstrates the utility of scenario-based association rule mining for both operational optimization and customer engagement. While the findings provide actionable insights for clinic management, further validation with practitioners and implementation in multi-clinic settings are recommended to confirm real-world applicability and enhance generalizability.