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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Atim, Sandi Badiwibowo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Advanced Encryption Standard (AES) Cryptography Application Design Allwine, Allwine; Atim, Sandi Badiwibowo; Afdhaluddin, Muhammad
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 1 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i1.22746

Abstract

As technology advances, the need for secure data transmission and storage increases. Encryption and decryption are essential processes to ensure data confidentiality and integrity. Encryption transforms original data into unreadable form during transmission, while decryption restores it to its original state for the recipient. This guarantees that unauthorized parties cannot access the data. Cryptosystems have evolved over time, and with the rapid growth of communication technologies, stronger standards are needed. AES (Advanced Encryption Standard), based on the Rijndael algorithm, has become the current standard for encryption. AES can encrypt and decrypt 128-bit data blocks with key lengths of 128, 192, or 256 bits, addressing the limitations of older algorithms and providing enhanced data security to protect confidentiality in modern cryptosystems.
Rule-Based Expert System Model with Backward Chaining Algorithm for Symptom-Based Skin Disease Diagnosis Atim, Sandi Badiwibowo; Ibrahim, M. Yhogha Ismail Ibn
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4416

Abstract

A rule-based expert system was a computational model designed to emulate expert decision-making using a knowledge base and inference algorithms. This research developed a rule-based expert system model with a backward chaining algorithm to diagnose skin diseases based on clinical symptoms. Backward chaining, a goal-driven inference method, started with a disease hypothesis (e.g., psoriasis) and verified related symptoms (e.g., kemerahan, sisik keperakan), enabling efficient differentiation of skin diseases with overlapping symptoms, such as dermatitis, psoriasis, and scabies. The model provided advantages in handling uncertainty, produced accurate diagnoses, and supporting interactive symptom verification. Developed using a knowledge base from credible sources like WHO and AAD, the model was intended to assist in clinical decision-making. The results showed that the backward chaining algorithm effectively improved the accuracy and efficiency of diagnosing skin diseases based on patient-reported symptoms
Co-Authors Afdhaluddin, Muhammad Allwine Ibrahim, M. Yhogha Ismail Ibn