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Journal : Journal of Advanced Computer Knowledge and Algorithms

Implementation of Tsukamoto Fuzzy Logic to Determine Sheep Livestock Population Based on Gender and Age Category in Aceh Tamiang Regency Agusniar, Cut; Khaira, Raziatul; Ramadan, Rifki
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 3 (2024): Journal of Advanced Computer Knowledge and Algorithms - July 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i3.16877

Abstract

This study implements the Tsukamoto Fuzzy Logic method to determine the sheep livestock population based on gender and age categories in Aceh Tamiang Regency. The population data of male and female sheep are categorized into three age groups: young, adolescent, and adult. The processes of fuzzification, fuzzy inference, and defuzzification are used to model the data and produce more accurate population predictions. Fuzzy rules are applied to consider the combination of membership levels of each age category and gender. The analysis results indicate that the sheep livestock population in Aceh Tamiang Regency is 5553,69007. The Tsukamoto Fuzzy Logic method has proven effective in handling uncertainty and variability in livestock population data, providing flexibility in complex data-driven decision-making. This study makes a significant contribution to the utilization of fuzzy logic methods for planning and managing livestock populations and can serve as a reference for policymakers in the livestock sector.
Implementation of the Secure Hashing Algorithm-512 (SHA-512) for Sign-Up Page Security in the KelasSeru Tutoring System Agusniar, Cut; Fazira, Ira; Wahyunita, Laili
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.20320

Abstract

Security for user authentication Security for user authentication in the sign-up process is an important aspect in protecting data from unauthorized access. This study aims to implement the Secure Hashing Algorithm-512 (SHA-512) algorithm on the sign-up page of the website-based KelasSeru tutoring system using Flask, to improve the security of user data, especially passwords. SHA-512 was chosen because of its ability to produce a 512-bit hash that cannot be returned to its original form, making it more resistant to cyber attacks such as bruteforce collision attacks. The research methodology includes developing a Flask-based application, validating input, and encrypting passwords before saving them to the database. This encryption process ensures that passwords are not stored in plaintext, but in hash form that is difficult to crack. The results show that SHA-512 is effective in maintaining password confidentiality and improving overall system security. In addition, the website also displays additional features and a different page display if the person logging in is an admin or users. This study proves that implementing SHA-512 on the sign up page can provide significant protection against cyber threats and ensure user data remains secure, providing a sense of comfort and trust for its users.
Utilizing K-Means Clustering for Grouping Student Achievement Data to Evaluate Learning Activeness Agusniar, Cut; Mulya Ulfa, Septia; Rhomadhona, Herfia
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22591

Abstract

Learning basically aims to foster student activity and creativity through various learning experiences and interactions. Teachers are an important part of the process of improving the quality of education. In addition, the success of the learning process depends on student activity. The world of education needs to improve the quality of students and their performance by using existing facilities, infrastructure and human resources. One way information systems can be used to improve student achievement and quality is by analyzing grades based on students' academic abilities, discipline and way of behaving.The aim of this research is to group students based on academic scores, disciplinary scores and attitude scores using the K-Means Clustering algorithm, so that the cluster results can be used as a reference in improving student scores in the next learning process. In this research, the elbow method was used to determine the optimal number of clusters. Students will be grouped into clusters. Visualization and correlation analysis between value variables is carried out to provide further insight into the distribution of data and the relationship between its values.