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Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Published by Asosiasi Riset Ilmu Matematika dan Sains Indonesia
ISSN : 30326249     EISSN : 30465419     DOI : 10.62383
Core Subject : Science,
Computer Science & IT
Jurnal ini adalah jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam yang bersifat peer-review dan terbuka. Bidang kajian dalam jurnal ini termasuk sub rumpun Ilmu Komputer, dan Ilmu Pengertahuan Alam.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 112 Documents
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Implementasi Algoritma Naive Bayes dalam Penentuan Karyawan Terbaik Wulan Dari; Dian Mayasari
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 4 (2026): Juli : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i4.1041

Abstract

Human resources (employees) are essential assets that determine the sustainability and development of a company. Employee performance evaluation processes for selecting the best employees often encounter challenges due to manual procedures, subjectivity, and lengthy assessment times. Therefore, an objective, accurate, and computerized decision support system is required to assist management in making effective decisions. This study aims to develop a system for determining the best employees using the Naive Bayes algorithm as a classification method. The Naive Bayes algorithm is a probabilistic classifier that predicts decisions by calculating probability values based on the frequency and combination of attributes within a dataset. The implementation of this method is expected to minimize errors in employee selection caused by limited evaluation criteria and human judgment. The results of this approach indicate that the use of a computerized decision support system can improve the efficiency, accuracy, and objectivity of employee performance assessments compared with conventional methods. Thus, the proposed system can support management in determining the best employees more effectively.
Implementasi Algoritma Miller-Rabin untuk Pengujian Bilangan Prima Menggunakan Python Sufri Adiyatno; Muhammad Sadno; Aswani Aswani; Arga Wiradarma; Rifa’atus Shalihah
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 4 (2026): Juli : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i4.1050

Abstract

Prime numbers play a crucial role in modern cryptography, necessitating fast and accurate primality-testing methods. The Miller-Rabin algorithm is an efficient probabilistic method; however, the literature discussing its practical implementation and empirical evaluation remains limited. This study aimed to implement the Miller-Rabin algorithm using Python and evaluate its classification accuracy and execution time. This implementative research tested prime and composite numbers of sizes 32, 64, 128, 256, and 512 bits. The algorithm was implemented using Python's built-in pow() function for efficient modular exponentiation. The experimental results demonstrated that the implemented Miller–Rabin algorithm correctly identified all prime numbers as probably prime and all composite numbers as composite in all test cases. Furthermore, the computational time evaluation revealed that the execution time was directly proportional to the bit length of the numbers and the number of testing iterations. This execution time remains highly efficient for numbers of up to 512 bits. The implications of this research highlight that the Python implementation of Miller-Rabin is highly effective, serving as an excellent educational medium and a foundational tool for developing systems requiring large prime number testing.

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