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This Author published in this journals
All Journal Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Systems and Information Technology
Firza Septian
Universitas Serelo Lahat
Computer Science & IT Engineering

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Journal : Journal of Intelligent Systems and Information Technology

 1 
Performance Optimization of Document Clustering for Harry Potter Series Comments using Cosine Similarity Firza Septian; Arief Zikry; Nina Dwi Putriani
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.30

Abstract

This research delves into the distinctive realm of comment clustering, focusing on the extensive discourse generated by the Harry Potter series. Leveraging a dataset from Kaggle, the study aims to optimize document clustering using cosine similarity within the K-Means algorithm. The research addresses the nuanced dynamics of sentiment and preferences within the Harry Potter fan community. A comprehensive methodology involves data collection, preprocessing, TF-IDF initialization, K-Means clustering with varying distance metrics, and result evaluation. The dataset of 491 respondents unveils diverse gender, geographical, and age distributions, adding complexity to the analysis. The K-Means clustering results highlight predominant positive sentiment, emphasizing the enduring popularity of the series. The study's originality lies in its focus on the Harry Potter cultural phenomenon, contributing to sentiment analysis and fan engagement discourse. The implications extend to researchers, practitioners, and enthusiasts seeking a deeper understanding of online discussions surrounding iconic media franchises.
Exploring the Performance of Whale Optimization Algorithm on Rosenbrock's Function Firza Septian
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.35

Abstract

Optimization of complex and nonlinear functions is essential across various domains, from engineering and finance to artificial intelligence and machine learning. Rosenbrock's function stands as a fundamental benchmark for evaluating optimization algorithms due to its highly nonlinear and multimodal nature. Among the multitude of optimization algorithms, the Whale Optimization Algorithm (WOA) has garnered attention for its inspiration from the social behavior of humpback whales. However, its performance on Rosenbrock's function remains relatively unexplored. This paper aims to investigate the effectiveness of the WOA specifically on Rosenbrock's function through rigorous experimentation and analysis. By evaluating convergence speed, solution accuracy, and robustness, this study sheds light on WOA's behavior when confronted with the challenges posed by Rosenbrock's function. Comparative analysis with other optimization algorithms further elucidates WOA's adaptability and scalability. The findings contribute valuable insights for selecting suitable optimization algorithms in real-world applications and advance understanding of optimization algorithms' behavior in challenging landscapes.
Co-Authors Arief Zikry Iski Mediansyah Muhammad Sulkhan Nurfatih Nina Dwi Putriani