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All Journal JAIS (Journal of Applied Intelligent System) Science Technology and Management Journal (STMJ)
Andriana, Wiwin
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering

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Implementation of the K-Nearst Neighbor (k-NN) Algorithm in Classification of Angora and Country Cats Andriana, Wiwin; Wisnumurti, Reza; Lestari, Yuni; Indriyono, Bonifacius Vicky; Wibowo, Dibyo Adi; Udayanti, Erika Devi
Journal of Applied Intelligent System Vol. 8 No. 1 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i1.7129

Abstract

There are so many types of mixed cats from various cat breeds, so many people find it difficult to identify and classify them. Therefore, we need a method that can classify the type of cat breeds. In this study the authors used the K-Nearest Neighbor (k-NN) algorithm to make it easier to recognize and classify cat breeds based on certain characteristics. The author took samples of 2 types of cat races, namely the Anggora race and the Kampung race. The implementation stage is to determine the euclidean distance and sort it and then determine the value of K to find the nearest neighbor. In testing, the authors used 50 training data and 50 test data with 6 attributes used, namely body shape, nose width, nose height, food type, hair type and hair length. The results of the classification of cat breeds using the k-NN method obtained an accuracy rate of 94% and an error rate of 6%.
Metode dan Algoritma Dalam Sentimen Analisis: Systematic Literature Review Lutfina, Erba; Andriana, Wiwin; Wiratmaja, Sanina Quamila Putri; Febrianti, Ervina
Science Technology and Management Journal Vol. 4 No. 2 (2024): Agustus 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v4i2.274

Abstract

This study highlights the advantages of sentiment analysis using algorithms in understanding public opinion, especially in the context of the increasing complexity of digital content. To investigate and present the latest developments in sentiment analysis, this study uses the Systematic Literature Review (SLR) method to identify and evaluate previously developed sentiment analysis methods. The research steps involve identifying significant sentiment analysis methods, assessing advantages and disadvantages, and critically reviewing recent advances. By applying algorithms in this process, it is expected to be able to analyze and describe the development of sentiment analysis research comprehensively. Through the application of the SLR method, this study is expected to provide in-depth insights into trends, challenges, and opportunities for future research in sentiment analysis, create a better understanding of effective sentiment analysis methods, and detail the expected results that can be expected in the development of sentiment analysis.
Co-Authors Bonifacius Vicky Indriyono, Bonifacius Vicky Dibyo Adi Wibowo Erika Devi Udayanti Febrianti, Ervina Lutfina, Erba Wiratmaja, Sanina Quamila Putri Wisnumurti, Reza Yuni Lestari