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All Journal Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON)
Liza Afriyanti
Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Perbandingan Jarak Metrik pada Klasifikasi Jamur Beracun Menggunakan Algoritma K-Nearest Neighbor (K-NN) Andre Suarisman; Alwis Nazir; Fadhilah Syafria; Liza Afriyanti
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4511

Abstract

Mushrooms are organisms from the kingdom fungi that have a fleshy body structure and can be consumed, but there are some species of mushrooms that are not safe to eat and have specific characteristics, so distinguishing between edible and poisonous mushrooms can be tricky due to the almost identical appearance of various mushroom species. Errors in identifying edible mushrooms can impact the health of consumers who consume the mushrooms. Evaluating the performance of various methods on a dataset is a key step in determining the most suitable classification method. This research is about how to measure the performance of classification methods on toxic mushroom datasets using the K-Nearest Neighbor algorithm with several metrics such as euclidean, manhattan and minkowski, which is a method for classifying new data based on proximity to existing training data. The results obtained in this study with several distance metrics can be concluded that the accuracy value of the manhattan metric is better than the euclidean and minkowski metrics. Because the manhattan metric gets the highest accuracy result of 99% with K = 100 and the lowest 82% with K = 3000, while the euclidean metric gets accuracy results with a value of 98% with K = 100 and 72% with K = 3000, and the minkowski metric gets accuracy results with a value of 96% at K = 100 and 64% at K = 3000.
Analisis Perbandingan Algoritma C4.5 dan Modified K-Nearest Neighbor (MKNN) untuk Klasifikasi Jamur R. Rahmadhani; Alwis Nazir; Fadhilah Syafria; Liza Afriyanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7052

Abstract

Mushrooms are organisms that consist of several cells, contain spores, are eukaryotic (have a cell nucleus membrane), and do not have chlorophyll, so fungi depend on other organisms to get food. Mushrooms have very identical shapes, starting with size, shape, smell, and color. So it is difficult for ordinary people to differentiate between poisonous mushrooms and non-poisonous mushrooms. Mistakes in identifying mushrooms can have fatal consequences because they can cause poisoning when consuming mushrooms. Therefore, there is a need for education in classifying poisonous and non-poisonous mushrooms. By applying various classification algorithms, it can be determined which algorithm performs better. In previous research conducted by several researchers on classifying mushrooms, there were differences in the accuracy results for each algorithm. Therefore, this research will raise the question of how to measure or comparion algorithm performance in classification using the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm. The results obtained by comparion the performance of the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm in this research show that the C4.5 algorithm managed to obtain an accuracy level of 98.52%, precision of 98.55%, recall of 98.52%, and f1-score of 98.51%. In contrast, the Modified K-Nearest Neighbor (MKNN) algorithm using the value K=10 achieved an accuracy level of 96.62%, precision of 96.69%, recall of 96.62%, and f1-score value of 96.57%.
Co-Authors Alwis Nazir Andre Suarisman Fadhilah Syafria R. Rahmadhani