Maliki, Abdi (2023) PERBANDINGAN AKURASI KLASIFIKASI K-NEAREST NEIGHBOUR DAN WEIGHT K-NEAREST NEIGHBOUR TERHADAP IKAN CHANNA (GABUS HIAS) MENGGUNAKAN PENDEKATAN ALGORITMA GRAY-LEVEL-CO-OCCURANCE-MATRIX. Sarjana thesis, Universitas Siliwangi.
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02. Lembar Pengesahan Tugas Akhir.pdf Download (504kB) |
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03. Lembar Pengesahan Penguji Sidang TA.pdf Download (434kB) |
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04. Lembar Penyataan Keaslian Tugas Akhir.pdf Download (683kB) |
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05. Abstract.pdf Download (4kB) |
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08. Daftar Isi.pdf Download (20kB) |
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Abstract
Ornamental snakehead fish is one type of fish that is in great demand in the community as an ornamental fish. Ornamental cork fish has varied types and colours, each variation has its own name and is a selling point among ornamental cork fish lovers. But there are still many who do not know for sure the type of ornamental cork fish because the colours are varied and seem similar. Because of this, we need a system that can classify the types of ornamental snakehead fish in a system while taking into account the level of classification accuracy. Algorithms that are often used for the classification process include KNN, but in the process KNN does not pay attention to the weight of each classified data. KNN only sees the highest number of classes that become the label benchmark of the classification results. These problems can be overcome by using the Weight-KNN algorithm for the classification process. Weight-KNN performs a classification based on the nearest neighbour but still pays attention to the weight of each data. Based on this, this study will focus on comparing the classification results between the KNN and Weight-KNN algorithms on ornamental snakehead fish. The results obtained indicate that the Weight-KNN algorithm has a higher accuracy rate than KNN, which W-KNN is 83.6% while KNN is only 80.6%. Keyword: Snakehead, KNN, Weight-KNN, and Classification
| Item Type: | Thesis (Sarjana) |
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| Subjects: | Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science |
| Divisions: | Fakultas Teknik > Informatika |
| Depositing User: | Rema Puri Irma Sri Katon |
| Date Deposited: | 27 Aug 2024 04:05 |
| Last Modified: | 27 Aug 2024 04:05 |
| URI: | http://repositori.unsil.ac.id/id/eprint/13632 |
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