Komparasi Metode Klasifikasi Batik Menggunakan Neural Network Dan K-Nearest Neighbor Berbasis Ekstraksi Fitur Tekstur

Zaman, Badroe; Rifa’i, Ahmad; Hanif, Mohammad Burhan

doi:10.51519/journalisi.v3i4.213

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The K-Nearest Neighbor method is widely recognized as a popular classification algorithm used for predicting classes based on neighboring records or samples [19]. In the proposed method, the original image processing involves a series of steps, as illustrated in Figure 1.…”

Section: Proposed Methodsmentioning

confidence: 99%

Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization

Pambudi,

Fahrezi,

Badharudin

2023

J. Inf. Syst. Informatics

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The demand for brown sugar products often falls below the level of production, resulting in unsold goods when market demand surpasses the production capacity. This paper addresses the challenge faced by many brown sugar businesses in estimating production yields. Another issue, apart from production uncertainty, is the presence of a dataset with a significant nominal range. The study focuses on a specific brown sugar producing company in Indonesia. To address the production estimation problem, this research proposes the use of k-NN supervised learning as a forecasting method. However, instead of relying solely on k-NN, the study suggests employing z-score normalization to handle the dataset's large nominal range. The production data used for analysis spans from March 2019 to February 2022, comprising 144 weekly records. The dataset is divided into training and testing data, employing an 8:2 split validation ratio. The proposed method consists of several steps, including data normalization using z-score, processing k-NN based on the Minkowski distance, and concluding with the de-normalization process. The results demonstrate the successful implementation of the proposed method in predicting production levels. The evaluation indicates an average error margin of 3.34%, which is below the 5% threshold. The evaluation of predictive data for k-NN with z-score normalization proves effective in forecasting brown sugar production uncertainty and addressing the challenge of a large nominal range.

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Section: Proposed Methodsmentioning

confidence: 99%

Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization

Pambudi,

Fahrezi,

Badharudin

2023

J. Inf. Syst. Informatics

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“…Penelitian ini mengimplementasikan ekstraksi fitur GLCM (Gray Level Co-occurrence Matrix) untuk memperoleh informasi fitur dari citra dan metode K-NN (K-Nearest Neighbor) untuk melakukan klasifikasi berdasarkan fitur yang didapat. Metode ekstraksi fitur GLCM memanfaatkan komputasi statistik berdasarkan nilai piksel gambar dan korelasi antara dua piksel pada jarak dan arah sudut yang sudah ditentukan [8]. K-NN adalah suatu algoritma pembelajaran mesin bekerja dengan cara menghitung jarak antara data uji dengan data latih [9].…”

Section: Pendahuluanunclassified

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Asep Zainal Alfarizi,

Enny Itje Sela

2024

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Rhizome is a modification of plant stems that grow under the soil surface and function as a storage place for food reserves. This plants have internodes that function produce new shoots and roots. Rhizomes are commonly used by people as spices in cooking and herbal medicine. Rhizomes have many types, such as ginger, sand ginger, fingerroot, turmeric, galangal, and curcuma. These types have similarities to each other, such as texture, shape, and color. These similarities can cause problems such as difficulty in identifying the type of rhizome. The solution to this problem is a computer system that can classify the type of rhizomes. The system in this research was built using the K-Nearest Neighbor method and Gray Level Co-occurrence Matrix texture feature extraction. Research data amounted to 500 images with ginger, sand ginger, fingerroot, turmeric, and galangal classes. The stages of this research are data collection, image resizing, conversion to grayscale, GLCM feature extraction, storing the extraction results into dataframe, dividing data into train data and test data, classification with K-NN, and implement GUI to make operation easier. Accuracy results on this system get a value of 74% on test data and 64% on train data with value of K=11.

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Komparasi Metode Klasifikasi Batik Menggunakan Neural Network Dan K-Nearest Neighbor Berbasis Ekstraksi Fitur Tekstur

Cited by 2 publications

References 6 publications

Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization

Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

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