k-Nearest Neighbor (k-NN) Classification for Recognition of the Batik Lampung Motifs

Andrian, Rico; Naufal, Muhammad Adib; Hermanto, Bambang; Junaidi, Akmal; Lumbanraja, Favorisen Rosyking

doi:10.1088/1742-6596/1338/1/012061

Cited by 18 publications

(7 citation statements)

References 1 publication

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“…k-Nearest Neighbor (k-NN) is a supervised learning algorithm also known as category classification algorithm [17]. After the information gain process is complete, the classification process uses the k-NN to classify documents.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Effect of information gain on document classification using k-nearest neighbor

Perwira

Yuwono

Siswoyo

et al. 2022

regist. j. ilm. teknol. sist. inf.

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State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.

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Section: K-nearest Neighbormentioning

confidence: 99%

Effect of information gain on document classification using k-nearest neighbor

Perwira

Yuwono

Siswoyo

et al. 2022

regist. j. ilm. teknol. sist. inf.

View full text Add to dashboard Cite

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“…c) Feature Extraction Feature extraction is the process of taking features of an object that can describe the characteristics of the object. This stage aims to obtain information contained in an image and then serve as a reference to distinguish between one image and another (Andrian et al, 2019). The region can be defined in a global or local environment and distinguished by shape, texture, size, intensity, statistical properties, and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN)

Sarimole¹,

Syaeful²

2022

JAETS

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Durian is one of the most popular fruits because it has a delicious taste and distinctive aroma. It has different shapes and types, especially from thorns and different colors and has fruit parts that are also not the same as other parts. In terms of fruit selection, care must be taken because consumers generally still find it difficult to distinguish physically identified types of Durian fruit due to limited knowledge of the types of Durian fruit and require a relatively long time and accuracy in sorting. Therefore, there is a need for a method to sort the types of Durian fruit effectively and efficiently. Namely image segmentation based on the classification of the types of Durian fruit to help consumers. The method used is Gray Level Co-Occurrence Matrices for feature extraction, while to determine the proximity between the test image and the training image using the K-Nearest Neighbor method based on texture based on the color of the Durian fruit obtained. Extraction features using the GLCM method based on angles of 0°, 45°, 90° and 135°. Then the KNN method is used for the classification of characteristic results using K = 3. In this study, 1281 data training was used and 321 data testing was used, resulting in an accuracy of 93%.

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“…3) K-Nearest Neighbors model (KNN) predicts the category of test samples according to training sample k, which is the closest neighbor to the test sample and inserts it into a category with the greatest probability. Near or far distances to neighboring points can be calculated using the Euclidean distance equation (2) (Andrian, 2019). Fig.…”

Section: Fig 13 Svm Regressionmentioning

confidence: 99%

Developing a Flood Forecasting System With Machine Learning and Applying to Geographic Information System

PUNGCHING¹,

PILAILAR²

2022

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Floods are natural disasters that can damage lives, property, and the economy. Therefore, it is necessary to have a reliable and accurate flood forecasting system to provide early warning in time. Although several Mathematical models have been developed and used to forecast floods continuously for decades, most require up-to-date and specific physical data, including a high experience user, to provide and interpret the result. It is an obstacle for use in remote areas with incomplete information and a lack of specialists. This study, therefore, developed a real-time flood forecasting system with Machine Learning by applying a 2-variable sliding window technique to restructure the data, which can solve the problem of data limitation. Thung Song District Nakhon Si Thammarat Province was selected to test this newly developed model. By importing the water level data of two water level observed stations, SWR025 at the upstream and NKO001 at Thung Song Municipality, into five machine learning algorithms (Linear Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest) for forecasting the water level every 30 minutes for the next 5 hours. Their performance was compared by the MSE, MAE, and R 2, which ranged from 0.006-0.013, 0.044-0.063, and 0.518-0.750, respectively. The Random Forest was the most efficient algorithm for the 3-hour forecast with an efficiency value of MSE 0.006, MAE 0.044, and R 2 0.75. The developed ML flood forecasting model was validated by the flood data in November 2021 and showed good agreement. Then, the extent of the inundation area was evaluated by the mathematical model. Next, the water depth and surface elevation were transformed and applied to GIS. Finally, the flood risk areas on Google Maps under that specific rainfall are promptly notified to the people three hours before the flood occurs.

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k-Nearest Neighbor (k-NN) Classification for Recognition of the Batik Lampung Motifs

Cited by 18 publications

References 1 publication

Effect of information gain on document classification using k-nearest neighbor

Effect of information gain on document classification using k-nearest neighbor

Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN)

Developing a Flood Forecasting System With Machine Learning and Applying to Geographic Information System

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