Coastal Vulnerability Classification of the North Coast of Java using K-Nearest Neighbor

Ayu Novitasari Saputri, Arita Witanti


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Indonesia has many beaches that attract the attention of tourists, including the north coast of Java. In addition to visual appeal, there are also other potentials such as settlements, agriculture, fisheries, ports, ponds, and other resources. However, there is also a threat to coastal damage caused by, among other things, wave action, tides, abrasion, and tidal flooding. For this reason, in developing coastal areas on the north coast of Java, it is necessary to consider the potential for damage to the coast based on the physical condition of the coast and a system that can classify the vulnerability levels of coastal areas is also needed. The Coastal Vulnerability Index (CVI) can be determined and classified using for example the Gornitz formula, or using data driven model and machine learning based on the coastal parameter data. This study demonstrates how the K-Nearest Neighbor, also known as K-NN algorithm, can be used to classify the level of vulnerability of the coastal areas. This study uses 290 points (locations) along the northern coasts of Java. The parameters that determine the coastal vulnerability are mean sea level (MSL), mean significant wave height (MSWH), mean tidal range (MTR), shoreline changes, landforms and slopes. In this study, the classification of coastal vulnerability levels is classified into 4, namely “low, moderate, high and very high”. The K-NN system uses 80% of the data for training and 20% for testing, with the value of K = 1 to 10. The test results show that the K-NN method is capable of classifying the vulnerability levels of the North Coast of Java. From the test results for values of K = 1 to K = 10, and by randomizing the training data and test data gives an average accuracy rate of 86.21% to 97.13%, with the best K value obtained at K = 2.


Classification; Coastal vulnerability; K-NN algorithm;

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Journal of Integrated and Advanced Engineering (JIAE),
Published by:
Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI):

p-ISSN: 2774-602X
e-ISSN: 2774-6038
Journal URL:
Journal DOI: 10.51662/jiae

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