• Gunawan Rudi Cahyono Politeknik Negeri Banjarmasin
  • Nurmahaludin Nurmahaludin
  • Joni Riadi Politeknik Negeri Banjarmasin
  • Kun Nursyaiful Priyo Pamungkas Politeknik Negeri Banjarmasin
Keywords: Image, Haar-Like Feature, Adaboost


Efforts to monitor pest populations at a rice plant site are important because based on
information on the type and number of pests attacking rice crops, a suggestion of
controlling can be developed early so that potential losses resulting from pests can be
suppressed. Therefore a process is needed to identify and classify the pests that attack
and harm the rice plants. In this research will be designed rice pest classification using
image processing where in its processing using image from stem borer (moth). Feature
extraction of positive samples (pest image of moths) and negative samples (non-pest
image) using Haar Like Feature. While in the process of classification into a class of
moths and not moths using Adaboost algorithm by applying cascade classifier to get a
strong characteristic. The observed variable is the error rate generated in the process of
pest classification of moth and non pest. From the test result on positive samples obtained
identification rate of true positive (TP) = 90%, while false positive (FP) = 20%. For
negative sample test (non pest image) obtained true negative (TN) = 80%, while false
negative (FN) = 20%. From the test result of positive sample and negative samples
obtained the accuracy of pest moth identification results of 85%


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Cahyono, R.G., & Nurmahaludin, 2015, Identifikasi Hama Penggerek Batang
Pada Tanaman Padi Menggunakan Sensor TCS3200, Seminar Nasional
Hasil Penerapan Penelitian dan Pengabdian Kepada Masyarakat,
Universitas Tarumanegara
Gorunescu, F. (2011). Data Mining Concepts, Models and Techniques. Verlag
Berlin Heidelberg: Springer.
Jillela, R., & Ross, A., 2015, Segmenting Iris Images in the Visible Spectrum with
Applications in Mobile Biometrics, Pattern Recognition Letters, Vol. 57,
Lee, H., & Chen, Y., 2015, Image Based Computer Aided Diagnosis System for
Cancer Detection, Journal of Expert Systems With Application, Vol. 42
Li, P., etc., 2015, A Cloud Image Detection Method Based on SVM, Journal of
Neurocomputing, Vol. 169, pp : 34-42
Obaidullah, S., etc., 2015, Numeral Script Identification from Handwritten
Document Images, Journal of Procedia Computer Science, Vol. 54, pp :
585 - 594
Qing, Y., etc., 2012, An Insect Imaging System to Automate Rice Light-Trap Pest
Identification, Journal of Integrative Agriculture, Vol. 6, pp : 978-985
Qing, Y., etc., 2014, Automated Counting of Rice Planthoppers in Paddy Fields
Based on Image Processing, Journal of Integrative Agriculture, Vol.13