Classification Algorithm for Edible Mushroom Identification

research
  • 11 May
  • 2018

Classification Algorithm for Edible Mushroom Identification

Abstract:


Indonesia has 13% species of mushroom in the world but there is a very limited study on determining edible or poisonous mushroom. Classification process of poisonous mushroom or not will be easily conducted by learning machine using mining data as one of the ways to extract computer assisted knowledge. Currently, there are three comparisons of the best classification algorithms in data mining, namely: Decision Tree (C4.5), NaïveBayes and Support Vector Machine (SVM). The study method used is experiment with assisted tool of WEKA that has been testing in the comparison of the three algorithms. To conduct the testing, it is used the mushroom data of Agaricus and Lepiota family. The mushroom data were taken from The Audubon Society Field Guide to North American Mushrooms, in UCI machine learning repository. Results of the testing indicate that the C4.5 algorithm has the same accuracy level to the SVM by 100% however, from the speed aspect, process of the C4.5 algorithm is faster than the SVM.

REFERENSI

[1]  Rial Adity and Setia Hadi Purwono, Jamur – Info Lengkap dan Kiat Sukses Agribisnis. Depok, Indonesia/West Java: Agriflo, 2012. 

[2]  Kristianus Sunarjon Dasa, "Pemanfaatan bagas sebagai campuran media pertumbuhan jamur tiram putih," vol. 11, pp. 195-201, 2011.

[3]  Anna Rahkmawati, "Keanekaragaman jamur," Universitas Negeri Yogyakarta, Yogyakarta, Tech. rep. 2010. [Online]. staffnew.uny.ac.id/upload/132296143/pengabdian/ppm-2010-kehati.pdf

[4]  Bayu Mahardika Putra, "Klasifikasi Jamur ke Dalam Kelas Dapat Dikonsumsi Atau Beracun Menggunakan Algoritma VFI 5 (Studi Kasus: Famili Agaricus dan Lepiota)," IPB, Bogor, Laporan Akhir 2008.

[5]  Galieh Adi and Surya Pradana, "Identifikasi jamur beracun pada jenis jamur famili agaricus dan lepiota berdasarkan klasifikasi," Univeritas Nusantara PGRI Kediri, Kediri, Laporan Akhir 2016.

[6]  Sang Jun Lee and Keng Siau, "A review of data mining techniques," Industrial Management 

[7]  M. Adib Alkaromi, "Komparasi Algoritma Klasifikasi untuk dataset iris dengan rapid miner," ICTech, 2015.

[8]  Diego R. Amancio et al., "A systematic comparison of supervised classifiers," CoRR, vol. abs/1311.0, 2013. [Online]. http://arxiv.org/abs/1311.0202

[9]  Dwi Widiastuti, "Analisa Perbandingan Algoritma SVM, Naïve Bayes, dan Decission Tree dalam Mengklasifikasikan Serangan (Attack) pada Sistem Pendeteksi Intrusi," unpublished 2007. 

[10] Xindong Wu et al., "Top 10 algorithms in data mining," Knowledge and Information Systems, vol. 14, pp. 1-37, 2008. [Online].http://dx.doi.org/10.1007/s10115-007-0114-2 

[11] Wenefirda Tulit Ina, "Klasifikasi Data Rekam Medis Berdasarkan Kode Penyakit Internasional Menggunakan Algoritma C4.5," Jurnal Media Elektro, vol. 1, pp. 105-110, 2013.

[12] Ni Wayan Sumartini Saraswati, "Naïve Bayes Classifier Dan Support Vector Machines Untuk Sentiment Analysis," Seminar Nasional Sistem Informasi Indonesia, pp. 587-591, 2013.