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Catur Supriyanto
Universitas Dian Nuswantoro
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Journal : Journal of Intelligent Systems

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Penanganan Fitur Kontinyu dengan Feature Discretization Berbasis Expectation Maximization Clustering untuk Klasifikasi Spam Email Menggunakan Algoritma ID3 Safuan, .; Wahono, Romi Satria; Supriyanto, Catur
Journal of Intelligent Systems Vol 1, No 2 (2015)
Publisher : IlmuKomputer.Com

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (908.039 KB)

Abstract

Pemanfaatan jaringan internet saat ini berkembang begitu pesatnya, salah satunya adalah pengiriman surat elektronik atau email. Akhir-akhir ini ramai diperbincangkan adanya spam email. Spam email adalah email yang tidak diminta dan tidak diinginkan dari orang asing yang dikirim dalam jumlah besar ke mailing list, biasanya beberapa dengan sifat komersial. Adanya spam ini mengurangi produktivitas karyawan karena harus meluangkan waktu untuk menghapus pesan spam. Untuk mengatasi permasalahan tersebut dibutuhkan sebuah filter email yang akan mendeteksi keberadaan spam sehingga tidak dimunculkan pada inbox mail. Banyak peneliti yang mencoba untuk membuat filter email dengan berbagai macam metode, tetapi belum ada yang menghasilkan akurasi maksimal. Pada penelitian ini akan dilakukan klasifikasi dengan menggunakan algoritma Decision Tree Iterative Dicotomizer 3 (ID3) karena ID3 merupakan algoritma yang paling banyak digunakan di pohon keputusan, terkenal dengan kecepatan tinggi dalam klasifikasi, kemampuan belajar yang kuat dan konstruksi mudah. Tetapi ID3 tidak dapat menangani fitur kontinyu sehingga proses klasifikasi tidak bisa dilakukan. Pada penelitian ini,  feature discretization berbasis Expectation Maximization (EM) Clustering digunakan  untuk merubah fitur kontinyu menjadi fitur diskrit, sehingga proses klasifikasi spam email bisa dilakukan. Hasil eksperimen menunjukkan ID3 dapat melakukan klasifikasi spam email dengan akurasi 91,96% jika menggunakan data training 90%. Terjadi peningkatan sebesar 28,05% dibandingkan dengan klasifikasi ID3 menggunakan binning.
Penerapan Metode Average Gain, Threshold Pruning dan Cost Complexity Pruning Untuk Split Atribut Pada Algoritma C4.5 Rahayu, Erna Sri; Wahono, Romi Satria; Supriyanto, Catur
Journal of Intelligent Systems Vol 1, No 2 (2015)
Publisher : IlmuKomputer.Com

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (695.078 KB)

Abstract

C4.5 is a supervised learning classifier to establish a Decision Tree of data. Split attribute is main process in the formation of a decision tree in C4.5. Split attribute in C4.5 can not be overcome in any misclassification cost split so the effect on the performance of the classifier. After the split attributes, the next process is pruning. Pruning is process to cut or eliminate some of unnecessary branches. Branch or node that is not needed can cause the size of Decision Tree to be very large and it is called over- fitting. Over- fitting is state of the art for this time. Methods for split attributes are Gini Index, Information Gain, Gain Ratio and Average Gain which proposed by Mitchell. Average Gain not only overcome the weakness in the Information Gain but also help to solve the problems of Gain Ratio. Attribute split method which proposed in this research is use average gain value multiplied by the difference of misclassification. While the technique of pruning is done by combining threshold pruning and cost complexity pruning. In this research, testing the proposed method will be applied to datasets and then the results of performance will be compared with results split method performance attributes using the Gini Index, Information Gain and Gain Ratio. The selecting method of split attributes using average gain that multiplied by the difference of misclassification can improve the performance of classifiying C4.5. This is demonstrated through the Friedman test that the proposed split method attributes, combined with threshold pruning and cost complexity pruning have accuracy ratings in rank 1. A Decision Tree formed by the proposed method are smaller. Keyword: Decision Tree, C4.5, split attribute, pruning, over-fitting, gain, average gain.
Co-Authors . Safuan, . Abdollah, Mohd. Faizal Abdul Rachman Syam Tuasikal Abu Salam Ahmed, Foez Al Fahreza, Muhammad Daffa Alamsyah, Sayyidul Aulia Amalia Amalia Amalia, Syafira Rosa Amiral, Afinzaki Andreas Wilson Setiawan Antony Eka Aditya, Antony Eka Astuti, Yani Parti Bayu Satria, Zaky Indra Dyan Yuliana Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erna Sri Rahayu, Erna Sri Fauzi Adi Rafrastara Gede Doddy Tisna MS Guruh Fajar Shidik Hapsari Peni Heru Agus Santoso HIMAWAN WISMANADI Junta Zeniarja Lin, wei Jhe Majid, Nur Kholis Marjuni, Aris Mohammad Reza Maulana, Mohammad Reza Muchamad Arif Al Ardha Muljono Muljono Mulyanto, Edy Nining Widyah Kusnanik Nurhasan Nurhasan, Nurhasan Octaviani, Dhita Aulia Oman Somantri Paramita, Cinantya Pitaloka, Tia Amika Purwanto Purwanto Purwanto, Purwanto Rahadian, Arief Rizka Safriyani Rizki, Ainun Zulfikar Romi Satria Wahono Rusdiawan, Afif Rustam, Suhardi Safar, Noor Zuraidin Mohd Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja Wahyu Adi Nugroho Wakhidah, Elfa Wahyu Yang, Chung Bing YUSUF FUAD