Robust Facial Area Aquisition Based on Decision Tree

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

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KLASIFIKASI SELEKSI ATRIBUT PADA SERANGAN SPAM MENGGUNAKAN METODE ALGORITMA DECISION TREE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v14i2.874 ◽

2018 ◽

Vol 14 (2) ◽

pp. 145

Author(s):

Aji Sudibyo ◽

Taufik Asra ◽

Bakhtiar Rifai

Keyword(s):

Decision Tree

internet sangat biasa untuk sekarang ini, penggunaaan internetnya tak lepas dari penggunaan email, salah satu ancaman yang terjadi ketika menggunakan email adalah spam, spam merupakan pesan atau email yang tidak diinginkan oleh penerimanya dan dikirimkan secara massa. Penelitian tentang serangan spam didapat dari dataset spam sebanyak 4601 record yang terdiri 1813 record dianggap spam dan 278 data bukan spam dengan atribut awal sebanyak 57 atribute dengan 1 atribute class, pada ekperimen yang dilakukan menggunakan select attribute dengan decision tree menjadi 15 atribute dengan 1 atribute class dilakukan 3 percobaan pengujian dengan persentase atribute 30%, 50% dan 70% select atribute didapat hasil fitur select atribute sebesar 70% didapat hasil lebih baik dari 30% ataupun 50% dengan nilai accuracy sebesar 92.469%.

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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