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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding KOMMIT TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Kefarmasian Indonesia Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Science Annals of Mathematical Modeling
Zuherman Rustam
Universitas Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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PENDETEKSIAN JENIS DAN KELAS AROMA DENGAN MENGGUNAKAN METODE ONE-VS-ONE DAN METODE ONE-VS-REST Rustam, Zuherman; Kusumoputro, Benyamin; Widjaja, Belawati
Makara Journal of Science Vol. 7, No. 3
Publisher : UI Scholars Hub

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Abstract

Aroma classification using one-vs-one and one-vs-rest methods. Computational Intelligence used in pattern classification problem can be divided into two different parts, one based on Neural Network and the other based on Statistical Learning. The Statistical Learning discovered by Vapnik on 70-est decade. For the pattern classification, Vapnik developed hyperplane optimal separation, which is known as Support Vector Machines Method (SVM). In the beginning, SVM was designed only to solve binary classification problem, where data existing are classified into two classes. To classify data whose consist of more than two classes, the SVM method can not directly be used. There are several methods can be used to solve SVM multiclasses classification problem, they are One-vs-One Method and One-vs-Rest Method. Both of this methods are the extension of SVM binary classification, they will be discussed in this article so that we can see their performance in aroma classification process. Data of aroma used in this experiment is consisted of three classes of aroma, each of them has six classes. The division of this class is based on alcohol concentration mixed into each of those aromas. For example, for aroma A, there are six kinds of aroma A with different alcohol concentration: 0%, 15%, 25%, 30%, 45% and 75%. The performance of these methods is measured based on their ability to recognize and classify aroma, precisely and match with the right class or variety of data existed.
Comparing random forest and support vector machines for breast cancer classification Chelvian Aroef; Yuda Rivan; Zuherman Rustam
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14785

Abstract

There are more than 100 types of cancer around the world with different symptoms and difficulty in predicting itsappearance in a person due to its random and sudden attack method. However, the appearance of cancer is generally marked by the growth of some abnormal cell. Someone might be diagnosed early and quickly treated, but the cancerous cell most times hides in the body of its victim and reappear, only to kill its sufferer. One of the most common cancers is breast cancer. According to Ministry of Health, in 2018, breast cancer attacked 42 out of every 100.000 people in Indonesia with approximately 17 deaths. In addition, the Ministry recorded a yearly increase in cancer patients. Therefore, there is adequate need to be able to determine those affected by this disease. This study applied the Boruta feature selection to determine the most important features in making a machine learning model. Furthermore, the Random Forest (RF) and Support Vector Machines (SVM) were the machine learning model used, with highest accuracies of 90% and 95% respectively. From the results obtained, the SVM is a better model than random forest in terms of accuracy.
Co-Authors Ajeng Maharani Putri Aldi Purwanto Arfiani Arfiani Belawati Widjaja Benyamin Kusumoputro Bustamam, Alhadi Chelvian Aroef Devvi Sarwinda Fildzah Zhafarina Glori Stephani Saragih Glori Stephani Saragih Hamimah Alatas Ilsya Wirasati Ilsya Wirasati Jane Eva Aurelia Jane Eva Aurelia María Jesús Segovia Vargas Nadya Asanul Husna Nyoman A. Wibawa Pandelaki, Jacub Rivan Pratama Yuda Selly A. A. K Sri Hartini Sri Hartini Suryadi MT Widjaja, Belawati Wiwit Widhianto Yasirly Amalia Yuda Rivan ZUHELMI AZIZ