Garuda - Garba Rujukan Digital

Felix Indra Kurniadi

Tanri Abeng University

Author-ID : 3793823

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document

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Articles

Perbandingan Local Binary Pattern untuk Klasifikasi Sel Darah Putih Felix Indra Kurniadi
Ultimatics : Jurnal Teknik Informatika Vol 9 No 2 (2017): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

In recent year, a lot of researches try to overcome problem in recognition and classify white blood cells to help hematologists diagnose white blood cells disease such blood cancer, leukemia and AIDS. This paper compares several methods Local Binary Pattern such as Local Binary Pattern Uniform, Local Binary Pattern Rotation Invariant and Local Binary Pattern Rotation Invariant Uniform to classify five types of white blood cells using two classifier: Support Vector Machine and K-Nearest Neighbour. Index Terms—LBP, LBP-U, LBP-RI, LBP-RIU, white blood cells

Penggunaan Heaviside Activation Function pada Regresi Linear untuk Klasifikasi Diabetes Felix Indra Kurniadi; Vinnia Kemala Putri
Ultimatics : Jurnal Teknik Informatika Vol 10 No 1 (2018): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Diabetes is one of the diseases that rapidly increase in the world. One of the most used dataset for diabetes is Pima indian dataset. Pima indian have 8 features such as pregnancies, glucose, blood pressure, insulin, BMI, diabetes pedigree function and age. In this research we are comparing between Linear Regression using Heaviside Activation Function and Logistic Regression. Logistic regression gives better result compare linear regression using Heaviside Activation Function. Index Terms—Diabetes, Regresi, Heaviside Activation Function, Logistic Regression

Klasifikasi Diabetes Menggunakan Model Pembelajaran Ensemble Blending Vinnia Kemala Putri; Felix Indra Kurniadi
Ultimatics : Jurnal Teknik Informatika Vol 10 No 1 (2018): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Diabetes mellitus is one of the deadliest disease and it is increasing in occurrence through the world. This can be prevented by conducting early diagnosis and treatment. However, in developing countries, less than half of people with diabetes are diagnosed correctly which lead to lose of human lives. In this Big Data era, medical databases have enormous quantities of data about their patients. But this medical data may contain noise and a lot of useless information which may mislead the expert in making a decision for medical diagnosis. Data mining is a technique to that is very effective for medical applications for identifying patterns and extracting useful information for databases. This paper proposed a data mining approach using an ensemble blending method to tackle a diabetes prediction problem in Pima Indian Diabetes Dataset. We proposed a blending ensemble classifier approach using a combination of Decision Tree and Logistic Regression as base classifiers, and Support Vector Machine as a top blender classifier. Our approach reached accuracy of 81% and F1-score of 0.81 proves to be higher when compared with basic classifier without combination. Index Terms—diabetes, ensemble, data mining

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