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All Journal Jurnal Tika Infolitika Journal of Data Science
Vikki, Zakial
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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An Implementation of Hybrid CNN-XGBoost Method for Leukemia Detection Problem Hidayat, Taufiq; Hadinata, Edrian; Damanik, Irfan Sudahri; Vikki, Zakial; Irvanizam, Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.87

Abstract

Leukemia is a blood cancer in which blood cells become malignant and uncontrolled. It can cause damage to the function of the body's organs. Several machine learning methods have been used to automatically detect biomedical images, including blood cell images. In this study, we utilized a hybrid machine learning method, called a hybrid Convolutional Neural Network-eXtreme Gradient Boosting (CNN-XGBoost) method to detect leukemia in blood cells. The hybrid method combines two machine learning methods. We use CNN as the basic classifier and XGBoost as the main classification method. The aim of this methodology was to assess whether incorporating the basic classification method would lead to an enhancement in the performance of the main classification model. The experimental findings demonstrated that the utilization of XGBoost as the main classifier led to a marginal increase in accuracy, elevating it from 85.32% to 85.43% compared to the basic CNN classification. This research highlights the potential of hybrid machine learning approaches in biomedical image analysis and their role in advancing the early diagnosis of leukemia and potentially other medical conditions.
Penerapan Metode Weighted Product Melalui Pendekatan Logika Fuzzy Pada Kasus Pemilihan Ketua Osim Man 7 Bireuen vikki, Zakial
Jurnal Tika Vol 7 No 2 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v7i2.1265

Abstract

The use of computer technology has penetrated into various fields in completing work. Work that requires high accuracy, is carried out repeatedly and requires fast processing time is no longer appropriate for humans to do using conventional methods. Computer involvement has become an obligation in completing the work. One of the cases that require computer involvement is the case of decision making in an organization or company, or in computer science terms it is often called a decision support system. Many decision support models have been developed to improve the model's performance in performing calculations. In this study, a decision support model was also developed to choose the chairman of OSIM MAN 7 Bireuen using the Weighted Product (WP) method through a fuzzy logic approach as the initial weighting of criteria and alternatives. The results obtained are the calculation of the WP model more optimally with the help of weighting through a fuzzy logic approach. The final result of this research is the existence of a computerized system that is able to calculate alternative recommendations for the chairman of OSIM MAN 7 Bireuen, totaling 12 alternatives with 4 criteria
Klasifikasi Tren Penyakit Pasien Bedasarkan Dianogsa Icd-10 Di Rumah Sakit Umum Cut Meutia Menggunakan Metode C4.5 Vikki, Zakial; Yuswandi, Yuswandi
Jurnal Tika Vol 7 No 3 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v7i3.1441

Abstract

The rapid development of information technology has impacted many people getting more and more data every day, even excessive. so that the use of the data is not optimal. Likewise patient medical record data at Cut Meutia General Hospital in North Aceh by serving patients every day. The large number of patients handled automatically makes this hospital accommodate a lot of patient medical record data with various types of diseases so that a method is needed to pattern the patient's disease data. For this reason, this study aims to find trends in patient disease at North Aceh Cut Meutia General Hospital based on ICD-10 diagnostics using data mining techniques by analyzing the C4.5 method where the C4.5 method creates a classification model from a large data set so as to produce patterns the new data pattern forms a decision tree (Decision Tree) useful for exploring data, finding relationships between a number of input variables with a target variable. The data used in this study were obtained from the North Aceh Cut Meutia General Hospital for 2020-2021 based on 4 variable data, namely age, gender, address and ICD-10 diagnosis. ICD-10 is a diagnostic classification with international standards that is compiled based on a category system and reports in disease units according to criteria agreed upon by international experts. ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th revision)
Co-Authors Fachry Abda El Rahman Hadinata, Edrian Irfan Sudahri Damanik Irvanizam, Irvanizam Yuswandi Yuswandi, Yuswandi