Analisis Penerapan Metode Ensembled Learning Decision Tree Pada Klasifikasi Virus Hepatitis C

Charisma, Rifqi Alfinnur; Pamungkas, Sofiyudin; Saputra, Rifqi Akmal; Ramadhan, Nur Ghaniaviyanto; Adhinata, Faisal Dharma

doi:10.47065/josyc.v3i4.2064

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Determination of the attribute as the root by calculating the value of entropy and gain. The Entropy formula for calculating each attribute can be seen in Formula 1 [7].…”

Section: Analysis Methodsmentioning

confidence: 99%

Comparison of C4.5 and Naive Bayes Algorithm Methods in Prediction of Student Graduation on Time (Case Study: Information Systems Study Program)

Dikriani,

Indal Karim

2023

JournalofDinda.DataScience.IT.DataAnalytics

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In tertiary institutions, students become one of the important parameters in the evaluation of study program organizers. Prediction of student graduation is a special concern to know, early identification for students is needed as an important action. Information processing to predict student graduation is by implementing data mining. The implementation of data mining can be applied if a university, especially a study program, does not yet have an early classification in achieving student graduation on time. The ITTP Information System study program is one of the study programs that does not have an early identification of student graduation on time. Determination of graduation for SI ITTP Study Program students includes GPA, TOEFL scores, and total credits. The purpose of this research is to find out which attributes have the most influence in predicting graduation of ITTP IS Study Program students. The method used in this prediction is by using the classification of the C4.5 Algorithm and Naïve Bayes. The classification is used to determine which attributes have an effect on predicting student graduation on time and to compare the two classification methods. The results obtained are the training set size 70% which has the best accuracy when compared to other training set sizes. Comparing the accuracy between the two methods, it is known that the C4.5 algorithm has good accuracy when training set size is 70% and Naïve Bayes has higher accuracy when training set size is 75%. Decision tree C4.5 interprets that the most influential attribute is the GPA as the root of the decision tree to predict student graduation on time. The research is expected to be used as a reference for the ITTP IS Study Program in formulating student graduation policies on time and as a reference for further researchers in predicting in the same field.

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“…Determination of the attribute as the root by calculating the value of entropy and gain. The Entropy formula for calculating each attribute can be seen in Formula 1 [7].…”

Section: Analysis Methodsmentioning

confidence: 99%

Comparison of C4.5 and Naive Bayes Algorithm Methods in Prediction of Student Graduation on Time (Case Study: Information Systems Study Program)

Dikriani,

Indal Karim

2023

JournalofDinda.DataScience.IT.DataAnalytics

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“…It divides the data based on the features that provide the most information gain or other separation criteria. Decision trees are easy to interpret and can handle both numerical and categorical features [13], [14].…”

Section: Classification Modelmentioning

confidence: 99%

Classification of Drug Types using Decision Tree Algorithm

Putri,

Faz,

Hafizhulloh

2023

JournalofDinda.DataScience.IT.DataAnalytics

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The accurate classification of drugs plays a crucial role in various areas of pharmaceutical research and development. In recent years, machine learning techniques have emerged as powerful tools for drug classification tasks. This paper presents a study on drug classification using machine learning techniques implemented in Python. The objective of this research is to explore the effectiveness of different machine learning algorithms in accurately classifying drugs based on their molecular properties and characteristics. The dataset used in this study consists of a diverse collection of drug compounds with annotated class labels. Several popular machine learning algorithms, including decision trees are implemented and evaluated using Python's extensive libraries such as scikit-learn. The dataset is pre-processed to handle missing values, normalize features, and reduce dimensionality using appropriate techniques. Experimental results demonstrate the performance of each algorithm in terms of accuracy, precision, recall, and F1-score. The findings of this study highlight the potential of machine learning techniques in accurately classifying drugs and provide valuable insights into the selection and optimization of algorithms for drug classification tasks. The Python implementation serves as a practical guide for researchers and practitioners interested in applying machine learning for drug classification purposes.

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“…Metode decision tree adalah classifier yang mempartisi ruang secara rekursif. Simpul yang terdiri dari pohon berakar merupakan pohon keputusan [23]. Formula decision tree yang digunakan yaitu entropy (7) [24].…”

Section: Algoritma Prediksiunclassified

Pendekatan Algoritma Tree dalam Prediksi Populasi pada Smart Poultry

Nugroho¹,

Ramadhan²,

Wibowo³

et al. 2022

bits

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Intelligent systems for monitoring poultry in kennels are experiencing an increasing trend in several studies. Monitoring poultry is very important in the cage so that you can find out the chickens' condition and environment in the cage. Conditions that can be monitored include the weight of the chickens, whether or not there is enough water in a day, CO2 levels in the cages, air temperature, and humidity in the cages. Several studies have been conducted studies on monitoring poultry cages using IoT-based sensors. However, people have yet to predict the poultry population for tomorrow. So this study aims to predict the number of poultry populations in kennels based on related parameters. The prediction method used in this research is a decision tree and Support Vector Machine (SVM) to see which prediction method is better. The results evaluation techniques used in this study are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2. The experimental results show that using the decision tree method, and the results are MSE 61987.202, RMSE 248.972, MAE 85.086, and R2 0.969. Overall the results of the decision tree method are superior to SVM.

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Analisis Penerapan Metode Ensembled Learning Decision Tree Pada Klasifikasi Virus Hepatitis C

Cited by 3 publications

References 6 publications

Comparison of C4.5 and Naive Bayes Algorithm Methods in Prediction of Student Graduation on Time (Case Study: Information Systems Study Program)

Comparison of C4.5 and Naive Bayes Algorithm Methods in Prediction of Student Graduation on Time (Case Study: Information Systems Study Program)

Classification of Drug Types using Decision Tree Algorithm

Pendekatan Algoritma Tree dalam Prediksi Populasi pada Smart Poultry

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