Implementation of boosting on the C5.0 algorithm in the health development index data

Fajri, Mohammad; Ramadhan, Ashari; Hidayatullah, Rahmat

doi:10.22487/25411969.2021.v10.i1.15466

Cited by 3 publications

(2 citation statements)

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“…The results showed that the model's accuracy increased with 60 iterations, with a relatively small error in the 10th iteration. The final accuracy, sensitivity, and specificity obtained were 97.09%, 96.72%, and 97.62%, respectively (Fajri et al, 2021). The difference between the author's research and previous studies is a different dataset.…”

Section: Introductionmentioning

confidence: 73%

Performance Comparison of Mushroom Type Classification Based on Multi-Scenario Dataset Using Decision Tree C4.5 and C5.0

2022

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Indonesia has a tropical climate that supports mushroom growth. Mushroom classification into poisonous and non-poisonous mushrooms. Identification of the type of mushroom is vital because mushrooms, especially poisonous mushrooms, risk causing potential hazards to humans, such as causing serious illness and even death. This study aimed to identify the fungus type using a computational approach, namely the Decision Tree C4.5 and C5.0 Algorithms. This research contributes to using multi-scenario datasets and comparing the performance of the C4.5 and C5.0 decision tree algorithms. The dataset used is a fungal classification dataset obtained from kaggle.com. The method stages in this research are literature study, data collection, and data preprocessing, which includes a data cleaning process and a partitioning process for multi-scenario datasets. Afterwards, the Decision Tree Algorithms C4.5 and C5.0 were implemented using the sci-kit-learn library. The last step is to do a performance comparison using the confusion matrix. The results showed that identifying poisonous mushrooms using the Decision Tree C5.0 Algorithm obtained an accuracy of 97.05% for scenario 1, 97.00% for scenario 2, and 97.11% for scenario 3. At the same time, the Decision Tre C4.5 algorithm yielded an accuracy. by 96.92% for scenario 1, 96.90% for scenario 2, and 97.05% for scenario 3. Based on the comparison of the performance of the classification results, we conclude that the Decision Tree C5.0 algorithm in scenario 3 has the highest accuracy for fungal identification poisonous.

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Section: Introductionmentioning

confidence: 73%

Performance Comparison of Mushroom Type Classification Based on Multi-Scenario Dataset Using Decision Tree C4.5 and C5.0

2022

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“…In this study, the C5.0 Algorithm Method will be used with the application of this method [9], [10], the author can determine how satisfied or dissatisfied students are in carrying out the online teaching and learning process during the Covid 19 pandemic [11]. That way the school will take a policy or action in the offline or online learning process by dividing the time for class entry at the Simalungun University Foundation Tourism Vocational School.…”

Section: Introductionmentioning

confidence: 99%

Student Satisfaction Level Analysis Of Online Learning During Pandemic Covid 19 Using C5.0 Algorithm

James Hasudungan Sihombing

2022

JAIEA

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The Simalungun University Foundation Tourism Vocational School is a private school located in Pematangsiantar. At this time there are problems in the learning process. And currently the data collection used by researchers in obtaining student satisfaction data is by sampling. In this study, data were obtained from giving questionnaires to students of the Simalungun University Foundation Tourism Vocational School which were categorized using five variables, namely teaching methods, learning media, communication, teaching materials/modules, and learning duration. Data Mining using the C5.0 algorithm is proven in analyzing satisfaction in the teaching and learning process. Research result able to find out the results of the analysis of the level of student satisfaction with online learning and find the highest score, can help the Tourism Vocational School of the Simalungun University Pematangsiantar Foundation in optimizing online learning today.

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Bootstrap aggregating on logistic regression to labor force participation rate

Fajri¹,

Rais²,

Rizaldi³

2023

AIP Conference Proceedings

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Implementation of boosting on the C5.0 algorithm in the health development index data

Cited by 3 publications

References 0 publications

Performance Comparison of Mushroom Type Classification Based on Multi-Scenario Dataset Using Decision Tree C4.5 and C5.0

Performance Comparison of Mushroom Type Classification Based on Multi-Scenario Dataset Using Decision Tree C4.5 and C5.0

Student Satisfaction Level Analysis Of Online Learning During Pandemic Covid 19 Using C5.0 Algorithm

Bootstrap aggregating on logistic regression to labor force participation rate

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