Prediksi Kepuasan Mahasiswa Terhadap Tingkat Pelayanan Menggunakan Algoritma C4.5 (Decision Tree) (Studi Kasus : Fakultas Teknik Universitas Hamzanwadi)

Permana, Baiq Andriska Candra

doi:10.29408/jit.v1i1.892

Cited by 2 publications

(2 citation statements)

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“…Sel yang abnormal akan menyerang bagian tubuh yang ada disekitarnya. Menurut data WHO jumlah kematian yang disebabkan oleh penyakit kangker diseluruh dunia mencapai 1,8 kematian pada tahun 2022 dan kemungkinan akan terus meningkat pada tahun 2030 [1] .…”

Section: Pendahuluanunclassified

Penerapan Python Dalam Data Mining Untuk Prediksi Kangker Paru

Permana¹,

Djamaluddin²

2023

jit

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Lung cancer is one of the groups of cancer that causes the most deaths, including in Indonesia. Many people with lung cancer do not realize that they are infected with lung cancer, which causes delays in treating this disease. For this reason, it is necessary to have a method that has a good level of accuracy in making a prediction so that later with a good level of accuracy it can be a reference for the development of an Artificial Intelligence (AI) in the world of health to detect lung cancer early. The proposed study uses the c4.5 algorithm to predict the likelihood of patients with lung cancer by providing the final result in the form of the prediction accuracy of the proposed algorithm. To carry out data mining implementation using the Python programming language by utilizing the library that has been provided to make it easier to implement machine learning. In this study the use of c4.5 was able to predict with an accuracy rate of 86%. This level of accuracy can be said to be worthy of being used as a reference to be able to predict lung cancer patients based on the symptoms that appear in the patient

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Section: Pendahuluanunclassified

Penerapan Python Dalam Data Mining Untuk Prediksi Kangker Paru

Permana¹,

Djamaluddin²

2023

jit

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“…In the process, researchers refer to several previous studies that can support research, such as (1) Yuliana and Pratomo in predicting student satisfaction with the performance of lecturers at Politeknik Tedc Bandung. This research resulted in predicting student satisfaction with lecturer performance using the C.45 algorithm very well, which is shown by the accuracy rate of 94.62% [3], (2) Hidayah and Rozi in knowing the performance of the best employees at Universitas Mercu Buana Yogyakarta with the results of the application of the C4.5 algorithm has an accuracy level that is included in the excellent classification of 85.52% [4], and (3) Permana in predicting student satisfaction with the level of service at the Faculty of Engineering, Hamzanwadi University by utilizing the C4.5 algorithm and obtained the C4.5 algorithm can predict satisfaction with the highest accuracy value of 81.07% [5]. The studies mentioned show that the C4.5 algorithm produces a decision tree with an average accuracy of more than 80% in predicting the satisfaction or performance of lecturers.…”

Section: Introductionmentioning

confidence: 99%

C4.5 Algorithm Implementation to Predict Student Satisfaction Level of Lecturer’s Performance in the Covid-19 Pandemic

Ledoh,

Andreas,

Pandie

et al. 2023

JIIKM

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Implementation of education during the emergency period of Covid-19 in Higher Education was carried out at home through online/distance learning. The lecturer is one of the key holders of success in the learning process. Lecturer performance is a main factor needed to improve education and service quality in online learning. In this study, the authors implemented the C4.5 algorithm using RapidMiner 9.10 app to predict student satisfaction with lecturer performance during the Covid-19 pandemic. The data in this study were obtained from a questionnaire distributed to active students in the Computer Science Study Program (class of 2016 - 2021) at the University of Nusa Cendana with 942 records. The attributes used in this study were the lecturer's age, gender, suitability of learning media (SLM), and the competencies of Pedagogic Competence (PeC), Professional Competence (PrC), Personal Competence (PsC), and social competence (SC), with the level of student satisfaction as the target class divided into two, namely Satisfied and Dissatisfied. The dataset is processed using RapidMiner and produces 11 decision rules which show that the attribute PeC has the most significant influence on the level of student satisfaction with lecturer performance during the Covid-19 pandemic and the test results of the decision tree model using cross-validation. The test results show that the C4.5 algorithm has a good performance in predicting levels of student satisfaction with an accuracy rate of 94.8%, precision for the prediction class Dissatisfied and Satisfied of 92.23 % and 95.52%, and recall of the actual Dissatisfied and Satisfied classes of 85.2% and 97.77%.

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Prediksi Kepuasan Mahasiswa Terhadap Tingkat Pelayanan Menggunakan Algoritma C4.5 (Decision Tree) (Studi Kasus : Fakultas Teknik Universitas Hamzanwadi)

Cited by 2 publications

References 0 publications

Penerapan Python Dalam Data Mining Untuk Prediksi Kangker Paru

Penerapan Python Dalam Data Mining Untuk Prediksi Kangker Paru

C4.5 Algorithm Implementation to Predict Student Satisfaction Level of Lecturer’s Performance in the Covid-19 Pandemic

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