An overview of Machine Learning Technologies and their use in E-learning

Farhat, Ramzi; Mourali, Yosra; Jemni, Mohamed; Ezzedine, Houcine

doi:10.1109/octa49274.2020.9151758

Cited by 22 publications

(9 citation statements)

References 24 publications

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“…Random Forest yang berarti ialah menggabungkanbeberapa model (pohon keputusan) bekerja dengan cara menghasilkan banyak pohon keputusan secara acak dan menggabungkan hasil prediksi dari masing-masing pohon [6] [7], keputusan tersebut untuk membuat prediksi siswa terbaik yang lebih akurat [8] [9][10] [11] [6]. Diharap dengan adanya penelitian ini dapat menjadi jawaban atas klasifikasi siswa terbaik [12][13] [14].…”

Section: Pendahuluanunclassified

Best Student Classification using Ensemble Random Forest Method

Mrg,

Hasibuan

2024

SISTEMASI

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Education at Madrasah Aliyah Negeri 1 Medan considers religious values, ethics, leadership, and participation in extracurricular activities as an integral part of student character building. Therefore, it is necessary to develop a classification system that integrates these various aspects to ensure that the best students not only excel in academic exams but also have strong social, leadership, moral and extracurricular abilities. The purpose of this research is to implement the Random Forest Ensemble method in predicting the best students of MAN 1 Medan and build a system in predicting the best students of MAN 1 Medan using the Random Forest Ensemble method. The data used is 550 divided into 385 data as Training data and 165 Testing data. In the implementation of Random Forest with three decision trees formed from entropy calculations on 385 training data, followed by testing using 10 testing data from a total of 165 existing data, the results show that the model predicts 8 data as class 1 (best students) and 2 data as class 0 (normal students) from a total of 10 testing data. From the test results using 385 training data and 165 testing data, the Random Forest model predicted 70 data as the best students (class 1) and 95 data as normal students (class 0) with high precision for both classes (0.94 for class 0 and 0.99 for class 1), as well as high recall for both classes (0.92 for class 0 and 0.99 for class 1) The overall accuracy reached 0.96, confirming the model's ability to classify the data well overall.

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Section: Pendahuluanunclassified

Best Student Classification using Ensemble Random Forest Method

Mrg,

Hasibuan

2024

SISTEMASI

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“…The classification of data is called clustering in machine learning. Some examples of clustering algorithms include K-Means, Farthest-First Maximization-Expectation (EM), and others (Farhat, et al, 2020;Naranjo-Torres, et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color

Putra¹,

Novelan²,

Rizki³

2022

JAETS

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In this research, the process of applying the K-Nearst Neighbor (KNN) method will be carried out, which is a classification method for a collection of data based on the majority of categories and the goal is to classify new objects based on attributes and sample samples from training data. So that the desired output target is close to the accuracy in conducting learning testing. The results of the test of the K-Nearest Neighbor method. It can be seen that from the K values ??of 1 to 10, the percentage of the results of the analysis of the K-NN method is higher than the results of the analysis of the K-NN method. And from the K value that has been tested, the K 2 value and the K 9 value have the largest percentage so that the accuracy is also more precise. As for the results of testing the K-Nearest Neighbor method in data classification. As for the author's test using a variation of the K value of K-Nearest Neighbor 3,4,5,6,7,8,9. Has a very good percentage of accuracy compared to only K-NN. The test results show the K-Nearest Neighbor method in data classification has a good percentage accuracy when using random data. The percentage of variation in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9 has a percentage of 100%.

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“…The answer is achieved via the use of a machine learning (ML) method. Previously, people developed complex algorithms after extensive data analytics, and machines used the analyzed data to find answers [8]. Today, researchers have developed deep learning (DL), a subtype of machine learning (ML), which employs a deeper network.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

Wong

Juwono

Reine³

2022

ELKHA

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The goal of 6G communication networks requires higher transmission speeds, tremendous data processing, and low-latency communication. Orthogonal frequency-division multiplexing (OFDM), which is widely utilized in 5G communication systems, may be a viable alternative for 6G. It significantly reduces inter symbol interference (ISI) in the frequency-selective fading environment. Channel estimation is critical in OFDM to optimize system performance. Deep learning has been employed as an appealing alternative for channel estimation and signal detection in OFDM-based communication systems due to its better potential for feature learning and representation. In this study, we examine the deep neural network (DNN) layers created from long-short term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. We investigate the performance of the system under various conditions. The simulation results show that the signal bit error (SER) is equivalent to and better than that of the minimum mean squared error (MMSE) and least square (LS) methods.

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An overview of Machine Learning Technologies and their use in E-learning

Cited by 22 publications

References 24 publications

Best Student Classification using Ensemble Random Forest Method

Best Student Classification using Ensemble Random Forest Method

Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color

Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

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