Perbandingan Kinerja Algoritma Klasifikasi  Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Nalatissifa, Hiya; Gata, Windu; Diantika, Sri; Nisa, Khoirun

doi:10.32493/informatika.v5i4.7575

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Research (Susilowati et al, 2015) explained the meaning of SVM as a learning machine method that works with the principle of Structural Risk Minimization (SRM), aiming to find the best hyperplane that can separate two classes in input space. The following is equation of the Support Vector Machine is as follows (Nalatissifa et al, 2021). 𝑦 𝑖 𝐾(𝑥, 𝑥 𝑖 ) + 𝑏 (3) Feature space is a conversion method from input (dot product) that can only separate linear data into high-dimensional forms (feature space).…”

Section: Methodsmentioning

confidence: 99%

Optimization of Support Vector Machine Algorithm Using Stunting Data Classification

2023

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Several studies from Indonesia reveal that malnutrition and stunting are still severe concerns to be addressed in the future. The complexity of the problem of stunting or nutritional status requires the responsibility of all parties, including science and technology. The issue of monitoring and data collection related to stunting or the nutritional status of children in Indonesia, especially Medan City, North Sumatra Province, is an essential factor in determining the calculations carried out by each Community Health Center with many attributes. Currently, the Support Vector Machine method is a solution to increase government intervention's effectiveness in classifying malnutrition and stunting. However, the Support Vector Machine algorithm still needs to improve, namely the difficulty of selecting the right and optimal features for the attribute weights, causing a low prediction accuracy. Therefore, researchers aim to optimize the Support Vector Machine Algorithm with Particle Swarm Optimization using Linear, Polynomial, Sigmoid, and Radial Basic Function kernels. The results were obtained from research utilizing nutritional status data, that performance in improving the Support Vector Machine algorithm based on Particle Swarm Optimization using four kernel tests, namely Linear, Polynomial, Sigmoid, and Radial Basic Function obtained different results, not all kernels in this study can improve accuracy well. The best performance is using the Radial Basic Function kernel with an Accuracy value of 78%, Precision of 89%, Recall of 66%, and F1-Score of 72%, so it is feasible for accurate information regarding the classification of nutritional status.

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Section: Methodsmentioning

confidence: 99%

Optimization of Support Vector Machine Algorithm Using Stunting Data Classification

2023

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“…a) Naive Bayes One of the simplest probabilistic classification techniques based on the Bayes theorem, Naive Bayes is a popular classification technique and one of the top 10 data mining algorithms [13]. To make calculations simpler, Naive Bayes assumes that the impact on attribute values for specific classes is independent of the impact on other attribute values [14]. b) Support vector machine A guided learning technique for classification is the support vector machine.…”

Section: Literature Reviewmentioning

confidence: 99%

Opinion mining toward work from office policies on post-pandemic covid-19 by using supervised learning

Wicaksono,

Imam Yuadi,

Ira Puspitasari

2024

tekno

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Post-pandemic COVID-19, many companies have re-implemented work from office policies for their employees. However, the policy has been controversial among social media activists, especially in Twitter. The sentiment arose according to them, during the pandemic COVID-19 they believe that working from home has many advantages over working in an office. The emergence of 'work from office' sentiments is an interesting target for opinion-mining research. Opinion mining or sentiment analysis is a general research area of data mining that helps to explore and analyze existing views and opinions to obtain useful information. The analysis process involves the use of machine learning with several supporting algorithms. This study used four classification algorithm models of supervised learning, including naive Bayes, support vector machines, k-nearest neighbors, and random forests. The selection of those algorithms also aims to find out which model produced a good performance for the results. The performance results of each model were evaluated by the confusion matrix and the k-nearest neighbor algorithm model with an accuracy value of 96.62% was found to give the best results and to be the most used model in the classification process. On the other hand, the algorithm model that obtains the lowest accuracy is a random forest with 72.08%.

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“…SVM is a good algorithm for data classification [19] with the principle of finding the best hyperplane that serves as a separator of two data classes [20]. The best hyperplane is determined by measuring the hyperplane margin and finding its maximum point, margin is the distance between the hyperplane and the nearest point of each class and this closest point is called the support vector [21].…”

Section: Data Processing 1) Support Vector Machine (Svm)mentioning

confidence: 99%

Performance Comparison of the Kernels of Support Vector Machine Algorithm for Diabetes Mellitus Classification

Anggoro¹,

Permatasari²

2023

IJACSA

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Diabetes Mellitus is a disease where the body cannot use insulin properly, so this disease is one of the health problems in various countries. Diabetes Mellitus can be fatal and can cause other diseases and even lead to death. Based on this, it is important to have prediction activities to find out a disease. The SVM algorithm is used in classifying Diabetes Mellitus diseases. The purpose of this study was to compare the accuracy, precision, recall, and F1-Score values of the SVM algorithm with various kernels and data preprocessing. The data preprocessing used included data splitting, data normalization, and data oversampling. This research has the benefit of solving health problems based on the percentage of Diabetes Mellitus and can be used as material for accurate information. The results of this study are that the highest accuracy was obtained by 80% obtained from the polynomial kernel, the highest precision was obtained by 65% which was also obtained from the polynomial kernel, and the highest recall was obtained by 79% obtained from the RBF kernel and the highest f1-score was obtained by 70% obtained from RBF kernel.

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Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Cited by 8 publications

References 9 publications

Optimization of Support Vector Machine Algorithm Using Stunting Data Classification

Optimization of Support Vector Machine Algorithm Using Stunting Data Classification

Opinion mining toward work from office policies on post-pandemic covid-19 by using supervised learning

Performance Comparison of the Kernels of Support Vector Machine Algorithm for Diabetes Mellitus Classification

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