Machine Learning for Diagnosing Drug Users and Types of Drugs Used

Anggrawan, Anthony; Satria, Christofer; Nuraini, Che Ku; Lusiana, Lusiana; Dasriani, Ni Gusti Ayu; MaYadi,

doi:10.14569/ijacsa.2021.0121113

Cited by 3 publications

(6 citation statements)

References 17 publications

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“…In essence, the classification model is a popular data mining or machine learning model [23][24][25] and has its application in various fields of science [26]. The classification model is a predictive learning model through training data on the data set to identify the pattern of relationships between attributes and classes in the data set [27,28].…”

Section: Introductionmentioning

confidence: 99%

“…That is why there is a need for a system that can assist in predicting with reasonable accuracy the results. Machine learning can predict accurately [25]. Machine learning has artificial intelligence in carrying out its jobs.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has artificial intelligence in carrying out its jobs. Artificial intelligence [25,31] is today's learning technology widely used for various roles [31]. Through machine learning, it is possible to uncover hidden patterns in big data and classify them [32].…”

Section: Introductionmentioning

confidence: 99%

“…SVM is a classification method used as a training system for linear learning machines [35]. As a result, machine learning can accurately perform classification [25]. However, according to López and Index Terms-Classification, educational technology, machine learning, data mining, Support Vector Machine (SVM), Synthetic Minority Oversampling Technique (SMOTE).…”

Section: Introductionmentioning

confidence: 99%

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Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE

Anggrawan¹,

Hairani²,

Satria³

2023

IJIET

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Student graduation accuracy is one of the indicators of the success of higher education institutions in carrying out the teaching and learning process and as a component of higher education accreditation. So it is not surprising that building a system that can predict or classify students graduating on time or not on time is necessary for universities to monitor the exact number of students graduating on time using educational technology. Unfortunately, educational technology or machine learning with data mining approaches is less accurate in classifying classes with unbalanced data. Therefore, this research purpose is to build a machine learning system that can improve classification performance on unbalanced class data between students who graduate on time and graduate late. This study applies the Synthetic Minority Oversampling Technique (SMOTE) method to improve the classifying performance of the Support Vector Machine (SVM) data mining method. The results of the study concluded that using the SMOTE method increased the accuracy, precision, and sensitivity of the SVM method in classifying class data of unbalanced student graduation times. The SVM performance score rises by 3% for classification accuracy, 8% for classification precision, and 25% for classification sensitivity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE

Anggrawan¹,

Hairani²,

Satria³

2023

IJIET

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“…7. Anthony Anggrawan, Christofer Satria, Che Ku Nuraini, Lusiana, Ni Gusti Ayu Dasriani, and Mayadi (2021) built an intelligent application system to predict the use of drugs and the types of drugs used [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Electricity Usage with Back-propagation Neural Network

Hairani

Anggrawan

Candra

2022

IJECSA

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The use of electricity has become a need that is increasing day by day. So it is not surprising that the problem of using electricity has attracted the attention of many researchers to research it. Electricity users make various efforts and ways to save on the use of electrical energy. One of them is saving electricity usage by electricity users using electrical energy-efficient equipment. That is why the previous research confirms the need for interventions to reduce the use of electrical energy. Therefore, this study aims to predict electricity use and measure the performance of the anticipated results of electricity use. This study uses the back-propagation method in predicting the use of electricity. This study concluded that the backpropagation architectural model with better performance is the six hidden layer architecture, 0.4 learning rate, and the Root Means Square Error (RMSE) value of 0.203424. Meanwhile, the training data test results get the best architectural model on hidden layer 8 with a learning rate of 0.3 with an RMSE performance value of 0.035811. The prediction results show that the prediction of electricity consumption is close to the actual data of actual electricity consumption.

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