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All Journal Kharisma Tech
Alwiah Musdar, Izmy
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Computer Science & IT Engineering

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ANALISIS SENTIMEN BAKAL CALON PRESIDEN 2024 MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE PADA TWITTER Winarto, Wilson; Alwiah Musdar, Izmy; Hasniati, Hasniati
KHARISMA Tech Vol 19 No 1 (2024): Jurnal KHARISMATech
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v19i1.464

Abstract

Indonesian is a democratic country with a huge population and the largest Twitter users in the world. The 2024 presidential election in Indonesia is an interesting topic for Twitter users. Public tweets related to presidential candidates can be used to see a picture of public opinion on presidential candidates. The large number of incoming tweets about presidential candidates encourages the need for methods that help to see public opinion effectively. One method that can be used to classify public opinion effectively is Support Vector Machine (SVM). This method will classify whether a public opinion belongs to a positive or negative sentiment by finding the best hyperlane from both classification classes. The addition of the Kernel function to the Support Vector Machine is useful for dealing with data that is not linearly separated. The use of the K-Fold Cross Validation Method is intended so that data can alternately become test data so as to increase accuracy. Weighting is done using the Term Frequency Document Inverse Frequency (TF-IDF). System evaluation was carried out using a confusion matrix to measure the accuracy of the system in classifying the average accuracy using a linear kernel. The results of the classification obtained by Anies Baswedan get an accuracy of 78.28% and a precision of 81.298%. Ganjar Pranowo received an accuracy of 82.494% and a precision of 85.642%. Prabowo Subianto received an accuracy of 83.904% and a precision of 86.22%.
KLASIFIKASI CITRA KOMPONEN SEPEDA MOTOR MENGGUNAKAN ALGORITMA CNN DENGAN ARSITEKTUR MOBILENET Anggarkusuma, Renaldi; Alwiah Musdar, Izmy; Hasniati
KHARISMA Tech Vol 19 No 2 (2024): Jurnal KHARISMATech
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v19i2.430

Abstract

Image recognition is a sub-category of computer vision technology used to classify images into specific categories. The purpose of this research is to create a CNN model with the MobileNet architecture to classify motorcycle component images and measure the accuracy level produced by the model. The creation of the deep learning CNN model uses the TensorFlow library. The initial data for the training process consists of 50 images divided into 5 categories: spark plugs, brake pads, bearings, regulators, and roller housings. These data undergo augmentation techniques such as rotation, shifting, and image flipping. This research successfully developed a CNN model using the MobileNet architecture that can classify motorcycle component images. The MobileNet model was tested using 20 test data, with 10 of them subjected to a motion blur filter. The test results showed that the accuracy performance of the CNN model with the MobileNet architecture in classifying motorcycle component images is 85%, and the accuracy of image classification did not significantly decrease when the motion blur filter was applied.
Co-Authors Anggarkusuma, Renaldi Hasniati Hasniati, Hasniati Winarto, Wilson