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Taufiqurrahman
University of Darussalam Gontor, Indonesia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Lightweight convolutional neural network for khat naskhi and riq'ah classification Muhamad Taufiq Riza; Oddy Virgantara Putra; Taufiqurrahman
Jurnal Mantik Vol. 7 No. 1 (2023): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i1.3651

Abstract

Arabic writing has various types of khat that are complex and different from each other, so it requires proper classification to identify the type of khat used. This research uses the Lightweight Convolutional Neural Network (CNN) classsification method to recognize the types of khat naskhi and riq'ah on Arabic writing datasets. The evaluation results show that this classification model has an accuracy of 98.75% on training data and 100% on validation data, with a relatively fast processing time of 2s 375ms per step so that the model can be implemented well in systems that require high data processing speed and also devices that have limited resources. These results show that the classification model using the Lightweight CNN layer can be used as an effective alternative in classifying types of Arabic writing, especially in recognizing certain types of khat such as naskhi and riq'ah. Furthermore, this research can be developed using a larger and more diverse dataset, as well as evaluated and compared with other classification models to improve the performance of the model in recognizing more complex types of Arabic writing.
Comparison between naive bayes method and support vector machine in sentiment analysis of the relocation of the Indonesian capital Imam Rasyidin Muqsith Rizqi Prasetyo; Aziz Musthafa; Taufiqurrahman
Jurnal Mantik Vol. 7 No. 1 (2023): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i1.3669

Abstract

Moving the capital city of Indonesia has drawn pros and cons among the public. Therefore, it is important to analyze public sentiment towards moving the Indonesian capital to Kalimantan. In this study, we used data from Twitter and YouTube comments as many as 3895 and 1884 data, starting from 18 May to 6 July 2022. The purpose of this study was to classify public sentiment towards the move of the Indonesian capital city into positive, negative and neutral, as well as compare the results of sentiment analysis using the Naïve Bayes and Support Vector Machine methods. The K-Fold Validation method is used to measure the accuracy of sentiment analysis results. The results of the analysis show that SVM has better accuracy than Naïve Bayes with an accuracy percentage of 0.897 and 0.802 respectively. The resulting comment labels indicated that 56% were positive, 32% neutral, and 11% negative. In this study, we also compared the results of previous studies using the same method, namely Naïve Bayes and SVM. This research can assist the government in evaluating public opinion on the relocation of the Indonesian capital and can be a reference for future researchers in analyzing public sentiment in the future.
Co-Authors Aziz Musthafa Imam Rasyidin Muqsith Rizqi Prasetyo Muhamad Taufiq Riza Oddy Virgantara Putra