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All Journal IPTEK The Journal for Technology and Science CommIT (Communication & Information Technology) ComTech: Computer, Mathematics and Engineering Applications International Journal of Computer Science and Humanitarian AI
Widodo Budiharto
Bina Nusantara University
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

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Journal : CommIT (Communication

 1 
The Performance of Boolean Retrieval and Vector Space Model in Textual Information Retrieval Budi Yulianto; Widodo Budiharto; Iman Herwidiana Kartowisastro
CommIT (Communication and Information Technology) Journal Vol. 11 No. 1 (2017): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v11i1.2108

Abstract

Boolean Retrieval (BR) and Vector Space Model (VSM) are very popular methods in information retrieval for creating an inverted index and querying terms. BR method searches the exact results of the textual information retrieval without ranking the results. VSM method searches and ranks the results. This study empirically compares the two methods. The research utilizes a sample of the corpus data obtained from Reuters. The experimental results show that the required times to produce an inverted index by the two methods are nearly the same. However, a difference exists on the querying index. The results also show that the numberof generated indexes, the sizes of the generated files, and the duration of reading and searching an index are proportional with the file number in the corpus and thefile size.
End-to-End Steering Angle Prediction for Autonomous Car Using Vision Transformer Ilvico Sonata; Yaya Heryadi; Antoni Wibowo; Widodo Budiharto
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8425

Abstract

The development of autonomous cars is currently increasing along with the need for safe and comfortable autonomous cars. The development of autonomous cars cannot be separated from the use of deep learning to determine the steering angle of an autonomous car according to the road conditions it faces. In the research, a Vision Transformer (ViT) model is proposed to determine the steering angle based on images taken using a front-facing camera on an autonomous car. The dataset used to train ViT is a public dataset. The dataset is taken from streets around Rancho Palos Verdes and San Pedro, California. The number of images is 45,560, which are labeled with the steering angle value for each image. The proposed model can predict steering angle well. Then, the steering angle prediction results are compared using the same dataset with existing models. The experimental results show that the proposed model has better accuracy regarding the resulting MSE value of 2,991 compared to the CNN-based model of 5,358 and the CNN-LSTM combination model of 4,065. From the results of this experiment, the ViT model can replace the existing model, namely the CNN model and the combination model between CNN and LSTM, in predicting the steering angle of an autonomous car.
Co-Authors Alexander Agung Gunawan Antoni Wibowo Christian Tarunajaya Frederikus Fios Hanis Amalia Saputri Heri Ngarianto Ilvico Sonata Iman Herwidiana Kartowisastro Karina Dwinovera Mulia Nabeel Kahlil Maulana Oey, Kevin Wijaya Putri Nurmala Reinard Lazuardi Kuwandy Wikaria Gazali Yaya Heryadi Yulianto, Budi