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All Journal ComEngApp : Computer Engineering and Applications Journal CommIT (Communication & Information Technology) Jurnal Pendidikan dan Konseling Javanologi: International Journal of Javanese Studies
Antoni Wibowo
Bina Nusantara University
Religion Arts Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Other

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 1 
The Effectiveness of Image Preprocessing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract Lily Rojabiyati Mursari; Antoni Wibowo
Computer Engineering and Applications Journal Vol 10 No 3 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.795 KB) | DOI: 10.18495/comengapp.v10i3.386

Abstract

Optical Character Recognition (OCR) has been widely discussed in various topics in the rise of robotics, artificial intelligence and computer vision. OCR has become a solution in extracting characters from the image into machine-encoded text. This research aims to discuss character recognition from digital handwritten image. However, characters recognition problems using OCR has been more or less solved. OCR mainly implemented in reading characters from scanned of printed documents. In this research, image preprocessing including convert to grayscale, morphological operations and noise removal has been successfully boost the accuracy score of OCR performance. The average success outcome resulted to 79.26% in reading characters from the image.
Sentiment Analysis of Twitter User’s Opinions on Government’s Performance in dealing with COVID-19 in Indonesia Bagus Sujiwo; Antoni Wibowo
Jurnal Pendidikan dan Konseling (JPDK) Vol. 4 No. 6 (2022): Jurnal Pendidikan dan Konseling
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpdk.v4i6.8128

Abstract

Saat ini, cukup banyak masyarakat Indonesia yang menggunakan Twitter, sebuah jejaring sosial media yang menyediakan informasi berupa produk, iklan, dan promosi mengenai kritik, saran suatu isu, dan opini publik. Penelitian ini bertujuan untuk menyederhanakan dan meningkatkan pendeteksian suatu opini tanpa menggunakan metode yang memakan waktu, seperti kuesioner. Selain itu, ia membuat kumpulan data berdasarkan tweet pengguna berbahasa Indonesia. Label data dikumpulkan menggunakan metode k-fold cross-validation yang dibagi menjadi 10 bagian. Metode klarifikasi analisis sentimen dilakukan melalui studi banding antara tiga metode, yaitu Naive Bayes (NB), Support Vector Machine (SVM), dan Long Short-Term Memory (LSTM). Ketiga metode memberikan hasil yang sesuai untuk setiap sifat kepribadian tetapi SVM sedikit mengungguli yang lain.
Fish Classification System Using YOLOv3-ResNet18 Model for Mobile Phones Suryadiputra Liawatimena; Edi Abdurachman; Agung Trisetyarso; Antoni Wibowo; Muhamad Keenan Ario; Ivan Sebastian Edbert
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8107

Abstract

Every country in the world needs to report its fish production to the Food and Agriculture Organization of the United Nations (FAO) every year. In 2018, Indonesia ranked top five countries in fish production, with 8 million tons globally. Although it ranks top five, the fisheries in Indonesia are mostly dominated by traditional and small industries. Hence, a solution based on computer vision is needed to help detect and classify the fish caught every year. The research presents a method to detect and classify fish on mobile devices using the YOLOv3 model combined with ResNet18 as a backbone. For the experiment, the dataset used is four types of fish gathered from scraping across the Internet and taken from local markets and harbors with a total of 4,000 images. In comparison, two models are used: SSD-VGG and autogenerated model Huawei ExeML. The results show that the YOLOv3-ResNet18 model produces 98.45% accuracy in training and 98.15% in evaluation. The model is also tested on mobile devices and produces a speed of 2,115 ms on Huawei P40 and 3,571 ms on Realme 7. It can be concluded that the research presents a smaller-size model which is suitable for mobile devices while maintaining good accuracy and precision.
Javanese Batik Motifs and Ornamentation as Objects of Aesthetics and Generative Art with Pseudo-Algorithm Dewi Retno Sari Saputro; Yekti Widyaningsih; Antoni Wibowo
Jurnal Javanologi Vol 6, No 1 (2022): Javanologi Volume 6 No. 1: Desember
Publisher : Pusat Unggulan Ipteks Javanologi Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/javanologi.v6i1.71586

Abstract

The batik patterns that are known to be fractal are the reality that there are alternative perspectives that exist among Indonesian society and civilization which are unique relative to the general modern perspective. This uniqueness is important considering that fractals are a form of understanding geometry and system complexity. Pseudo-algorithm in batik is an ornamentation process: klowongan-isenharmonization which starts from the smallest fractional elements of batik motifs (fractals), has selfsimilarity and is carried out through iterative computational methods. Batik as a patterned aesthetic object has pseudo-algorithmic depiction rules that can be treated as a generative art form. Batik that can be developed as fractal batik is batik with geometric motifs. Fractals have initiated a change and presented scientific creativity and progressivity in several fields in the form of interdisciplinary. All computational patterns growth to find fractal character in batik can turn into the sources of creativity to create new motifs.Keywords: Javanese batik, ornamentation, Aesthetic, pseudo algorithm
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 Agung Trisetyarso Bagus Sujiwo Dewi Retno Sari Saputro, Dewi Retno Edi Abdurachman Ilvico Sonata Ivan Sebastian Edbert Lily Rojabiyati Mursari Muhamad Keenan Ario Suryadiputra Liawatimena Widodo Budiharto Yaya Heryadi Yekti Widyaningsih