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All Journal Technomedia Journal Jurnal Teknik Informatika (JUTIF)
Suriansyah, Muhammad
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Computer Science & IT Languange, Linguistic, Communication & Media

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Journal : Jurnal Teknik Informatika (JUTIF)

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Hybrid Neural Network-Based Road Damage Detection Using CNN-RNN and CNN-MLP Models Rahajoe, Ani Dijah; Suriansyah, Muhammad; Jr, Angelo A. Beltran
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4435

Abstract

Currently, there are many applications of image processing in various fields. One of them is the recognition of paved road images. Detection through images helps in handling infrastructure development roads. With the advancement of technology, especially in the field of deep learning, the process of detecting road damage can be done automatically and more efficiently. The road damage detection system can be integrated into the smart city system to monitor infrastructure conditions in real time. This study will use a combined deep learning algorithm between Convolutional Neural Network- Recurrent Neural Network (CNN-RNN) and as a comparison using Convolutional Neural Network- MultiLayer Perceptrons (CNN-MLP). The study aims to analyze the accuracy of using the CNN-RNN and CNN-MLP algorithms for detecting paved roads that have categories of undamaged roads, damaged roads, and damaged roads with holes. The detection of paved roads has complex details so an algorithm that has good performance with high accuracy is needed. The results of the study showed that the CNN-RNN hybrid had a better accuracy of 96.59 percent than the CNN-MLP hybrid model of 95.9 percent.  
REAL-TIME DROWSY FACE DETECTION FOR ONLINE LEARNING BASED ON RANDOM FOREST AND DECISION TREE ALGORITHMS Ani Dijah Rahajoe; Subekti, Mohamad Rafli Agung; Suriansyah, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1554

Abstract

In the current era, technology regarding artificial intelligence has developed rapidly and has been used in various areas of life. Face detection is one of the applications of Artificial Intelligence. This research aims to detect students' faces during the online learning process and succeeded in getting positive feedback when tested on students. Student detection includes drowsy and alertness. The method is via webcam in real-time so that the screen will show whether the student is drowsy or alert. In the trial, the teacher can find out who is in a drowsy and alert condition. On the other hand, students can find out that they fall into the drowsy or alert category. So that both parties immediately respond to what should be done based on the classification results. The algorithms used are Decision Tree and Random Forest. The accuracy results of the Random Forest algorithm are better than the Decision Tree algorithm, namely 65 percent, while the Decision Tree algorithm is 58 percent. The division of training data and test data uses a Kfold of 5. When Kfold is equal to 2, both algorithms have the highest accuracy, where Random Forest has an accuracy of 85 percent, and Decision Tre has an accuracy of 65 percent.
Co-Authors Ahmad Zaini Ani Dijah Rahajoe Jr, Angelo A. Beltran Muhammad Syaifuddin, Nur Puji Widodo, Aris Subekti, Mohamad Rafli Agung