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All Journal Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Science in Information Technology Letters
Awang Hendrianto Pratomo
Universitas Pembangunan Nasional Veteran Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Parking detection system using background subtraction and HSV color segmentation Awang Hendrianto Pratomo; Wilis Kaswidjanti; Alek Setiyo Nugroho; Shoffan Saifullah
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3251

Abstract

Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.
SISTEM PENGAWASAN DAN PERINGATAN DINI KEBENCANAAN PADA GOA TERINTEGRASI MENGGUNAKAN IOT Danang Arif Rahmanda; Awang Hendrianto Pratomo; Oliver Samuel Simanjuntak
Telematika Vol 17, No 2 (2020): Edisi Oktober 2020
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v1i1.3381

Abstract

Smart city merupakan sistem yang memberikan perkembangan pada kota yang digunakan dengan tujuan untuk lebih baik serta memberikan pelayanan terhadap masyarakat untuk memenuhi kehidupan yang layak. Peringatan dini atau Early Warning System (EWS) pada bidang lingkungan merupakan bagian dari lingkungan cerdas untuk memberikan sebuah peringatan dini suatu kejadian seperti kebencanaan yang diberitahukan kepada masyarakat. Penerapan sistem peringatan dini tersebut di terapkan pada lingkungan yang meliputi daerah wisata alam, salah satunya gua. Gua merupakan suatu lingkungan berupa bentukan akibat proses alam yang melubangi batuan. Dengan adanya sistem pengawasan dan peringatan dini saat ini yang merupakan sistem yang sangat dibutuhkan, mengingat bencana yang sering realtime terjadi dan terkadang yang tidak dapat diduga. Dari cara pengamatan dan pemberitahuan informasi yang lama mengenai keadaan didalam gua ini dapat dilakukan dengan cepat dengan data yang diperbaharui secara secara terus menerus. Sistem pengawasan yang dirancang dengan menggunakan sensor DHT11 Sebagai Sensor Kelembaban Udara, DS18 Sebagai Sensor Ruang Luar,BMP180 Sebagai Sensor Suhu Ruang Dalam dan Tekanan Udara, FC28 Sebagai Sensor Kelembaban Tanah, Rain Gaug Sebagai Sensor Curah Hujan. Data yang didapatkan dari masing-masing sensor akan dikirimkan kedalam database yang berada dalam cloud server, sehingga data akan terus diperbaharui. Hasil dari pengujian sensor didapatkan memiliki selisih yang tidak jauh dengan nilai nyata yang diuji dengan alat pengukur lain ketika daya atau tegangan yang diberikan pada sensor lebih tinggi. Pada tegangan 1 ampere untuk sensor suhu memberikan nilai 30 derajat selsius sedangkan dengan termometer adalah 32 derajat selsius, dan pada tegangan 5 ampere sensor suhu dan thermometer bernilai 29 derajat selsius diwaktu yang sama. Pada sensor kelembaban udara, dengan tegangan 1 ampere mendapatkan 63% sedangkan pada nilai nyatanya 65%, dan dengan tegangan 5 ampere, pada kelembaban nyata dan menggunakan sensor bernilai 77% diwaktu yang sama.
Image processing for student emotion monitoring based on fisherface method Awang Hendrianto Pratomo; Mangaras Yanu Florestyanto; Y I Sania; B Ihsan; H H Triharminto; Leonel Hernandez
Science in Information Technology Letters Vol 2, No 1: May 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i1.690

Abstract

Monitoring academic emotion is an activity to provide information from students' academic emotions in the class continuously. Some research in the image processing field had done for face recognition but had not been many studies on image processing to detect student emotions. This paper aims to determine the percentage of facial recognition with fisherface and academic emotional recognition by monitoring changes in students' facial expressions using facial landmarks in various distances, camera angles, light, and attributes used on objects. The proposed method uses facial image extraction based on fisherface method for presence. Furthermore, face identification will be made with Euclidean distance by finding the smallest length of training data with test data. Emotion detection is done by facial landmarks and mathematical calculations to detect drowsiness, focus, and not focus on the face. Restful web service is used as a communication architecture to integrate data. The success rate of applications with the fisherface method obtains 96% percent accuracy of face recognition. Meanwhile, facial landmarks and mathematical calculations are used to detect emotions, with 84 %.
Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification Awang Hendrianto Pratomo; Nur Heri Cahyana; Septi Nur Indrawati
Science in Information Technology Letters Vol 4, No 1 (2023): May 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i1.1182

Abstract

Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (GA), an optimization technique grounded in the principles of natural selection, were employed to identify the optimal hyperparameters for Convolutional Neural Networks (CNNs). The objective was to enhance the performance of the model, namely in the classification of photographs into two categories: those with face masks and those without face masks. The convolutional neural network (CNN) model, which was enhanced by the utilization of hyperparameters adjusted by a genetic algorithm (GA), demonstrated a commendable accuracy rate of 94.82% following rigorous testing and validation procedures. The observed outcome exhibited a 2.04% improvement compared to models that employed a trial and error approach for hyperparameter tuning. Our research exhibits exceptional quality in the domain of investigations utilizing Convolutional Neural Networks (CNNs). Our research integrates the resilience of Genetic Algorithms (GA), in contrast to previous studies that employed Convolutional Neural Networks (CNN) or conventional machine learning models without adjusting hyperparameters. This unique approach enhances the accuracy and methodology of hyperparameter tuning in Convolutional Neural Networks (CNNs). 
Co-Authors Alek Setiyo Nugroho B Ihsan Danang Arif Rahmanda H H Triharminto Leonel Hernandez Mangaras Yanu F Nur Heri Cahyana Oliver Samuel Simanjuntak Saifullah, Shoffan Septi Nur Indrawati Wilis Kaswidjanti Y I Sania