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All Journal Jurnal Sarjana Teknik Informatika International Journal of Artificial Intelligence Research KACANEGARA Jurnal Pengabdian pada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Signal and Image Processing Letters Science in Information Technology Letters Signal and Image Processing Letters
Adhi Prahara, Adhi
Universitas Ahmad Dahlan
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Neuroscience

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Real-time Facial Expression Recognition to Track Non-verbal Behaviors as Lie Indicators During Interview Setiawan, Arif Budi; Anwar, Kaspul; Azizah, Laelatul; Prahara, Adhi
Signal and Image Processing Letters Vol 1 No 1 (2019)
Publisher : Association for Scientic Computing and Electronics, Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.144

Abstract

During interview, a psychologist should pay attention to every gesture and response, both verbal and nonverbal language/behaviors, made by the client. Psychologist certainly has limitation in recognizing every gesture and response that indicates a lie, especially in interpreting nonverbal behaviors that usually occurs in a short time. In this research, a real time facial expression recognition is proposed to track nonverbal behaviors to help psychologist keep informed about the change of facial expression that indicate a lie. The method tracks eye gaze, wrinkles on the forehead, and false smile using combination of face detection and facial landmark recognition to find the facial features and image processing method to track the nonverbal behaviors in facial features. Every nonverbal behavior is recorded and logged according to the video timeline to assist the psychologist analyze the behavior of the client. The result of tracking nonverbal behaviors of face is accurate and expected to be useful assistant for the psychologists.
Gender Classification using Fisherface and Support Vector Machine on Face Image Fatkhannudin, Muhammad Noor; Prahara, Adhi
Signal and Image Processing Letters Vol 1 No 1 (2019)
Publisher : Association for Scientic Computing and Electronics, Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.147

Abstract

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.
Real-time Facial Expression Recognition to Track Non-verbal Behaviors as Lie Indicators During Interview Setiawan, Arif Budi; Anwar, Kaspul; Azizah, Laelatul; Prahara, Adhi
Signal and Image Processing Letters Vol. 1 No. 1: March 2019
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.144

Abstract

During interview, a psychologist should pay attention to every gesture and response, both verbal and nonverbal language/behaviors, made by the client. Psychologist certainly has limitation in recognizing every gesture and response that indicates a lie, especially in interpreting nonverbal behaviors that usually occurs in a short time. In this research, a real time facial expression recognition is proposed to track nonverbal behaviors to help psychologist keep informed about the change of facial expression that indicate a lie. The method tracks eye gaze, wrinkles on the forehead, and false smile using combination of face detection and facial landmark recognition to find the facial features and image processing method to track the nonverbal behaviors in facial features. Every nonverbal behavior is recorded and logged according to the video timeline to assist the psychologist analyze the behavior of the client. The result of tracking nonverbal behaviors of face is accurate and expected to be useful assistant for the psychologists.
Gender Classification using Fisherface and Support Vector Machine on Face Image Fatkhannudin, Muhammad Noor; Prahara, Adhi
Signal and Image Processing Letters Vol. 1 No. 1: March 2019
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.147

Abstract

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.
Classification of Tiles using Convolutional Neural Network Ramadayanti, Susanti Aulia; Prahara, Adhi
Mobile and Forensics Vol. 3 No. 2 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v3i2.5643

Abstract

Tiles are one of the building materials with various types that can make a residence more elegant, attractive, and colorful. However, not all people know about the types of tiles and their advantages. Therefore, a Convolutional Neural Networks (CNN) based method is proposed to make it easier for people to accurately recognize tiles based on their types and know their advantages. The purpose of this paper is to classify the types of tiles using CNN which is based on VGG16 model. The proposed method classifies tiles into 6 classes, namely granite, limestone, marble, motifs, mosaics, and terrazzo. This research uses 186 training data, 96 validation data and 60 test data with image resolution of 224x224. Based on the experiments, the training process produces 100% of training accuracy and 94% of validation accuracy. The testing process achieves 98.33% accuracy which can be concluded that the proposed CNN model able to classify the types of tiles well.
Motion Capture Technique with Enhancement Filters for Humanoid Model Movement Animation Habibillah, Ahmad Yasin; Prahara, Adhi; Murinto, Murinto
Mobile and Forensics Vol. 5 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v5i1.6534

Abstract

The definition of 3D animation is a representation of objects that are made into animation using characters or objects to look more alive and real. Making 3D animation itself requires a long process and a large amount of funding. This is because most 3D animated films still use key-framing technology which causes the process to make an animation to take a lot of steps. In this research, a motion capture technique with an enhancement filter is proposed to make humanoid movement animation using Kinect 2.0. The method consists of several steps such as recording every skeleton joint of human movements using a Kinect sensor, filtering the movements to minimize the shakiness and jitter from Kinect data, mapping skeleton data to the bones of a rigged humanoid model, and recording each movement to make animation. The final result is in the form of a 3D animation of modern dance movements. The method is tested by measuring the similarity between the 3D humanoid model and the user movement. From the 10 animations of modern dance generated by the method and performed by the user, a questionnaire to measure the MRI and MSE value is distributed and the result achieves 4.27 on a scale of 5 for the averaged MRI score and 0.0539 for the MSE score. The MSE value is less than 5% which means the system is categorized as acceptable.
Betta fish classification using transfer learning and fine-tuning of CNN models Munif, Rihwan; Prahara, Adhi
Science in Information Technology Letters Vol 5, No 1 (2024): May 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i1.1378

Abstract

Betta fish, known as freshwater fighters, are in demand because of their beauty and characteristics. These betta fish such as Crowntail, Halfmoon, Doubletail, Spadetail, Plakat, Veiltail, Paradise, and Rosetail are hard to recognize without knowledge about them. Therefore, transfer learning of Convolutional Neural Network models was proposed to classify the betta fish from the image. The transfer learning process used a pre-trained model from ImageNet of VGG16, MobileNet, and InceptionV3 and fine-tuned the models on the betta fish dataset. The models were trained on 461 images, validated with 154 images, and tested on 156 images. The result shows that the InceptionV3 model excels with 0.94 accuracies compared to VGG16 and MobileNet which acquire 0.93 and 0.92 accuracy respectively. With good accuracy, the trained model can be used in betta fish recognition applications to help people easily identify betta fish from the image.
Rekonstruksi 3D Untuk Model Wajah Virtual Akademik Menggunakan Sensor Kinect 2 Rani, Siti Sofia; Prahara, Adhi
Jurnal Sarjana Teknik Informatika Vol. 9 No. 1 (2021): Februari
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v1i1.19014

Abstract

Massive multiplayer online game (MMOG) seperti world of warcraft, aion atau second life telah mendapatkan perhatian luar biasa pada perkembangan game vitual. Salah satu kelebihan MMOG pada game virtual setiap player dapat berkomunikasi secara langsung yang di wakili dengan karakter visual tiga dimensi. MMOG juga mendukung grafik permainan pada komputer hingga permainan yang digunakan menggunakan karakter visual tiga dimensi menjadi terlihat nyata.Penelitian ini memanfaatkan alat sensor kinect 2 dan Microsoft Kinect yang membantu untuk merekam avatar tiga dimensi yang dapat dipersonalisasikan. Dari perkembangan alat sensor yang bernama Kinect 2 sensor dapat mempermudah rekontruksi 3D untuk model wajah pada avatar game virtual dan di proses menggunakan teknik modeling 3D hingga visual dari hasil sensor Kinect 2 menggambarkan tampak nyata dari player dalam bentuk visual.Penelitian ini menghasilkan rekontruksi 3D untuk model wajah pada avatar game virtual akademik menggunakan sensor Kinect 2. Hasil pengujian SUS untuk uji modelling dan visual avatar 3D menghasilkan nilai rata-rata 41,6 dari sekala 5, maka masuk kategori acceptable yang artinya aplikasi dapat diterima. 
Pengenalan Rumah Adat Indonesia Menggunakan Teknologi Markerless Augmented Reality Romadhoni, Ilham Fauzi; Prahara, Adhi
Jurnal Sarjana Teknik Informatika Vol. 10 No. 2 (2022): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v10i2.21811

Abstract

Saat ini untuk belajar mengenai rumah adat Indonesia ketika belajar di sekolah, hanya dipelajari dari buku kebudayaan Indonesia, dimana hanya terdapat gambar dan beberapa penjelasan tanpa diketahui makna dari bentuk bangunannya. Dari hasil survey dapat diketahui bahwa 35,7% pelajar SMP sederajat tidak mengetahui, 30.6% mungkin mengetahui dan 33.7% yang mengetahui makna dari bangunan rumah adat yang diajarkan. Tujuan dari penelitian ini adalah mengembangkan media edukasi dengan mengimplementasikan teknologi Markerless Augmented Reality tentang Rumah Adat Indonesia. Pengembangan media edukasi ini menggunakan Teknologi Markerless Based Tracker Surface Tracking yang merupakan salah satu jenis metode Augmented Reality. Dengan menggunakan metode ini siswa tidak memerlukan penanda khusus dan dapat mempermudah siswa karena hanya cukup mengandalkan device saja untuk memunculkan obyek 3D. Pengumpulan data kebutuhan system dilakukan dengan cara obesvasi dan wawancara kepada siswa. Software untuk membuat aplikasi ini menggunakan Audancity, Sketchup dan Unity. Pengujian aplikasi menggunakan pengujian black box dan pengujian Sistem Usaility Scale. Pengujian dilakukan menggunakan metode Black box test dengan hasil persentase sebagai berikut: 94,12% untuk pengujian fungsionalitas system, 88,89% untuk pengujian kemiripan obyek 3d bangunan, 80% untuk pengujian kualitas audio deskripsi dan, Pengujian SUS dengan hasil sebesar 76.31%. Dengan demikian, dapat disimpulkan bahwa aplikasi yang dikembangkan layak digunakan
Gender classification using fisherface and support vector machine on face image Fatkhannudin, Muhammad Noor; Prahara, Adhi
Signal and Image Processing Letters Vol 1, No 1 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.86

Abstract

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.
Co-Authors A.A. Ketut Agung Cahyawan W Abdillah, Aldi Khoirul Ahmad Azhari Alqorni, Wais ALYA MASITHA Anggisa, Ahmad Bramdimas Anwar, Kaspul Arif Budi Setiawan Banjaransari, Muhammad Fatkhannudin, Muhammad Noor Fatkhannudin, Muhammad Noor Habibillah, Ahmad Yasin Herman Yuliansyah, Herman Indikawati, Fitri Indra Jefree Fahana Laelatul Azizah, Laelatul Munif, Rihwan Murinto Murinto Pramesti, Mara Indar Ramadayanti, Susanti Aulia Rani, Siti Sofia Romadhoni, Ilham Fauzi Son Ali Akbar