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All Journal International Journal of Electrical and Computer Engineering Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Electrical Engineering and Computer Science Journal of Practical Computer Science (JPCS)
Yazid Aufar
Politeknik Hasnur
Computer Science & IT Electrical & Electronics Engineering Engineering

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Face recognition based on Siamese convolutional neural network using Kivy framework Yazid Aufar; Imas Sukaesih Sitanggang
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp764-772

Abstract

Human face recognition is a vital biometric sign that has remained owing to its many levels of applications in society. This study is complex for free faces globally because human faces may vary significantly due to lighting, emotion, and facial stance. This study developed a mobile application for face recognition and implemented one of the convolutional neural network (CNN) architectures, namely the Siamese CNN for face recognition. Siamese CNN can learn the similarity between two object representations. Siamese CNN is one of the most common techniques for one-shot learning tasks. Our participation in this study determined the efficiency of the Siamese CNN architecture with the enormous quantity of face data employed. The findings demonstrated that the suggested strategy is both practical and accurate. The method with augmentation produces the best results with a total data set of 9000 face images, a buffer size of 10000, and epochs of 5, producing the minimum loss of 0.002, recall of 0.996, the precision of 0.999, and F1-score of 0.672. The proposed method gets the best accuracy of 98% with test data. The Siamese CNN model is successfully implemented in Python, and a user interface and executables are built using the Kivy framework.
Robusta coffee leaf diseases detection based on MobileNetV2 model Yazid Aufar; Tesdiq Prigel Kaloka
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6675-6683

Abstract

Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%.
Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture Yazid Aufar; Muhammad Helmy Abdillah; Jiki Romadoni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4622

Abstract

In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes.
User Experience Design Tes Berbasis Mobile sebagai Link and Match Lowongan Kerja Industri dengan Politeknik di Kalimantan Selatan Wahyu Ridhoni; Yazid Aufar; Emma Valensia Aurum
Journal of Practical Computer Science Vol. 4 No. 1 (2024): Mei 2024
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v4i1.4634

Abstract

Link and Match antara Perguruan Tinggi Vokasi dengan Industri di Kalimantan Selatan dinilai masih jauh dari harapan. Terdapat kesenjangan karena standar capaian pembelajaran belum sesuai dengan standar yang diinginkan dari industri. Selain itu serapan lulusan untuk bekerja masih rendah. Penelitian ini bertujuan membangun Platform tes lowongan kerja berbasis mobile dalam rangka mempererat sinergi antar Politeknik dengan industri di Kalimantan Selatan. Berfokus pada User Experience Design dengan membangun prototipe dan mengujinya. Perusahaan tinggal memilih program studi apa saja yang bisa mengikuti tes suatu lowongan pekerjaan dan secara langsung informasi diterima oleh masing-masing lulusan Program Studi. Platform mobile merupakan pilihan yang paling memungkinkan karena di Indonesia perangkat mobile lebih banyak digunakan. Selain itu tidak seperti website dan program desktop, pada aplikasi mobile dapat dilakukan pembatasan untuk keperluan yang spesifik agar tes berlangsung bebas dari kecurangan. User Experience Design dilaksanakan dengan kerangka kerja UXD FLIP dan menggunakan tool desain Figma. Hasil pengujian User Experience Design menunjukkan bahwa prototipe yang sudah dikembangkan telah valid dengan nilai 80,5, sehingga dinyatakan telah fix dan dapat dijadikan acuan dalam proses coding.
Co-Authors Abdillah, Muhammad Helmy Aurum, Emma Valensia Jiki Romadoni Sitanggang, Imas S. Tesdiq Prigel Kaloka Wahyu Ridhoni