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All Journal International Journal of Electrical and Computer Engineering Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Electrical Engineering and Computer Science
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.
Enhancing SDGs in Barito Kuala: Enterprise Architecture for Smart Village Development using TOGAF ADM 9.2 in Sungai Pitung Yazid Aufar; Tesdiq Prigel Kaloka; Marhusin Marhusin; Muhammad Aditia; Salmah Salmah; M. Junaidi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.6023

Abstract

Sustainable village development is essential for realizing the Sustainable Development Goals (SDGs), specifically regarding poverty eradication (SDG 1), inequality reduction (SDG 10), and the establishment of sustainable communities (SDG 11). This study formulates a smart village framework for Sungai Pitung Village, Barito Kuala, by utilizing the Village Information System (SID) to enhance local governance and economic conditions. The methodology employs TOGAF ADM 9.2, covering the Preliminary phase, Architecture Vision, Business Architecture, Information Systems Architecture, and Technology Architecture. The novelty of this research lies in the strategic integration of Village Development Index (IDM) indicators and Village SDGs (Ministerial Regulation No. 13 of 2020) directly into the architectural blueprints. This approach ensures that the resulting digital roadmap is legally and contextually compliant with Indonesian rural mandates. The framework is validated through a formal evaluation of research results with relevant stakeholders to ensure it addresses the digital divide and operational inefficiencies. The findings provide a holistic IT architecture that standardizes technology catalogs and aligns village services with national development targets, ultimately fostering an enhanced quality of life for rural inhabitants.
Co-Authors Abdillah, Muhammad Helmy Jiki Romadoni M. Junaidi Marhusin , Muhammad Aditia Salmah Salmah Sitanggang, Imas S. Tesdiq Prigel Kaloka