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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization ComTech: Computer, Mathematics and Engineering Applications
Tanuar, Evawaty
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Computer Science & IT Engineering Mathematics

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Analisis E-Bisnis Terhadap Amazon dan Aquarelle Tanuar, Evawaty; Yosanny, Agustinna
ComTech: Computer, Mathematics and Engineering Applications Vol 1, No 2 (2010): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v1i2.2589

Abstract

The internet and digital world is one thing that needs to be taken into account by the Company. Business through Internet, known as e-business, is another way to increase the relation between company and customers or prospective customers. Analysis from the view point of customers and the integration of technologies was conducted on 2 examples sites that well known in doing online business but have different history on how it started the e-commerce. They are Amazon and Aquarelle. By comparing the two sites, the characteristics of e-commerce sites could be studied. As a result, there are striking differences between these two sites, where Amazon is more oriented to sales, while Aquarelle more on customer-oriented impact to the design and implementation of their e-business. 
Deep Learning Algorithms and Optimizers: Enhancing the Evaluation of Signature Authenticity Haris Rangkuti, Abdul; Tanuar, Evawaty; Kusuma, Verdiant Jonathan; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2625

Abstract

Given the rapid technological advancements, security has become an essential human need that must be addressed. For example, a signature, which serves as a unique identifier or mark on a document, is vital in verifying and legalizing its contents. This study aims to utilize image processing techniques to identify patterns in signature images. Generally, a signature is a handwritten depiction used to authorize a document, indicating that the signing party acknowledges and agrees to its contents. However, this practice exposes signatures to the risk of forgery by dishonest individuals. Therefore, it is crucial to implement a security system for identity recognition using a biometric system for verification and identification. Verification involves determining whether the signature belongs to a previously identified individual and assessing its authenticity. This study employs deep learning algorithms, enhanced by optimizer methods, to improve accuracy performance in signature recognition for authenticity. Additionally, classification methods such as Linear SVM and RbfSVM are utilized. Several experiments were conducted, with VGG16 paired with the Adam optimizer yielding the highest accuracy of 0.9923. This was closely followed by VGG19 with Adagrad and Xception with RMSprop, achieving an accuracy of 0.9915. The training and validation accuracy processes revealed that the CNN VGG19 and VGG16 models with the Adam optimizer consistently achieved an accuracy exceeding 99%. Based on these experimental findings, the accuracy for detecting genuine and fake signatures can be clearly distinguished with an accuracy of over 99%, demonstrating the validity of this approach.
Enhancing touchless smart locker systems through advanced facial recognition technology: a convolutional neural network model approach Rangkuti, Abdul Haris; Tanuar, Evawaty; Yapson, Febriant; Sijoatmodjo, Felix Octavio; Athala, Varyl Hasbi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3262-3273

Abstract

As the world recovers from COVID-19, demand for contactless systems is increasing, promising safety and convenience. Touchless technology, particularly public locker security systems that use facial recognition and hand detection, is advancing rapidly. The system minimizes physical contact, increasing user safety. It uses advanced models such as multi-task cascaded convolutional networks (MTCNN) and RetinaFace, FaceNet512, ArcFace, and visual geometry group (VGG)-Face for face detection and recognition, with a combination of RetinaFace, ArcFace, and L2 norm Euclidean or cosine as the most effective distance metric method, where the accuracy reaches 96 and 90%. 'Yourvault', an application demonstrating this efficient security feature, provides notifications for mask detection, facial authenticity and locker status, offering a solution to the problem of convenience and security of public spaces. Future research could investigate the impact of photo age on facial recognition accuracy, potentially making touchless systems more efficient. In general, the application of this technology is an important step towards a safer and more comfortable world after the pandemic. This model approach can be followed up with more optimal facial recognition.
Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods Rangkuti, Abdul Haris; Hasbi Athala, Varyl; Haridhi Indallah, Farrel; Tanuar, Evawaty; Muliadi Kerta, Johan
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1359

Abstract

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.
Co-Authors Athala, Varyl Hasbi Haridhi Indallah, Farrel Haris Rangkuti, Abdul Hasbi Athala, Varyl Kusuma, Verdiant Jonathan Muliadi Kerta, Johan Rangkuti, Abdul Haris Sijoatmodjo, Felix Octavio Yapson, Febriant Yosanny, Agustinna