The purpose of this research is to propose an enterprise architecture framework for planning a drinking water company blueprint. In drinking water companies, it is very important to ensure that the systems and information technology used meet business needs effectively and efficiently. However, the information system that supports the company's operations still needs to be improved, to get better operational quality. In this case, companies need a framework that can assist companies in designing and developing business architectures that strengthen competitive advantage, optimize operational performance, and ensure compliance with applicable regulations and standards. Therefore, the author proposes the selection of an enterprise architecture framework based on The Open Group Architecture Framework or TOGAF. The Open Group Architecture Framework is a widely used framework for developing and implementing enterprise architectures. TOGAF consists of four main components, namely business architecture, application architecture, technology architecture, and data architecture. Enterprise Architecture helps companies develop application and technology architectures that can accelerate product and service innovation and improve operational efficiency. Data architecture, managing and utilizing data effectively in making the right business decisions. By adopting the TOGAF-based Enterprise Architecture framework, water companies optimize the use of information systems and technology, increase flexibility in anticipating changes in community needs and accelerate innovation in products and services.

https://jurnal.polgan.ac.id/index.php/sinkron/article/download/12477/1719

Proposed use of TOGAF-Based Enterprise Architecture in Drinking Water Companies

An information technology consulting firm that specializes in Global Positioning Systems provides fleet management services for many of its clients. The systems currently used by companies require more advanced modernization to ensure optimal service delivery. To overcome this challenge, a proposed enterprise architecture on system fleet management is presented in this paper. The proposed enterprise architecture is a comprehensive solution that includes the necessary hardware, software and operational processes to improve fleet management services. The proposed architecture is based on the Enterprise Architecture, which enables the integration of various systems and applications used by companies. The proposed architecture includes modules for vehicle tracking, fuel management, maintenance scheduling and driver performance monitoring. These modules work together to provide real-time data on fleet operations, enabling companies to make informed decisions regarding their fleet management services. The proposed architecture also incorporates an easy-to-use interface that simplifies the fleet management process and enhances customer satisfaction. The proposed system is scalable and easily adaptable to meet service requirements across multiple customers. In conclusion, the proposed enterprise architecture for system fleet management provides a comprehensive solution to the current challenges faced by companies as a corporate fleet service provider. The proposed architecture will improve service, reduce costs, and increase customer satisfaction.

/pdf/proposed-enterprise-architecture-on-system-fleet-management-30g3bbm6.pdf

Proposed Enterprise Architecture on System Fleet Management: PT. Integrasia Utama

The development of the Generative Adversarial Network is currently very fast. First introduced by Ian Goodfellow in 2014, its development has accelerated since 2018. Currently, the need for datasets is sometimes still lacking, while public datasets are sometimes still lacking in number. This study tries to add an image dataset for supervised learning purposes. However, the dataset that will be studied is a unique dataset, not a dataset from the camera. But the image dataset by doing the augmented process by generating from the existing image. By adding a few changes to the augmentation process. So that the image datasets become diverse, not only datasets from camera photos but datasets that are carried out with an augmented process. Camera photos added with painting images will become still images with a newer style. There are many studies on Style transfer to produce images in drawing art, but it is possible to generate images for the needs of image datasets. The resulting force transfer image data set was used as the test data set for the Convolutional Neural Network classification. Classification can also be used to detect specific objects or images. The image dataset resulting from the style transfer is used for the classification of goods transporting vehicles or trucks. Detection trucks are very useful in the transportation system, where currently many trucks are modified to avoid road fees

Style Transfer Generator for Dataset Testing Classification

Detection of a drowsy driver is an important aspect of driving safety. For this reason, it is necessary to have technology to carry out early detection before fatigue occurs. Mainly focused on driver fatigue that occurs at night. Analysis can be done quickly and accurately. These conditions can be sent via data so that they can be monitored and analyzed in real time. The results of the analysis can be sent by communication via the internet network. In addition, it functions as an early warning and can be used as logging or records that can be stored. This research does not discuss data communication but makes a prototype for detecting sleepy drivers. Prototype created using the Convolutional Neural Network Algorithm. The detection area is in the eye and testing is carried out with the brightness level of the light. In this study, building a prototype to detect signs of driver fatigue using the Convolutional Neural Network algorithm. The detection area used is in the eye, by testing at different light brightness levels. The dataset used in this study consists of a series of eye images, which are divided into two classes, namely open eyes, and closed eyes. After conducting the training process on Convolutional Neural Network, we get results of detection accuracy reaching 90%.

/pdf/drowsy-detection-in-the-eye-area-using-the-convolutional-1fy7wr3j.pdf

Drowsy Detection in the Eye Area using the Convolutional Neural Network

Internal Combustion Engines are known for their unique sound characteristics. Through these sound characteristics, an experienced car mechanic will be able to diagnose the type of engine damage just by listening to the sound. This reduces the need to disassemble components to pinpoint machine faults which also contributes to a significant reduction in overall repair time. The main aim of this paper is to build a process to identify faulty machines through engine noise analysis with visual data to determine machine faults at an early stage. By capturing various types of engine sounds, data visualization uses healthy engine sounds and broken engine sounds obtained from cars as well as various types of broken engine sounds that are usually found in vehicles. This audio data will be used in audio signal processing combined with a linear regression classification algorithm. Visualization data can distinguish various types of sounds that are commonly found in damaged or damaged engines such as clicks, ticks, knocks and other types of sounds to determine the types of damage that are usually found in internal combustion engines. The data used comes from Kaggle, which is public data which is widely used as general data for data science activities. Visually, data from vehicle engines can be seen from the data on which car brand is the best in terms of sound. The results using linear regression show the R-squared score (R^2) or also called the coefficient of determination reaching 91.95%.

/pdf/diagnostic-on-car-internal-combustion-engine-through-noise-144hpxfj.pdf

Diagnostic on Car Internal Combustion Engine through Noise

This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.

/pdf/detects-damage-car-body-using-yolo-deep-learning-algorithm-38gk5ff8.pdf

Alessandro Enriqco Putra Bayu Wedha

Papers

Proposed Enterprise Architecture on System Fleet Management: PT. Integrasia Utama

Style Transfer Generator for Dataset Testing Classification

Drowsy Detection in the Eye Area using the Convolutional Neural Network

Diagnostic on Car Internal Combustion Engine through Noise

Detects Damage Car Body using YOLO Deep Learning Algorithm