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
Sebagai satu negara kepulauan terbesar di dunia, Indonesia dengan jumlah pulau lebih dari 17,000 dan tahun 2020 jumlah penduduk mencapai 270,20 juta orang. Melihat kondisi geografi dan jumlah penduduknya maka industri logistik merupakan industri yang menghadapi banyak tantangan terutama untuk perusahaan logistik bisa mencapai efisiensi, keamanan dan mendapatkan keuntungan. Untuk itu penelitian ini difokuskan pada perancangan enterprise architecture untuk industri truk logistik, guna mendukung tercapainya target perusahaan. Pada penelitian ini model bisnis digambarkan dalam business model canvas, dari sembilan aspek bisnis menjadi satu kesatuan dengan tujuan perusahaan. Anterprice architecture divisualisasikan dengan menggunakan elemen dari Archimate Core Framework untuk mendapatkan hasil enterprise sistem yang terintegrasi antara elemen bisnis, elemen data dan aplikasi, beserta elemen teknologi. Berdasarkan wawancara dengan ahli, dalam studi ini menitik beratkan penelitian pada 3 sumber pendapatan utama, yaitu; pendapatan dari pengiriman barang secara regular, pendapatan dari pengiriman barang secara khusus (proyek) dan pendapatan dari pengangkutan limbah dan bahan kimia. Hasil dari penelitian ini adalah enterprise architecture pada industri truk logistik beserta pemodelan dalam archimate. Penelitian ini diharapkan dapat memberikan masukan bagi pelaku bisnis truk logistik dalam melakukan pengembangan dan pemanfaatan teknologi informasi agar dapat bertahan dan meningkatkan daya saing di persaingan global.
Biaya logistik di Indonesia masih tergolong mahal yang disebabkan kurangnya infrastruktur, teknologi, kemampuan sumber daya manusia, kebijakan logistik pemerintah, terjadinya bencana alam, serta seringnya pungutan liar. Pelanggan belum menerima informasi secara real time. Hal ini dapat berdampak kepada kepuasan pelanggan serta terlambatnya proses pembayaran dari pelanggan. Untuk menjawab tantangan-tantangan ini, pelaku usaha trucking diharuskan untuk melakukan inovasi serta meningkatkan kinerja dan utilisasi kendaraan yang dimiliki terutama dengan pemanfaatan teknologi internet of things (IoT). Implementasi teknologi IoT pada perusahaan trucking memerlukan perencanaan enterprise architecture, sehingga teknologi yang diimplemntasikan sesuai dengan kebutuhan bisnis. Pada jurnal ini akan membahas bagaimana pemanfaatan teknologi IoT dalam mendukung tujuan bisnis dan proses operasional pada core process perusahaan trucking di Indonesia, serta memberikan rekomendasi enterprise architecture sesuai TOGAF yang dapat diimplementasikan pada core process bisnis trucking di Indonesia. Rekomendasi enterprise architecture divisualisasikan melalui archimate, sehingga dapat dengan mudah dipahami dan diadptasi oleh pelaku usaha bisnis trucking atau pemerintah.
Currently, many roads in Indonesia are damaged. This is due to the presence of large vehicles and large loads that often pass. The more omissions are carried out, the more damaged and severe the road is. The central government and local governments often carry out road repairs, but this problem is often a problem. Damaged roads are indeed many factors, one of which is the road load. The road load is caused by the number of vehicles that carry more than the specified capacity. There are many methods used to monitor roads for road damage. The weighing post is a means used by the government in conducting surveillance. This research is not a proposal to monitor the road, but this is only to create a model for the purpose of detecting heavily or lightly loaded vehicles. This research is to classify using Convolutional Neural Network (CNN) with pre-trained Resnet50. The model generated from the Convolutional Neural Network training process reaches above 90%. Generate Image deep learning algorithms such as the Generative Adversarial Network currently generate a lot of synthetic images. The testing dataset that will be used is generated from style transfer. The model is tested using a testing dataset from the generated style transfer. Style transfer is a method of generating images by combining image content with image styles. The model is pretty good at around 92% for training and 88% for testing, can it detect image style transfer? The Convolutional Neural Network model is said to be good if it is able to recognize the image correctly, considering that the accuracy of the model is very good. One of the reasons why the training model is good but still makes errors during testing, then the image dataset is overfitting
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.
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%.
The weight measurement system is carried out manually using a manual scale. The existing weighing system is still considered inefficient because it takes a long time if it is done repeatedly and there are too many errors in its measurement. To overcome this, an electronic weighing device was designed using the NodeMCU ESP 8266 microcontroller as a controller and a load cell as a sensor. This journal presents the development of electronic weighing indicators for digital measurements. The purpose of this system is to read the measured weight in conventional analog form to digital form, achieving high precision in measurement and calibration. The components used in this research are Load Cell, Load Cell Hx711 amplifier, NodeMCU ESP 8266 microcontroller, and LCD module. In this study, a 4 kg load cell was used. The load cell sends the output signal of the measured mechanical weight to the Hx711 module which amplifies and sends the output to the NodeMCU microcontroller. The microcontroller calibrates the output signal with the help of the load cell amplifier module before sending the converted signal to digital form to the LCD module for display. The developed system has proven that digital electronic weighing systems can be low cost, miniature, discrete, and can take accurate readings without errors
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