In recent years, Solar power plants are currently developed rapidly, where solar power plants don't cause environmental damage. This generator utilizes sunlight as its input source which environmentally friendly, when sunlight is converted into direct current and voltage that can be stored in batteries. Therefore, the solar energy industry needs to have high efficiency, competitive pricing, and durability. With the right information technologies and developing application systems, it can help to optimize business processes and integrating information systems. But the fact there are many industries that do not apply information technology correctly into their business. In this research, provide to the best solution for applying the enterprise architecture framework correctly. The purpose of this paper is to design a manufacturing industry information system that is in accordance with the business model canvas and enterprise architecture. The design also discusses the development of a large and integrated information system with other systems. The study of Business Model Canvas is a business model that can explain and focus on nine business aspects with a solid strategy. Studies of the ArchiMate core Framework is the framework used to classify elements of the ArchiMate core language. These two methods are expected to be able to design an enterprise information system architecture consisting of business architecture, application architecture, information architecture and technology architecture as a result of the study. The result is a more complete system design to meet system requirements for users, integrated with existing system modules and no system duplication occurs.
Currently adoption of mobile phones and mobile applications based on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of 91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of 99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.
Object recognition in images is one of the problems that continues to be faced in the world of computer vision. Various approaches have been developed to address this problem, and end-to-end object detection is one relatively new approach. End-to-end object detection involves using the CNN and Transformer architectures to learn object information directly from the image and can produce very good results in object detection. In this research, we implemented ResNet-50 in an End-to-End Object Detection system to improve object detection performance in images. ResNet-50 is a CNN architecture that is well-known for its effectiveness in image recognition tasks, while DETR utilizes Transformers to study object representations directly from images. We tested our system performance on the COCO dataset and demonstrated that ResNet-50 + DETR achieves a better level of accuracy than DETR models that do not use ResNet-50. In addition, we also show that ResNet-50 + DETR can detect objects more quickly than similar traditional CNN models. The results of our research show that the use of ResNet-50 in the DETR system can improve object detection performance in images by about 90%. We also show that using ResNet-50 in DETR systems can improve object detection speed, which is a huge advantage in real-time applications. We hope that the results of this research can contribute to the development of object detection technology in images in the world of computer vision.
The development of Deep Learning technology is very good at detecting Objects. One of them is detection on the vehicle number plate. This method can be applied to Computer Vision to process images using DensetNet121, NasNetLarge, VGG16 and VGG19 methods. The most basic difference between Machine Learning and Deep Learning is the inclusion of a Hidden Layer and what distinguishes the Deep Learning process using neurons as a process from input, process to output. Feature extraction is done directly with the Deep Learning process. In terms of time, training models with Deep Learning are very long, when compared to Machine Learning. The dataset comes from Kaggle, then training is carried out with four Deep Learning models, resulting in a model. There are differences in conducting the training process. Before carrying out the Training process, a pre-paration process from the Image Dataset is carried out. The dataset is divided into two parts, the Training Dataset and the Testing Dataset. After the training model is completed, it is continued with the Testing process and measuring the performance of the model's accuracy. The accuracy of the four models resulting from Deep Learning training is also presented
Dewasa ini masyarat banyak memanfaatkan teknologi Internet, untuk berbagai kebutuhan. Mulai dari berbelanja, transportasi dan dunia pendidikan memanfaatkan Internet sebagai layanan digital. Peralatan dalam mengakses Internet pun banyak dan sangat beragam, mulai dari personal komputer, laptop sampai perangkat komunikasi seperti perangkat seluler. Perangkat seluler saat ini yang cukup banyak variasinya dan digunakan masyarakat adalah perangkat seluler berbasis sistem operasi Android. Dalam situasi ini mendorong pihak-pihak tertentu memanfaatkan celah untuk mencari keuntungan, salah satunya pembuatan Malicious Software (Malware). Keberadaan Malware sangat meresahkan, dimana pertumbuhan malware sangat cepat. Fenomena Malware yang terus bertumbuh inilah yang menjadikan peneliti berfokus untuk menganalisa Malware dengan memanfaatkan teknologi kecerdasan buatan. Tujuan dari penelitian ini adalah menganalisa file-file APK Android dengan metode anlisa statis dan melakukan klasifikasi keluarga Malware dan bukan Malware atau file APK Normal. File-file APK Malware dan bukan Malware di unduh dari Canadian Institute for Cyber Security, Google Play dan APK Pure. File-file tersebut dilakukan fitur ekstraksi untuk digenerate dan disimpan menjadi Malware dataset. Malware dataset tersebut dilakukan training menggunakan algoritma pembelajaran mesin. Pembelajaran mesin yang digunakan adalah Naïve Bayes, K-Nearest Neighbor dan Decision Tree. Pengukuran performansi akurasi dan perbandingan antara Naïve bayes, K-Nearest Neighbor dan Decision Tree yang merupakan bagian dari Pembelajaran Mesin.
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