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"Penelitian dilakukan untuk mengembangkan sistem keamanan rumah dengan webcam yang sudah ada saat ini. Sistem keamanan saat ini menggunakan webcam hanya untuk merekam dan menyimpan kejadian dalam bentuk video file. Hal tersebut dirasa kurang cukup aman dikarenakan saat kejadian berlangsung seperti perampokan tidak ada notifikasi kepada pemilik rumah. Maka pada penelitian ini ditambahkan sebuah fitur pada sistem untuk mendeteksi wajah penghuni rumah dan akan memberikan notifikasi ketika ada wajah yang tidak dikenal. Sistem ini juga akan digabungkan dengan akses pintu otomatis menggunakan solenoid door lock. Metode yang digunakan adalah Deep Learning Matric untuk implementasi pada face recognition yang digunakan untuk akses kunci pintu rumah.Hasil yang didapatkan dari pengujian sistem cukup baik, dimana sistem dapat membedakan penghuni rumah dengan orang asing dengan beberapa kriteria pengujian, diantaranya dengan pengujian jarak webcam dengan orang pada siang sekitar pukul 12.00 sampai 13.00 dan malam hari sekitar pukul 19.00 sampai 20.00 dengan menghadap kearah webcam, pengujian banyaknya wajah yang terdeteksi webcam dalam satu frame, dan notifikasi kepada penghuni rumah. Berdasarkan hasil pengujian dan analisis, banyaknya wajah yang tertangkap bisa lebih dari 3 orang dan untuk persentase akurasi pada pengujian siang hari didapatkan sebesar 91.11% sedangkan pada malam hari sebesar 80%. Dari persentase yang didapatkan, pendeteksian pada siang hari lebih baik dan akurat dibandingkan pada malam hari dikarenakan intensitas cahaya yang mempengaruhi kerja dari algoritma face recognition.

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The study was conducted to develop a home security system with a webcam that already exists today. The current security system only uses the camera to record and save events in the form of video files. This is not enough safe because when the incident took place such as a robbery there was no notification to the homeowner. Therefore, in this study added some feature for a system to recognize faces of homeowner and will provide notifications when there are faces that are not known. This system will also be combined with automatic door access using solenoid door lock. The method used is the Deep Learning Matric for the implementation of face recognition which will used for door lock access.

The results obtained from the testing of the system are quite good where the system can distinguish between homeowner and strangers with several testing criteria, including testing the distance of the camera with people at noon around 12:00 to 13:00 and the night around 19:00 to 20:00 by facing the camera, testing the number of faces detected by cameras, and notifications to residents. Based on the results of testing and analysis, many faces can be caught more than 3 people and for the percentage of accurated in daytime testing obtained by 91.11% while at night by 80%. From the percentage obtained, the detection during the day is better and more accurate than at night due to the light intensity that affects the work of the face recognition algorithm.

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"Diagnosis otomatis kanker kulit dari lesi kulit dengan menggunakan gambar dermoskopi masih merupakan tugas yang menantang bagi kecerdasan buatan khususnya pada Artificial Neural Network dengan menggunakan deep learning. Penggunaan arsitektur yang tepat pada klasifikasi merupakan faktor penting dalam membuat diagnosis otomatis yang akurat. Meski demikian, model-model klasifikasi yang sudah terbuat tersebut masih belum dapat sempurna melakukan kategorisasi pada penyakit lesi kulit. Pada riset ini dilakukan penggantian arsitektur model klasifikasi yang digunakan dengan menggunakan arsitektur terbaru seperti EfficientNet B0 dan B1. Model EfficientNet B0 terbaik dengan menggunakan augmentasi saja memiliki akurasi, presisi, recall, dan f1-score sebesar 91%, 76%, 68%, dan 71% sedangkan EfficientNet B1 terbaik dengan menggunakan augmentasi dan class weight memiliki akurasi, presisi, recall, dan f1-score sebesar masing-masing 89%, 78%, 73%, dan 73%. Model EfficientNet B1 terbaik tersebut dapat mengungguli model state of the art yang ada dengan kenaikan recall dan f1-score sebesar 2% dan 12% dari model semi-supervised. Model juga dapat diimplementasikan dengan graphical user interface sehingga dapat digunakan oleh dokter spesialis kulit dalam pemeriksaan dermoskopi.Automatic diagnosis of skin cancer from skin lesions using dermoscopy images is still a challenging task for artificial intelligence, especially in Artificial Neural Networks using deep learning. The use of the correct architecture in the classification is an important factor in making an accurate automatic diagnosis. However, the classification models that have been made are still not able to perfectly categorize skin lesions. In this research, a replacement of the classification model architecture used by using the latest architectures such as the EfficientNet B0 and B1 was conducted. The best EfficientNet B0 model that only used augmentation has the accuracy, precision, recall, and f1-scores of 91%, 76%, 68%, and 71% while the best EfficientNet B1 that used augmentation and class weights has the accuracy, precision, recall, and f1-score of 89%, 78%, 73%, and 73%, respectively. The best EfficientNet B1 model can outperform the existing state of the art model with an increase in recall and f1-score by 2% and 12% from the semi-supervised model, respectively. The model can also be implemented with a graphical user interface so that dermatologist can use it in dermoscopy examinations."

"Penetration testing is often considered an art as much as it is a science, but even an artist needs the right brushes to do the job well. Many commercial and open source tools exist for performing penetration testing, but it's often hard to ensure that you know what tools are available and which ones to use for a certain task. Through the next ten chapters, we'll be exploring the plethora of open source tools that are available to you as a penetration tester, how to use them, and in which situations they apply. Open source tools are pieces of software which are available with the source code so that the software can be modified and improved by other interested contributors. In most cases, this software comes with a license allowing for distribution of the modified software version with the requirement that the source code continue to be included with the distribution. In many cases, open source software becomes a community effort where dozens if not hundreds of people are actively contributing code and improvements to the software project. This type of project tends to result in a stronger and more valuable piece of software than what would often be developed by a single individual or small company. While commercial tools certainly exist in the penetration testing space, they're often expensive and, in some cases, too automated to be useful for all penetration testing scenarios. There are many common situations where the open source tools that we will be talking about fill a need better and (obviously) more cost effectively than any commercial tool. The tools that we will be discussing throughout this book are all open source and available for you to use in your work as a penetration tester."

"Permasalahan parkir seperti efektifitas, efisiensi parkir, serta akuntabilitas biaya parkir merupakan masalah-masalah parkir yang selama ini terjadi pada sistem parkir manual. Oleh karena itu, diperlukan sebuah sistem perpakiran yang terstruktur dengan baik dan mampu menawarkan berbagai macam solusi dari permasalahan-permasalahan perparkiran yang ada. Salah satu solusi untuk membangun sistem tersebut yaitu dengan menggunakan RFID (Radio Frequency Identification). Pada skripsi ini dilakukan rancang bangun sistem parkir otomatis berbasis RFID reader DL-910 dan tag EPC Gen2 dengan fitur sistem debit biaya parkir via SMS. Sistem parkir otomatis ini menggunakan RFID reader DL-910 untuk mendeteksi keluar masuknya kendaraan dari gerbang parkir. Data keluar masuknya kendaraan yang terdeteksi oleh RFID tersebut akan diolah secara langsung oleh software aplikasi yang terinstalasi pada komputer untuk kemudian disimpan ke dalam database sistem parkir. Sistem parkir ini juga menggunakan teknologi SMS (Short Message Service) untuk pengisian debit biaya parkir. Pemilik kendaraan yang telah terdaftar pada database pengelola parkir dapat mengisi debit biaya parkir mereka melalui SMS. Hasil pengujian terhadap sistem parkir ini, baik dari aspek fungsionalitas maupun aspek durabilitas sistem, menunjukkan bahwa sistem ini dapat bekerja dengan baik. Jarak baca sistem pakir ini dapat mencapai jarak sejauh 3 m pada kondisi tag berada di dalam mobil dan 12,5 m pada kondisi tag tidak berada di dalam mobil.

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Parking problems like parking effectiveness, parking efficiency, and fee parking transparency are parking problems which occur in manual parking up till now. Therefore, it is needed a parking system which highly structured and overcome with that of parking system. One of the best solutions for building the system use RFID (Radio Frequency Identification). This thesis design and construct automatic parking system based on RFID reader DL-910 and tag EPC Gen2 technologies which have feature parking fee debit via SMS.

This automatic parking system use RFID reader DL-910 to identify vehicle which go in and out the parking gateway. Data vehicle from RFID reader will be proceed by application software which is installed in computer and then are saved to parking system database. The parking system also uses SMS (Short Message Service) for filling parking fee. Users who have been registered at database parking system can fill their parking fee via SMS.

The result of testing this parking system, either functionality aspect or durability aspect, show that this parking system do well. Distance read this parking system can reach 3 m when the tag place in the car and 12.5 m when the tag not place in the car."