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Abstrak :
Meskipun pandemi COVID-19 sudah mereda yang ditandai dengan banyak negara yang melonggarkan pembatasanpembatasan, namun masih ditemui kasus dan kematian yang disebabkan oleh COVID-19. Salah satu metode pendeteksian COVID-19 adalah dengan menggunakan citra CT scan yang di-training menggunakan arsitektur berbasis deep learning. Namun, ketersediaan dataset publik mengenai hal tersebut sangat terbatas. Untuk mengatasi hal itu, diperlukan metode pembuatan citra sintesis berbasis GAN (generative adversarial networks) yang diharapkan dapat meningkatkan performa dari arsitektur deep learning. Salah satu arsitektur GAN yang dapat digunakan yakni TinyGAN yang memiliki parameter training yang lebih sederhana dari GAN namun tidak mengurangi performa yang dihasilkan. Hasil augmentasi citra sintesis menggunakan TinyGAN tersebut kemudian dibandingkan dengan metode berbasis GAN lainnya, seperti BigGAN yang mana diharapkan mengurangi cost komputasi sehingga dapat digunakan pada perangkat yang terbatas dari segi resource. Dari hasil percobaan yang telah dilakukan menunjukkan bahwa penggunaan augmentasi citra mampu meningkatkan performa secara keseluruhan, yakni akurasi sebesar 98.42% dan F1-score sebesar 98.48% dengan metode VGG 16 serta dalam pengujian menggunakan aplikasi berbasis web model mampu memprediksi dengan benar dan waktu running terbilang singkat, yakni 0.0036 detik. Dalam hal evaluasi kualitas citra, metode TinyGAN dalam hal inception score menghasilkan hasil yang lebih baik, yakni sebesar 2.2037 daripada metode BigGAN yang bernilai 2.03502. Sedangkan dalam hal frechet inception distance metode TinyGAN menghasilkan hasil yang lebih baik, yakni sebesar 39.833 daripada metode BigGAN yang bernilai 40.601. ......Although the COVID-19 pandemic has subsided, which is marked by many countries easing restrictions, there are still cases and deaths caused by COVID-19. One to detect COVID-19 is to use CT scan images trained using a deep learning-based architecture. However, the availability of public datasets on this subject is very limited. To overcome this, a synthetic image generation method based on GAN (generative adversarial networks) is needed that is expected to improve the performance of the deep learning architecture. One of the GAN architectures that can be used is TinyGAN which has simpler training parameters than GAN but does not reduce the resulting performance. The results of the synthetic image augmentation using TinyGAN are then compared with other GAN-based methods, such as BigGAN which is expected to reduce computational costs so that it can be used on devices that are limited resources. From the results of experiments that have been carried out, it shows that the use of image augmentation resulted in increased performance (accuracy of 98.42% and F1-score of 98.48% using VGG16 method) and in testing using a web-based application model. able to predict correctly and the running time is relatively short, which is 0.0036 seconds. In terms of evaluating image quality, the TinyGAN method in terms of inception score produces better results, which is equal to 2.2037 than the BigGAN method which has a value of 2.03502. Whereas in terms of frechet inception distance the TinyGAN method produces better results, namely 39,833 compared to the BigGAN method which has a value of 40,60

Abstrak :
Tesis perancangan ini membahas tentang pembongkaran film Inception (2010) karya Christopher Nolan untuk menelusuri potensi ruang insepsi dalam perancangan ruang arsitektural. Konsep ruang arsitektural ini diharapkan dapat mempengaruhi target (mark) sebagai pengguna ruang melalui penanaman ide secara diam-diam di mana target tidak menyadari adanya intervensi dari ruang yang didesain serta menganggap ide yang ditanam melalui konsep-konsep utama adalah ide yang muncul dengan sendirinya. Target (mark) yang akan mengalami proses insepsi adalah penderita demensia, penderita yang memiliki penurunan progresif fungsi kognitif dan kelakuan. Konsep utama sebagai bagian dari proses inception adalah persepsi, memori, scenario, layer, dan labyrinth. Metode desain yang dilakukan adalah kajian teknis terhadap nursing home, pembongkaran dan pengembangan slayer oranye, dan arah kiblat. Pengembangan metode ini dilakukan melalui model series House of Ariadne dan diintervensi pada konteks perumahan dosen sektor Timur lama Universitas Syiah Kuala, Banda Aceh. Hasil yang diperoleh dari penelitian desain ini adalah sebuah konsep ruang insepsi sebagai wayfinding bagi penderita demensia yang mengalami penurunan kemampuan memori dan visual-spasial. ......This design thesis explains about the idea of inception space from Inception (2010), a movie directed by Christopher Nolan to explore the inception space potential in designing architectural space. The concept of architectural space is expected to affect the target (mark) as user by planting the idea 'secretly' in which the target is unaware of the intervention of a space and considers the idea planted through the main concepts is presented itself. Target (mark) which will undergo a process of inception is the person with dementia, people who have a progressive decline in cognitive function and behavior. The main concept as part of the inception process are perception, memory, scenario, layer, and labyrinth. The design method are studies on the nursing home, the development of "slayer oranye", and Qiblah direction. The development of this method is done through the series model House of Ariadne and intervened in old East sector of Universitas Syiah Kuala lecturer housing context, Banda Aceh. Results obtained is a concept of space inception as wayfinding for dementia with memory and visuospatial deficit.

Abstrak :
ABSTRAK
A machine learning approach has been used in this work to categorize jewelry images into five different classes. This classification was achieved by using the convolutional neural network (CNN). The objective was to find different approaches that can be competent for the image classification and recognition. The images used in this work are drawn directly from the jewelry industries and companies. The first technique uses support vector machine along with the features that were extracted from the input images using AlexNet. The second method involves the use of Inception v3 model for performing the same. Upon experimenting, it was derived that both the approaches performed well, however, Inception v3 was found to be more successful by 0.9%. The Inception v3 was then further taken to train the dataset from scratch which resulted in better consistency.