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"From the time of the earliest settlements the Europeans had seen America as a New World which would give them unlimited opportunity in the future. This image was cherished especially by those who had left their homelands for greater economic opportunities. Between 1620 and 1635 England suffered from economic difficulties. Many people lost their jobs. This deteriorating condition was aggravated by insufficient crops, because most of the lands were used for sheep-raising to meet the increasing demand for wool for England's expanding wool industries. To the first immigrants, then, the new land became the land of hope. Many settlers believed that God had granted them the American land as a second chance for them to live on after the first chance had been ruined in their previous home country. America was really a place of relief for the people who were weary of too much suffering. It was stated that when the first immigrants were approaching the American shore, they felt that "air at 12 leagues' distance smelt as sweet as a new-blown garden-. (US IS, 1978: 1-3). Those people considered America just like the second Garden of Eden they were always dreaming about. The vast, virgin forest, extending along the eastern seaboard from the north to the south, and rich in natural resources, resembled the earthly paradise and then became the object of pursuit for the suffering people. This Edenic possibility continued to be a strong attraction and since America's beginning, the development of American society had been marked by the great tide of immigrants toward the west. This Edenic dream also became a motivating factor for the waves of later immigrants to the new world in the centuries which followed, especially in the 19th Century, where the Edenic dream was transformed into myth that had spread among the pioneers who moved west. The people believed that because the western areas were still virgin, a promising future lay ahead, in the treasure island which became a rich source of raw materials for the development of the country. .As the coastal seaboard became densely populated and the soil became incapable of producing grain, these unfavorable conditions stimulated migration to the western regions, and there came a steady stream of men and women who left their coastal farms and villages to take advantage of the frontier life of the continent. "

"ABSTRAKSalah satu mitos paling terkenal di Amerika adalah mitos American Adam. Fakta bahwa mitos American Adam digunakan dalam berbagai macam produk budaya, seperti buku, film, dan lagu-lagu tidak bisa lagi disangkal kebenarannya. Tulisan ini akan membahas representasi dari mitos American Adam yang digambarkan dalam film The Dreamers 2004 karya sutradara asal Italia Bernardo Bertulloci. Dengan menyesuaikan fase mitos American Adam yang dikemukakan oleh R.W. B. Lewis 1955 , tulisan ini akan mengikuti jejak mitos American Adam melalui perkembangan psikologis sang pemeran utama, Matthew. Selain itu, representasi dari American Adam dalam The Dreamers 2004 telah membawa perspektif yang berbeda terhadap Amerika melalui lensa perfilman Eropa, khususnya produksi perfilman Italia.

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ABSTRACTOne of the distinguished myths of America is the American Adam. The fact that the American Adam is applied within several contemporary cultural products, such as books, films, and songs are cannot be denied. This study will discuss the representation of the American Adam that is depicted in the film The Dreamers 2004 by an Italian director, Bernardo Bertulloci. By adjusting the phases of the American Adam from R. W. B. Lewis 1955 , this research will trace the track of the American Adam through Matthew rsquo s psychological development, as the main character in this film. Moreover, the representation of the American Adam in The Dreamers 2004 has brought a different perspective towards America through the lens of European cinema, specifically Italian cinema production. "

"Mata merupakan salah satu indera terpenting bagi kehidupan manusia. Umumnya, banyak manusia yang mengabaikan gangguan fungsi penglihatan, dimana gangguan fungsi penglihatan ini mengindikasikan awal mula penyakit mata. Penyakit mata adalah gangguan fungsi penglihatan berkisar dari gangguan fungsi penglihatan ringan hingga gangguan fungsi penglihatan berat yang dapat menyebabkan kebutaan. Dalam melakukan diagnosa terhadap pasien gangguan fungsi penglihatan memiliki jenis penyakit mata yang diderita, diperlukan tahapan pemeriksaan retina dengan ophthalmoscopy atau fotografi fundus. Setelah itu, seorang dokter spesialis mata menganalisis jenis penyakit mata yang diderita pasien tersebut. Namun, karena terbatasnya sarana fasilitas kesehatan dan dokter spesialis mata yang memeriksa dan mengoperasi. Oleh karena itu, dibutuhkan alat deteksi dini dengan menggunakan data citra agar pasien gangguan penglihatan dapat ditangani sebelum pasien menderita gangguan fungsi penglihatan berat atau dapat mengalami kebutaan. Pada penelitian ini, diusulkan oleh peneliti model klasifikasi citra fundus ke dalam kelas normal, katarak, glaukoma, dan retina disease menggunakan Convolutional Neural Network (CNN) dengan arsitektur AlexNet. Data citra yang digunakan merupakan data fundus image retina yang berasal dari website kaggle. Sebelum data citra fundus image masuk ke dalam proses training model, dilakukan tahapan preprocessing pada data citra fundus image. Pada tahapan proses training dalam CNN digunakan fungsi optimasi untuk meminimalkan fungsi loss. Adapun fungsi optimasi yang digunakan dalam penelitian ini adalah Adam dan diffGrad. Hasil penelitian ini menunjukkan bahwa kedua fungsi optimasi tersebut memiliki hasil evaluasi training yang tidak jauh berbeda pada kedua fungsi optimasi. Keunggulan menggunakan kedua fungsi optimasi ini adalah mudah diterapkan. Pada penelitian ini didapatkan training loss terkecil sebesar 0,4838, validation loss terkecil sebesar 0,6658, dan training accuracy terbaik sebesar 0,8570 yang dimiliki oleh fungsi optimasi Adam. Sedangkan untuk validation accuracy terbaik sebesar 0,7189 yang dimiliki oleh fungsi optimasi diffGrad. Sedangkan running time tercepat pada proses training model sebesar 2840,9 detik yang dimiliki oleh fungsi optimasi diffGrad. Setelah tahapan proses training, dilakukan evaluasi dengan data testing. Secara keseluruhan, apabila dilihat dari hasil testing yang terbaik dimiliki oleh fungsi optimasi Adam dengan nilai accuracy sebesar 63%, recall sebesar 63%, dan precision sebesar 63%. Sedangkan running time tercepat pada proses testing model adalah 5,4 detik yang dimiliki oleh fungsi diffGrad. Dapat disimpulkan bahwa metode CNN menggunakan Arsitektur AlexNet dan fungsi optimasi Adam memberikan performa terbaik dalam mendeteksi penyakit mata pada data fundus image.

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The eyes are one of the most important senses for human life. Generally, many people ignore visual impairment, where this visual impairment indicates the onset of eye disease. Eye disease is a visual impairment ranging from mild visual impairment to severe visual impairment which can lead to blindness. In diagnosing patients with visual impairment who have the type of eye disease they suffer, it is necessary to carry out a retinal examination with ophthalmoscopy or fundus photography. After that, an ophthalmologist analyzes the type of eye disease the patient is suffering from. However, due to limited medical facilities and ophthalmologists who examine and operate. Therefore, an early detection tool is needed using image data so that visually impaired patients can be treated before the patient suffers from severe visual impairment or can go blind. In this study, researchers proposed a model for classifying fundus images into normal, cataract, glaucoma, and retinal disease classes using Convolutional Neural Network (CNN) with AlexNet architecture. The image data used is retinal fundus image data from the Kaggle website. Before the fundus image data enters the training model process, a preprocessing stage is carried out on the fundus image data. At this stage of the training process in CNN, an optimization function is used to minimize the loss function. The optimization functions used in this study are Adam and differed. The results of this study indicate that the two optimization functions have training evaluation results that are not much different from the two optimization functions. The advantage of using these two optimization functions is that they are easy to implement. In this research, the smallest training loss is 0.4838, the smallest validation loss is 0.6658, and the best training accuracy is 0.8570 which is owned by the Adam optimization function. As for the best validation accuracy of 0.7189 which is owned by the diffGrad optimization function. Meanwhile, the fastest running time in the model training process is 2840.9 seconds, which is owned by the diffGrad optimization function. After the stages of the training process, evaluation is carried out with data testing. Overall, when viewed from the testing results, Adam's optimization function is the best with an accuracy value of 63%, recall of 63%, and precision of 63%. Meanwhile, the fastest running time in the model testing process is 5.4 seconds, which is owned by the diffGrad function. It can be concluded that the CNN method using AlexNet Architecture and Adam's optimization function provides the best performance in detecting eye diseases in fundus image data."