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"Skripsi ini membahas mengenai kualitas pelayanan pengajuan bantuan kegiatan mahasiswa di pusat pelayanan mahasiswa terpadu (PPMT) Universitas Indonesia, dalam hal ini pelayanan pengajuan bantuan kegiatan mahasiswa melayani pengajuan bantuan pada lembaga kemahasiswaan di tingkat universitas dan kegiatan level nasional atau internasional. Penelitian ini dilakukan terhadap pengurus lembaga kemahasiswaan di tingkat universitas untuk menganalisis bagaimana kualitas pelayanan menurut persepsi pengurus lembaga kemahasiswaan ditingkat universitas sebagai mahasiswa penerima layanan. Teori yang digunakan dalam penelitian ini daalah teori Parasuraman et.al (SERVQUAL). Teori Parasuraman et.al (SERVQUAL) dalam penelitian ini terdiri dari lima dimensi yaitu reliability, responsiveness, assurance, emphaty, dan tangibility. Metode penelitian yang digunakan dalam penelitian ini adalah pendekatan kuantitatif dengan teknik pengumpulan data melaui kuesioner dan wawancara mendalam. Kesimpulan hasil penelitian ini didapatkan bahwa menurut persepsi pengurus lembaga kemahasiswaan ditingkat universitas kualitas pelayanan pengajuan bantuan kegiatan mahasiswa pada dimensi tangibility didapatkan skor terbanyak yaitu 136.8, sedangkan skor terendah ada pada dimensi responsiveness dengan skor 129. Dari dimensi tersebut, indikator yang mendapatkan penilaian paling rendah yaitu indikator bantuan dana yang diterima memuaskan, layanan yang diberikan tepat waktu, dan layanan yang diberikan sesuai kebutuhan. Ketiga indikator yang masih mendapat penilaian rendah tersebut menandakan masih adanya masalah dan ketidaksesuaian pelayanan yang seharusnya didapatkan oleh pengguna layanan di pusat pelayanan mahasiswa terpadu, dalam hal ini pengurus lembaga kemahasiswaan ditingkat universitas.

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This thesis discusses the quality of service for the submission of student activity assistance at the University of Indonesia integrated student service center (PPMT), in this case the service for submitting student assistance serves the submission of assistance to student organizations at the university level and national or international level activities. This research was conducted on administrators of student organizations at the university level to analyze how the quality of service according to the perceptions of administrators of student organizations at the university level as students receiving services. The theory used in this study is the theory of Parasuraman et.al (SERVQUAL). Parasuraman et.al (SERVQUAL) theory in this study consisted of five dimensions, namely reliability, responsiveness, assurance, empathy, and tangibility. The research method used in this study is a quantitative approach with data collection techniques through questionnaires and in-depth interviews.

The conclusion of this study found that according to the perceptions of management of student organizations at the university level the service quality of submitting student assistance to the tangibility dimension obtained the highest score of 136.8, while the lowest score was in the responsiveness dimension with a score of 129. From that dimension, the indicator that received the lowest rating was indicators of funds received are satisfactory, services provided on time, and services provided as needed. The three indicators that still get low ratings indicate that there are still problems and service mismatches that should be obtained by service users at the integrated student service center, in this case the management of student organizations at the university level."

"Denial of Service adalah salah satu serangan siber yang dapat mengakibatkan gangguan layanan dan kerugian finansial. Akibat dari serangan DoS tentunya akan memberikan dampak buruk dan sangat merugikan. Untuk dapat menanggulangi dan meminimalisir dampak serangan DoS, dirancanglah sebuah sistem deteksi serangan DoS dan klasifikasi serangan yang terjadi menggunakan machine learning. Pada penelitian ini, akan dilakukan perancangan sistem deteksi serangan DOS melalui pengumpulan traffic data yang dikumpulkan oleh Wireshark dan dikonversi menggunakan CICFlowMeter. Serangan DoS dilancarkan oleh GoldenEye, HULK, dan SlowHTTPTest. Pengklasifikasian diterapkan pada salah satu dataset pada CICIDS2017, menggunakan algoritma Random Forest, AdaBoost, dan Multi-layer Perceptron. Hasil akurasi klasifikasi tertinggi adalah Random Forest sebesar 99,68%, hasil rata-rata Cross-Validation tertinggi juga dipegang oleh Random Forest sebesar 99,67%, dan untuk perbandingan performa antara hasil algoritma yang dilakukan oleh penulis dan paper konferensi DDOS Attack Identification using Machine Learning Techniques yang menjadi acuan, hasil yang paling mendekati adalah Random Forest dengan besar yang sama.

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Denial of Service is a cyberattack that can result in service disruption and financial loss. The consequences of a DoS attack will certainly have a bad and very detrimental impact. To be able to overcome and minimize the impact of DoS attacks, a DoS attack detection system and classification of attacks that occur using machine learning was designed. In this research, a DOS attack detection system will be designed by collecting traffic data collected by Wireshark and converted using CICFlowMeter. DoS attacks were launched by GoldenEye, HULK, and SlowHTTPTest. Classification was applied to one of the datasets in CICIDS2017, using the Random Forest, AdaBoost, and Multi-layer Perceptron algorithms. The highest classification accuracy result is Random Forest at 99.68%, the highest average Cross-Validation result is also held by Random Forest at 99.67%, and for performance comparison between the algorithm results carried out by the author and the conference paper DDOS Attack Identification using Machine Learning Techniques are the reference, the closest result is Random Forest with the same size."

"As concern grows over the relevance of a master?s degree to the professional work of librarianship, more and more schools will be looking to incorporate service learning into the student experience. Roy brings together authors from the top-tier schools to outline their programs and surrounding efforts andProvides examples of how to incorporate service learning into library and information science educationGives an overview of the history of service-learningOutlines the student, faculty, and field supervisor rolesService Learning serves as the rare educational resource that will tie professional and formalized education together."

""This book focuses on an in-depth assessment on strategies and instructional design practices appropriate for the flipped classroom model, highlighting the benefits, shortcoming, perceptions, and academic results of the flipped classroom model"--"

"Perkembangan teknologi yang pesat mempengaruhi lingkungan pembelajaran yaitu membentuk lingkungan pembelajaran modern, salah satu bentuk lingkungan belajar modern tersebut adalah kelas belajar pintar. Aplikasi teknologi terbukti telah meningkatkan ketertarikan belajar serta kualitas dari edukasi. Untuk memiliki hasil yang maksimal, institusi yang menyelenggarakan kelas belajar pintar, membutuhkan analisis terhadap faktor yang memiliki pengaruh terhadap kelas belajar pintar, agar dari faktor- faktor tersebut dapat dibentuk strategi untuk meningkatkan dan mempercepat tingkat adopsi kelas belajar pintar.Penelitian ini bertujuan untuk merumuskan strategi guna mengakomodir tingkat adopsi pengguna kelas belajar pintar, dalam hal ini dosen dan mahasiswa, dengan mengembangkan model konseptual menggunakan kombinasi instrumen dari theory of planned behavior (TPB) dan preference instrument of smart classroom learning environment (PI-SCLE). Pengambilan data dilakukan dengan menyebarkan kuesioner kepada mahasiswa dan dosen di lingkungan Fakultas Teknik, Universitas Indonesia. Selanjutnya, partial least squares (PLS) digunakan untuk menganalisis kedua model.Metode why how laddering digunakan untuk perumusan dan pengembangan strategi, serta metode strategy to mission matrix digunakan untuk validasi dan pemilihan strategi. Berdasarkan analisis model mahasiswa, 9 hipotesis diterima, dan 3 hipotesis ditolak. Sedangkan pada analisis model dosen, 5 hipotesis diterima dan 5 hipotesis di tolak. Berdasarkan perumusan dan pengembangan strategi menggunakan why how laddering, 24 rekomendasi strategi diajukan, kemudian 4 strategi dipilih sebagai prioritas atau fokus utama berdasarkan hasil pengolahan data menggunakan strategy to mission matrix.

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The rapid development of technology creates a modern learning environment, one of which is smart learning class. The application of technology is increasing the learning interest and quality of education. In order to have a maximum output, the institution in which the smart learning class will be adopted have to analyze certain factors that could be enhanced to accommodate students and teachers, to formulate strategies therefore, the system will be well adopted, in a manner of time.

This paper aims to develop recommendations of strategy, to increase the adoption rate and timeline towards smart learning class. Conceptual Model for smart learning class for student and lecturer’s adoption was build by using the combination instruments from theory of planned behavior (TPB) and preference instrument of smart classroom learning environments (PI-SCLE), to analyze the influential factors related to smart class adoption. This research was conducted using the questionnaire for lecturers and students in engineering faculty, Universitas Indonesia. The data was analyzed using Partial Least Square (PLS) method for hypotheses testing.

Why how laddering method was used to formulate and develop the strategy recommendation, and strategy to mission matrix will be used to validate and choose the appropriate strategies. From the student model, 9 hypotheses are accepted and 3 hypotheses are rejected, and from the lecturer model, 5 hypotheses are accepted and 5 hypotheses are rejected. 24 strategies recommendations were formulated using why how laddering method, and 4 strategies are chosen as priorities for implementation by using strategy to mission matrix."

"ABSTRAKDalam beberapa tahun terakhir, Deep Learning DL telah menarik banyak perhatian dalam penelitian pemelajaran mesin. Metode ini telah berhasil dipakai untuk berbagai aplikasi pada pemrosesan suatu, robotika, pengenalan fonetik, pencarian informasi dan bahkan analisa molekul. Meskipun DL telah berhasil sukses untuk diterapkan dalam berbagai bidang aplikasi, training yang diperlukan pada metode ini tidaklah mudah. Sejumlah cara telah diusulkan untuk membuat proses training DL menjadi lebih optimal, beberapa diantanya dengan menambahkan proses pre-training, memutuskan beberapa jaringan dalam lapisan, ataupun mengganti fungsi aktivasi dan metode gradien standar yang dipergunakan. Disertasi ini menggunakan pendekatan lain dalam optimasi DL, yaitu memakai algoritme metaheuristik. Secara umum disertasi ini dibagi dalam dua bagian besar. Bagian pertama adalah studi awal penelitian yang difokuskan pada beberapa eksperimen yang berkaitan dengan algoritme metaheuristik dan aplikasi DL dalam klasifikasi citra. Bagian kedua dari disertasi berkaitan dengan penerapan algoritme metaheuristik dalam DL. Hasil pada bagian ini misalnya untuk optimasi metode Convolutional Neural Nework CNN menggunakan dataset CIFAR10, diperoleh untuk Top-1 error pada validasi adalah 99,05 . Hasil ini lebih baik dari nilai akurasi CNN asli sebesar 88,21 , fine-tuning CNN menggunakan Harmony Search yang diusulkan G. Rosa dkk sebesar 78,28 , dan bahkan State of the art saat ini sebesar 96,53 dengan Fractional Max-Pooling.

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ABSTRACTIn recent years, deep Learning DL has drawn many attention in machine learning research. This method has been successfully used in various applications, such as sound process, robotics, phonetic identification, information retrieval, and even molecule analysis. Although DL has been successful to be applied in many fields, it is difficult to train in this method. Various attempts and methods has been proposed to make the DL training process become more optimum, some of them are by adding pre training process, drop out some networks in the layer, or by replacing activation function and standard gradient method being used. This dissertation takes another way to optimize a DL, i.e. using metaheuristic algorithms. Overall, this dissertation will be divided into two main parts. The first part is a preliminary study of research, focusing on several experiments which were related to the metaheuristic algorithm and DL application in image classification. The second part of this dissertation is related to application of metaheuristic algorithm in DL. The results in this part, for example, the optimization of CNN method using CIFAR10 dataset for Top 1 error in validation is 99.05 . This result is higher than the accuracy level from original CNN 88,21 , fine tuning CNN using Harmony Search suggested by G. Rossa et.al 78.28 , and even ldquo State of the art rdquo right now using Fractional Max Pooling 96.53 "