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"Large Language Model (LLM) generatif merupakan jenis model machine learning yang dapat diaplikasikan dalam industri jurnalisme, khususnya dalam proses pembuatan dan validasi berita. Namun, LLM memerlukan sumber daya yang besar untuk operasionalnya serta membutuhkan waktu proses inferensi yang relatif lama. Penelitian ini bertujuan untuk mengembangkan layanan web machine learning yang memanfaatkan LLM generatif untuk proses pembuatan dan validasi berita. Tujuan lainnya adalah menciptakan sistem dengan mekanisme manajemen beban yang efisien untuk meminimalkan waktu inferensi. Pengembangan melibatkan beberapa tahap, yakni analisis kebutuhan stakeholder, perancangan desain dan arsitektur, implementasi, serta evaluasi. Dalam implementasi layanan web machine learning, pengembangan ini berfokus pada manajemen GPU untuk meningkatkan kecepatan proses inferensi LLM. Selain itu, dilakukan implementasi design pattern untuk meningkatkan skalabilitas dalam penambahan model machine learning. Untuk manajemen beban, dikembangkan dua mekanisme, yaitu load balancer dan scheduler. Implementasi load balancer memanfaatkan NGINX dengan metode round-robin. Sedangkan untuk scheduler, digunakan RabbitMQ sebagai antrean, dengan publisher menerima permintaan dan subscriber mendistribusikan permintaan ke layanan yang tersedia. Berdasarkan API Test, layanan ini berhasil melewati uji fungsionalitas dengan waktu respons API sekitar 1-2 menit per permintaan. Evaluasi performa pada kedua mekanisme manajemen beban menunjukkan tingkat keberhasilan 100%, dengan waktu respon rata-rata meningkat seiring dengan peningkatan jumlah request per detik. Pengelolaan beban dengan load balancer menghasilkan waktu respon yang lebih cepat, sementara pengelolaan beban dengan scheduler menghasilkan mekanisme yang lebih efektif pada proses koneksi asinkron.

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Generative Large Language Model (LLM) is a type of machine learning model that can be applied in the journalism industry, especially in the process of news generation and validation. However, LLM requires large resources for its operation and requires a relatively long inference process time. This research aims to develop a machine learning web service that utilizes generative LLM for news generation and validation. Another goal is to create a system with an efficient load management mechanism to minimize inference time. The development involves several stages, namely stakeholder needs analysis, design and architecture, implementation, and evaluation. In the implementation of machine learning web services, this development focuses on GPU management to increase the speed of the LLM inference process. In addition, the implementation of design patterns is done to improve scalability in adding machine learning models. For load management, two mechanisms are developed: load balancer and scheduler. The load balancer implementation utilizes NGINX with the round-robin method. As for the scheduler, RabbitMQ is used as a queue, with the publisher receiving requests and the subscriber distributing requests to available services. Based on the API Test, the service successfully passed the functionality test with an API response time of about 1-2 minutes per request. Performance evaluation on both load management mechanisms showed a 100% success rate, with the average response time increasing as the number of requests per second increased. The use of a load balancer results in faster response times, while load management with a scheduler results in a more effective mechanism for asynchronous connection processes."

"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."