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Berbeda dengan arsitektur monolith, arsitektur microservice dapat melakukan scaling secara independen pada service tertentu saja, memberikan fleksibilitas yang lebih besar dalam menanggapi lonjakan traffic dan tentunya lebih menghemat resource dibanding monolith. Tidak semua service pada suatu aplikasi perlu dilakukan scaling, hanya service dengan load processing tertinggi saat menerima banyak request yang perlu dilakukan horizontal scaling untuk menghemat resource. Tetapi penentuan service yang harus dilakukan scaling harus dilakukan secara benar agar sesuai dengan kebutuhan pengguna. Salah satu metode yang bisa digunakan adalah Customer Behavior Model Graph (CBMG) dengan melihat probabilitas perpindahan halaman yang dilakukan oleh pengguna. Dari metode tersebut dapat ditemukan halaman yang paling sering diakses oleh pengguna sebelum akhirnya ditentukan service dengan load processing tertinggi. Salah satu teknik yang dimiliki oleh kubernetes adalah Horizontal Pod Autoscaling (HPA) yang memungkinkan untuk melakukan scaling hanya pada salah satu pod. Pada kubernetes, service lebih dikenal sebagai pod. Dari pengimplementasian HPA didapatkan bahwa pada percobaan terjadi penurunan access failure rate dari sebelum implementasi sebesar 17.19% dan 20.52% dan setelah implementasi turun menjadi 4.86% dan 5.44%. Selain itu terdapat kenaikan throughput pada percobaan dari sebelum implementasi sebesar 25.00 request/detik dan 41.30 request/detik, setelah implementasi didapatkan sebesar 39.30 request/detik dan 51.10 request/detik. Pada percobaan lainnya didapatkan sebelum implementasi sebesar 4.60 request/detik dan 4.20 request/detik, setelah implementasi didapatkan sebesar 15.50 request/detik dan 13.80 request/detik. Dari hasil implementasi bisa dilihat bahwa melakukan peningkatan pada salah satu pod sudah cukup untuk meningkatkan kinerja aplikasi website dengan resource yang tersedia dan dapat dioptimalkan dengan maksimal. Implementasi dilakukan pada salah satu aplikasi website microservice teastore, dengan strategi scaling berdasarkan CBMG, optimasi yg dilakukan berhasil menurunkan access failure rate dan meningkatkan throughput, meksipun menggunakan jumlah resource yang sama. dengan kata lain, setelah strategi yang dirancang diimplementasikan, penggunaan resource menjadi lebih optimal untuk melayani request-request yang ada.

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Unlike monolithic architecture, microservice architecture can independently scale specific services, providing greater flexibility in responding to traffic spikes and, of course, saving more resources compared to monoliths. Not all services in an application need to be scaled; only pods with the highest load processing when receiving many requests need to be horizontally scaled to save resources. However, determining which services need scaling must be done properly to meet user needs. One method that can be used is the Customer Behavior Model Graph (CBMG), which looks at the probability of user page transitions. From this method, the most frequently accessed pages by users can be identified before determining the service with the highest load processing. One technique available in Kubernetes is Horizontal Pod Autoscaling (HPA), which allows scaling to be done only on specific pods. From the implementation of HPA, it was found that there was a decrease in the access failure rate from before implementation by 17.19% and 20.52%, and after implementation, it decreased to 4.86% and 5.44%. Additionally, there was an increase in throughput from before implementation by 25.00 requests/second and 41.30 requests/second, after implementation, it was found to be 39.30 requests/second and 51.10 requests/second. In another experiment, before implementation was 4.60 requests/second and 4.20 requests/second, after implementation, it was 15.50 requests/second and 13.80 requests/second. Improving the performance of one pod is sufficient to enhance the performance of the website application with the available resources and can be optimized to the maximum. The implementation was carried out on one microservice website application, making it better than monolithic architecture, which needs to scale the entire application."

"Tree Based Of Consistency Objective Function For Evaluation Alignment (T-COFFEE) merupakan algoritma untuk menyelesaikan permasalahan multiple sequence alignment (MSA). Algoritma ini menggabungkan dua teknik, pertama Tree Based yang merupakan Progressive Alignment dan kedua Consistency Objective Function berupa extending library. Pada pembahasan skripsi ini akan digunakan data sequence dari database ensembl yang terdiri dari database DNA atau protein yang akan diproses dengan global alignment (Needleman-Wunsch) dan local alignment (Smith- Waterman) dengan harapan informasi yang dihasilkan pada akhir pensejajaran akan menggambarkan hasil penyejajaran yang optimal. Proses pembentukan primary dan extended library pada T-COFFEE membutuhkan waktu lama sehingga untuk mempercepat waktu proses T-COFFEE digunakan teknik komputasi paralel Graphic Processing Unit (GPU). Skripsi ini akan menjelaskan algoritma T-COFFEE, algoritma paralel T-COFFEE, serta mengukur efisiensi dari kedua algoritma tersebut.

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Tree Based Of Consistency Objective Function For Evaluation Alignment (T-COFFEE) is an algorithm to solve the problem of multiple sequence alignment (MSA). This algorithm combines two techniques, first is Tree-Based with Progressive Alignment and second is Consistency of Objective Function by extending library. In this skripsi, we use the data from the ensembl database that consisting of DNA or protein data. Those data will be processed by the global alignment (Needleman-Wunsch) and local alignment (Smith-Waterman) that is expected to give optimal alignment result at the end of the alignment process. The generating of Primary and Extended Library is the most time consuming, hence to speed up the T-COFFEE process, a parallel version of T-COFFEE algorithm is needed by implementing parallel computing on Graphic Processing Unit (GPU). In this skripsi, the T-COFFEE algorithm, the parallel T-COFFEE algorithm, and the measurement of their speed up and efficiency will be discuss."

"Klasifikasi aksi multi-objek berdasarkan video RGB aerial merupakan tantangan kompleks yang dapat berguna untuk pengembangan sistem keamanan. Terdapat dua pendekatan jaringan saraf tiruan yang umum digunakan dalam sistem pengenal berbasis kerangka, Convolutional Neural Network (CNN) dan Graph Convolutional Network (GCN). Pendekatan CNN lebih efektif dalam mempelajari fitur spatio-temporal, lebih kuat terhadap noise dalam estimasi pose, dan dapat menangani skenario multi-objek dengan komputasi yang lebih ringan. Penelitian ini meliputi pengembangan pengenal aksi manusia dengan pendeteksi spatio-temporal berbasis kerangka menggunakan pendekatan 3D Convolutional Neural Network (3D-CNN). Pendeteksi spatio-temporal memungkinkan sistem untuk mengenali tiap-tiap aksi yang simultan dilakukan oleh multi-objek dalam satu rekaman video. Percobaan dilakukan menggunakan sejumlah pre-trained dataset dan menggunakan dataset video RGB aerial primer yang dilatih terhadap model pengenal aksi berbasis video frontal, dengan menerapkan metode transfer learning. Proses tranfer learning dilakukan dengan dataset khusus untuk menghasilkan model pelatihan yang memiliki akurasi tinggi. Pelatihan memberi keluaran berupa model jaringan saraf tiruan dengan nilai akurasinya. Pengujian dilakukan menggunakan data video untuk mengetahui ketepatan model. Dari model yang diperoleh, akan dilakukan analisis terhadap keberhasilan dan keakuratan metode dalam mengenali aksi manusia.

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Multi-object action recognition based on aerial RGB video is a complex challenge that can be useful for security system development. There are two commonly used artificial neural network approaches in skeleton-based recognition systems, Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN). CNN approach is more effective in learning spatio-temporal features, more robust to noise in pose estimation, and can handle multi-object scenarios with lighter computation. This research involves developing a human action recognition with skeleton-based spatio-temporal detection using a 3D Convolutional Neural Network (3D-CNN) approach. Spatio-temporal detection allows the system to recognize each simultaneous action performed by multiple objects in a single video footage. Experiments were conducted using a number of pre-trained datasets and using a primary aerial RGB video dataset trained on a frontal video-based action recognition model, by applying the transfer learning method. The transfer learning process is performed with a specific dataset to produce a high-accuracy training model. The training outputs an artificial neural network model with its accuracy value. Testing is done using video data to determine the accuracy of the model. From the model obtained, the success and accuracy of the method in recognizing human actions will be analyzed."

"This refereed volume arose from the editors' recognition that physical scientists, engineers, and applied mathematicians are developing, in parallel, solutions to problems of parallelization. The cross-disciplinary field of scientific computation is bringing about better communication between heterogeneous computational groups, as they face this common challenge. This volume is one attempt to provide cross-disciplinary communication. Problem decomposition and the use of domain-based parallelism in computational science and engineering was the subject addressed at a workshop held at the University of Minnesota Supercomputer Institute in April 1994. The authors were subsequently able to address the relationships between their individual applications and independently developed approaches. This book is written for an interdisciplinary audience and concentrates on transferable algorithmic techniques, rather than the scientific results themselves. Cross-disciplinary editing was employed to identify jargon that needed further explanation and to ensure provision of a brief scientific background for each chapter at a tutorial level so that the physical significance of the variables is clear and correspondences between fields are visible."

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Pada skripsi ini dipresentasikan desain rangkaian novel dc-dc flyback converter untuk mendukung teknologi 48 volt. Kebaruan dari rangkaian ini adalah pengaplikasian parallel input seried output dan umpan balik pada dc-dc flyback converter untuk mengubah 12V DC ke 48V DC. Disamping itu penggunaan transformator pada rangkaian yang berjumlah lebih dari satu berfungsi untuk membagi beban kerja induksi magnetik pada inti transformator. Desain umpan balik memanfaatkan arduino untuk memproses sinyal umpan balik sehingga menggerakan komponen penghasil sinyal PWM berdasarkan sinyal umpan balik yang diterima dan tegangan keluaran yang dihasilkan mencapai 48 volt meskipun dilakukan variasi beban. Rangkaian usulan terdiri atas keempat model yang berbeda, yaitu model tanpa umpan balik, model arduino-TL494CN, arduino saja, dan arduino-MCP4725-TL494CN. Hasil dari penelitian ini menyatakan model arduino-MCP4725-TL494CN memiliki presisi yang tinggi yaitu nilai defiasi yang paling rendah 0.02765 %⁄int dan tidak mengalami overshoot pada inisiasi rangkaian.

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This essay presented dc-dc flyback converter for supporting 48 volt technology. This circuit merge parallel input seried output and feedback application. The new design development consist of parallel input seried output and feedback in dc-dc flyback converter to converter 12V DC to 48 V DC. Aside that, the function of using transformer more than one in circuit is for splitting magnetic induction workload for transformer core. Feedback design using arduino is for proccessing feedback signal so that driving PWM generator component based on received feedback signal and output voltage reach at 48 volt even the load is changing. The proposed circuit consist of four different model, that are no feedback model, arduino-TL494CN model, arduino only, and arduino-MCP4725-TL494CN model. Result of this research stated arduino-MCP4725-TL494CN model has the lowest deviation value 0.02765 %⁄int and preventing overshoot at circuit initiation.

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"Performance Optimization of Numerically Intensive Codes offers a comprehensive, tutorial-style, hands-on, introductory and intermediate-level treatment of all the essential ingredients for achieving high performance in numerical computations on modern computers. The authors explain computer architectures, data traffic and issues related to performance of serial and parallel code optimization exemplified by actual programs written for algorithms of wide interest. The unique hands-on style is achieved by extensive case studies using realistic computational problems. The performance gain obtained by applying the techniques described in this book can be very significant.The book bridges the gap between the literature in system architecture, the one in numerical methods and the occasional descriptions of optimization topics in computer vendors' literature. It also allows readers to better judge the suitability of certain computer architecture to their computational requirements. In contrast to standard textbooks on computer architecture and on programming techniques the book treats these topics together at the level necessary for writing high-performance programs. The book facilitates easy access to these topics for computational scientists and engineers mainly interested in practical issues related to efficient code development."