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"ABSTRAKTeknologi di bidang perangkat lunak dan perangkat keras semakin berkembang cepat. Masalah keterbatasan kapasitas suatu komputer memicu berkembangnya sebuah inovasi yang disebut dengan High Performance Computing HPC . HPC merupakan sekumpulan komputer yang digabungkan dalam sebuah jaringan dan dikoordinasi oleh software khusus. Cloud Computing merupakan paradigma yang relatif baru dalam bidang komputasi. Pada penelitian ini dilakukan pengujian terhadap performansi High Performance Computing Cluster HPCC berbasis cloud menggunakan layanan OpenStack dalam menjalankan fungsi dasar Message Passing Interface. Pengujian dilakukan menggunakan program Mpptest dan SIMPLE-O. Penggunaan server yang tidak mendukung hypervisor KVM pada pengujian point-to-point communication dapat menurunkan performansi HPCC berbasis cloud sebesar 3,1 - 12,4 dibandingkan dengan HPCC berbasis non-cloud. Pada pengujian point-to-point communication dengan 2 server yang mendukung hypervisor KVM, HPCC berbasis cloud unggul dibandingkan HPCC berbasis non-cloud sebesar 1,6 ndash; 2,7 . Pada pengujian performansi HPCC dalam melakukan fungsi MPI collective communication tidak ditemukan perbedaan berarti antara kedua cluster dimana HPCC berbasis non-cloud mengungguli HPCC berbasis cloud sebesar 0 - 1,4 . Pada pengujian menggunakan program SIMPLE-O didapati performansi HPCC berbasis cloud dan non-cloud imbang jika semua instance dijalankan dengan server yang mendukung hypervisor KVM, apabila terdapat instance yang dijalankan server tanpa dukungan KVM maka HPCC berbasis non-cloud unggul 96,2 dibandingkan HPCC berbasis cloud. Ketersedian modul KVM pada server yang menjadi host suatu instance sangat berpengaruh terhadap performansi HPCC berbasis cloud.

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ABSTRACTSoftware and hardware technologies have been developing rapidly. Capacity limation problems found in computers triggered a development of a new innovation called High Performance Computing HPC . HPC is a cluster of computers in a network coordinated by a special software. Cloud Computing is a new paradigm in computation field. In this research, series of test are done to find out the performance of cloud and non cloud based High Performance Computing Cluster HPCC while running basic functions of Message Passing Interface. Tests are done using Mpptest and SIMPLE O program. By using a server that does not support KVM in point to point communication test could decrease the performance of cloud based HPCC by 3,1 to 12,4 compared to non cloud based HPCC. During the test of point to point communication using 2 servers that support KVM hypervisor, cloud based HPCC is ahead of non cloud based HPCC by 1,6 to 2,7 . During the test of collective communication, there are no significant differences between performances of the two cluster, with non cloud based HPCC is ahead by 0 to 1,4 compared to cloud based HPCC. During the test using SIMPLE O program, the two cluster is even in term of performance as long as every instance is run by servers that support KVM hypervisor, if there is an instance that is run by a server that does not support KVM hypervisor then the performance of non cloud based HPCC is still ahead by 96,2 compared to cloud based HPCC. During the performance testing of HPCC while running collective communication, noticable performance difference between cloud and non cloud based HPCC was not found. The availability of KVM module in a server that is used to host an instance is really essential to the cloud based HPCC performance."

"Skripsi ini akan membahas tentang pengembangan sistem penilaian esai otomatis untuk esai pendek bahasa Jepang dengan menerapkan metode K-Means Clustering untuk mengelompokkan setiap topik pertanyaan dan Analisis Semantik Laten untuk membuat penilaian. Sistem yang dikembangkan untuk membantu memudahkan pemeriksaan esai yang saat ini masih dilakukan secara manual. Pengembangan sistemnya sendiri dilakukan dengan menggunakan bahasa pemrograman Python. Terdapat 5 skenario pengujian yang dilakukan dengan memvariasikan jenis masukan hiragana dan romaji serta proses eliminasi stopwords. Dari hasil yang diperoleh dan analisis yang dilakukan, bentuk atau jenis input teks yang digunakan serta penggunaan parameter seperti stopwords berpengaruh terhadap akurasi penilaian yang diperoleh. Sistem penilaian esai otomatis yang dikembangkan mampu memperoleh tingkat akurasi tertinggi sebesar 89% dengan menggunakan input berupa huruf romaji dan tanpa proses eliminasi stopwords.

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This thesis will discuss about the development of an automatic essay grading system for short Japanese essays by applying the K-Means Clustering method to group each question topic and Latent Semantic Analysis to make an assessment. The system developed to help facilitate essay checking is currently still being done manually. The development of the system itself is carried out using the Python programming language. There are 5 test scenarios carried out by varying the types of hiragana and romaji inputs and the stopwords elimination process. From the results obtained and the analysis carried out, the form or type of text input used and the use of parameters such as stop words have an effect on the accuracy of the assessment obtained. The developed automatic essay scoring system was able to obtain the highest accuracy rate of 89% by using input in the form of Romaji letters and without the stopwords elimination process."

"Grading is a process for decision making using information from evaluation of learning result whether using a test instrument or not[1]. Grading with essay is on option to evaluate level of knowledge of the students, but essay grading is not giving an objective view to each student. Essay grading by many of researcher is considered a good tools to evaluate result of a learning process and so, to evaluate level of intuition like synthesis and analysis. [2]. This research is intended to create an automatic essay grading which is called SIMPLE (SIsteM PeniLaian Esei otomatis) using Latent Semantic Analysis (LSA) as one of the method to extract and represent sentence using mathematical calculation or statistic from large amount of text [3]. Mathematical calculation is done by mapping with or without word from matrix group of word Furthermore, this research is done by implementing weight feature on web based automatic essay grading using Indonesian language. Testing is done by comparing result from system that using weight word and system that not using weight word Testing has succeeded with 82.56-96.42 percentage agreement with human raters for system using weight word."