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"The annual Workshop on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in all aspects of algorithm engineering, including the implementation, tuning, and experimental evaluation of algorithms and data structures. ALENEX 2008, the tenth workshop in this series, was held in San Francisco, California on January 19, 2008. The workshop was sponsored by SIAM, the Society for Industrial and Applied Mathematics, and SIGACT, the ACM Special Interest Group on Algorithms and Computation Theory."

"The annual Workshop on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in all aspects of algorithm engineering, including the implementation and experimental evaluation of algorithms and data structures. The workshop was sponsored by SIAM, the Society for Industrial and Applied Mathematics, and SIGACT, the ACM Special Interest Group on Algorithms and Computation Theory. The aim of ANALCO is to provide a forum for the presentation of original research in the analysis of algorithms and associated combinatorial structures."

"The annual Workshop on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in all aspects of algorithm engineering, including the implementation and experimental evaluation of algorithms and data structures. "

"This research presents significant studies in experimental analysis or in the implementation, testing, and evaluation of algorithms for realistic environments and scenarios. The paper presentations address specific applied areas that present unique challenges in their underlying algorithmic problems as well as methodological issues and standards in the context of empirical research on algorithms and data structures. This proceedings collects extended versions of the 12 papers that were selected for presentation from a pool of 38 submissions."

"The ALENEX workshop provides a forum for the presentation of original research in the implementation and experimental evaluation of algorithms and data structures. This volume collects extended versions of the 12 papers that were selected for presentation."

"The 12 revised full papers were carefully reviewed and selected from numerous submissions. The papers address all aspects of fault tolerance and exception handling, safety modeling, supporting evolution, resilience in service-oriented computing, and applying formal methods in case studies."

"Sistem Penilaian Esai Otomatis (Simple-O) telah dibuat menggunakan algoritma K-Means dan metode Latent Semantic Analysis (LSA). Jawaban karangan siswa pertama-tama akan diklasifikasikan ke dalam kelas-kelas sesuai dengan topik masing-masing nomor, dan akan memisahkannya dari jawaban siswa yang tidak sesuai konteks kemudian akan dilakukan proses LSA yang merepresentasikan kata ke dalam matriks, yang kemudian matriks direduksi menggunakan Singular Value Decomposition dan dilanjutkan dengan mencari norma frobenius yang merupakan nilai dari setiap soal. Pada penelitian ini dilakukan uji coba dengan menggunakan 4 skenario dan hasil penelitian SIMPLE-O menggunakan algoritma K-Means dan LSA menghasilkan akurasi rata-rata sebesar 74% yaitu hasil skenario pengujian 1

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An Automatic Essay Assessment System (Simple-O) has been created using the K-Means algorithm and the Latent Semantic Analysis (LSA) method. Students' essay answers will first be classified into classes according to the topic of each number, and will separate them from student answers that do not fit the context then an LSA process will be carried out which represents the word into a matrix, which is then reduced by using Singular Value. Decomposition and continue by looking for the Frobenius norm which is the value of each question. In this study, trials were carried out using 4 scenarios and the results of the SIMPLE-O research using the K-Means and LSA algorithms produced an average accuracy of 74%, namely the results of the test scenario number 1."

"Dalam suatu ujian, terutama di tingkat universitas, terdapat berbagai macam bentuk soal yang harus dikerjakan oleh mahasiswa. Salah satu bentuk yang banyak digunakan adalah ujian berupa esai. Namun ketika jawaban sudah terkumpul, terdapat kendala yang dihadapi oleh dosen, yaitu melakukan penilaian esai yang banyak memakan waktu dan tenaga. Oleh karena itu, dikembangkanlah suatu sistem untuk membantu pekerjaan dosen tersebut.Simple O merupakan sistem yang dikembangkan di Departemen Teknik Elektro Universitas Indonesia. Mulai dikembangkan pada tahun 2007, Simple O merupakan suatu sistem yang dapat melakukan penilaian terhadap perkerjaan mahasiswa yang bersifat esai. Tujuan dari diciptakannya sistem ini adalah untuk membantu dosen dalam melakukan penilaian terhadap ujian yang telah dilakukan mahasiswa.Simple O menggunakan algoritma Latent Semantic Analysis (LSA) ketika pertama kali dikembangkan. Kemudian sistem tersebut dikembangkan hingga menjadi algoritma Generalized Latent Semantic Analysis (GLSA) dan pada akhirnya dikembangkan algoritma Hybrid.

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On some tests, especially in university, there are lots of types of questions that must be done by the students. One of them is essay. But when the answers are collected, there is a problem that is faced by the lecturer, the amount of time and energy that need to use by them. Because of that, people developped a system that can help the lecturer.

Simple O is a system that is developped in Electrical Engineering Department, University of Indonesia. The development was started in 2007, and it is a system that can grade the work of the students, in the form of essay. The purpose of the invention of this system is to help the lecturer in giving grades to the tests that have been done by the students.

Simple O uses LSA algorithm when the first time of its development. Then the system has been developped into Generalized Latent Semantic Analysis (GLSA) and finally it becomes Hybrid algorithm."

"Skripsi ini membahas tentang perbandingan dua algoritma untuk sistem penilaian esai otomatis (Simple-O), yaitu generalized latent semantic analysis (GLSA) laplacian eigenmaps embedding (LEM) dan hybrid indexing. Kedua algoritma tersebut dibandingkan untuk mengetahui cara kerja kedua algoritma tersebut, kecepatan proses, dan hasil penilaiannya. Perbandingan cara kerja dilakukan dengan membandingkan pseudocode dari masing-masing algoritma. Kecepatan proses dihitung untuk mengetahui algoritma yang lebih cepat dalam menilai esai.Algoritma GLSA hybrid indexing merupakan pengembangan dari algoritma LEM. Perbedaan mendasar dari kedua algoritma tersebut adalah pada perlakuan kata benda dan kata-kata selain kata benda. Penelitian ini menggunakan sampel delapan soal yang dikerjakan oleh 48 mahasiswa (384 data). Dari hasil penelitian, GLSA LEM memiliki total waktu proses 46.51454 detik lebih cepat dari GLSA hybrid indexing. Sedangkan rata-rata waktu proses GLSA LEM dan GLSA hybrid indexing untuk menilai satu jawaban adalah 6-6.6 detik. Hasil penilaian dari GLSA LEM dan GLSA hybrid indexing memiliki tingkat kemiripan tertinggi 95,83% dan terendah 16,67%. Dari percobaan sebanyak delapan soal, lima diantaranya memiliki tingkat kemiripan lebih dari 83,33%.

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This thesis discusses the comparison between two algorithms which used in automated essay grading system (Simple-O). The two algorithms are generalized latent semantic analysis (GLSA) embedding laplacian eigenmaps (LEM) and hybrid indexing. Both algorithms are compared to determine how the algorithms works, processing time, and the scores. Pseudocode can be used to determine how the algorithms are working, The processing time is calculated to find out which algorithm is faster in assessing essays.

GLSA hybrid indexing algorithm is a development from GLSA LEM. The fundamental difference of the two algorithms is in the treatment of a subset of nouns and words other than nouns. This research using samples of eight questions which filled by 48 students (384 data). From the research, GLSA LEM has a total processing time of 46.51454 seconds faster than GLSA hybrid indexing. While the average processing time GLSA LEM and hybrid GLSA indexing to grade the answer is 6 to 6.6 seconds. GLSA LEM and GLSA hybrid indexing grades have the highest similarity level of 95.83% and 16.67% for the lowest similarity level. From the eight questions, five questions have similarity level more than 83.33%."