Judul: Named entity tagging untuk dokumen berbahasa Indonesia menggunakan metode berbasis aturan
Abstrak: The main objective of this research is to implement named entity tagging in Indonesian documents. Rule-based method is used for this research. This method uses various rules to identify words or phrase to know if that words or phrase can be classified as named entity. Named entity form obtained from the named entity tagging can be classified in six forms, there are <NAME>, <ORGANIZATION>, <LOCATION>, <CURRENCY>, <DATE>, <TIME>, and <NUMBER> to identify the name of people, organizations, places, currency, date, time, and number. In addition of using rules, dictionary is also used to identify named entity <NAME>, <ORGANIZATION>, and <LOCATION>. The evaluation is done by using 91 document samples from a total of 1.000 documents. Evaluation is based on rules and performance of the system. Evaluation based on the rule is done by comparing the result from the manual test with the result from the system test. The results of the evaluation can identify the accuracy level of the system. There are 3.599 named entities classificated from the total 3.641 named entities in 91 documents. There are 99 unclassified named entities, 87 wrong classified named entities. Entities are classified consist of 514 NAME named entities, 576 ORGANIZATION named entities, 1.376 LOCATION named entities, 117 CURRENCY named entities, 341 DATE named entities, 4 TIME named entities, and 680 NUMBER named entities. From this evaluation, it can be concluded that the more documents are analyzed, the better level of identification can be found. In the evaluation of system performance, evaluation is done by the time and the number of words, and by the time and the many of named entities. From this evaluation, it also concluded that the number of words and the many of named entities contained in the document is very influential on the time required in the process of named entity tagging.
Keyword: Information Retrieval, Question Answering, Document Preprocessing, Named Entity Tagging, Named Entity Recognition, Rule-Based

Judul: Penerapan Metode Rule-Based Dengan Unsupervised  Learning Untuk Pelabelan Dokumen Berbahasa  Indonesia
Abstrak: Penelitian ini menerapkan metode Rule-based dengan Unsupervised Learning untuk pelabelan dokumen teks berbahasa Indonesia. Metode Rule-based menggunakan pola kata untuk menentukan label dari kata yang tidak diketahui. Pola diperoleh melalui proses pembelajaran otomatis dan diurutkan berdasarkan frekuensi kemunculan. Pelabelan kata yang ambigu atau tidak diketahui labelnya dilakukan dengan cara melihat pola kata sekitar dan mengambil pola kata dengan frekuensi kemunculan terbesar. Penambahan metode pengujian jenis imbuhan pada sistem diharapkan dapat meningkatkan pengenalan label pada dokumen. Penelitian menggunakan 102 dokumen teks yang terdiri dari 305.989 token untuk proses pelatihan dan menghasilkan 7.706 rule. Basis data rule yang diperoleh dari proses pembelajaran dan basis data perubahan jenis kata berdasarkan imbuhan digunakan untuk proses pengujian sistem. Pengujian menggunakan 52 dokumen teks yang terdiri dari 131.719 token. Pengujian menghasilkan 97,82 % token yang berhasil dikenali . Pengujian manual terhadap 3 dokumen yang terdiri dari 431 token menghasilkan tingkat kebenaran 85,85%.
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Judul: Approximation of the Median of Poisson Distribution Using the Relation of Poisson-Gamma Distribution
Abstrak: A major drawback of median is that median cannot usually be defined in closed form, even in the cases where their distribution functions are known. This study gives an approximation of the closed form for the median of the Poisson distribution by using the relation of Poisson-Gamma distribution. The approximation uses elementary techniques based on the monotonicity of certain sequences involving tail probabilities of the Poisson distribution and the Central Limit Theorem. Furthermore, a closed form expression for the mean absolute deviation 􀜧|􀜰􀰒 􀵆 􀜯􀝁􀝀􁈺􀜰􀰒􁈻| is also studied. The median is relevant because of the fact that it minimizes the mean absolute deviation of a Poisson random variable (􀜰􀰒􁈻 by considering median as central tendency.
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