Judul: Fungsi Agregat pada Categorical Data Skyline Search untuk Pencarian Dokumen Multi-Keyword
Abstrak: Tinjauan pustaka merupakan langkah awal dalam memulai penelitian untuk pemahaman yang lebih mendalam tentang minat penelitian, namun untuk menemukan literatur yang relevan dengan minat penelitian tidaklah mudah dan membutuhkan waktu. Skyline query merupakan metode yang telah dikenal untuk memilih sejumlah kecil objek data yang menarik, yang tidak didominasi oleh objek lain, suatu objek p dikatakan mendominasi objek q jika p sama dengan q pada semua atributnya, dan p setidaknya lebih baik dari q pada satu atribut. Categorical Data Skyline Search (CDSS) adalah algoritme yang dapat digunakan untuk memfilter objek Skyline dalam tipe data kategorikal seperti dokumen. Algoritme ini menggunakan Extended Distance Wu and Palmer (DEWP) untuk menghitung jarak antara kueri pengguna dan keyword dokumen, setiap keyword dokumen dan kueri pengguna direpresentasikan sebagai node dalam ontologi ACM CCS. Namun, pada penelitian ini tidak disertakan penjelasan lengkap bagaimana menghitung jarak antara kueri dan keyword jika keyword berjumlah lebih dari satu. Penelitian ini bertujuan untuk menggunakan algoritme CDSS untuk mencari dokumen Skyline yang diwakili oleh lebih dari satu keyword dengan menambahkan fungsi agregat (rata-rata, minimum, maksimum) pada algoritme CDSS khususnya dalam perhitungan DEWP. Penelitian ini menggunakan dokumen tesis jurusan ilmu komputer IPB University sebagai sumber data. Keyword dokumen diekstraksi menggunakan metode Term Frequency-Inverse Term Frequency (TF-IDF). Keyword yang terkumpul dipetakan dalam pohon ontologi campuran yang mengacu pada ontologi Association of Computing Machinery Computing Classification System 2012 dan Computer Science Ontology (CSO) sebagai standar ontologi dalam ilmu komputer. Algoritme Skyline query yang digunakan untuk menentukan dokumen Skyline adalah Block nested Loop (BnL). Metode evaluasi yang digunakan adalah perhitungan rasio Skyline berdasarkan jumlah dokumen Skyline yang diperoleh dari masing-masing fungsi agregat dalam CDSS. Berdasarkan nilai rasio Skyline, CDSS dengan menggunakan DEWP maksimum memiliki rasio terbaik dibandingkan dengan DEWP rata-rata dan DEWP minimum., Literature review is the first step in starting research for a deeper understanding of research interests, but finding literature relevant to research interests is not easy and takes time. Skyline query is a well-known method for selecting a small number of data objects of interest, which are not dominated by other objects, an object p is dominate object q if p is equal to q on all its attributes, and p is at least better than q on one attribute. Categorical Data Skyline Search (CDSS) is an algorithm that can be used to filter Skyline objects in categorical data types such as documents. This algorithm uses Distance Extended Wu and Palmer (DEWP) to calculate the distance between user query and document keyword, each document keyword and user query is represented as a node in the ACM CCS ontology. However, this research does not include a complete explanation of how to calculate the distance between a query and a keyword if keyword is more than one. This research aims to use the CDSS algorithm to search for Skyline documents that are represented by more than one keyword by adding an aggregate function (average, minimum, maximum) to the CDSS algorithm, especially in DEWP calculations. This research uses the thesis document of the computer science department of IPB University as the data source. Document keywords were extracted using
Keyword: block nested loop, categorical data skyline search, ontology, term frequency inverse term frequency, thesis

Judul: Penentuan Subjek Otomatis Berbasis DDC pada Dokumen Perpustakaan miMenggunakan Algoritme Lin Similarity
Abstrak: Subject classification for library document with Dewey Decimal Classification (DDC) system is difficult to perform manually. The goal of this research is to make an application that automatically do perform subject classification for library document using similarity method. We use Natural Language ToolKit (NLTK) with Wordnet module to find similarity between keyword and DDC class. DDC is a hierarchy classification. We use Lin Similarity to find similarity between two words, with Brown corpus for Information Content (IC) of Wordnet. Wordnet can find similarity for only noun and verb, so we do not process other kinds of word. We use 30 documents combination of theses and dissertations in Bogor Agriculture University. We use 3 different methods to decide the relevant class of DDC which is similar to a document keyword. The first method is maximum-maximum method, the second one is maximum-average method, and the third method is maximum-minimum method. The first method results in 6 documents having the same main class, 2 documents having the same division class, and 0 document having the same section class. The second method results in 5 documents having the same main class, 1 document having the same division class, and 0 document having the same section class. The third method results in 3 documents having the same main class, 2 documents having the same division class, and 0 document having the same section class.
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Judul: Co-infection of Infectious Myonecrosis Virus (IMNV) and Vibrio harveyi in Pacific White Shrimp (Litopenaeus vannamei)
Abstrak: L. vannamei production in Indonesia was growing intensively. In 2006, IMN (infectious myonecrosis) disease was confirmed spread to Indonesia shrimp farm and declined shrimp production. The study was conducted to investigate effect of dose of V. harveyi on co-infection with IMNV in L. vannamei as well as development of viral infection. Shrimps juvenile were oral infected with IMNV infected shrimps 10 % feeding rate during 3 days and co-infected with 106, 107 and 108 cfu/ml V. harveyi. Mortality rate was 0 % in control and single infection of V. harveyi except 108 cfu/ml treatment. Mortality pattern demonstrated on co-infection was faster and higher than single IMNV infection in 14 days observation. The density of green colony Vibrio in hepatopancreas of co-infected shrimps collected in 2, 4, 6, 8 and 10 days post infection were higher than V. harveyi single infected (significantly in 10 days post infection). There were no difference of IMN disease development between co-infection and IMNV single infection. It was confirmed by visual gross sign appeared, tissue and lymphoid organ histophatology, organ abnormality, and PCR test. In conclusion, IMN disease caused higher and faster mortality on co-infection with V. harveyi, but not affect to IMN disease development.
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