Judul: Web Document Clustering Through Metafile Generation for Digraph Structuring Using Document Index Graph Algorithm
Abstrak: Nowaday, the increased volume of data, especially on text documents and their implications for the issue of the accuracy of the search results and information retrieval has led to the development and the use of data management and analysis techniques. The technique is used to split the document into different groups so that the documents contained in a group will contain the same topic and related to each other. Therefore we need a method of grouping documents in order to facilitate the retrieval of information according to user needs. Clustering is a technique that can be used to discover linkages between documents. This technique separates a set of documents into several groups or clusters by calculating the similarity between documents. Documents that have been clustered, will help users finding the information needed and will increase the speed of access to that information. The scope of this research consists of : 1) the test and training documents using REUTERS newswire-21578; 2) algorithm generates output in metafile form that will be used as input to represent the structure of digraphs. Research methods perform literature studies, data preprocessing, implementation of Document Index Graph (DIG) algorithm, generating the metafile for digraphs construction, digraphs representation, and analysis of clustering result. Instead of three core processes tokenization , stop-word removal and stemming, data preprocessing stage is concerned with dimentional reduction mechanism. Dimentional reduction will determine the document frequency threshold values before clustering process. The results of data preprocessing will be followed by the implementation of the DIG algorithm. The algorithm calculates the weight of words that often appears in the document being processed. The results bring a bag of words that frequently appear more than 20 times. The output of this result is written into a metafile that will be used as input for the digraph structuring and representation. This research analyzes the results by calculating precision, recall and accuracy percentage on clustering result. DIG algorithm implementations using dimentional reduction mechanism through data preprocessing stage is able to produce an accuracy above 70 %.
Keyword: Document Index Graph, Clustering, REUTERS dataset, metafile, digraph

Judul: Grouping Scientific Publication Documents Based on Field of Expertise Using Agglomerative Hierarchical Clustering Method
Abstrak: Dengan setiap pakar memiliki keahlian yang berbeda, dan keahlian ini dapat diidentifikasi melalui dokumen-dokumen yang paling menunjukkan kemampuannya. Sebuah model klasterisasi diperlukan untuk mengklasifikasikan karya ilmiah secara otomatis ke dalam domain keahlian yang berbeda. Dengan adanya pengelompokan ini menjadi salah satu cara untuk mengklaim atau memvalidasi dari bidang kepakaran seseorang. Penelitian ini menggunakan algoritma Latent Dirichlet Allocation (LDA) untuk mengurangi dimensi data dan menggunakan metode Agglomerative Hierarchical Clustering yang ditingkatkan dengan dan T-distributed Stochastic Neighbor Embedding (T-SNE) sebagai klasterisasi dokumen publikasi ilmiahnya. Temuan penelitian ini secara efektif mengelompokkan dokumen, yang dibuktikan dengan nilai koefisien silhouette sebesar 0.544 dengan nilai yang lebih tinggi menunjukkan klaster yang lebih baik. Model yang dibangun dievaluasi dengan membandingkan cluster yang dihasilkan dengan klaim yang dinyatakan. Hasilnya, 25% hasil pencocokan tidak sesuai dan 75% sesuai., Each expert has different expertise, and this expertise can be identified through the documents that best demonstrate his or her skills. A clustering model is needed to automatically classify scientific papers into different domains of expertise. This clustering is one way to claim or validate one's area of expertise. This research uses the Latent Dirichlet Allocation (LDA) algorithm to reduce the dimensionality of the data and uses the Agglomerative Hierarchical Clustering method enhanced with and T-distributed Stochastic Neighbor Embedding (T-SNE) as its clustering of scientific publication documents. The findings of this study effectively cluster the documents, as evidenced by the silhouette coefficient value of 0.544 with higher values indicating better clusters. The built model was evaluated by comparing the resulting clusters with the stated claims. As a result, 25% of the matching results did not match and 75% matched.
Keyword: clustering, lda, t-SNE, dokumen, klasterisasi, agglomerative hierarchical clustering, documents

Judul: Kawin Berulang Pada sapi, Kausa Kuman Aspesifik Dan Pengobatannya
Abstrak: Kawin berulang pada sapi betina adalah merupakan bantuk atau geja1a inferti1itas, dimana sapi-sapi yang menderita kawin beru1ang tersebut mempunyai siklus berahi yang normal atau hampir normal dan sudah dikawinkan dua ka1i atau 1ebih dengan pejantan atau semen pejantan fertil, tapi tetap belum bisa bunting. Pemeriksaan klinis terhadap sapi tersebut tidak memberikan gambaran tertentu yang dapat menerangkan kegaga1an konsepsi yang dialami (Roberts, 1971).
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