Judul: Text Feature Weighting for Summarization of Document Bahasa Indonesia Using Genetic Algorithm.
Abstrak: This thesis aims to perform text feature weighting for summarization of document bahasa Indonesia using genetic algorithm. There are eleven text features, i.e, sentence position (f1), positive keywords in sentence (f2), negative keywords in sentence (f3), sentence centrality (f4), sentence resemblance to the title (f5), sentence inclusion of name entity (f6), sentence inclusion of numerical data (f7), sentence relative length (f8), bushy path of the node (f9), summation of similarities for each node (f10), and latent semantic feature (f11). We investigate the effect of the first ten sentence features on the summarization task. Then, we use latent semantic feature to increase the accuracy. All feature score functions are used to train a genetic algorithm model to obtain a suitable combination of feature weights. Evaluation of text summarization uses F-measure. The F-measure directly related to the compression rate. The results showed that adding f11 increases the F-measure by 3.26% and 1.55% for compression ratio of 10% and 30%, respectively. On the other hand, it decreases the F-measure by 0.58% for compression ratio of 20%. Analysis of text feature weight showed that only using f2, f4, f5, and f11 can deliver a similar performance using all eleven features.
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Judul: Peringkasan Teks Bahasa Indonesia dengan Pemilihan Fitur C4.5 dan Klasifikasi Naive Bayes
Abstrak: This research summarized Indonesian text documents using naive bayes (NB) classification method. Segmentation of the documents into sentences and feature computation are the initial stages of training the system to determine which sentences are classified as summary. The classification used 11 features (f1-f11). The features are selected using C4.5 decision tree to determine the features that affect the summary, reduce the number of features and speed up the summarization. The accuracy of summarization using 10 features (f1-f10) was 34.63%, 37.96%, and 28.14% for compression rate (CR) of 10%, 20%, and 30%, respectively. Adding f11 and C4.5 produced an accuracy of 52.45%, 51.49% and 51.35% for CR 10%, 20%, and 30%, respectively. Text summarization using NB classification, C4.5 feature selection, and additional f11 feature produced better accuracy and faster summarization.
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Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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