Penggunaan Teknik Feature Weighting Untuk Pembersihan Noise Pada Halaman Situs Berita Berbahasa Indonesia

A web page usually consists of information in every page blocks displayed. In some cases, news content displayed in a news website are not entirely relevant or are unrelated to the main content such as navigation panel, copyright, user guide, links, news summary, various advertisement etc. Information blocks irrelevant to the main content is known as web pages noise. This research applies feature weighting technique to improve classification results by detecting a noise in pages of a website. Using feature weighting technique the web is first modelled with Document Object Model(DOM) tree and Compressed Structure Tree(CST) to obtain the general structure and compare the information blocks in awebsite.Information obtained is used to measure and evaluate the importance level of each node created by Compressed Structureed Tree(CST). Based on the tree created and the importance level of each node, this method assign weights on each individual word (feature) in each content block. The weights will be used in web mining process

Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns. The Development and Evaluation of New Web Mining Methods that enhance Information Retrieval and improve the Understanding of User¿s Web Behavior in Websites and Social Blogs.

The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer. With these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs

Using webcrawling of publicly available websites to assess E-commerce relationships

We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates

Deteksi dan Penandaan Noise Pada Halaman Web Berita Berbahasa Indonesia Menggunakan Teknik Feature Weighting

ABSTRAKSI: Sebuah website di Internet memiliki banyak konten informasi dalam tiaptiap blok halaman yang ditampilkan. Kemudian tidak seperti kebanyakan data atau teks konvensional lainnya, suatu halaman web selain mengandung konten utama juga mengandung banyak blok informasi yang tidak berhubungan dengan konten utama misalnya, panel navigasi, copyright, user guide, links, sinopsis suatu berita, berbagai macam iklan dan lain-lain. Dalam hal ini blok-blok informasi yang tidak relevan dengan konten utama pada suatu halaman web disebut sebagai web pages noise.Dalam tugas akhir ini akan digunakan teknik feature weighting untuk meningkatkan performansi hasil klasifikasi dengan mendeteksi noise yang ada pada halaman website. Dengan teknik feature weighting ini suatu halaman web pertama kali akan dimodelkan dengan pohon struktur Dokumen Object Model (DOM) tree dan Compressed structure tree(CST) untuk memperoleh struktur umum dan membandingkan blok-blok informasi dalam suatu website. Informasi yang didapatkan digunakan untuk melakukan pengukuran dan mengevaluasi tingkat kepentingan dari masing-masing node yang terbentuk dari compress struktur tree(CST).Berdasarkan tree yang terbentuk dan tingkat kepentingan dari nilai node yang didapatkan, metoda ini memberikan bobot pada masing-masing individual word (feature) pada masing-masing blok kontent. Hasil pembobotan (weight) akan digunakan dalam proses web mining.Kata Kunci : CST, DOM, deteksi noise, eliminasi noise, web mining.ABSTRACT: A Website on the Internet has shown a lot of information content in each block. Unlike conventional data or text, web pages not only have a main content but also typically contain a large amount of information that is not part of the main content of the pages, e.g., navigation bars, copyright, user guide, links, synopsis and also advertisement. The blocks information that is not the main content or irrelevant information in web pages is called web pages noise.On this final project, feature weighting technique will be used to improve performance of classification with detection the noisy information in web pages. First, web pages will be modelled with structure tree Documents Object Model (DOM) tree and Compressed Structure Tree (CST) to capture the common structure and compare information block in a website. The Information that is captured will be used to measure and evaluate the importance of each node which is built from Compressed Structure Tree.Based on the CST and the importance of weighting value, this method will put on a weight to each feature in each content block. The weighting result will be used to web mining process.Keyword: CST, DOM, Noise Detection, Elimination, web minin

Development of bambangan (Mangifera pajang) carbonated drink

Mangifera pajang Kostermans or bambangan is a popular fruit among Sabahan due to its health and economic values. However, the fruit is not fully commercialized since it is usually been used as traditional cuisine by local people. Thus, development of bambangan fruit into carbonated drink was conducted to produce new product concept. The objectives of this study were to conceptualize, formulate, evaluate consumer acceptance, and determine physicochemical properties and nutritional composition of the accepted product. Method used in conceptualising the product was based on questionnaire. The consumer acceptance was evaluated based on descriptive and affective tests with four product formulations tested. The physicochemical properties on carbon dioxide volume, colour, pH, total acidity, total soluble solid (TSS) and viscosity were highlighted, meanwhile nutritional composition on fat, protein, carbohydrates and energy content were determined. About 77% respondents gave positive feedback, and 69% respondents decided this product is within their budget. The formulation of 5% bambangan pulp, 70% water, 25% sugar and 0.2% citric acid was highly accepted in descriptive and affective tests with 4.4 and 6.39 mean scores, respectively. The physicochemical properties and nutritional composition of the acceptance product were in optimum value except for colour, total acidity and TSS. Overall, this study showed that the product has high potential to be commercialized as new product concept, and heritage of indigenous people can be preserved when this fruit is known regionally