PENDIDIKAN AGAMA ISLAM (PAI) MULTIKULTURAL PERSPEKTIF PEMBELAJARAN INTEGRATIF

Tulisan ini mengkaji tentang PAI multikultural sebagai model pembelajaran integratif yang memadukan antara ilmu pengetahuan dan agama. Dari aspek konten, multikulturalisme mengkaji keragaman bangsa, suku, warna kulit, bahasa, agama dan keragaman lain yang terbentang dalam realitas sosial kehidupan manusia sebagai ayat-ayat Allah yang bersifat kauniyah. Sedangkan PAI mempelajari normativitas ajaran Islam dan dimensi historis yang banyak termuat dalam al-Qur’an sebagai ayat-ayat Allah yang bersifat qauliyah. Pembelajaran kedua bidang keilmuan tersebut sama-sama sebagai proses pencarian kebenaran yang merujuk kepada Allah sebagai episentrum kebenaran Hakiki dan sumber ilmu pengetahuan. Dari konsep pembelajaran integratif yang dikemukakan pemikir Islam dan Barat, PAI multikultural berada pada pola relasi antardispliner, yaitu integrasi antara ilmu umum dan ilmu agama. PAI multikultural sebagai pembelajaran integratif tergambar pada aspek: tujuan, materi, metode, media dan sumber belajar dan penilaian yang saling terintegrasi dalam membentuk kompetensi beragama peserta didik secara integral, yaitu: keterpaduan antara unsur duniawi dan ukhrawi, antara dimensi spritual dan intelektual, antara ranah personal dan sosial dalam konteks membangun harmoni kehidupan warga sekolah, masyarakat dan bangsa Indonesia yang pluralistik dari segala aspeknya.

A Bayesian Network and Ontology Based Semantic Annotation Framework

The internet has become an attractive avenue for global e-business, e-learning, knowledge sharing, etc. Due to continuous increase in the volume of web content, however, it is not practically possible for a user to extract information by browsing and integrating data from a huge amount of web sources retrieved by the existing search engines. The semantic web technology aims to answer this and many other information extraction related issues by providing a suite of tools for integrating data from different sources. To take full advantage of semantic web, however, it is necessary to annotate existing web pages with semantics. Another difficulty that logically arises while accessing information over the web is the presence of unstructured, ungrammatical and incoherent format such as online advertisements, emails, reports etc. This thesis aims to answer few of the concern raised above and presents a semantic annotation framework that is capable of extracting relevant data from unstructured, ungrammatical and incoherent data sources and semantically annotating it. The semantic annotation framework is named BNOSA and it employs ontology and Bayesian network to perform semantic annotation. As the data is unstructured and ungrammatical, the framework exploits the use of context keywords along with domain knowledge to find the location of the data of interest in relevant data sources. Due to the variable size of information available on different web pages, it is often the case that the extracted data contains missing values for certain variables of interest or it may extract more than one value (conflicting values). It is desirable in such situations to predict the missing values and to resolve the conflicts by selecting the most relevant value. BNOSA employs Bayesian networks for missing value prediction and conflict resolution. The framework is extensible as it is capable of dynamically linking any problem domain given a pre-defined ontology and a corresponding Bayesian network. Experiments have been conducted to analyze the performance of BNOSA on several problem domains. The sets of corpora used in the experiments belong to selling-purchasing websites where product information is entered by ordinary web users in a structure free format. The results show that BNOSA performs better than the other recently proposed semantic annotation frameworks.