Algoritme Genetika untuk Mengurangi Galat Prediksi Metode Item-based Collaborative Filtering

Sistem rekomendasi adalah suatu teknik dan perangkat lunak yang dapat memberikan suatu anjuran yang bermanfaat bagi pengguna dalam proses pengambilan keputusan. Metode yang banyak digunakan dalam sistem rekomendasi adalah collaborative filtering. Pengembangan dari pendekatan untuk mengurangi kesalahan prediksi sudah menjadi sebuah topik penelitian yang aktif dalam sistem rekomendasi dengan collaborative filtering, karena akurasi dari prediksi memainkan peran yang sangat penting untuk preferensi kepada pengguna. Salah satu permasalahan yang terjadi pada collaborative filtering adalah jika persebaran rating secara keseluruhan jarang, yang menyebabkan sulit untuk mengidentifikasi neighborhood yang benar dan relevan untuk membuat prediksi. Terdapat  dua macam metode collaborative filtering yaitu user-based collaborative filtering dan item-based collaborative filtering. Metode item-based memberikan kualitas prediksi yang lebih baik daripada metode user-based.Optimasi metode user-based collaborative filtering dengan algoritme genetika untuk memperbaiki nilai kemiripan user mampu mengurangi galat prediksi. Pada penelitian ini dikembangkan optimasi sistem rekomendasi dengan menerapkan algoritme genetika untuk memperbaiki nilai kemiripan item. Penelitian ini menggunakan dataset movielens dan book-crossing sebagai bahan evaluasi. Evaluasi dilakukan dengan variasi jumlah neighbors terhadap metode pengukur kemiripan yang digunakan, seperti: cosine similarity, pearson correlation dan adjusted cosine. Usulan pendekatan mampu mengurangi galat prediksi dari sistem rekomendasi secara signifikan. Galat prediksi terendah diperoleh metode adjusted-cosine berevolusi pada kedua dataset

Computational Intelligence for the Micro Learning

The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis. The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results