Judul: Pembuatan Modul Rekomendasi pada OpenCart Menggunakan Metode Item-Based Collaborative Filtering
Abstrak: This research aims to add a recommender module in OpenCart CMS. One of the method is item-based collaborative filtering that can reduce the execution time of calculation. This study uses adjusted cosine similarity to calculate the similarity between books, weighted sum method to calculate the books rate prediction, and mean absolute error to calculate recommendacy accuracy. In order to get the recommendation, user have to login and give ratings to the books. Then, adjusted cosine similarity calculates the similarity between books based on user’s rate. Based on the similarities between books, weighted sum method calculates the books rate prediction. Before a book is recommended to the user, type of book from the prediction are first matched with the type of book rated by the user. This research uses 300 books and 30 users. The result shows that only 17 users can get recommendations. Evaluation is conducted by analyzing the execution time and recommendacy accuracy. It is found that the execution time is 1.60 seconds and the mean absolute error is 0.15.
Keyword: 

Judul: Pemodelan Personalisasi Rekomendasi Buku dengan Pendekatan Association Rule Mining
Abstrak: Perpustakaan X telah menyediakan Online Public Access Catalog (OPAC) sebagai sarana penelusuran informasi koleksi buku. Namun, terkadang informasi buku yang muncul tidak relevan sesuai kebutuhan pengguna. Salah satu cara mengatasi masalah ini dengan sistem personalisasi rekomendasi buku yang sesuai kebutuhan pengguna. Sebagian besar penelitian berdasarkan sejarah transaksi peminjaman buku dalam memberikan rekomendasi buku kepada pengguna dan menggunakan algoritme Apriori untuk menghasilkan aturan asosiasi antar buku. Dalam penelitian ini hasil analisis dari aturan asosiasi dilanjutkan untuk rancangan model personalisasi rekomendasi buku. Tujuan penelitian ini adalah menerapkan association rule mining pada data transaksi peminjaman buku di Perpustakaan X; merumuskan rekomendasi buku berdasarkan aturan asosiasi dari pendekatan association rule mining; membuat model personalisasi rekomendasi buku pada Perpustakaan X dengan pendekatan association rule mining. Model personalisasi rekomendasi buku dibuat dengan menerapkan association rule mining pada data transaksi peminjaman buku lalu merumuskan rekomendasi buku berdasarkan aturan asosiasi dari pendekatan associaton rule mining. Berdasarkan perumusan rekomendasi buku dari hasil aturan asosiasi dirancang sebuah model personalisasi rekomendasi buku untuk Perpustakaan X. Manfaat penelitian ini adalah memberikan informasi buku sesuai dengan kebutuhan pengguna melalui perancangan model personalisasi rekomendasi buku. Metode dalam penelitian ini adalah association rule mining menggunakan algoritme Apriori. Tools yang digunakan dalam association rule mining menggunakan algoritme Apriori ini adalah Waikato Environment for Knowledge Analysis yang dikenal dengan singkatan WEKA. Data yang digunakan dalam penelitian adalah data transaksi peminjaman buku di Perpustakaan X tahun 2016-2017 sebanyak 5,509 data transaksi peminjaman buku. Data tersebut dikelola dalam basis data transaksi peminjaman buku menggunakan aplikasi Senayan Library Information Management System (SLiMS). SLiMS merupakan software open source yang dilisensikan di bawah GPL 3 dan digunakan oleh Perpustakaan X dalam melayani pengguna perpustakaan. Tahapan penelitian meliputi identifikasi masalah; pengumpulan data transaksi peminjaman buku; melakukan praproses data terhadap data transaksi peminjaman buku; menerapkan algoritme Apriori yang bertujuan untuk menentukan pola peminjaman buku; melakukan penyusunan rekomendasi buku berdasarkan aturan asosiasi; membuat perancangan model personalisasi rekomendasi buku Hasil penelitian menunjukkan bahwa aturan yang ditemukan pada minimum support 0.1% dan minimum confidence 10% menghasilkan 42 aturan asosiasi. Hubungan aturan asosiasi yang kuat yaitu C657 (Akuntansi) dan C658 (Manajemen Umum) dengan nilai support 2.6% dan confidence 14%. Perumusan rekomendasi buku dilakukan dengan memilih maximum support kemudian memilih maximum confidence. Model perancangan sistem personalisasi rekomendasi buku yang diusulkan terintegrasi dengan web dan e-mail pengguna dengan menggunakan data pengguna dan data transaksi peminjaman buku.
Keyword: Algoritme Apriori, Association Rule Mining, Data Transaksi Perpustakaan, Personalisasi, Rekomendasi Buku

Judul: Phylogeography and Species Limits in the Papuan White Snake (Micropechis ikaheka) (Reptilia: Serpentes: Elapidae)
Abstrak: Papua white snake, Micropechis ikaheka, has been distingushed into two subspecies M.i. ikaheka and M.i. fasciatus. However, due to lack of data on their morphology, distribution and ecology, both subspecies often ignored in scintific discussions and refers only byas , Micropechis ikaheka. This study was designed to document the differences between M.i. ikaheka and M.i fasciatus by using scales characteristics and reproductive organ or hemipenes. The study also examine, zoogeography, habitat, diet and cannibalism of both subspecies to better understanding the the ecology of one of the most venomous snakes in the world. This study consists of three topics with method and also different in the size samples, respectively. The first study is the histology and microtopography of the skin were studied using paraffin method and Scanning Electron Microscope (SEM). The second is the hemipenis were examined by following Myers and Cadle (2003), and Zaher and Prudente (2003). The third is the abdominal surgery was carried out to document diet type and the investigate phenomenon of cannibalism in the snake. Specimens for this study came from the collection from Museum Zoologicum Bogoriense (MZB) and Universitas Negeri Papua’s Laboratory (LZU). The research result also indicated that the markers of skin and hemipenis described the same structure and microornamentation in all M. ikaheka groups being compared. Based specimens examined morphological descriptions do not s support the further differentiation in Micropechis. In short, two markers that used in this study could not sufficiently indicate the differences in the level of species and subspecies of M. ikaheka. The study, however, were able to delineate zoogeography and skin color patterns, which fall into four groups: M. i. ikaheka; M. i. fasciatus. M. i. ssp. “intermediate”; and M. i. ssp “black”. This study also revealed that color variations did not overlap in their geographic distribution. It is therefore, it suggested that environmental factors may contribute to the skin color variations. It also assumed that habitat discontinuous and biogeography factors may also cause the loss of the connection between the original populations (vicarians) or follow colonization events (dispersal). The examined of 22 stomach contents of M. ikaheka shows this species is carnivorous, and prey on vertebrates such as amphibians, fish, reptiles, and Mammals. These snakes are opportunistic predators since they prey on all that can be captured, but mainly reptiles (61.5%), followed by mammals and fish (both 15.4%), and amphibians (7.7%). Although categorized as terrestrial animal, the diet study showed that M. ikaheka snakes often go into the water to hunt for fish (the sample from Yapen island), including eel (the sample from Aru island). Based on prey items indentified, there is evidence that M. ikaheka is cannibal. At this time, there still no record on reason for cannibalism, but it is assumed that cannibalism was due to competition between populations and means to control population.
Keyword: geographic variation, white snake, papua