Evaluasi Daftar Stopword Bahasa Indonesia

Pada sistem temu kembali informasi berbentuk teks maupun text mining, terdapat proses pengindeksan. Teks diproses dengan tujuan mengintisarikan informasi berbentuk teks tersebut. Salah satu proses yang dilakukan adalah stopword filtering,  beberapa kata yang tidak layak diindeks diabaikan berdasar sebuah daftar. Di dalam sistem berbahasa Indonesia, terdapat beberapa versi daftar stopword yang tersedia bebas. Penelitian ini bertujuan mengevaluasi daftar yang telah tersedia tersebut. Tujuan akhir dari penelitian ini adalah telaah daftar yang tersedia berdasarkan tata bahasa Indonesia, cara penyusunan, dan kebiasaan perambah internet. Dari hasil telaah diperoleh fakta bahwa daftar yang tersedia dibangun dengan analisis frekuensi kemunculan kata pada sebuah korpus (corpus) teks, tanpa memperhatikan jenis kata ataupun kebiasaan pengguna internet. Hasil lain penelitian ini  adalah beberapa rekomendasi lebih lanjut bagi para peneliti di bidang ini ketika membutuhkan daftar stopword bahasa Indonesia, yaitu daftar yang memperhatikan jenis kata dan kebiasaan pengguna internet melalui mesin perambah yang tersedia.AbstractMost of text-based information retrieval system uses indexing process. The system processes the texts in order to obtain the information essence. One of the process is stopword filtering, several words are being ignored based on a stopword list. Several Indonesian stopword list are available openly. Therefore, this paper evaluates the available lists based on Indonesian formal grammar, its preparation technique, and internet surfer habit. The results show all of the list are developed by term frequency analysis based on a text corpus. This paper also provides several recommendations for researcher both in text mining and text-based information retrieval field, developing stoplist by the word type and internet surfer habit

Pengembangan Sistem Pengenalan Wajah Dengan Metode Pengklasifikasian Hibrid Berbasis Jaringan Fungsi Basis Radial Dan Pohon Keputusan Induktif

Face recognition is a difficult task mostly because of the inherent variability of the image formation process ranging from the position/cropping of the face and its environment (distance and illumination) is totally controlled, to those involving little or no control over the background and viewpoint. Moreover, those are allowing for major changes in facial appearance due to factors expression, aging, and accessories such as glasses or changes in hairstyle. A solution has been proposed by considering hybrid classification architectures deal with the benefit of robustness via consensus provided by ensembles of Radial Basis Functions (RBF) networks and categorical classification using decision trees. A specific approach considers an ensemble of RBF Networks through its ability to cope with variability in the image formation. The experiments were carried out on images drawn randomly 50 unique subjects totalling to 500 facial images with rotation ± 50 encoded in greyscale. The faces are then normalized to account for geometrical and illumination changes using information about the eye location. Specifically performance true positive by Ensambles RBF1 (ERBF1) increased on ± 13,86% measures up to RBF while ERBF2 by ± 15,93%. On the contrary the false negative rate decreased by amount of ±5,8% for ERBF1 and somewhat less to ±5,6% for ERBF2. When the connectionist ERBF model is coupled with an Inductive Decision Tree - C4.5 - the performance improves over the case while only the connectionist ERBF module is used