Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.

One of the most common issue in information retrieval is documents ranking. Documents ranking system collects search terms from the user and orderly retrieves documents based on the relevance. Vector space models based on TF.IDF term weighting is the most common method for this topic. In this study, we are concerned with the study of automatic retrieval of Islamic Fiqh (Law) book collection. This collection contains many books, each of which has tens to hundreds of pages. Each page of the book is treated as a document that will be ranked based on the user query. We developed class-based indexing method called inverse class frequency (ICF) and book-based indexing method inverse book frequency (IBF) for this Arabic information retrieval. Those method then been incorporated with the previous method so that it becomes TF.IDF.ICF.IBF. The term weighting method also used for feature selection due to high dimensionality of the feature space. This novel method was tested using a dataset from 13 Arabic Fiqh e-books. The experimental results showed that the proposed method have the highest precision, recall, and F-Measure than the other three methods at variations of feature selection. The best performance of this method was obtained when using best 1000 features by precision value of 76%, recall value of 74%, and F-Measure value of 75%.

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AbstrakIdentifikasi manusia berbasis data gigi umum digunakan dalam forensik. Dalam

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Background: Mandibular bone on panoramic radiographs has been proven to be useful for identifying postmenopausal women with low skeletal bone mineral density. One of the important parts of mandibular bone is trabecular bone. Trabecular bone architecture is one of the factors that governs bone strength and may be categorized as a contributor to bone quality. Purpose: The purposes of this study were to develop a computer-aided system for measuring trabecular bone line strength on panoramic radiographs in identifying postmenopausal women with osteoporosis and to clarify the diagnostic efficacy of the system. Methods: Reduction and expansion of trabecular bone sample images using a two level Gaussian pyramid for removing noises and small segments were first introduced. Then, line strength at each pixel was calculated based on its existence on the trabecular bone with emphasizes line segment which has similar orientation with the root of tooth. The density was measured with respect to line strength of segment structure which has similar orientation with the root of tooth, either on the left and the right in the mandibular bone. Number of pixels in the line segment area was compared with a threshold value to determine whether normal or osteoporosis. Results: From experiment on 100 data, the accuracy of 88%, sensitivity of 92%, and specificity of 86.7% were achieved. Conclusion: The computer-aided system of trabecular bone analysis may be useful for detecting osteoporosis using panoramic radiographs.Latar belakang: Tulang mandibula pada panoramik radiografi telah banyak diteliti dan terbukti mampu digunakan untuk mengidentifikasi wanita pasca menopause dengan menggunakan bone mineral density rendah. Salah satu bagian tulang mandibula yang penting adalah tulang trabekula. Arsitektur tulang trabekula merupakan salah satu dari faktor-faktor yang mempengaruhi kekuatan tulang dan dapat digolongkan sebagai kontributor bagi kualitas tulang. Tujuan: Penelitian ini bertujuan untuk membangun sebuah sistem dengan bantuan komputer untuk mengukur kekuatan garis pada tulang trabekula dan menggunakannya untuk mendeteksi osteoporosis pada wanita postmenopause. Metode: Dilakukan sampling pada sebagian tulang mandibular yang menghasilkan sebuah sampel citra. Sampel citra ini selanjutnya diperbaiki dari derau (noise) dengan menggunakan piramida Gaussian dua level. Kekuatan garis pada tiap piksel dihitung berdasarkan orientasi segmen garis tulang trabekula yang sejajar dengan akar gigi. Setelah dilakukan binerisasi, luasan segmen yang dihasilkan dihitung dan dibandingkan dengan sebuah nilai ambang. Bila luasan melebihi nilai threshold maka dikategorikan sebagai normal. Sebaliknya bila luasan dibawah nilai threshold, dikategorikan sebagai osteoporosis. Hasil: Berdasarkan eksperimen terhadap 100 data, sistem mampu mencapai akurasi identifikasi sebesar 88%, sensitivitas 92%, dan spesifisitas 86,7%. Kesimpulan: Sistem analisa trabecular bone dengan bantuan komputer ini dapat digunakan oleh para dokter gigi untuk mendeteksi osteoporosis menggunakan panoramik radiografi.