Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer. Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.
Behaviour of traders mixing beef and pork is very detrimental to consumers, especially followers of Islam because it is related to legal or forbidden food. So, consumers must be protected from these rogue traders. However, differentiating beef and pork is not easy for ordinary people, especially if you only see from one information that is the colour or texture. In this paper, we proposed a new combination of extraction features based on texture and colour features for the classification of beef and pork. The feature of the texture is to see the local information optimally by using a local optimal-oriented pattern (LOOP) so that it can provide better texture information. The colour features that will be used are hue, saturation, and value (HSV). Texture and colour features are combined into one, so that more enrich the information used. The combination of optimal local-oriented pattern features and hue saturation value gives increased accuracy for the classification of pork and beef. The results of tests that have been done show that the success rate of calcification by using a combination of features has increased. accuracy obtained is equal to 99.16 percent, recall 100 percent and precision 98.36 percent. this shows that by utilizing the colour features and texture features can provide improved classification due to increased information that can be used to do the classification.
Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.
Twitter merupakan salah satu layanan media sosial yang sering digunakan (popular) sebagai sarana komunikasi antar pengguna. Kepopuleran twitter tersebut membuat spammer melakukan spam demi tujuan dan keuntungan pribadi. Bot spammer merupakan penyalahgunaan user pada media sosial Twitter. Spammer menyebarkan spam secara bertubi-tubi pada pengguna lain. Spam ini dilakukan bertujuan untuk mencapai trending topik. Aktivitas spam dilakukan dengan meniru pola perilaku pengguna asli agar tidak terdeteksi sebagai tindakan penyalahgunaan Twitter. Penelitian ini mengusulkan pembobotan TF-IDF untuk mendeteksi akun spammer di Twitter berdasarkan tweet dan representasi retweet dari tweet. Tujuan dari penelitian ini adalah untuk mendeteksi Bot Spammer atau Human menggunakan teknik klasifikasi meggunakan algoritma naive bayes. Hasil percobaan terbaik pada pembagian 70% data latih dan 30% data uji mendapatkan akurasi 92% dengan precision dan recall sebesar 100% dan 87.5%. Hal ini menunjukan berhasil mendeteksi akun bot spammer di Twitter.
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