Beef consumption is quite high and expensive in the world. In Indonesia, beef prices are relatively expensive because the meat supply chain from farmers to the market is quite long. The high demand for beef and the difficulty of obtaining meat are factors in the high price of meat. This makes some meat traders cheat by mixing beef and pork (oplosan). Mixing beef and pork is detrimental to beef consumers, especially those who are Muslim. In this paper, we proposed a new strategy for identifying beef, pig, and mixed meat utilizing Fuzzy learning vector quantization (FLVQ) Based on the color and texture aspects of the meat. The HSV (Hue saturation value) approach is used for color features, whereas the GLCM (Gray level co-occurrence matrix) method is used for texture features. This study makes use of primary data collected from the Pasar Bawah Tourism and Cipuan Market in Pekanbaru, Riau Province. The data set consists of 600 photos, 200 each of beef, pork, and mixed. Based on the test scenario, the coefficient of fuzzyness and learning rate affect the accuracy of meat image identification. The proposed strategy has succeeded in classifying pork, beef and mixed meat with the best percentage of accuracy results in theclasses of beef and pork, beef and mixed, pork and mixed meat, respectively, at 100%, 97.5%, and 95%. This demonstrates that the proposed strategy has succeeded in classifying the image of pork, beef, and mixed.

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.

Tomatoes (Lycopersiconeculentum Mill) are vegetables that are widely produced in tropical and subtropic areas. Accordingto (Harllee) tomatoes are grouped into 6 levels of maturity, namely green, breakers, turning, pink, light red, and red. One waythat can be used to classify the level of maturity of tomatoes in the field of informatics is to utilize digital image processingtechniques. This study classifies the maturity of tomatoes using K-Nearest Neighbor (KNN) based on the Red Green Blue andHue Saturation Value color features. The KNN algorithm was chosen as a classification algorithm because KNN is quite simplewith good accuracy based on the minimum distance using Euclidean Distance. The research conducted received the highestaccuracy result of 91.25% at the value of K = 7 with the test data 80. This shows that the KNN algorithm successfully classifiedthe maturity of tomatoes by utilizing the color image of RGB and HSV.