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Abstrak Beberapa tahun belakangan ini, muncul perusahaan-perusahaan penyedia jasa transportasi yang menggunakan aplikasi berbasis android dalam proses pelayanannya atau biasa disebut dengan transportasi online. Hal ini dilakukan untuk meningkatkan pelayanan terhadap pengguna jasa transportasi . Hadirnya transportasi online seperti Gojek, Grab dan Uber menimbulkan masalah sosial antara supir transportasi online dan supir transportasi non aplikasi. Penyebabnya dikarenakan sebagian besar masyarakat beralih menggunakan transportasi online, sehingga pendapatan supir transportasi non aplikasi menurun. Pada penelitian ini, dilakukan analisa sentimen terhadap tweet berbahasa Indonesia tentang transportasi online dengan menggunakan metode pembobotan Hybrid TF-IDF dan kNN sebagai metode klasifikasinya. Hasil terbaik dari pengujian cross validation pada uji variable k adalah k=5 dengan nilai akurasi 70%, presisi kelas positif 68%, presisi kelas negatif 75%, recall kelas positif 82%, recall kelas negatif 59%,  f-measur kelas positif 74% dan f-measure kelas negatif 65%. Abstract  In recent years, there are same new companies which uses android applications in their service process or commonly called with online based transportation. This case do for improve their service to online based transportations customers. The presence of online based transportation like Gojek, Grab, and Uber inflict social problem between online based transportations drivers with non-aplication based  transportations drivers. The is due to most of the people change over to online based transportation, so non-aplication based  transportations drivers income decreased. In this research,  sentiment analysis against Indonesian tweets about online based transportation by using Hybrid TF-IDF weighting method and kNN classification method. The best cross validation result on the test k variable are k=5 with accuracy value  70%, positive class precision 68%, negative class precision 75%, positive class recall 82%, negative class recall 59%, positive class f-measure 74%, and negatif class f-measure 65%.

Analisa Sentimen Tweet Berbahasa Indonesia dengan Menggunakan Metode Pembobotan Hybrid TF-IDF pada Topik Transportasi Online