Analisis Sentimen Self-driving car dengan Sentiment Confident Terbaik
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Self-driving car merupakan inovasi dalam teknologi transportasi khususnya kendaraan roda empat yang merupakan bagian dari revolusi Industri 4.0. Self-driving car. Self-driving car mulai banyak bermunculan dari berbagai macam perusahaan, diantaranya yang cukup besar adalah perusahaan Google, Tesla, CMU, Toyota, Audi, Volvo, Nissan dan Merchedes Benz. Terkait dengan mulai banyaknya perusahaan yang mengembangkan Self-driving car, banyak masyarakat yang memberikan pandangannya terkait dengan teknologi ini. Dari data yang didapat dari https://data.world terkait dengan Selfdriving car memiliki lima class attribute, diantaranya sangat negative, agak negative, netral, agak positif, sangat positif. Dari data tersebut diambil hanya data yang memiliki nilai sentiment confident terbaik, yaitu satu. Metode klasifikasi yang digunakan adalah metode bayes, yaitu Naïve Bayes, Bayes Net, Naïve Bayes Multinomial, Naïve Bayes Multinomial Text dan Naïve Bayes Multinomial Updateable. Hasil klasifikasi yang didapat dengan metode Naïve Bayes sebesar 67,92, Bayes Net sebesar 79,84, Naïve Bayes Multinomial sebesar 82,23, Naïve Bayes Multinomial Text sebesar 78,11, Naïve Bayes Multinomial Updateable 82,23, dan Naïve Bayes Updateable 67,92.
Kata kunci— self-driving car, sentiment analisys, analisis sentiment, mobil otomatis.
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M. Bojarski et al., “End to End Learning for Self-driving cars,” arXiv:1604.07316 [cs], Apr. 2016.
N. J. Goodall, “Can you program ethics into a self-driving car?,” IEEE Spectrum, vol. 53, no. 6, pp. 28–58, Jun. 2016.
“‘Self-Driving-Car’, Masa Depan Big Data di Depan Mata,” idBigData, 10-Jul-2017.
Google, Self-driving car Test: Steve Mahan. .
“BMW : Bersama Apple Bikin Mobil Self Driving,” Car Review Indonesia. [Online]. Available: https://carreview.id/news/bmwbersama-apple-bikin-mobil-self-driving/8916. [Accessed: 23-Apr2019].
M. König and L. Neumayr, “Users’ resistance towards radical innovations: The case of the self-driving car,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 44, pp. 42– 52, Jan. 2017.
A. H. Mirza, “APPLICATION OF NAIVE BAYES CLASSIFIER ALGORITHM IN DETERMINING NEW STUDENT ADMISSION PROMOTION STRATEGIES,” 1, vol. 1, no. 1, pp. 14–28, Mar. 2019.
M. Idris, “IMPLEMENTASI DATA MINING DENGAN ALGORITMA NAÏVE BAYES UNTUK MEMPREDIKSI ANGKA KELAHIRAN,” Pelita Informatika: Informasi dan Informatika, vol. 18, no. 1, pp. 160–167, Apr. 2019.
G. Isabelle, W. Maharani, and I. Asror, “Analysis on Opinion Mining Using Combining Lexicon-Based Method and Multinomial Naïve Bayes,” presented at the 2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018), 2019
N. Saputra, “ANALISIS SENTIMEN DENGAN PREPROCESSING KATA (SENTIMENT ANALISYS WITH LEXICON PREPROCESSING),” 1, vol. 7, no. 1, pp. 45–57, 2018.
N. Saputra, “Analisis Sentimen Mahasiswa Terhadap Universitas.”
L. Jiang, L. Zhang, L. Yu, and D. Wang, “Class-specific attribute weighted naive Bayes,” Pattern Recognition, vol. 88, pp. 321–330, Apr. 2019.
W. Xu, L. Jiang, and L. Yu, “An attribute value frequency-based instance weighting filter for naive Bayes,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 31, no. 2, pp. 225–236, Mar. 2019.
L. Jiang, L. Zhang, C. Li, and J. Wu, “A Correlation-Based Feature Weighting Filter for Naive Bayes,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 2, pp. 201–213, Feb. 2019.
G. H. John and P. Langley, “Estimating Continuous Distributions in Bayesian Classifiers,” arXiv:1302.4964 [cs, stat], Feb. 2013.
D. Soni, “Introduction to Bayesian Networks,” Towards Data Science, 08-Jun-2018. [Online]. Available: https://towardsdatascience.com/introduction-to-bayesian-networks81031eeed94e. [Accessed: 11-May-2019].
M. S. Geraldi and E. Ghisi, “Short-term instead of long-term rainfall time series in rainwater harvesting simulation in houses: An assessment using Bayesian Network,” Resources, Conservation and Recycling, vol. 144, pp. 1–12, May 2019.
S. Kadam, A. Gala, P. Gehlot, A. Kurup, and K. Ghag, “Word Embedding Based Multinomial Naive Bayes Algorithm for Spam Filtering,” in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018, pp. 1–5.
“Sentiment Self-driving cars - dataset by crowdflower,” data.world. [Online]. Available: https://data.world/crowdflower/sentiment-selfdriving-cars. [Accessed: 10-May-2019].
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