Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Aries Maesya; Yaya Heryadi; Yulyani Arifin; Lukas; Wayan Suparta

Judul : Telematics Work Field Review Text Classification Using the Naïve Bayes Method

Abstrak :
Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%.

Tahun : 2022 Media Publikasi : Seminar Internasional

Kategori : Prosiding No/Vol/Tahun : - / - / 2022

ISSN/ISBN : -

PTN/S : Universitas Pakuan Program Studi : ILMU KOMPUTER

Bibliography :
Ming-Hui Huang and Roland T. Rust, "Artificial intelligence in service", Journal of Service Research, vol. 21, no. 2, pp. 155-172, 2018.

Ronald H. Coase, "Economics and contiguous disciplines" in The organization and retrieval of economic knowledge, Routledge, pp. 481-495, 2019.

Muhammad Haseeb et al., "Industry 4.0: A solution towards technology challenges of sustainable business performance", Social Sciences, vol. 8, no. 5, pp. 154, 2019.

Zhaoyu Zhai et al., "Decision support systems for agriculture 4.0: Survey and challenges", Computers and Electronics in Agriculture, vol. 170, pp. 105256, 2020.

O. Somantri, "Text mining untuk klasifikasi kategori cerita pendek menggunakan naïve bayes (nb)", Jurnal Telematika, vol. 12, no. 1, pp. 7-12, 2017.

B. Yanuargi, "Analisis Sentemen Terhadap Aplikasi Bukalapak Sebelum IPO dan Sesudah IPO Menggunakan Algoritma Naive Bayes", JNANALOKA, pp. 17-25, 2022.

A. Zanuardi and H. Suprayitno, "Analisa karakteristik kecelakaan lalu lintas di jalan ahmad yani surabaya melalui pendekatan knowledge discovery in database", Jurnal Manajemen Aset Infrastruktur & Fasilitas, vol. 2, no. 1, 2018.

Amelec Viloria et al., "Methodology for the design of a student pattern recognition tool to facilitate the teaching-learning process through knowledge data discovery (big data)", International conference on data mining and big data, 2018.

Dery Anjas Ramadhan and Erwin Budi Setiawan, "Analisis Sentimen Program Acara di SCTV pada Twitter Menggunakan Metode Naive Bayes dan Support Vector Machine", eProceedings of Engineering, vol. 6, no. 2, 2019.

G. N. M. Nata and P. P. Yudiastra, "Preprocessing Text Mining pada email box berbahasa Indonesia", E-Proceedings KNS&I STIKOM Bali, pp. 479-483, 2017.

A. Filcha and M. Hayaty, "Implementasi Algoritma Rabin-Karp untuk Pendeteksi Plagiarisme pada Dokumen Tugas Mahasiswa", JUITA: Jurnal Informatika, vol. 7, no. 1, pp. 25-32, 2019.

Mujaddid Izzul Fikri, Trifebi Shina Sabrila and Yufis Azhar, "Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter", Smatika Jurnal, vol. 10, no. 02, pp. 71-76, 2020.

A. D. Wibisono, S. D. Rizkiono and A. Wantoro, "Filtering Spam Email Menggunakan Metode Naive Bayes", Telefortech: Journal Of Telematics And Information Technology, vol. 1, no. 1, pp. 9-17, 2020.

M.S.H. Simarangjir, "Studi Perbandingan Algoritma-Algoritma Stemming Untuk Dokumen Teks Bahasa Indonesia", Jurnal Inkofar, vol. 1, no. 1, 2017.

Y. D. Pramudita, S. S. Putro and N. Makhmud, "Klasifikasi Berita Olahraga Menggunakan Metode Naïve Bayes dengan Enhanced Confix Stripping Stemmer", J. Teknol. Inf. dan Ilmu Komput, vol. 5, no. 3, pp. 269, 2018.

B. Gunawan, H. Sastypratiwi and E. E. Pratama, "Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes", JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 4, no. 2, pp. 113-118, 2018.

Muhammad Abbas et al., "Multinomial Naive Bayes classification model for sentiment analysis", IJCSNS Int. J. Comput. Sci. Netw. Secur, vol. 19, no. 3, pp. 62, 2019.

M. Syukri Mustafa, Muh Rizky Ramadhan and Angelina P. Thenata, "Implementasi Data Mining untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier", Creative Information Technology Journal, vol. 4, no. 2, pp. 151-162, 2018.

E. R. M. Razaq, D. W. Jacob and F. Hamami, "Analisis Sentimen Kepuasan Mahasiswa Terhadap Pembelajaran Online Selama Pandemi Covid-19 Pada Media Sosial Twitter Menggunakan Perbandingan Algoritma Klasifikasi", eProceedings of Engineering, vol. 8, no. 5, 2021.

W. T. H. Putri and R. Hendrowati, "Penggalian Teks Dengan Model Bag of Words Terhadap Data Twitter", Jurnal Muara Sains Teknologi Kedokteran dan Ilmu Kesehatan, vol. 2, no. 1, pp. 129-138, 2018.

F. S. Pamungkas and I. Kharisudin, "Analisis Sentimen dengan SVM NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter", PRISMA Prosiding Seminar Nasional Matematika, vol. 4, pp. 628-63, 2021.

URL : https://ieeexplore.ieee.org/document/10118757

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Detail Karya Ilmiah Dosen

Aries Maesya; Yaya Heryadi; Yulyani Arifin; Lukas; Wayan Suparta

Judul	:	Telematics Work Field Review Text Classification Using the Naïve Bayes Method
Abstrak	:	Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%.
Tahun	:	2022	Media Publikasi	:	Seminar Internasional
Kategori	:	Prosiding	No/Vol/Tahun	:	- / - / 2022
ISSN/ISBN	:	-
PTN/S	:	Universitas Pakuan	Program Studi	:	ILMU KOMPUTER
Bibliography	:	Ming-Hui Huang and Roland T. Rust, "Artificial intelligence in service", Journal of Service Research, vol. 21, no. 2, pp. 155-172, 2018. Ronald H. Coase, "Economics and contiguous disciplines" in The organization and retrieval of economic knowledge, Routledge, pp. 481-495, 2019. Muhammad Haseeb et al., "Industry 4.0: A solution towards technology challenges of sustainable business performance", Social Sciences, vol. 8, no. 5, pp. 154, 2019. Zhaoyu Zhai et al., "Decision support systems for agriculture 4.0: Survey and challenges", Computers and Electronics in Agriculture, vol. 170, pp. 105256, 2020. O. Somantri, "Text mining untuk klasifikasi kategori cerita pendek menggunakan naïve bayes (nb)", Jurnal Telematika, vol. 12, no. 1, pp. 7-12, 2017. B. Yanuargi, "Analisis Sentemen Terhadap Aplikasi Bukalapak Sebelum IPO dan Sesudah IPO Menggunakan Algoritma Naive Bayes", JNANALOKA, pp. 17-25, 2022. A. Zanuardi and H. Suprayitno, "Analisa karakteristik kecelakaan lalu lintas di jalan ahmad yani surabaya melalui pendekatan knowledge discovery in database", Jurnal Manajemen Aset Infrastruktur & Fasilitas, vol. 2, no. 1, 2018. Amelec Viloria et al., "Methodology for the design of a student pattern recognition tool to facilitate the teaching-learning process through knowledge data discovery (big data)", International conference on data mining and big data, 2018. Dery Anjas Ramadhan and Erwin Budi Setiawan, "Analisis Sentimen Program Acara di SCTV pada Twitter Menggunakan Metode Naive Bayes dan Support Vector Machine", eProceedings of Engineering, vol. 6, no. 2, 2019. G. N. M. Nata and P. P. Yudiastra, "Preprocessing Text Mining pada email box berbahasa Indonesia", E-Proceedings KNS&I STIKOM Bali, pp. 479-483, 2017. A. Filcha and M. Hayaty, "Implementasi Algoritma Rabin-Karp untuk Pendeteksi Plagiarisme pada Dokumen Tugas Mahasiswa", JUITA: Jurnal Informatika, vol. 7, no. 1, pp. 25-32, 2019. Mujaddid Izzul Fikri, Trifebi Shina Sabrila and Yufis Azhar, "Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter", Smatika Jurnal, vol. 10, no. 02, pp. 71-76, 2020. A. D. Wibisono, S. D. Rizkiono and A. Wantoro, "Filtering Spam Email Menggunakan Metode Naive Bayes", Telefortech: Journal Of Telematics And Information Technology, vol. 1, no. 1, pp. 9-17, 2020. M.S.H. Simarangjir, "Studi Perbandingan Algoritma-Algoritma Stemming Untuk Dokumen Teks Bahasa Indonesia", Jurnal Inkofar, vol. 1, no. 1, 2017. Y. D. Pramudita, S. S. Putro and N. Makhmud, "Klasifikasi Berita Olahraga Menggunakan Metode Naïve Bayes dengan Enhanced Confix Stripping Stemmer", J. Teknol. Inf. dan Ilmu Komput, vol. 5, no. 3, pp. 269, 2018. B. Gunawan, H. Sastypratiwi and E. E. Pratama, "Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes", JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 4, no. 2, pp. 113-118, 2018. Muhammad Abbas et al., "Multinomial Naive Bayes classification model for sentiment analysis", IJCSNS Int. J. Comput. Sci. Netw. Secur, vol. 19, no. 3, pp. 62, 2019. M. Syukri Mustafa, Muh Rizky Ramadhan and Angelina P. Thenata, "Implementasi Data Mining untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier", Creative Information Technology Journal, vol. 4, no. 2, pp. 151-162, 2018. E. R. M. Razaq, D. W. Jacob and F. Hamami, "Analisis Sentimen Kepuasan Mahasiswa Terhadap Pembelajaran Online Selama Pandemi Covid-19 Pada Media Sosial Twitter Menggunakan Perbandingan Algoritma Klasifikasi", eProceedings of Engineering, vol. 8, no. 5, 2021. W. T. H. Putri and R. Hendrowati, "Penggalian Teks Dengan Model Bag of Words Terhadap Data Twitter", Jurnal Muara Sains Teknologi Kedokteran dan Ilmu Kesehatan, vol. 2, no. 1, pp. 129-138, 2018. F. S. Pamungkas and I. Kharisudin, "Analisis Sentimen dengan SVM NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter", PRISMA Prosiding Seminar Nasional Matematika, vol. 4, pp. 628-63, 2021.
URL	:	https://ieeexplore.ieee.org/document/10118757