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Journal : JEEE- Journal of Electrical And Electronics Engineering

Analisis Sentimen Media Sosial Opini Ujian Nasional Berbasis Komputer menggunakan Metoda Naive Bayes Fajar Priyono; Surti Kanti; Iqbal Dzulfiqar I; Imam Amirulloh; Endang Sri P; Alvi Alvi; Didi Rosiyadi
Journal of Electrical And Electronics Engineering Vol 1, No 2 (2016): JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (519.105 KB) | DOI: 10.33021/jeee.v1i2.189

Abstract

Ujian Nasional merupakan proses tolak ukur kemampuan hasil belajar siswa dan siswi selama proses belajar di sekolah, seiring perkembangan zaman, terdapat perubahan pada proses ujian nasional, yaitu sistem pengerjaan ujian nasional secara terkomputerisasi atau dapat di sebut dengan Computer Based Test (CBT). Dengan adanya Ujian Nasioal Berbasis komputer tentu menjadi bahasan-bahasan baru bagi masyarakat, baik bahasan pro dan kontra sehingga banyak masyarakat yang melontarkan opini-opininya melalui media sosial. Penelitian ini telah membahas mengenai analisa sentimen opini ujian nasional berbasis komputer. Sample yang di ambil sebanyak 181 kalimat sentimen yang di olah menggunakan algoritma Naive Bayes dengan mengelompokkan data menjadi tiga kelas Sentimen Positif, Netral, Negatif. Hasil pengolahan data menunjukan kelas sentimen netral memiliki nilai tertinggi sebesar 79% dan nilai terendah di peroleh kelas negatif dengan nilai 0.09%. Sedangkan tingkat akurasi ketiga kelas sentimen mencapai 100%.Keywords— Naive Bayes, Opini, Sentimen, Twitter The National exam is a benchmark of students’ learning capability result during learning process in schools. As the era develops, national exam is changing in its process. Nowadays, the test uses Computer Based Test (CBT) system. The existence of this system leads to new topics for society. Consequently, pro and contra opinions are thrown by them through social media. Therefore, this research discuses about analyses of sentiment opinions on CBT national exam. Naive Bayes’ Algorithm is used for processing 181 data samples in form of sentiment sentences. The data samples are grouped into three classess; Positive, Neutral, and Negative. The results of data processing have shown that Neutral sentiment gains the highest percentage 79% while the Negative sentiment is the lowest with value 0.09% Overall, accuracy degree of the three sentiment classes reach 100%.Keywords— Naive Bayes, Opini, Sentimen, Twitter
Co-Authors -, Cucu Abdul Mahatir Najar Abdullah, Ifnu Abdul Aziz Ade Cahyana Adiwijaya Adnyana, I Ketut Widhi Agus Indra Jaya Agus Salim Akbari Basuki Akbari Indra Basuki Alvi Alvi Aridarma, Dedi Bambang Joko Triwibowo Basuki, Akbari Indra Bobby Suryo Prakoso Bobby Suryo Prakoso Cahya, El Rangga Un Chandra Nugraha Chandra Nugraha, Chandra Cucu Cucu Daniati Uki Eka Saputri Dedi Ariadarma Dedi Aridarma Deni Rusdiaman Dwi, F Lia Dwiza Riana Eko Arif Riyanto, Eko Arif Eko Saputro Endang Sri P Fajar Pramono Fajar Priyono Fariszal Nova Arviantino Fauzi, Fariz Furqon Hensan Muttaqien Hadi Susanto Harianto Harianto Heru Sukma Utama Heru Sukma Utama Heru Susanto Horng, Shi Jinn Husain, Syepry Maulana Ifnu Abdul Aziz abdullah Imam Amirulloh Ipin Sugiyarto Iqbal Dzulfiqar I Irawan, Rama Irwansyah Saputra Iwan Setiawan Iwan Setiawan Jamil, Muh. Juninisvianty, Tri Kurniawan W. Handito Kurniawan, Rohim Kusnadi - Kusnadi Muh. Jamil Muhammad Syahrani Mulyanto, Yudi Nana Suryana Nana Suryana Nana Suryana nanang ruhyana Nila Hardi Nuryani Nuryani Prakoso, Bobby Suryo Prasetyo, Heri Putra, Yeffry Handoko Qhomar, Mohammad Arifin Nurul Qurrota Ayun Hariyanto Putra RACHMAT HIDAYAT Rama Irawan Riski Annisa Salim, Taufik Ibnu Salsabila, Unik Hanifah Saputra, Surya Fajar Seimahuira, Syarah Sidiq, Muhammad Fajar Sriyadi Sriyadi Sugiyarto, Ipin Surti Kanti Surya Fajar Saputra Sutiyono Sutiyono Suwanda Aditya Aaputra Taufik Iqbal Ramdhani Tiska Pattiasina Utama, Heru Sukma Windu Gata Yeffry Handoko Putra Yusnan Hasani Siregar Yusuf Arif Setiawan Zulfikar Fauzi