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All Journal Jurnal Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Techno.Com: Jurnal Teknologi Informasi Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information Systems Engineering and Business Intelligence Riau Journal of Computer Science Jurnal Teknologi dan Sistem Komputer JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal ULTIMA InfoSys Jurnal ULTIMATICS Jurnal Informatika Jurnal Informatika dan Sistem Informasi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Prosiding Seminar Nasional Program Pengabdian Masyarakat Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Atma Inovasia GIAT: Teknologi untuk Masyarakat International Journal of Information Technology and Computer Science Applications (IJITCSA) SmartComp International Journal of Information Technology and Business JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yuan Lukito
Universitas Kristen Duta Wacana
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Jurnal Transformatika

 1 
PENERAPAN SENTIMENT ANALYSIS PADA HASIL EVALUASI DOSEN DENGAN METODE SUPPORT VECTOR MACHINE Valonia Inge Santoso; Gloria Virginia; Yuan Lukito
Jurnal Transformatika Vol 14, No 2 (2017): January 2017
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v14i2.439

Abstract

The quality of lectures can be determined by some feedbacks from students. From the feedbacks, we can give appreciations for those lectures who get good feedback from students, and evaluations for those who get bad feedback. The problem is classifying large size of feedbacks manually isn’t effective and took a lot of time. Therefore, we need a system that can classify feedbacks automatically. These feedbacks will be classified into positive, negative, and neutral, usually called as sentiment analysis. Sentiment analysis implementation can be done by several methods, one of them that has a good accuracy is Support Vector Machine (SVM). SVM performance in this research is measured with the level of accuracy. The number of accuracy indicate the success level of system. The conclusion of this research is factors that affects the accuracy. The factors are the range of each classes and number of unique words in the training document.
Co-Authors Aditya Wikan Mahastama Antonius R. Chrismanto, Antonius R. Antonius Rachmat Chrismanto Argo Wibowo Astuti, Laksmita Widya Budi Susanto Budi Susanto Daniel Felix Setiawan Dody Ivana Erinda Resha Astanti F. Yogiswara Adinugraha P Ginting Pebrindanov Gloria Virginia Halim Budi Santoso Hartono, Arfiananda Dhede Hizkia Juan Suryanto Hizkia Juan Suryanto, Hizkia Juan Katon Wijana Katon Wijana Katon Wijana Kevin Valiant Kristian Hartono, Renaldi Laurentius Kuncoro Probo Saputra Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lukas Chrisantyo Lukas Chrisantyo Naibaho, Yosep Parlindungan Nugroho Agus Haryono Pranatantri, Shalommita Purwadi, Joko Raden Gunawan Santosa Ragil Yoga Irawan Restyandito Rosa Delima Sagara Mahardika Sunaryo Setiawan, Daniel Felix Susilo, Anton Valonia Inge Santoso Verianto, Eko Wahju Satria Wibowo Widi Hapsari Willy Sudiarto Raharjo