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All Journal Jurnal Rekayasa Sistem Industri Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) JURNAL ABDI MASYARAKAT INDONESIA (JAMIN)
Triwulandari SD
Universitas Trisakti
Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Industrial & Manufacturing Engineering Other

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Journal : Jurnal Rekayasa Sistem Industri

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Analisis Sentimen Mahasiswa untuk Perbaikan Desain Afektif Ruang Kelas Jurusan Teknik Industri, Universitas Trisakti Anik Nur Habyba; Novia Rahmawati; Triwulandari SD
Jurnal Rekayasa Sistem Industri Vol. 10 No. 1 (2021): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.97 KB) | DOI: 10.26593/jrsi.v10i1.4319.27-34

Abstract

Improving the affective classroom design is essential to maximize student performance and learning achievement. The comfort and performance achievement of Trisakti University Industrial Engineering students are influenced by the affective design of classrooms. This study aimed to use sentiment analysis in the classification of students’ perceptions of the affective classroom design. Student sentiment classification is done using a Support Vector Machine (SVM). The questionnaire analysis results also showed perceptions about the subjects that were considered the most difficult (statistics). The classrooms had a positive sentiment: FGTSC, a sample for the next stage of classroom design formulation. The results show what impressions and things the students consider in choosing the FGTSC class. Some examples of the dominant kansei word are “comfortable,” this shows that students really care about the comfort of a classroom in the learning process. The word kansei for the design concept was collected from students' perceptions of the “positive” label. Design elements that need to be improved include equipment that is less comfortable to use, less lighting, walls with graffiti and uncomfortable seating. The classification results using three SVM types Kernel linear, radial and polynomial obtained linear have the best accuracy value (76%). These results indicate that the classification of student sentiment has the maximum results with SVM linear kernel (dot) type. This method will be used in classifying student sentiment on the results of improving classroom design.  
Co-Authors Dimas Aulia Kalimantoro Dorina Hetharia Emelia Sari Habyba, Anik Nur Iveline Anne Marie Nora Azmi Novia Rahmawati Pudji Astuti Wahyuningsih Santosa Winnie Septiani