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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Teknologi Informatika dan Komputer
Evta Indra
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis Perbandingan Decision Tree, Support Vector Machine, dan Xgboost dalam Mengklasifikasi Review Hotel Trip Advisor Hansen Christanto; Julfikar Rahmad; Stiven Hamonangan Sinurat; Daniel Ryan Hamonangan Sitompul; Andreas Sitomorang; Dennis Jusuf Ziegel; Evta Indra
Jurnal Teknologi Informatika dan Komputer Vol 9, No 1 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i1.1429

Abstract

Jaringan media sosial pada saat ini terus berkembang dan berdampak pada industri perhotelan. Pelanggan dan traveler telah memposting hasil review secara online untuk menunjukkan tingkat kepuasan mereka terhadap hotel dan berbagi pengalaman terkait hotel yang dikunjungi dengan pelanggan lain yang ada di seluruh belahan dunia. Situs web yang bergerak dalam pariwisata dan perhotelan berkembang pesat secara online seperti Trip advisor. Trip advisor merupakan platform penyedia layanan perjalanan dan pemesanan hotel. Penelitian menggunakan teknik analisis sentimen untuk mengkategorikan opini pengguna yang bernilai negatif maupun positif dengan bantuan kecerdasan buatan yaitu Machine Learning. Penelitian ini menguji tiga algoritma Machine Learning, yaitu Decision Tree Classifier, Support Vector Machine (SVM) dan Xgboost Classifier, dalam melakukan analisis sentimen terhadap review hotel di platform Trip advisor. Hasilnya menunjukkan bahwa Xgboost memiliki tingkat keakuratan (accuracy) yang paling tinggi, mencapai 99%, dibandingkan dengan Decision Tree (97%) dan Support Vector Machine (98%). Dengan demikian, Xgboost dianggap sebagai algoritma terbaik untuk melakukan analisis sentimen pada review hotel di Trip advisor. 
Visual Attention Analysis of Perspective Images Using the Eye Tracking Method Purba, Andres Taruli; Br Purba, Laura Natalia; Haliza, Della; Siagian , Hendricus; Simanjuntak, Pransisko Oktavianus; Evta Indra
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5133

Abstract

In this research, eye tracking was applied to observe students' visual attention to three perspective images, each of which has a Region of Interest (RoI). In initial research, it was found that the majority of students faced difficulties in concentrating during the learning process. The aim of this research is to analyze the visual attention of perspective drawings in adolescents in an effort to increase learning concentration. Eye tracking was used as a research instrument to monitor the eye movements of 70 students objectively and in real-time who were guided by giving assignments to look for certain objects. This study showed that in terms of perception speed and focus duration, female participants outperformed male participants. However, overall the level of concentration of teenagers cannot be said to be good. These findings provide important knowledge for educators in creating more effective visual content to improve student concentration and understanding.
Co-Authors Andreas Sitomorang Br Purba, Laura Natalia Daniel Ryan Hamonangan Sitompul Dennis Jusuf Ziegel Haliza, Della Hansen Christanto Julfikar Rahmad Purba, Andres Taruli Siagian , Hendricus Simanjuntak, Pransisko Oktavianus Stiven Hamonangan Sinurat