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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Teknologi Informatika dan Komputer INOVTEK Polbeng - Seri Informatika
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.
Android Application Prototype for Detecting Mould on Bread using Machine Learning Frissy Siregar; Daniel Haganta Barus; Clara Stephanie Bernadeth Piay; Evta Indra
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/bptwhn82

Abstract

Mould contamination in bread poses a serious health risk if not detected early, especially in the food industry, which still relies heavily on manual visual inspection. This study aims to develop a prototype Android application capable of automatically detecting mould on bread using a machine learning approach based on the MobileNetV2 architecture. The classification model was trained on a dataset of 666 bread images, consisting of 533 training and 133 validation samples. Training was carried out over 37 epochs using data augmentation techniques and a learning rate of 0.0001. The results demonstrated consistent accuracy improvements and loss reductions without signs of overfitting. The model achieved 94% testing accuracy, with a precision, recall, and F1-score of 0.94 for both "Mouldy" and "Non-Mouldy" classes. The confusion matrix showed 125 correct predictions out of 133 test images. This research addresses the gap in lightweight and practical solutions for mobile-based mould detection. Unlike previous studies that used heavier models such as VGG16 or ResNet, this study shows that MobileNetV2 can achieve high performance with lower computational demands, making it suitable for real-world Android applications. The trained model was integrated into a simple Android interface, allowing users to upload images and instantly receive classification results. For future improvement, this prototype can be enhanced by incorporating object detection or image segmentation techniques such as YOLOv5 or U-Net to enable not only classification but also the localisation of mould areas in real-time.
Co-Authors Andreas Sitomorang Br Purba, Laura Natalia Clara Stephanie Bernadeth Piay Daniel Haganta Barus Daniel Ryan Hamonangan Sitompul Dennis Jusuf Ziegel Frissy Siregar Haliza, Della Hansen Christanto Julfikar Rahmad Purba, Andres Taruli Siagian , Hendricus Simanjuntak, Pransisko Oktavianus Stiven Hamonangan Sinurat