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All Journal Jurnal Ilmiah Informatika Komputer EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis Sistemasi: Jurnal Sistem Informasi Sebatik Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JURNAL LENTERA BISNIS G-Tech : Jurnal Teknologi Terapan Journal of Information Technology (JIfoTech) Infact: Jurnal Sains dan Komputer Jurnal Pengabdian Masyarakat Indonesia (JPMI)
Maya Sari
Institut Shanti Bhuana
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Social Sciences Other

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Implementasi Metode CNN Berbasis Transfer Learning dengan Arsitektur MobileNetV2 dalam Klasifikasi dan Pemetaan Tempat Wisata Mira; Christian Cahyaningtyas; Maya Sari; Yuliana
Jurnal Ilmiah Informatika Komputer Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2025.v30i3.56

Abstract

The growth of tourism in the digital era encourages the use of social media as a source of visual data for destination analysis. This study aims to classify and map tourist attractions in West Kalimantan using a transfer learning-based Convolutional Neural Network (CNN) method with the MobileNetV2 architecture. A total of 454 images were collected through web scraping from the Instagram account @enjoykalbar, then through a process of elimination, augmentation, normalization, and manual labeling based on the West Kalimantan Disporapar tourism categories, namely Hills, Beaches, Cascades, Culture, Lakes, Rivers, Caves, and Forests. The dataset was divided into training data (70%), validation (20%), and test (10%). The model was built by freezing the initial layers of MobileNetV2 and adding a classification head, then drilled for 20 epochs using the Adam Optimizer and EarlyStopping and ReduceLROnPlateau callbacks. The training results showed a training accuracy of 95.8%, validation accuracy of 88.1%, and test accuracy of 80%. Further evaluation using the classification report yielded an overall accuracy of 89%, with an average precision of 0.93, a recall of 0.86, and an F1-score of 0.88. The model was then integrated into a category- and coordinate-based interactive mapping system to display the distribution of tourist attractions across 12 districts and 2 cities. The results demonstrate that the CNN transfer learning approach is effective for tourism image classification and supports spatial visualization in tourism promotion and planning.
Co-Authors Cahyaningtyas, Christian Dedy Eligia Monixa Salfarini Gudiato, Candra Kusnanto Kusnanto Lili Lerian sari Listra Frigia Missianes Horhoruw Mira Nikodemus Talis Noviyanti. P Nurcahyo, Azriel Christian Pranata, Devon Surya Sabinus Beni Yeremia Niaga Atlantika Yuliana