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Pengembangan Media Virtual Tour 360 Derajat Menggunakan Platform Lapentor Untuk Promosi Ekowisata Gampong Nusa Aceh Besar Kahvi, Farizaki; Islamadina, Raihan
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 2 (2026): InfoTekjar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i2.13106

Abstract

Penelitian ini bertujuan mengembangkan media Virtual Tour 360 derajat menggunakan platform Lapentor sebagai sarana promosi ekowisata di Gampong Nusa, Aceh Besar. Media promosi konvensional dinilai belum mampu memberikan gambaran kondisi destinasi wisata secara realistis. Metode penelitian yang digunakan adalah Research and Development (RD) dengan model ADDIE, meliputi tahap analisis, desain, pengembangan, implementasi, dan evaluasi. Media Virtual Tour dikembangkan dengan menampilkan beberapa titik lokasi utama ekowisata Gampong Nusa dalam bentuk panorama 360 derajat. Tahap implementasi dilakukan melalui uji coba kepada 15 responden menggunakan instrumen kuesioner. Hasil penelitian menunjukkan bahwa media Virtual Tour 360 derajat memperoleh persentase sebesar 80% pada aspek tampilan media dan kemudahan penggunaan, serta persentase sebesar 73% pada aspek kemampuan media dalam menyajikan gambaran kondisi ekowisata secara lebih nyata. Media ini berpotensi menjadi alternatif promosi ekowisata yang efektif dan informatif.
Optimasi Segmentasi Kepala Janin Berbasis U-Net Melalui Preprocessing Citra USG Putri Salsabila; Raihan Islamadina
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.33961

Abstract

Fetal Head Circumference (HC) measurement using Ultrasound (USG) imagery is a crucial biometric parameter for estimating gestational age and monitoring fetal growth rate. However, automated interpretation is often hindered by speckle noise, low contrast, and blurred object boundaries inherent in USG images. This study aims to optimize the performance of a U-Net architecture with backbone ResNet-34 for fetal head segmentation through image preprocessing and data augmentation techniques. The proposed method integrates Anisotropic Diffusion for noise reduction and CLAHE (Contrast Limited Adaptive Histogram Equalization) to enhance boundary features, alongside geometric augmentations (rotation, flip) and median blur. The model was trained on 799 training images and validation with 80:20 ratio and 200 test images from a public dataset. Results indicate that the proposed preprocessing significantly improves segmentation performance compared to the baseline. The Intersection over Union (IoU) score increased from 0.9440 to 0.9526, and the Dice Similarity Coefficient (DSC) get 0.9757. Although preprocessing visually intensified certain artifacts, it effectively enhanced feature distinctiveness for the model. Based on the segmentation output, biometric estimation was conducted using ellipse fitting. This study concludes that U-Net optimized with Anisotropic Diffusion and CLAHE preprocessing shows significant potential as an assistive tool for medical professionals, enabling faster biometric measurement while maintaining the necessity for clinical verification.