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All Journal Journal of Innovative and Creativity Journal of Education Transportation and Business
Bambang Irawan
Muhadi Setiabudi University
Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Transportation Other

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Pemetaan Stasiun Kereta Api di Kabupaten Brebes Berbasis Web Dwi Angga Fahrezi; Bambang Irawan; Agyztia Premana
Journal of Education Transportation and Business Vol 1, No 2 (2024): Desember 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetbus.v1i2.3386

Abstract

Kabupaten brebes terletak di Provinsi Jawa Tengah. Ibu Kota Kabupaten Brebes terdapat di Kecamatan Brebes. Kabupaten brebes terdapat banyak Stasiun Kereta Api sehingga diperlukan platform yang dapat menyajikan Informasi Geografis yang cepat dan akurat. Metode yang digunakan dalam penelitian ini metode Deskriptif menggunakan pendekatan Sistem Informasi Geografis (SIG) untuk memetakan lokasi stasiun kereta api. Hasil dari penelitian ini adalah peta berbasis web yang menampilkan informasi tentang Stasiun Kereta Api. Sistem ini diharapkan dapat memudahkan masyarakat dalam mengakses informasi perkeraapian.
Penerapan Vision Transformer Untuk Klasifkasi Sampah Rumah Tangga Prasista Dhiyaul Haq; Bambang Irawan
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.7826

Abstract

The increasing volume of Household waste requires an accurate and efficient automatic waste sorting system. This study aims to apply Vision Transformer (ViT) for image-based household waste clasification. The dataset was divided inti training and validation sets and prepared to match the Vision Transformer archtecture. The ViT-Base Patch16-224 model was trained using the AdamW optimizer with a learning rate of 0.0002, batch size of 16, and 15 training epoch. Model performence was evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Experimental results show that the proposed model achieved an overall accuracy of 95%. The inorganic class obtained a precision of 0.9, recall of 0.96, and F1-score of 0.95, while the organic class achived a precision of 0.94, recall of 0.93, F1-score of 0.94. these result indicate that self-attention mechanism in Vision Transformer effectively extracts global visual features and improves clasification stability. Therefore, Vision Transformer dermonstrates strong potential for implementasi in intelligent automatic waste sorting systems.
Co-Authors Agyztia Premana Dwi Angga Fahrezi Prasista Dhiyaul Haq