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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika
Risnandar
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Computer Science & IT Engineering

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Remote Sensing Scene Classification using ConvNeXt-Tiny Model with Attention Mechanism and Label Smoothing Rachmawan Atmaji Perdana; Aniati Murni Arimurthy; Risnandar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5731

Abstract

Remote Sensing Scene Classification (RSSC) is the discrete categorization of remote sensing images into various classes of scene categories based on their image content. RSSC plays an important role in many fields, such as agriculture, land mapping, and identification of disaster-prone areas. Therefore, a reliable and accurate RSSC algorithm is required to ensure the accuracy of land identification. Many existing studies in recent years have used deep learning methods, especially CNN combined with attention modules to solve this problem. This study focuses on solving the RSSC problem by proposing a deep learning-based method (CNN) with the ConvNeXt-Tiny model integrated with Efficient Channel Attention Module (ECANet) and label smoothing regularization (LSR). The ConvNeXt-Tiny model shows that a persistent superior outperforms the ‘large’ model in convinced metrics. The ConvNeXt-Tiny model also has a huge advantage in high-precision positioning and higher classification accuracy and localization precision in a variety of complicated scenarios of remote sensing scene recognition. The experiments in this study also aim to prove that the integration of the attention module and LSR into the basic CNN network can improve precision, because the attention module can strengthen important features and weaken features that are less useful for classification. The experimental results proved that the integration of ECANet and LSR in the ConvNeXt-Tiny base network obtained a higher precision of 0.38% in the UC-Merced dataset, 0.7% in the AID, and 0.4% in the WHU-RS19 dataset than the ConvNeXt-Tiny model without ECANet and LSR. The ConvNeXt-Tiny model with ECANet integration and LSR obtained an accuracy of 99.00±0.41% in the UC-Merced dataset, 95.08±0.20% in AID, and 99.50±0.31% in the WHU-RS19 dataset.
Metode Computational Thinking untuk Peningkatan Kemampuan Bahasa Pemrograman Python Siswa SMK : (Studi Kasus: SMK Asshiddiqiyah Karangpawitan, Garut) Risnandar; Fawwaz Al Maki, Wikky
DIMASLOKA: Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika Vol 1 No 1 (2022): Januari
Publisher : Fakultas Ilmu Komputer Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/dimasloka.v1i1.6

Abstract

Kemampuan memprogram salah satu bahasa pemrograman komputer banyak menghadapi masalah dalam kemampuan berpikir komputasi (computational thinking) bagi sebagian siswa Sekolah Menengah Kejuruan (SMK) jurusan Rekayasa Perangkat Lunak (RPL). Sebagian siswa merasa frustasi dengan tingkat kesulitaan algoritma dan berpikir komputasi dari berbagai bahasa pemrograman komputer dan cenderung bingung saat pembuatan aplikasinya. Dalam penelitian ini,kami mengeksplorasi kemampuan siswa SMK menggunakan bahasa pemrograman Python untuk menganalisis data science kebutuhan dunia kerja setelah lulus. Kami melakukan reformasi dalam berpikir komputasi saat melakukan perencanaan teknik pengajaran, metode logika berpikir komputasi, dan efisiensi baris kode program. Teknik dan metode pengajaran computational thinking yanga kami usulkan dalam penelitian ini mudah diterima oleh para siswa. Hasil Indek Kepuasan Siswa (IKS) menunjukkan bahwa jumlah kuesioner terisi sebanyak 30 sampel responden yang dibuatkan dalam 5 pertanyaan dengan bobot 5 kriteria BRRT, diperoleh nilai NRR untuk pertanyaan-1 sebesar 3,53 (NRRT=0,07); pertanyaan-2 sebesar 3,00 (NRRT=0,06); pertanyaan-3 sebesar 3,13 (NRRT=0,06); pertanyaan-4 sebesar 3,93 (NRRT=0,08); dan pertanyaan-5 sebesar 3.90 (NRRT=0,08). Selanjutnya jumlah NRRT sebesar 0.35 untuk setiap bobot per pertanyaan adalah 20.Indeks Kepuasan Siswa (IKS) memperlihatkan hasil sebesar 7,00 yang berarti mendapatkan Grade A (Sangat Baik).
Co-Authors Aniati Murni Arimurthy Rachmawan Atmaji Perdana Wikky Fawwaz Al Maki