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All Journal Jurnal Filsafat Indonesia Indonesian Journal of Aerospace
Emir Mauludi Husni, Emir Mauludi
Institut Teknologi Bandung
Aerospace Engineering Astronomy Education

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Perkembangan Paradigma Metode Klasifikasi Citra Penginderaan Jauh dalam Perspektif Revolusi Sains Thomas Kuhn Ambarwari, Agus; Husni, Emir Mauludi; Mahayana, Dimitri
Jurnal Filsafat Indonesia Vol. 6 No. 3 (2023)
Publisher : Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jfi.v6i3.53865

Abstract

The rapid improvement of remote sensing technology has given rise to three paradigms of remote sensing image classification methods, namely pixel-based, object-based, and scene-based. This article aims to explain or reveal the development of remote sensing image classification methods and their relationship with Thomas Kuhn's scientific revolution process (pre-paradigm, normal science, anomaly, crisis, and scientific revolution) that occurs in the development of these classification methods. The preparation of this article uses a descriptive qualitative method. Reference sources are journal articles collected from the Scopus database with topics related to classification and remote sensing. Other reference sources are data extracted from review articles. From all the references collected, a literature study is then carried out by analyzing the article's title, abstract, and overall content. After that, the stages of the scientific revolution related to the development of classification methods in remote sensing images were described. Based on the review of the articles, it can be explained that the development of classification methods for remote sensing imagery began in the 1970s when the Landsat satellite was first launched. In this early period, the classification method used was based on pixels or sub-pixels, because the spatial resolution of remote sensing imagery was shallow. As remote sensing technology developed, in the 2000s a new approach was discovered that was more efficient than the pixel-based approach for classifying high-resolution imagery, namely object-based classification methods. Then, with the release of the land use dataset (UC-Merced) in the 2010s, scene-based remote sensing image interpretation began to be used, as pixel- and object-based methods were insufficient to classify correctly.
ALGORITMA PERINGATAN DINI PENCURIAN IKAN PADA DATA AUTOMATIC IDENTIFICATION SYSTEM (AIS) BERBASIS TERESTRIAL DAN SATELIT (ILLEGAL FISHING EARLY WARNING ALGORITHM FOR TERESTRIAL AND SATELLITE-BASED AUTOMATIC IDENTIFICATION SYSTEM (AIS) DATA) Husni, Emir Mauludi; R. S., Muhammad Riksa Andanawari; Triharjanto, Robertus Heru
Indonesian Journal of Aerospace Vol. 14 No. 2 Desember (2016): Jurnal Teknologi Dirgantara
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.jtd.2016.v14.a2385

Abstract

Pencurian ikan merupakan kegiatan yang menyebabkan kerugian sangat besar untuk Indonesia, sementara wilayah perairan Indonesia yang luas membuat kegiatan pengawasan pencurian ikan tersebut menjadi sulit dilakukan. Peraturan internasional yang mewajibkan setiap kapal di atas 300 GT untuk mengirimkan data menggunakan AIS menjadi kesempatan untuk mendeteksi kapal-kapal yang melakukan pencurian ikan. Kemampuan Indonesia untuk mendeteksi sinyal AIS dari satelit LAPAN-A2/Orari memperbesar kesempatan tersebut. Penelitian ini bertujuan membangun bagian dari sistem peringatan dini aktivitas pencurian ikan, berdasarkan data AIS yang diterima oleh sensor di garis pantai dan di satelit. Proses pendeteksian dilakukan dengan menganalisa data perjalanan dari sistem AIS. Jenis-jenis pencurian ikan yang dapat dideteksi oleh algoritma ini adalah trans-shipment, penggunaan pukat harimau, pelanggaran zona teritorial, pelanggaran tidak melapor, pelanggaran wilayah penangkapan, dan pelanggaran tidak mengaktifkan pemancar sinyal AIS. Algoritma yang digunakan adalah metode Ray Casting, untuk menentukan suatu kapal berada dalam satu wilayah atau tidak. Perbaikan performa algoritma ini dilakukan dengan melakukan proses multithreading menggunakan kode Python. Algoritma diuji dengan data AIS dari LAPAN-A2/Orari dan data simulasi. Hasil menunjukkan bahwa algoritma yang dirancang untuk sistem analisis peringatan dini pencurian ikan (illegal fishing) dengan data AIS berhasil mendeteksi 6 jenis pelanggaran sesuai ketentuan Kementerian Kelautan dan Perikanan (KKP) Republik Indonesia yang telah disebutkan di atas dengan menggunakan data simulasi.
Co-Authors Ambarwari, Agus Dimitri Mahayana R. S., Muhammad Riksa Andanawari Triharjanto, Robertus Heru