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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmu Komputer dan Agri-Informatika Biogenesis: Jurnal Ilmiah Biologi Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Medria Kusuma Dewi Hardhienata
Department Of Computer Science, Faculty Of Mathematic And Natural Science, IPB University, Bogor, West Java, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Immunology & microbiology

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Klasifikasi Daerah Penangkapan Ikan Menggunakan Algoritma Random Forest dan Support Vector Machine Kurnianto, Andi; Imas Sukaesih Sitanggang; Medria Kusuma Dewi Hardhienata
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 2 (2024)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.2.100-110

Abstract

Kondisi ekonomi nelayan tradisional masih berada di lingkaran kemiskinan sehingga diperlukan solusi untuk meningkatkan kesejahteraan. Salah satu solusi adalah dengan menggunakan teknologi informasi mengenai daerah penangkapan ikan, sehingga nelayan dapat menghemat bahan bakar dan menambah jumlah tangkapan. Informasi daerah penangkapan ikan dapat di tentukan dengan cara mengolah data citra satelit dan menggunakan teknologi machine learning. Penelitian ini bertujuan membuat model yang dapat melakukan menklasifikasi daerah penangkapan ikan menggunakan algoritma Random Forest dan Support Vector Machine menggunakan data citra satelit laut jawa dan sekitarnya dari tahun 2019-2021 dengan menggunakan parameter klorofil, suhu permukaan laut, salinitas, ketinggian dan suhu air laut. Hasil penelitian ini menunjukan parameter klorofil mempunyai peran paling besar sebesar 77.14% dalam menentukan daerah penangkapan ikan. Hasil nilai precision yang dihasilkan algoritma Support Vector Machine (99.83%) lebih tinggi dibanding dengan yang dihasilkan algoritma Random Forest (99.80%). Meski demikian model klasifikasi yang dihasilkan algoritma Random Forest mempunyai nilai accuracy (99.90%), recall (100%) dan F1 score (99.90%) yang lebih tinggi dibanding dengan yang dihasilkan algoritma Support Vector Machine dengan nilai accuracy (99.89%), recall (99.96%) dan F1 score (99.89%).
Modeling Human Mobility by Train on the Spread of COVID-19 in East Java Province Using Distance-Decay PageRank Algorithm Rizha Al-Fajri; Medria Kusuma Dewi Hardhienata; Yeni Herdiyeni
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27285

Abstract

Since early 2020, the world has been dealing with the COVID-19 outbreak. A person who has been infected with COVID-19 has the potential to transmit the virus to others. This study aims to model human mobility by train using the spatial network in East Java Province. This research examines the relationship between human mobility by train and the spread of COVID-19 in East Java Province. The spatial network is formed based on train stations and train trips, and the model was created using the Distance-decay PageRank algorithm. This research has modeled human mobility using the train in East Java Province. The result shows that human mobility by train is highly correlated with the spread of COVID-19 in East Java Province, with a correlation coefficient of 0.7 (r = 0.7).Since early 2020, the world has been dealing with the COVID-19 outbreak. A person who has been infected with COVID-19 has the potential to transmit the virus to others. This study aims to model human mobility by train using the spatial network in East Java Province. This research examines the relationship between human mobility by train and the spread of COVID-19 in East Java Province. The spatial network is formed based on train stations and train trips, and the model was created using the Distance-decay PageRank algorithm. This research has modeled human mobility using the train in East Java Province. The result shows that human mobility by train is highly correlated with the spread of COVID-19 in East Java Province, with a correlation coefficient of 0.7 ( = 0.7).
Co-Authors Agus Buono Amin Elhan Auzi Asfarian Husin Alatas Iis Rodiah Imas Sukaesih Sitanggang Julio Adisantoso Kahfi Gunardi Karlisa Priandana Kurnianto, Andi Laksono Trisnantoro Michael Julyus Christopher Manullang Mohamad Solahudin Mushthofa Peter John Morley Purnajaya, Akhmad Rezki Qarry Atul Chairunnisa Rizha Al-Fajri Siti Nur Hasanah Sony Hartono Wijaya Syarifah Aini Wisnu Ananta Kusuma WULANDARI Yeni Herdiyeni