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All Journal Jurnal Teknologi Perikanan dan Kelautan Jurnal Ilmu Komputer
Maulidia, Raisa
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science

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Model Prediksi Umur Kepiting Berdasarkan Data Morfometrik dan Gender: Pendekatan Model Support Vector Regression Ramadhani, Tirta Samudera; Mudaim, Syarifah; Sabitta, Valin Rizkia; Maulidia, Raisa
Jurnal Ilmu Komputer Vol 16 No 2 (2023): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2023.v16.i02.p07

Abstract

Crab is one of the most important marine commodities and resources in Indonesian waters, both economically and ecologically. Crab age determination can provide a better understanding of crab growth and development, so that crab farming can be carried out effectively, efficiently, and profitably for its supporters. In addition, determining the age of the crab can also help the sustainability of the crab population. This study was conducted using support vector regression (SVR) modeling to predict crab age by establishing a predictive relationship between the dependent variable (x) and the independent variable (y). The attributes of the dependent variable (x) include length, diameter, height, and weight. While the independent variable (y) only includes crab age. SVR modeling is carried out to show predicted data with actual data, where the results of the SVR modeling will be evaluated based on the results of the RMSE value test. This study resulted in an RMSE value of 0.019814 so it can be said that the model to predict crab age is very accurate. The purpose of this research is to develop a statistical model that can predict crab age based on morphometric data and crab gender using the Support Vector Regression (SVR) model approach.
APPLICATION OF INVERSE DISTANCE WEIGHTED (IDW) INTERPOLATION IN DETERMINING WAVE HEIGHT IN THE WATERS OF THE SUNDA STRAIT: PENERAPAN INTERPOLASI INVERSE DISTANCE WEIGHTED (IDW) DALAM MENENTUKAN TINGGI GELOMBANG LAUT DI PERAIRAN SELAT SUNDA Arifin, Willdan Aprizal; Daud, Anton; Maulsyid, Ramzan Pradana; Maulidia, Raisa; Handyanto, Lukman; Sutrisno, Rifki Andreana
Jurnal Teknologi Perikanan dan Kelautan Vol 16 No 3 (2025): AGUSTUS 2025
Publisher : Fakultas Perikanan dan Ilmu Kelautan, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24319/jtpk.16.268-281

Abstract

The Sunda Strait is one of the busiest transportation routes in Indonesia, which has great potential in the fields of shipping, fisheries, and tourism. In addition to its potential, the Sunda Strait is also faced with challenges in the form of high wave risks that can jeopardize safety and disrupt the smooth running of maritime activities. The availability of wave data is an important aspect in maintaining safety and maritime activities. This research aims to visualize Inverse Distance Weighted (IDW) interpolation of wave height to provide more accurate and detailed information in the waters of the Sunda Strait for the benefit of the maritime sector. In this study, the IDW method was applied to wind data at three Automatic Weather Stations (AWS) points around the Sunda Strait region. Before the application of IDW, the Delaunay Triangulation method was used to ensure the optimization of sample points used to perform interpolation. The results showed that the significant wave height tended to be higher in the southwest monsoon than in the northeast monsoon. During the observation period 2022-2024, the maximum significant wave height was recorded at 2.06 meters, and the minimum one was close to zero. The application of the IDW method successfully visualizes the spatial distribution of wave height in detail, thereby supporting decision-making in risk mitigation and shipping safety in the Sunda Strait.
Co-Authors Daud, Anton Handyanto, Lukman Maulsyid, Ramzan Pradana Mudaim, Syarifah Ramadhani, Tirta Samudera Sabitta, Valin Rizkia Sutrisno, Rifki Andreana