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All Journal Jurnal Kebijakan Perikanan Indonesia Parameter: Journal of Statistics
Anugrahnu, Joannes Fregis Philosovio
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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ADAPTIVE SYNTHETIC IMPLEMENTATION ON RANDOM FOREST IN ARCHIPELAGIC FISHING PORT OF PEMANGKAT NESSYANA DEBATARAJA, NAOMI; Kusnandar, Dadan; Anugrahnu, Joannes Fregis Philosovio
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17279

Abstract

Random Forest is one of the classification methods employed in data mining. One of the problems in data mining classification is the problem of unbalanced class data This phenomenon arises when the data classes utilized do not have identical instances. Imbalance class data causes the classification results to be biased towards the majority class. Adaptive Synthetic (ADASYN) can be used to deal with this problem. ADASYN generates synthetic data by assigning different importance of minority class samples and then producing synthetic data with similar characteristics. The implementation of ADASYN is suitable for fishery production data, which will experience the problem of unbalanced class data. Fish production is part of the measured fishery. This study aims to classify the value of measured fishery production at PPN Pemangkat through Random Forest Classification using ADASYN to handle the imbalance class data problem and compare the results with those without ADASYN implementation. This study uses four predictor variables which include fishing gear types (), number of trip days (), number of crew (), and the total weight of fish () with production value as response variable (). Accuracy, precision, recall, specificity, and G-mean are the model performance indicators used. The results showed that ADASYN successfully handles the problem of unbalanced class data in Random Forest classification. Accuracy is increased from to , Specificity is increased from to , Precision from to , and G-Mean from to . The decrease in recall is negligible due to the small amount, so the Random Forest classification with ADASYN is better than without ADASYN
STRATEGI PENINGKATAN EKSPOR HASIL LAUT MELALUI KEBIJAKAN PERIKANAN TERUKUR MENGGUNAKAN RANDOM FOREST ALGORITHM Anugrahnu, Joannes Fregis Philosovio; Etika, Ezra; Sumarni, Sania Lina; Debataraja, Naomi Nessyana; Lestyowati, Yoke; Priyanto, Dwi Ari
Jurnal Kebijakan Perikanan Indonesia Vol 17, No 2 (2025): (November) 2025
Publisher : Badan Penyuluhan dan Pengembangan Sumberdaya Manusia Kelautan dan Perikanan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/jkpi.17.2.2025.105-113

Abstract

Kebijakan penangkapan ikan terukur merupakan kebijakan penangkapan hasil laut yang disertai oleh pengendalian kuota penangkapan dan area tangkapannya. Kepatuhan terhadap kebijakan ini sebaiknya ditingkatkan seiring dengan bertahannya sektor ekspor hasil laut di tengah menurunnya sektor perekonomian akibat pandemi covid-19. Eco-preservation fishing merupakan salah satu solusi yang dapat ditawarkan. Penelitian ini menggunakan data sekunder produksi ikan tahun 2020 yang didapatkan dari Pelabuhan Perikanan Nusantara Pemangkat Kabupaten Sambas. Variabel yang digunakan dalam penelitian ini adalah berat tangkapan (Y), gross tonnage (X1), alat tangkap (X2), pembongkaran kapal (X3), jumlah hari perjalanan (X4), jumlah ABK (X5), dan banyak penangkapan ikan (X6). Random forest algorithm digunakan untuk mengklasifikasikan subset sampel dan variabel pelatihan yang dipilih secara acak sehingga menghasilkan beberapa pohon keputusan. Hasil analisis menunjukkan nilai hasil prediksi sebesar 81,32% dan termasuk dalam kategori baik. Variabel X6 memiliki nilai MDG terbesar daripada variabel lainnya sehingga memiliki kontribusi paling besar dalam mengklasifikasi penyebab hasil tangkapan di PPN Pemangkat tahun 2021. Kemudian variabel lainnya yang memiliki kontribusi terbesar berturut-turut adalah X3, X1, X5, X4, dan X2.
Co-Authors Dadan Kusnandar Debataraja, Naomi Nessyana Etika, Ezra NESSYANA DEBATARAJA, NAOMI Priyanto, Dwi Ari Sumarni, Sania Lina Yoke Lestyowati