Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Kebijakan Perikanan Indonesia Parameter: Journal of Statistics
Anugrahnu, Joannes Fregis Philosovio
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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

 Penelitian ini bertujuan menyusun strategi dalam menerapkan eco-preservation fishing dalam kerangka kebijakan Penangkapan Ikan Terukur (PIT) di PPN Pemangkat, Kabupaten Sambas. Dengan menggunakan data sekunder mengenai produksi ikan tahun 2020, peneliti mengidentifikasi faktor-faktor penting yang perlu dijadikan dasar dalam penyusunan kebijakan agar penerapan eco-preservation fishing dapat berjalan efektif sekaligus mendukung kinerja ekspor setelah masa pandemi. Variabel yang dianalisis mencakup berat hasil tangkapan (Y), ukuran kapal/gross tonnage (X1), jenis alat penangkapan (X2), waktu pembongkaran (X3), jumlah hari perjalanan (X4), jumlah ABK (X5), dan frekuensi penangkapan (X6). Algoritma random forest digunakan untuk mengklasifikasikan serta mengevaluasi tingkat pengaruh masing-masing variabel. Model yang dihasilkan memiliki akurasi prediksi sebesar 81,32% (kategori baik). Frekuensi penangkapan (X6) menunjukkan penurunan rataan Gini terbesar, sehingga menjadi variabel yang paling berpengaruh, diikuti oleh ukuran kapal (X1), jenis alat (X2), dan hari perjalanan (X4). Dari temuan tersebut, ditemukan strategi prioritas, yaitu : (1) penetapan kuota berdasarkan effort (per trip/kapal) dan ukuran kapal, (2) pembatasan serta penggantian alat tangkap yan glebih ramah lingkungan, (3) pengaturan durasi dan jumlah hari operasi, serta (4) peningkatan kepatuhan melalui pengawasan waktu bongkar. Hasil penelitian ini memberikan strategi untuk penerapan eco-preservation fishing dalam skema PIT di PPN Pemangkat yang didasarkan pada bukti empiris dari data tahun 2020. Measured fisheries management (PIT) is a policy of capturing marine products accompanied by control over its quotas and areas. Compliance with this policy should be increased along with the sustainability of the marine product exports sector amidst the decline in the economic sector due to the covid-19 pandemic. Eco-preservation fishing is one of the solutions that can be offered. This study uses secondary data on marine products production in 2020 obtained from Archipelago Fishing Port (AFP) of Pemangkat, Sambas Regency. Random forest algorithm is used to classify randomly selected subsets of the sample and training variables to produce several decision trees. The results of the data testing test show the predicted value of 81.32% and is included in the good category. The variable in this research is weight of catches (Y), gross tonnage (X1), fishing gear type (X2), ship unloading time (X3), number of travel days (X4), number of crew members (X5), and the number of catches (X6). X6 has the largest mean decrease gini value than the other variables, so it has the biggest contribution in classifying the causes of catches in Pemangkat AFP as per 2021.
WATER QUALITY ANALYSIS IN THE RESIDENTIAL AREAS OF PONTIANAK CITY USING THE GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION METHOD NESSYANA DEBATARAJA, NAOMI; Kusnandar, Dadan; Lestari, Fika Dian; Anugrahnu, Joannes Fregis Philosovio
Parameter: Journal of Statistics Vol. 5 No. 2 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i2.17900

Abstract

Water quality is a key indicator of a community’s health and welfare, yet it has deteriorated significantly due to pollution caused by human activities. This study aimed to evaluate Geographically Weighted Logistic Regression’s (GWLR) ability to handle spatial nonstationarity in the relationship between explanatory factors and water quality status in Pontianak City, and to compare its performance with logistic regression. Three modelling approaches were applied to classify water as polluted or non-polluted: (i) logistic regression with spatially invariant) parameters; (ii) GWLR with a fixed Gaussian kernel, producing spatially varying parameters using a fixed bandwidth; and (iii) GWLR with an adaptive Gaussian kernel, producing spatially varying parameters using an adaptive bandwidth. Model performance was compared using Akaike’s Information Criterion (AIC) and classification accuracy. The GWLR model with a fixed Gaussian kernel produced an AIC of 22.52, whereas the logistic regression model produced a slightly lower AIC of 22.39; both models achieved a classification accuracy of 92.86%, with the adaptive-kernel GWLR showing comparable classification performance. These results indicate that, for the parameter settings considered, GWLR offered performance comparable to, but not substantially better than logistic regression for modelling the factors affecting water quality, despite its capacity to address spatial nonstationarity.
Co-Authors Dadan Kusnandar Debataraja, Naomi Nessyana Etika, Ezra Lestari, Fika Dian NESSYANA DEBATARAJA, NAOMI Priyanto, Dwi Ari Sumarni, Sania Lina Yoke Lestyowati