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 2 Documents
Search
Journal : Parameter: Journal of Statistics

 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
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