Pendeteksian dan Determinan Overfishing di Indonesia: Penerapan Analisis Klaster dan Regresi Logistik Biner - Johan, Muhammad Fazlan; Zareka, Andi Muh. Zulfadhil; Kesumawijaya, Anak Agung Istri Anggita; Kurniasari, Agustin; Maharani, Jessica; Putri, Nimas Ayu Eka (Seminar Nasional Official Statistics, 2024)
Overfishing causes a decline in fish stocks, imbalance in marine ecosystems, and economic losses for the fisheries sector. This study aims to obtain a model that is able to detect overfishing in various provinces in Indonesia using a combination of cluster analysis and logistic regression. The data used in this research is secondary data obtained from the Central Statistics Agency (BPS) and the Indonesian Ministry of Maritime Affairs and Fisheries (KKP) which includes information related to marine fish production in Indonesia in 2022. From the study conducted, it was found that the provincial data are classified into two clusters where the second cluster was classified as overfished provinces. Based on the analysis carried out, the best model for modeling overfishing is a logistic regression model with two predictor variables, which are exports and fish consumption rates. Thus, it is hoped that this research can serve as a guide for the government in formulating sustainable policies to reduce the number of overfishing in Indonesia in the following years.