PENERAPAN METODE REGRESI DALAM ANALISIS TINGKAT KONSUMSI IKAN DI JAWA TIMUR

The Indonesian marine ecosystem, rich in water resources, makes the fishing sector important for the economy, food, and jobs. East Java, with the highest catch production in Indonesia, has huge potential in the fishing sector. Despite its high potential, fish consumption in Indonesia is still relatively low due to lack of awareness, unoptimal distribution, and other factors. The study aims to analyze the level of fish consumption in Eastern Java and determine the best regression method to predict the rate of fish intake based on the region and the types of available commodities. This study uses three regression methods, namely Linear Regression, Support Vector Regression (SVR), and Gradient Boosting Machine. (GBM). Data visualization is done using bar diagrams, and results are validated using the determination coefficient R2 which is then analyzed descriptively. The Sumenep region has the highest level of fish consumption in East Java during the period 2018-2020. Whereas the commodities with the highest consumption are Tuna, Tongkol, Cakalang (TTC) Diawetkan. The GBM method showed its best performance and proved to be the most effective and accurate in predicting the level of fish consumption in East Java with a perfect determination coefficient (0,9999), compared to Linear Regression (0,8755) and SVR (0,9825).

Authors

Institutions

Publication Details

Journal
METHODIKA Jurnal Teknik Informatika dan Sistem Informasi
Published
2024-09-20
DOI
https://doi.org/10.46880/mtk.v10i2.2982
Primary Topic
Economic Growth and Fiscal Policies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PENERAPAN METODE REGRESI DALAM ANALISIS TINGKAT KONSUMSI IKAN DI JAWA TIMUR

Hikmal Akbar Fitransyah, Mahathir Muhamad Ashari, Naufal Rahaditya Aryadi, Willdan Aprizal Arifin
METHODIKA Jurnal Teknik Informatika dan Sistem Informasi
Economic Growth and Fiscal Policies
article

PENERAPAN METODE REGRESI DALAM ANALISIS TINGKAT KONSUMSI IKAN DI JAWA TIMUR

Hikmal Akbar Fitransyah, Mahathir Muhamad Ashari, Naufal Rahaditya Aryadi, Willdan Aprizal Arifin
article en

Abstract

The Indonesian marine ecosystem, rich in water resources, makes the fishing sector important for the economy, food, and jobs. East Java, with the highest catch production in Indonesia, has huge potential in the fishing sector. Despite its high potential, fish consumption in Indonesia is still relatively low due to lack of awareness, unoptimal distribution, and other factors. The study aims to analyze the level of fish consumption in Eastern Java and determine the best regression method to predict the rate of fish intake based on the region and the types of available commodities. This study uses three regression methods, namely Linear Regression, Support Vector Regression (SVR), and Gradient Boosting Machine. (GBM). Data visualization is done using bar diagrams, and results are validated using the determination coefficient R2 which is then analyzed descriptively. The Sumenep region has the highest level of fish consumption in East Java during the period 2018-2020. Whereas the commodities with the highest consumption are Tuna, Tongkol, Cakalang (TTC) Diawetkan. The GBM method showed its best performance and proved to be the most effective and accurate in predicting the level of fish consumption in East Java with a perfect determination coefficient (0,9999), compared to Linear Regression (0,8755) and SVR (0,9825).

METHODIKA Jurnal Teknik Informatika dan Sistem InformasiVol. 10(2)
Serang Raya University (ID)
Openalex Percentile: Top 79%
Economic Growth and Fiscal Policies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.