SIMULASI ALGORITMA REGRESI LINEAR DALAM PERANCANGAN SISTEM INFORMASI RANTAI PASOK KEMARITIMAN INDONESIA (STUDI KASUS : TPI KUALA BUBON)

NABILLAH, IDA (2020) SIMULASI ALGORITMA REGRESI LINEAR DALAM PERANCANGAN SISTEM INFORMASI RANTAI PASOK KEMARITIMAN INDONESIA (STUDI KASUS : TPI KUALA BUBON). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The volume of octopus commodity exports has increased, and stocks in an area will be uneven and excessive, and that the demand for octopus in several destination countries in Asia, Europe and America has increased significantly. This study aims to estimate the octopus supply based on historical data from 2014 to 2018. After further predictions, it is necessary to evaluate the prediction model used. The research method used to predict commodity outcomes is linear regression, aiming to determine the effect of one or several variables on one variable. Furthermore, the evaluation model used uses the Mean Absolute Percentage Error (MAPE). MAPE gives a clue how much forecast error compared to the actual value of the series. Furthermore, the production results can predict 70% and MAPE results by 30%, it can be said that the results of linear regression can forecast feasible models. Key words: algorithm, maritime, linear regression, information systems, UML (Unified Modeling Language). Volume ekspor komoditas gurita mengalami kenaikan dan stok di suatu daerah akan tidak merata dan berlebih, serta bahwa permintaan gurita di beberapa negara tujuan di Asia, Eropa dan Amerika telah meningkat secara signifikan. Penelitian ini bertujuan untuk untuk memperkirakan pasokan gurita berdasarkan data historis dari tahun 2014 sampai 2018. Setelah dilakukan prediksi selanjutnya maka diperlukan untuk mengevaluasi model prediksi yang digunakan. Metode penelitian yang digunakan untuk memprediksi hasil komoditas yaitu dengan regresi linier, yang bertujuan untuk mengetahui pengaruh antara satu atau beberapa variabel terhadap satu buah variable. Selanjutnya evaluasi model yang digunakan menggunakan Mean Absolute Percentage Error (MAPE). MAPE memberikan petunjuk seberapa besar kesalahan peramalan dibandingkan dengan nilai sebenarnya dari series tersebut. Selanjutnya hasil produksi dapat diprediksi 70% dan hasil MAPE sebesar 30% maka dapat dikatakan bahwa hasil regresi linier memiliki kemampuan model peramalan yang layak. Kata kunci: algoritma, kemaritiman, regresi linear, sistem informasi, UML (Unified Modelling Language).

Item Type: Thesis (S1)
NIM/NIDN Creators: 41816110091
Uncontrolled Keywords: algoritma, kemaritiman, regresi linear, sistem informasi, UML (Unified Modelling Language)
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem > 003.5 Computer Modeling and Simulation/Model dan Simulasi Komputer
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 511 General Principles of Mathematics > 511.8 Mathematical Models, Mathematical Simulation/Matematika Model, Simulasi Matematika
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: Dede Muksin Lubis
Date Deposited: 30 Oct 2023 02:27
Last Modified: 30 Oct 2023 02:27
URI: http://repository.mercubuana.ac.id/id/eprint/83502

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