PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY

In this paper, we present a novel approach to data-driven neuro-fuzzy modeling, which aims to create accurate monthly inflow and outflow forecast of money (M0) in Bali Province. The data is monthly time series included some religious ceremony identification variables and a monthly dummy variable fro...

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Main Authors: I KADEK MENTIK YUSMANTARA, G.K. GANDHIADI, LUH PUTU IDA HARINI
Format: Article
Language:English
Published: Universitas Udayana 2021-08-01
Series:E-Jurnal Matematika
Online Access:https://ojs.unud.ac.id/index.php/mtk/article/view/77425
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spelling doaj-c0a579813eb14bc89162030a16970ed62021-09-17T03:23:16ZengUniversitas UdayanaE-Jurnal Matematika2303-17512021-08-0110315616210.24843/MTK.2021.v10.i03.p33677425PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZYI KADEK MENTIK YUSMANTARA0G.K. GANDHIADI1LUH PUTU IDA HARINI2Universitas UdayanaUniversitas UdayanaUniversitas UdayanaIn this paper, we present a novel approach to data-driven neuro-fuzzy modeling, which aims to create accurate monthly inflow and outflow forecast of money (M0) in Bali Province. The data is monthly time series included some religious ceremony identification variables and a monthly dummy variable from January 2011 to March 2019. Well known, Bali Province has unique cultures, the only one province which Hinduism majority religion in Indonesia, and listed as top tourism destination in the world. The neuro-fuzzy models were created using ANFIS architecture and sliding window time series analysis, then simulated using walk forward validation, interpreted using MAPE, and NRMSE. Based on the simulation of the last 24 months, the model of inflow obtained MAPE 23.33% (worth considering) and NRMSE 18.68% (accurate). Meanwhile, the model of outflow obtained MAPE 19.24% (accurate) and NRMSE 8.71% (very accurate). These models and their pieces of information could assist the central bank in Bali Province to prepare cash for money (M0) outflow and managed technic for counting money (M0) inflow.https://ojs.unud.ac.id/index.php/mtk/article/view/77425
collection DOAJ
language English
format Article
sources DOAJ
author I KADEK MENTIK YUSMANTARA
G.K. GANDHIADI
LUH PUTU IDA HARINI
spellingShingle I KADEK MENTIK YUSMANTARA
G.K. GANDHIADI
LUH PUTU IDA HARINI
PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY
E-Jurnal Matematika
author_facet I KADEK MENTIK YUSMANTARA
G.K. GANDHIADI
LUH PUTU IDA HARINI
author_sort I KADEK MENTIK YUSMANTARA
title PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY
title_short PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY
title_full PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY
title_fullStr PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY
title_full_unstemmed PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY
title_sort prediksi inflow dan outflow uang kartal di provinsi bali dengan metode neuro-fuzzy
publisher Universitas Udayana
series E-Jurnal Matematika
issn 2303-1751
publishDate 2021-08-01
description In this paper, we present a novel approach to data-driven neuro-fuzzy modeling, which aims to create accurate monthly inflow and outflow forecast of money (M0) in Bali Province. The data is monthly time series included some religious ceremony identification variables and a monthly dummy variable from January 2011 to March 2019. Well known, Bali Province has unique cultures, the only one province which Hinduism majority religion in Indonesia, and listed as top tourism destination in the world. The neuro-fuzzy models were created using ANFIS architecture and sliding window time series analysis, then simulated using walk forward validation, interpreted using MAPE, and NRMSE. Based on the simulation of the last 24 months, the model of inflow obtained MAPE 23.33% (worth considering) and NRMSE 18.68% (accurate). Meanwhile, the model of outflow obtained MAPE 19.24% (accurate) and NRMSE 8.71% (very accurate). These models and their pieces of information could assist the central bank in Bali Province to prepare cash for money (M0) outflow and managed technic for counting money (M0) inflow.
url https://ojs.unud.ac.id/index.php/mtk/article/view/77425
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AT luhputuidaharini prediksiinflowdanoutflowuangkartaldiprovinsibalidenganmetodeneurofuzzy
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