Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Inferensi Berdikari : Jurnal Ekonomi dan Statistik Indonesia Priviet Social Sciences Journal
Muayyad Muayyad
Politeknik Negeri Bali
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Forecasting Tourist Arrivals in Bali: A Grid Search-Tuned Comparative Study of Random Forest, XGBoost, and a Hybrid RF-XGBoost Model Waciko, Kadek Jemmy; Susanti, Leni Anggraini; Muayyad, Muayyad; Fakhrurozi, Rifqi Nur
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23334

Abstract

Tourism planning, infrastructure growth, and economic stability. This study presents an extensive comparative evaluation of Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and a novel Hybrid RF-XGBoost model in the prediction of monthly international tourist arrivals. A full time series dataset of a ten-year period (2014–2024) from the Central Bureau of Statistics of Bali was used for training and testing the models. Hyperparameter optimization using Grid Search with cross-validation (Grid Search CV) was used for all the machine learning models to obtain best predictive performance. Two robust metrics, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), were used to assess forecasting accuracy. Results show that the Random Forest model outperforms all competitors with lowest RMSE (41,772.68) and MAPE (6.30%), indicating high forecasting precision and robustness, especially during structural breaks such as the COVID-19 pandemic. The hybrid model also performs well, with LSTM indicating higher error rates, illustrating its shortcomings on small-to-medium-scale tourism time series. Besides, the study provides six-month ahead predictions (January–June 2025) with 95% prediction intervals, showing an ongoing trend of recovery. The findings affirm the superiority of bagging-based ensemble methods over polynomial-based methods in capturing nonlinearity, seasonality, and exogenous shocks in tourist demand. The study plugs the growing amount of data-driven tourism analytics by offering a reproducible, high-precision forecasting model for developing countries and seasonally driven destinations.
Improving Hierarchical Tourism Forecasting through the ARIMA-OC (ARIMA Based on The Optimal Combination) Method Waciko, Kadek Jemmy; Muayyad; Susanti, Leni Anggraini
Jurnal Ekonomi Dan Statistik Indonesia Vol. 5 No. 3 (2025): Berdikari: Jurnal Ekonomi dan Statistik Indonesia (JESI)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jesi.05.03.02

Abstract

The research explores the ARIMA-OC method (ARIMA based on the Optimal Combination approach) for Hierarchical Forecasting. In this approach, the ARIMA model is used to forecast each individual time series, and the Optimal Combination (OC) technique is applied to merge these initial forecasts into an updated set of predictions. The study compares the ARIMA model with the Exponential Tail Smoothing (ETS) model, with both models being integrated using five different strategies: the Bottom-up approach (BU), the Top-down approach using Forecasted Proportion (TDFP), two Top-down approaches based on Historical Proportions (TDHP1 and TDHP2), and the Optimal Combination approach (OC). To assess how ARIMA-OC performs with small samples, a simulation was carried out, revealing that ARIMA-OC surpasses the other methods according to the MASE metric. Furthermore, non-parametric tests like the Friedman test and the Nemenyi post-hoc test were used to validate the effectiveness of Hierarchical Forecasting.
The effect of influencer credibility and brand experience on Conrad Bali hotel's Instagram brand image Kadek Sri Utami; Nyoman Indah Kusuma Dewi; Muayyad Muayyad
Priviet Social Sciences Journal Vol. 6 No. 6 (2026): June 2026
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v6i6.1951

Abstract

The disparity between the growth in Conrad Bali Hotel's Instagram following and the audience's perception of the brand serves as the driving force behind this study. The purpose of this study is to partially and concurrently examine how influencer credibility and brand experience affect the Conrad Bali Hotel's Instagram brand image. 110 active Conrad Bali Hotel Instagram followers who were exposed to marketing content on the platform were included in the quantitative analysis using a purposive sampling technique. Questionnaires were used to gather data, which were then analyzed using multiple linear regression, validity tests, reliability, classical assumptions, t-test, F-test, and coefficient of determination. With a computed t-value of 4.840 and a significance level of <0.001, the findings demonstrated that influencer credibility had a favorable and substantial impact on brand image. With a computed t-value of 5.236 and a significance of <0.001, brand experience also had a favorable and substantial impact on brand image. With a computed F-value of 137.898 and a significance of <0.001, both factors simultaneously had a favorable and substantial impact on brand image. According to the R Square value of 0.720, influencer credibility and brand experience account for 72% of the variation in brand image. This finding confirms that credible influencers and engaging brand experiences through Instagram can strengthen the brand image of Conrad Bali Hotel. Thus, Conrad Bali Hotel is advised to continue optimizing the use of influencers and engaging and authentic Instagram content to strengthen its brand image amidst the competitive hospitality industry.
Co-Authors Kadek Jemmy Waciko Kadek Sri Utami Leni Anggraini Susanti Nyoman Indah Kusuma Dewi Rifqi Nur Fakhrurozi