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All Journal VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Journal of System and Computer Engineering INFINITY : UKI TORAJA JOURNAL OF INFORMATION TECHNOLOGY
Adha, Muhammad Sofwan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Journal : Journal of System and Computer Engineering

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Prediction of Protein Content of Shredded Goldfish Based on Physical Characteristics and Processing Process Using Random Forest Regression Method Damayanti, Irene Devi; Adha, Muhammad Sofwan; Pairunan, Lisna Junita
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2270

Abstract

Shredded goldfish is a processed fishery product that has high nutritional value, especially in its protein content. This study aims to predict the protein content in shredded goldfish based on the physical characteristics of the ingredients (moisture, ash, fat, and crude fiber content) and processing parameters (temperature and frying time) using the Random Forest method. The data used consisted of 10 samples of proximate analysis results and were divided into training data (67%) and test data (33%). The model was evaluated using MAE, MSE, RMSE, and R-squared metrics. The evaluation results showed that the model produced an MAE of 0.5649, MSE of 0.5409, RMSE of 0.7354, and R² of 0.0898. The low R² value indicates that the model is still not optimal in explaining variations in the target data. The prediction of protein levels for new data with certain characteristics resulted in a value of 20.16%, which is still within the range of actual values. This research shows the potential of using machine learning methods in predicting the nutritional value of food products, although increased accuracy is still needed through additional data and exploration of other models. It is recommended that the frying temperature is 155°C to 160°C and the frying time is 11 minutes to 13 minutes to maintain optimal protein levels.
Bayesian-Optimized Prophet for Tourism-Based Regional Government Revenue Forecasting Adha, Muhammad Sofwan; Karuru, Sakti Swarno; Angel, Feby; Joling, Jesika
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2373

Abstract

Accurate hotel tax revenue forecasting is critical for supporting proactive fiscal planning in tourism-dependent local governments . Hotel tax revenues in these regions exhibit high volatility influenced by seasonal tourism patterns, visitor preferences, economic conditions, and external shocks such as the COVID-19 pandemic . Traditional time series forecasting methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing struggle to capture complex seasonal patterns and accommodate multiple external factors . Recent advances in time series forecasting—particularly Facebook's Prophet framework—offer automatic decomposition of trend, seasonality, and holiday effects, plus the ability to integrate external regressors . However, Prophet's performance is highly sensitive to hyperparameter configurations, and default settings often produce suboptimal results on volatile data . Bayesian Optimization has emerged as an efficient technique for hyperparameter tuning, achieving convergence with significantly fewer iterations compared to exhaustive grid search . This study develops and validates a Bayesian-Optimized Prophet Framework for forecasting monthly hotel tax revenue in Kabupaten Tana Toraja, a cultural tourism destination in Indonesia, over 60 months (January 2020–December 2024) encompassing normal conditions, pandemic disruption, and recovery phases. The optimized model achieved Mean Absolute Percentage Error (MAPE) of 9.59% compared to baseline Prophet's 33.72%—a 71.55% improvement in forecasting accuracy. Mean Absolute Error (MAE) reduced from Rp 11.76 million to Rp 3.34 million per month. Robustness testing during COVID-19 pandemic demonstrated model stability with MAPE ≤15% despite >60% revenue decline. The framework provides 24-month forecasts (2025–2026) with 95% confidence intervals and decision-support capability with lead-time advantage of 3–6 months for early revenue shortfall detection. This research contributes a reproducible, efficient methodology for hyperparameter tuning in time series forecasting within fiscal planning domain, applicable to other tourism-dependent regions and tax categories.
Co-Authors Angel, Feby Damayanti, Irene Devi Dambe, Melisanti Luther Eko Suripto Pasinggi Joling, Jesika Juprianus Rusman Karuru, Sakti Swarno Muhammad, Merson Padang, Semuel Yakobus Pairunan, Lisna Junita Rongko, Yunita Toding Sari, Dersan Srivan Palelleng Upa, Samrius