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All Journal JURIKOM (Jurnal Riset Komputer) TIN: TERAPAN INFORMATIKA NUSANTARA RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI
Nur Rofiq
Universitas Pamulang, Tangerang Selatan
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Prediksi Harga Bawang Merah menggunakan Algoritma Fuzzy Inference System (FIS) Nur Rofiq; Agus Salim
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 3 No. 4 (2023): RESOLUSI Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v3i4.677

Abstract

Consumption of shallots in Indonesia is still relatively large. This affects price movements, production to market needs. Associated with changes in the amount of production with public consumption needs affect the price variations in each period. Shallot price conditions in the market that experience changes can affect losses or profits for shallot entrepreneurs. This creates problems in the sale of bawal because the price of onions is difficult to predict. To minimize losses, a system or technology is needed that can help predict shallot prices. As an illustration of the shallot entrepreneurs. Shallot price prediction system can be done using the calculation method "Algorithm Fuzzy Inference System (FIS) Sugeno method". The use of this algorithm does not require independent assumptions, homoscedasticity, and normally distributed residuals which are often not found in the data so that this method is considered suitable for predicting data that has extreme values. The price of shallots is influenced by two variables, namely the amount needed by the amount of market demand. The test results show a Mean Square Error (MSE) value of 137.671697. then the Root Mean Square Error (RMSE) value is the result of the square of the Mean Square Error (MSE) value, namely: 1.0541 The Mean Absolute Percentage Error (MAPE) value which has an error rate of 40%.
A Comparative Analysis of XGBoost and Random Forest for Time Series Based Stock Price Prediction with Directional Movement Evaluation Andri Fahmi; Nur Rofiq
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9197

Abstract

Stock price prediction remains a complex task due to the dynamic nature of financial time series and the difficulty of extracting informative patterns from historical price movements. This study addresses the need to better understand whether the choice of model or the design of time series features plays a more dominant role in prediction performance. The objective of this research is to comparatively evaluate Extreme Gradient Boosting (XGBoost) and Random Forest for stock price prediction using engineered time series features, while also assessing their ability to capture directional price movements. The proposed approach applies a structured pipeline involving data preprocessing, extraction of time series features (lag, moving average, and volatility), and evaluation using a time-aware data split to preserve temporal order. Unlike conventional studies that focus solely on prediction accuracy, this research integrates both regression-based evaluation (RMSE, MAE, and R²) and directional movement analysis using confusion matrix, along with feature importance interpretation to understand model behavior. The experimental results, based on 1,258 daily stock price records, show that XGBoost achieved an RMSE of 457.97, MAE of 345.28, and R² of 0.884, while Random Forest obtained an RMSE of 462.01, MAE of 351.02, and R² of 0.882. The difference in R² (0.002 or 0.2%) indicates that both models perform comparably, with no substantial performance gap. Directional evaluation further reveals that both models are more accurate in predicting upward trends than downward movements. These findings suggest that feature engineering plays a more critical role than model selection in this context, providing a practical contribution to the development of stock prediction systems.
Klasifikasi Spesies Suara Burung Menggunakan YAMNet dan Random Forest untuk Konservasi Alam Syaeful Machfud; Simon Simarmata; Nur Rofiq
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9443

Abstract

Monitoring and identification of bird species is an important aspect of biodiversity conservation, but manual identification methods based on direct observation and expert listening still have limitations in terms of time, cost, and subjectivity. Other challenges arise due to variations in the quality of sound recordings, the presence of environmental noise, and the similarity in vocalization patterns between species that make it difficult to automate the classification process. This study aims to develop an automatic classification system of bird species based on acoustic signals by combining the YouTube Audio Event Network (YAMNet) model and the Random Forest algorithm. YAMNet is utilized to extract spectral log-Mel features that represent the frequency and temporal characteristics of bird sounds, while Random Forest is used as a classifier to determine species based on those features. The dataset used is the Sound of 114 Species of Birds till 2022, which includes species variation, recording duration, and complex acoustic conditions. The results showed that the features produced by YAMNet were able to form separation between species visually through Principal Component Analysis (PCA), although there was still overlap in species with similar vocalization characteristics. Evaluation using the confusion matrix shows that some species can be classified with a high degree of accuracy, while misclassification occurs mainly in species with similar frequency patterns. Receiver Operating Characteristic (ROC) analysis yields Area Under Curve (AUC) values of up to 0.98 in certain species, indicating the model's excellent discriminating ability. These findings suggest that the integration of YAMNet and Random Forest has the potential to be an efficient and reliable solution to support automated bird species identification systems in nature conservation.
Co-Authors Agus Salim Andri Fahmi Simon Simarmata Syaeful Machfud