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All Journal NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Sinkron : Jurnal dan Penelitian Teknik Informatika
Christian Dwi Suhendra
University of Papua
Computer Science & IT Mathematics

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Implementation of Geometric Brownian Motion to Predict Crude Oil Prices Feby Seru; Christian Dwi Suhendra; Agung Dwi Saputro
Numerical: Jurnal Matematika dan Pendidikan Matematika Vol. 6 No. 2 (2022)
Publisher : Institut Agama Islam Ma'arif NU (IAIMNU) Metro Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25217/numerical.v6i2.2674

Abstract

Crude oil has a vital role in the economic growth of a country because crude oil is a source of energy driving the economy. To maintain economic stability, the price of crude oil in the coming period needs to be anticipated by making predictions on world crude oil commodity prices. One of the models that can be used to predict crude oil prices in the short term is Geometric Brownian Motion (GBM). This study aims to implement the GBM model to predict crude oil prices during the Covid-19 pandemic and measure the model's accuracy. This study made crude oil price predictions with several iterations of 50, 100, and 1000. The results showed that the smallest MAPE value was carried out 1000 times in iterations, namely 2.13%. Based on the MAPE value, it can be concluded that the results of crude oil price predictions using GBM have a high level of accuracy.
Implementation of Geometric Brownian Motion to Predict Crude Oil Prices Feby Seru; Christian Dwi Suhendra; Agung Dwi Saputro; Gautam Makwana; H Elizabeth
Numerical: Jurnal Matematika dan Pendidikan Matematika Vol. 6 No. 2 (2022)
Publisher : Institut Agama Islam Ma'arif NU (IAIMNU) Metro Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25217/numerical.v6i2.2674

Abstract

Crude oil has a vital role in the economic growth of a country because crude oil is a source of energy driving the economy. To maintain economic stability, the price of crude oil in the coming period needs to be anticipated by making predictions on world crude oil commodity prices. One of the models that can be used to predict crude oil prices in the short term is Geometric Brownian Motion (GBM). This study aims to implement the GBM model to predict crude oil prices during the Covid-19 pandemic and measure the model's accuracy. This study made crude oil price predictions with several iterations of 50, 100, and 1000. The results showed that the smallest MAPE value was carried out 1000 times in iterations, namely 2.13%. Based on the MAPE value, it can be concluded that the results of crude oil price predictions using GBM have a high level of accuracy.
Customer Complaint Classification at PT Pos Indonesia Manokwari Using Naive Bayes and Random Forest Rizhmaria Ester Vieta Saphira; Christian Dwi Suhendra; Lilis Indrayani
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16280

Abstract

Customer complaints represent an important source of information for evaluating service quality and improving organizational performance. However, the increasing volume of complaints received by PT Pos Indonesia Manokwari makes manual complaint classification inefficient and time-consuming. This study aims to compare the performance of Naive Bayes and Random Forest algorithms for customer complaint classification using the Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction method. The dataset consisted of 1,490 customer complaint records collected from the Customer Complaint Handling (CCH) system and categorized into twelve complaint classes. The research process included data cleaning, case folding, stopword removal, TF-IDF transformation, dataset splitting, model training, and performance evaluation. The classification models were evaluated using accuracy, precision, recall, F1-weighted score, F1-macro score, and 5-fold cross-validation. The experimental results showed that Random Forest achieved better performance than Naive Bayes. Random Forest obtained an accuracy of 87.92%, precision of 85.22%, recall of 87.92% an F1-weighted score of 86.30%, and an F1-macro score of 70.85%, while Naive Bayes achieved an accuracy of 84.90%, an F1-weighted score of 84.00%, and an F1-macro score of 48.41%. The cross-validation results produced an average accuracy of 71.81%. Although Random Forest achieved the highest hold-out accuracy, the cross-validation results indicate performance variation across different data partitions, which may be caused by class imbalance among complaint categories. These findings demonstrate that Random Forest is more effective for multiclass customer complaint classification and can support the development of automated complaint management systems at PT Pos Indonesia Manokwari.
Co-Authors Agung Dwi Saputro FEBY SERU Gautam Makwana H Elizabeth Lilis Indrayani Rizhmaria Ester Vieta Saphira