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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Hamzah Arof
Department of Electrical Engineering, University of Malaya, Malaysia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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COMPARISON OF XGBOOST AND RANDOM FOREST METHODS IN PREDICTING AIR POLLUTION LEVELS Akas Yekti Pulih Asih; Firman Yudianto; Puguh Triwinanto; Rachman Sinatriya Marjianto; Teguh Herlambang; Hamzah Arof
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0785-0796

Abstract

Air is one of the elements needed by living things, including humans, to survive. The air quality in an area also affects the health and quality of human life and its surrounding environment. However, with the current phenomenon, the influence of the increasing number and mobility of humans actually degrades air quality, caused by the pollutants produced. For further impacts, poor air quality can reduce human life expectancy. Big cities in Indonesia, such as Surabaya, also experience the same thing due to the lack of public awareness of air pollution. The biggest contributors to air pollution are motor vehicles and industrial activities that emit carbon monoxide (CO), nitrogen oxides (NO), ozone (O3), and other particles (PM10). This condition is addressed by the Surabaya City Government by installing air condition measuring devices at points considered prone to pollution. This device works to measure urban air conditions daily and provides data that can be utilized to establish strategic policies. By utilizing the data, in this research, we implemented two prediction methods from machine learning technology, namely XG Boost and Random Forest. In accordance with the objective of this research, both methods will be compared for accuracy in predicting air pollution levels in Surabaya based on Ozon (O3) substance within the period of January 1, 2020, to December 31, 2020. Both of them have a similarity in that they implement tree-ensemble based, which are appropriate for handling non-linear data. The XG Boost method managed to achieve the best error value of 0.0510, and the Random Forest method reached the best error value of 0.0468.
IMPLEMENTATION OF KALMAN FILTER, RECURRENT NEURAL NETWORK, AND DECISION TREE METHOD TO FORECAST HIV CASES IN EAST JAVA Nurwijayanti Nurwijayanti; Firman Yudianto; Panca Radono; Rizky Amalia Sinulungga; Mochammad Romli Arief; Rahayu Budi Utami; Hamzah Arof
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1473-1484

Abstract

HIV (Human Immunodeficiency Virus) is a virus that infects cells in the body and weakens the human immune system, making it more susceptible to various diseases. Meanwhile, the symptoms of the disease arising from HIV itself are referred to as AIDS (Acquired Immune Deficiency Syndrome). Approximately 50% of people with AIDS in Indonesia are adolescents. Until now, HIV/AIDS has ranked second in East Java province. HIV/AIDS is classified as a dangerous disease because of the risk of death. Unfortunately, there is no treatment method or vaccine that could prevent this disease. This monitoring program to prevent the development of dangerous health cases such as HIV/AIDS is very helpful for local governments. Along with the development of information technology, the emergence rate of new HIV/AIDS cases can now be forecasted using machine learning as a monitoring tool to support. This machine learning-based monitoring program works with past data for statistical analysis. In this study, the methods used are Kalman Filter, Recurrent Neural Network, and Decision Tree. The Kalman Filter is a type of filter method that is used to predict the state of a dynamic, stochastic, linear, discrete system. A Recurrent Neural Network (RNN) is a development of a Neural Network. RNN deals with input sequence/time-series data by individual sector at each step and preserves the information it has captured at previous time steps in a hidden state. A Decision Tree is one of the classic tree-based prediction methods. The best error value (RMSE) achieved by each method is 0.0885 for the Kalman Filter, then for the Recurrent Neural Network method achieved 0.198, and the Decision Tree method successfully achieved 0.0287.
Co-Authors Akas Yekti Pulih Asih Firman Yudianto Firman Yudianto Mochammad Romli Arief Nurwijayanti Nurwijayanti Panca Radono Puguh Triwinanto Rachman Sinatriya Marjianto Rahayu Budi Utami Rizky Amalia Sinulungga Teguh Herlambang