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All Journal Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram
Yahya Darmawan
Faculty of Mathematics and Natural Sciences, University of Sumatera Utara, Medan
Biochemistry, Genetics & Molecular Biology Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Other

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Identification of Rainfall events on Climate Phenomena in Medan based on Machine Learning Deassy Eirene Diana Doloksaribu; Kerista Tarigan; Richard Mahendra Putra; Yahya Darmawan
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol 11, No 2: April 2023
Publisher : IKIP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v11i2.7738

Abstract

Indonesia has diverse topographical conditions that result in Indonesia having a unique climate. One of the unique climate elements to be studied is rainfall, because rainfall has a different pattern in each region, this different rainfall pattern is caused by several climate phenomena factors that affect the rainfall pattern, including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO). Medan City is the capital of North Sumatra province which is one of the areas in the flood-prone category in North Sumatra, where the factor of flooding is due to rainfall events in a long period of time, so the author wants to know which climatic phenomena factors can affect rainfall events in Medan city by using Machine Learning technology through the Matlab application, where in this study has a method by forming four combination models, namely the combination of the influence of IOD, SOI and MJO; second combination of IOD and SOI; third combination of SOI and MJO; and fourth combination of MJO and IOD, these four combinations will be the rainfall value of the four models. Furthermore, the rainfall value of the model is compared with the observed rainfall value and verification test using Mean Absolute Error (MAE) and correlation. Then the calculation of the comparison between the four rainfall models with the observed rainfall obtained the lowest MAE value during the SOI and MJO phenomenon of 15.0 mm and the highest correlation value during the IOD and SOI and SOI and MJO phenomena. So it is concluded that the combination of SOI and MJO has the best verification value. This shows that based on Machine Learning modeling, the model shown as the best predictor in Medan city is when the model combination consists of SOI and MJO.
Development of an Automated Temperature Calibration Monitoring System Using Internet of Things for the Regional Meteorology, Climatology, and Geophysics Agency (Bmkg) in Medan Humam Maulana; Kerista Tarigan; Syahrul Humaidi; Yahya Darmawan
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol 11, No 2: April 2023
Publisher : IKIP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v11i2.7819

Abstract

The Regional Meteorology, Climatology, and Geophysics Agency (BMKG) plays a crucial role in providing accurate and reliable services related to meteorology, climatology, and geophysics. Temperature observation is one of the important tasks carried out by the BMKG as it is essential for weather and climate forecasting, as well as for predicting natural disasters. To ensure the accuracy of the data, the thermometers used for temperature observation must be in good working condition and calibrated regularly. According to the Republic of Indonesia Law No. 31, Article 48, Year 2009 on Meteorology, Climatology, and Geophysics (MKG), all observation equipment must be in good working condition and calibrated regularly. Calibration is a crucial step in ensuring the accuracy and operational fitness of the observation equipment. The International Organization for Standardization (ISO) / International Electrotechnical Commission (IEC) 17025:2017 also emphasizes the importance of ensuring the quality and accuracy of all measurement instruments. The Calibration Laboratory at the BMKG Regional Office I in Medan is accredited with ISO/IEC 17025:2017 by the National Accreditation Committee (KAN). However, the calibration process can be time-consuming and requires constant monitoring to achieve stable data. During temperature and humidity calibration, the calibration laboratory's environment must be conditioned to maintain the performance of sensitive instruments that are susceptible to environmental changes. This study aims to design an automated temperature calibration monitoring system using the Internet of Things (IoT) to improve the efficiency of the calibration process and achieve maximum calibration results at the BMKG Regional Office I in Medan. The system will enable the calibration personnel to monitor the calibration process remotely and receive real-time data, allowing for more effective analysis and decision-making.
Implementation of Monte Carlo Simulation in Evaluation of The Uncertainty of Rainfall Measurement Romeo Kondouw; Kerista Tarigan; Syahrul Humaidi; Marhaposan Situmorang; Mardiningsi Mardiningsi; Yahya Darmawan
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol 11, No 2: April 2023
Publisher : IKIP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v11i2.7820

Abstract

Many factors trigger the uncertainty of rainfall measurement. Several factors can be related to the instruments, weather conditions, and acquisition methods. The degree of uncertainty could be obtained through the calibration process. In principle, rain gauges are calibrated based on the standard process ruled by ISO/IEC 17025 using the law of propagation of uncertainty (LPU). However, LPU requires complex and complicated mathematical calculations. An alternative approach is needed to evaluate measurement uncertainty besides the LPU method. This research used the Monte Carlo method to determine the uncertainty during the rainfall measurement. This method involves repeated random simulations by providing probability distribution on the input and output of rainfall measurement. The results showed that the Monte Carlo method can accurately determine the uncertainty of rainfall measurement. In addition, the uncertainty analysis also showed that instrument inaccuracy is the most significant factor that causes the uncertainty of rainfall measurement.
Co-Authors Deassy Eirene Diana Doloksaribu Humam Maulana Mardiningsi Mardiningsi Marhaposan Situmorang Richard Mahendra Putra Romeo Kondouw Syahrul Humaidi Tarigan, Kerista