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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Jurnal Sains Materi Indonesia Journal of Information Technology and Computer Science Jurnal Penelitian Pendidikan IPA (JPPIPA) INDONESIAN JOURNAL OF APPLIED PHYSICS Jurnal Elektronika dan Telekomunikasi Journal of Physics and Its Applications Indonesian Physical Review
Handoko, Djati
Department Of Physics Faculty Of Mathematic And Sciences Universitas Indonesia, Depok 16424 Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Materials Science & Nanotechnology Physics

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Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN Danitasari, Finkan; Ryan, Muhammad; Handoko, Djati; Pramuwardani, Ida
Jurnal Penelitian Pendidikan IPA Vol 10 No 1 (2024): January
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i1.5906

Abstract

Bidirectional LSTM (BiLSTM) is an extension of LSTM which can improve model efficiency and accuracy in classification scenarios based on time series data or longer time series data repeatedly. This research uses the BiLSTM algorithm to build a daily weather forecast model at Soekarno-Hatta Airport. The model built will assist forecasters in making weather forecasts on a local scale. This research is expected to be implemented and able to increase the verification value of Soekarno-Hatta Airport weather forecasts to support flight safety in Indonesia. The dataset used is hourly surface air weather parameter data (synoptic data) of Soekarno-Hatta Meteorological Station for the period January 2018 - December 2022. There is an imbalance in the data set, so the SMOTE and ADASYN techniques are used to handle the problem. The output of this research is weather conditions categorised into sunny, sunny cloudy, cloudy, light rain, moderate rain, heavy rain, and thunder rain. The results obtained will go through model verification and evaluation by finding the accuracy value by comparing the weather forecast model output with actual weather data using a multi-category contingency table. The BiLSTM - ADASYN model obtained the highest average accuracy value compared to other models, which was 83.2%.
Developing 1-D velocity model along Matano Fault Zone, Sulawesi, Indonesia Madona, Madona; Rosid, Mohammad Syamsu; Handoko, Djati; Sianipar, Dimas Salomo Januarianto
Journal of Physics and Its Applications Vol 8, No 1 (2026): February 2026
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v8i1.29859

Abstract

The Matano Fault, with a slip rate of ~ 20 mm/year, is the most active strike-slip fault in Sulawesi after the Palu-Koro Fault. As a result, this region exhibits a high level of seismicity. Unfortunately, a number of studies that have been conducted only involve a less dense network of stations and global velocity models. This study aims to obtain an optimum velocity model using the VELEST program, which reliably represents the actual condition of the study area. The data used in this study consists of hypocenter, origin times, and P-wave arrival times from earthquakes (Mw ≥ 3), each containing at least six clearly identified P-wave phases. These data were obtained from 317 events that occurred within the region bounded by 120.10°E – 122.20°E and 2.99°S – 1.66°S during the period from January 2022 to March 2025. To determine the optimum 1-D velocity model, four initial models were tested, namely Koulakov, Arimuko, Crust, and Bunaga. These models were evaluated based on RMS, the stability test of the updated velocity model, uncertainty assessment using bootstrap test, and their consistency with previous studies. The evaluation results indicate that the Arimuko Model is the most reliable, as it provides the lowest RMS value, stable hypocenter relocations (±6–7 km), bootstrap results showing narrow uncertainty intervals, and consistency with earlier studies that identified a low-velocity zone at depths of 0–3 km. The result of this study is expected to serve as a reference for earthquake relocation and seismicity analysis at the Matano Fault Zone.
Flatline Anomaly Detection in Automatic Weather Station Air Temperature Sensor Data Using LSTM Autoencoder Supriyatna; Soekirno, Santoso; Martarizal; Handoko, Djati
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026): In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i4.14486

Abstract

The quality of air temperature data from Automatic Weather Stations (AWS) is crucial for meteorological analysis, climatology, and early warning systems. However, flatline anomalies, a condition where sensor values ​​tend to remain constant over a period of time, can degrade data quality and are often not optimally detected by conventional rule-based quality control (QC) methods. Previous research is also limited in specifically examining flatline detection, with most studies focusing on general anomalies and not integrating deep learning approaches with operational quality control systems. This study proposes a data-driven approach using a Long Short-Term Memory Autoencoder (LSTM-AE) combined with Level-1 QC. The novelty of this study lies in the use of a normal-only training scheme, anomaly threshold determination based on the reconstruction error distribution, and post-detection diagnosis to identify flatline characteristics. The methods include QC filtering, sliding window formation, model training, threshold determination, and anomaly detection. The results show stable model performance with an anomaly threshold value of 0.01177 (MSE). Of the 985,730 data windows, approximately 0.578% were detected as anomalies, indicating that flatline occurrences are relatively small but still significant to data quality. Most anomalies are short-lived and discontinuous, indicating localized sensor noise. This study demonstrates that LSTM-AE is effective as an adaptive flatline detection method and has the potential to be implemented as an automated QC module in AWS systems to improve data reliability.
Co-Authors Alifta, Dimas Aliwarga, Harry Kasuma Aliwarga, Harry Kusuma Arief Sudarmaji, Arief Auriwan Yasper Azwar Manaf Budhy Kurniawan Danitasari, Finkan Dewi, Fitria Yunita Finkan Danitasari Hapsara Hadi Carita Jati Harry Kasuma Aliwarga Harun Al Rasyid Ramadhany Hasan Fahad Hifzhi, Affan Kim, Dong-Hyun Madona, Madona Martarizal Maulana Putra Pradana, Ilham S Pramuwardani, Ida Putra, Maulana Rosid, Mohammad Syamsu Rosid, Mohammad Syamsu Rosid Ryan, Muhammad S. Hi. Rauf, Faris zulkarnain Sianipar, Dimas Salomo Januarianto Soekirno, Santoso Sugiarto Supriyatna Syaefudin, Mohamad Anwar Tito Wahyu Purnomo Yasper, Auriwan