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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 Journal of Physics and Its Applications Indonesian Physical Review Makara Journal of Science
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 Finkan Danitasari; Muhammad Ryan; Djati Handoko; Ida Pramuwardani
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%.
Flatline Anomaly Detection in Automatic Weather Station Air Temperature Sensor Data Using LSTM Autoencoder Supriyatna; Santoso Soekirno; Martarizal; Djati Handoko
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026)
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.
A Comparison of CNN-based Image Feature Extractors for Weld Defects Classification Purnomo, Tito Wahyu; Ramadhany, Harun Al Rasyid; Jati, Hapsara Hadi Carita; Handoko, Djati
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 1 (2024): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i1.72509

Abstract

Classification of the types of weld defects is one of the stages of evaluating radiographic images, which is an essential step in controlling the quality of welded joints in materials. By automating the weld defects classification based on deep learning and the CNN architecture, it is possible to overcome the limitations of visually or manually evaluating radiographic images. Good accuracy in classification models for weld defects requires the availability of sufficient datasets. In reality, however, the radiographic image dataset accessible to the public is limited and imbalanced between classes. Consequently, simple image cropping and augmentation techniques are implemented during the data preparation stage. To construct a weld defect classification model, we proposed to utilize the transfer learning method by employing a pre-trained CNN architecture as a feature extractor, including DenseNet201, InceptionV3, MobileNetV2, NASNetMobile, ResNet50V2, VGG16, VGG19, and Xception, which are linked to a simple classification model based on multilayer perceptron. The test results indicate that the three best classification models were obtained by using the DenseNet201 feature extractor with a test accuracy value of 100%, followed by ResNet50V2 and InceptionV3 with an accuracy of 99.17%. These outcomes are better compared to state-of-the-art classification models with a maximum of six classes of defects. The research findings may assist radiography experts in evaluating radiographic images more accurately and efficiently.
Co-Authors Alifta, Dimas Aliwarga, Harry Kusuma Arief Sudarmaji, Arief Auriwan Yasper Azwar Manaf Budhy Kurniawan Dede Djuhana Dewi, Fitria Yunita Finkan Danitasari Finkan Danitasari Harry Kasuma Aliwarga Hasan Fahad Hifzhi, Affan Ida Pramuwardani Jati, Hapsara Hadi Carita Kim, Dong-Hyun M. Husna Al Hasa Madona, Madona Martarizal Masrukan Masrukan Maulana Putra Muhammad Ryan Pradana, Ilham S Purnomo, Tito Wahyu Putra, Maulana Ramadhany, Harun Al Rasyid Rosid, Mohammad Syamsu S. Hi. Rauf, Faris zulkarnain Santoso Soekirno Sianipar, Dimas Salomo Januarianto Sigit, Rohmad Siti Mardiah, Siti Sugiarto Supriyatna Syaefudin, Mohamad Anwar Tito Wahyu Purnomo Wisnu Ari Adi