Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Predictive maintenance (PdM) is indicated state of the machine to perform a schedule of maintenance based on historical data, integrity factors, statistical inference methods, and engineering approaches that are currently often applied to aircraft maintenance. The Predictive maintenance on aircraft to avoid the worse event (failure) and get information about the status of aircraft machines by applied on Machine Learning (ML) to get high accuracy and precision. The research aims to look for the method and technique of ML, which is the best applied on PdM for aircraft in accuracy indicators. The techniques of ML have been divided by classification and regression, which are compared on three ML methods: Random Forest (RF), Support Vector Machine (SVM), and simple LSTM. The result of the study for classification technique are LSTM 98,7%, SVM 95,6%, and RF 900,3%. On other hand, Regression technique for ML result on MAE and RMSE are LSTM 13,55 and 22,13, SVM 15,77 and 20,51, RF 15,06 and 19,98. Classify technique is better and faster than regression when calculating the PdM on an aircraft engine. The LSTM method of ML is the best applied to it because of the accuracy higher and time process faster than other methods in this study. Finally, the LSTM method is highly recommended while using with classify technique on ML to determine the PdM on an aircraft engine.

The Essence of Poetry Application as Indonesian Pop Songs Lyrics in the Perspective of Utilitarianism Ethical Axiology

Background: The existence of literature among the younger generation is increasingly difficult to detect. Meanwhile, literature plays an important role in character education as well as the existence of bahasa Indonesia as the national language. One form of literature whose popularity is declining is poetry. Using poems as song lyrics is a new way to deliver poetry. Aim: This study aims to determine the essence of poetries application as Indonesian pop song lyrics on the spread of literature. Methods: The research was conducted using a qualitative approach with an open questionnaire data collection method and interview. The population is Faculty of Humanities (FIB) of Universitas Airlangga students from the cohort of 2019-2021. The sample was taken using a random sampling technique with the number 40 respondents for the questionnaire and 14 participants for the interview. For the questionnaire, the respondents were asked to interpret three lyric snippets of popular pop songs in Indonesia. The purpose of the questionnaire was to measure the respondent's level of understanding of the song lyrics so that conclusions could be drawn. The data analysis method was done using the classification technique, which was grouping the answers from the questionnaire into four categories, namely: 1). completely understand, 2). somewhat understand, 3). do not quite understand, and 4). do not understand at all. Results: The results show that the use of poetry as song lyrics has an effect on increasing respondents' literary understanding. Recommendation: Younger generation should utilize songs as media for dissemination as well as learning literature, especially poetry. Limitation: However, further in-depth research is needed due to the limitations of this study. The respondents and participants involved are still restricted to FIB students from Universitas Airlangga, hence the resulting data does not represent the young generation from all over Indonesia.

EVALUATION OF EFFECTIVENESS OF PATCH BASED IMAGE CLASSIFICATION TECHNIQUE USING HIGH RESOLUTION WORLDVIEW-2 IMAGE

The aim of the current study was to evaluate the performance of patch-based classification technique in land use/land cover classification and to investigate the effect of patch size in thematic map accuracy. To reach desired goal, recently proposed ensemble learning classifiers (i.e., XGBoost and CatBoost) were utilized to classify produced image patches obtained from high-resolution WorldView-2 (WV-2) satellite image. . In order to analyse the effect of varying patch size on classification accuracy, three different window sizes (i.e., 3 × 3, 7 × 7 and 11 × 11) were applied to WV-2 imagery for extracting image patches. Constructed image patches were classified using XGBoost and CatBoost ensemble learning classifiers and thematic maps were constructed for varying patch sizes. Results showed that while XGBoost and CatBoost showed similar classification performances for varying patch size and the estimated highest overall accuracy were %68, %82 and %92 for 11x11, 7 × 7 and 11 × 11 patch sizes, respectively. These findings confirmed that defining class boundaries on the high-resolution image using smaller patches increases the accuracy of thematic maps. In addition, results of patch-based classification were compared the results of LULC maps produced by same classifiers using pixel-based classification method. Overall accuracy of pixel-by-pixel classification of WV-2 image reached to about %94. Furthermore, CatBoost showed superior classification performance in all time compared to XGBoost. All in all, pixel-based CatBoost was found to be more successful in LULC mapping of fine resolution image.