Deforestation in Indonesia is in a status that is quite alarming. From year to year, deforestation is still happening. The decline in fauna and the diminishing biodiversity are greatly affected by deforestation. This paper proposes a bioacoustics-based forest quality assessment tool using Nvidia Jetson Nano and convolutional neural networks (CNN). The device, named GamaDet, is a portable physical product based on the microprocessor and equipped with a microphone to record the sounds of birds in the forest and display the results of their analysis. In addition, a Google Collaboratorybased GamaNet digital product is also proposed. GamaNet requires forest recording audio files to be further analyzed into a forest quality index. Testing the forest recording for 60 seconds at an arboretum forest showed that both products could work well. The GamaDet takes 370 seconds, while the GamaNet takes 70 seconds to process the audio data into a forest quality index and a list of detected birds.

The control loop in the industry is a component that must be maintained because it will determine the plant's performance. Most industrial controllers experience oscillations with various causes, such as noise, oscillation, backlash, dead band, hysteresis, random variation, and poor controller tuning. The oscillation diagnosis system, which can understand the oscillation type characteristics, is built based on machine learning because it is dynamic and not based on specific rules. This study developed an online oscillation diagnosis program using the extreme gradient boosting (XGBoost) method. The data was obtained through the simulation of the Tennessee Eastman process. The data is segmented on specific window sizes, and then time series feature extraction is performed. The extraction results are then used to build an XGBoost model capable of performing oscillation diagnosis tasks. There are seven types of oscillations tested in this study. The model that has been made is implemented online with the help of sliding windows. The results show that the XGBoost model performs best when the data window size is 100, with the accuracy performance and the F1 score of the model in classifying the type of oscillation being 0.918 and 0.905, respectively. The model can detect the type of oscillation with an average diagnosis time of 712 seconds on diagnostic tests.