In this research, an intelligent system for detecting cassava leaf disease has been developed by utilizing the MobileNetV2 deep learning model and displaying it using a python graphical user interface (GUI). There are five disease classes used in this study, namely Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD) and Healthy. The results showed that the overall accuracy of the test data obtained was 65,6%. The GUI application program was made to be operated efficiently for beginners and can be used by cassava farmers in the field.

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Through this translator program, it is craved that it can avail the general public to understand foreign language videos, can be useful in the world of education and technology, and can avail the persons with disabilities be up to communicate. The method used is a classification method that functions to detect the flow of shapes to instruct the class attribute as the task of the input attribute by generating automatic output through three stages, namely Machine Learning, Natural Language Processing, and Speech. The results showed that 90.38% of videos were successfully translated into text and audio, 9.62% of videos failed to be translated because the owner limited public interaction, and 89%-97% synchronization between text and audio. In this research, a text and audio translator program has been created using the Application Programming Interface (API). This program is a configuration of deep learning, machine translation, and text-to-speech designed using the high-level programming language python. The system used is a predictive system in which the system tries to predict the output equally the wishes of the user.

Photoacoustic spectroscopy can appl in various fields including in the fields of biology (measuring trachea volume and observing insect breathing patterns), medicine (a measurement of internal disease biomarkers through respiratory gases), environment (measuring NO2 gas in the environment near roads), and agriculture (measurement ethylene gas in postharvest fruit). The existing photoacoustic spectroscopy still has a large size and high operating costs, so it is necessary to design photoacoustic spectroscopy that is portable and low operating costs. In this research, designing an infrared diode laser that can be modulated using software using Visual Studio. There are two tests to see the characteristics of the devices made in this study, namely Arduino testing and testing of the software programs created. Arduino testing resulted in a calibration factor of fo = 0.9068fi + 109.33. Meanwhile, software testing resulted in a calibration factor of fo = 0.7343fi + 462.74. The two tests that have been carried out have different output results. The software output that is created has a smaller calibration factor than the direct output of the Arduino program.