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All Journal International Journal of Engineering Continuity Jurnal Pengabdian Masyarakat Sultan Indonesia
Diana Rusjayanti
Universitas Wiralodra
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Pengamatan Dampak Pengaruh Kelembahan Suhu Bagi Pelaku Usaha Tanaman Jamur Diana Rusjayanti; Tion Sutiyono; Taufik Hidayat
Jurnal Pengabdian Masyarakat Sultan Indonesia Vol. 1 No. 1 (2024): Abdisultan
Publisher : Sultan Publsiher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/abdisultan.v1i1.196

Abstract

White oyster mushrooms are woody mushrooms that can be consumed and have high nutritional content including calcium, carbohydrates, iron, potassium and phosphorus. This time we are doing community service to mushroom plant business actors, aiming to find out the obstacles faced by business actors cultivating this mushroom plant, so that by analysing the impact of temperature on mushroom plants, we can contribute suggestions for white oyster mushroom plant business actors, so that crop yields increase. In observing this temperature, we used the method of interviewing business actors, then from the sources we interviewed, we made an analysis and we also made observations on mushroom plants. From the results of our observations we concluded that to get good mushroom production the temperature must be maintained around 24-28 Degrees Celsius, if the temperature is too high then the harvest will not be good. From the results of the observations made so that mushroom plant business actors can productively maintain the temperature in oyster mushroom plants so that the quality of the harvest is good.
Electronic Nose Based on Sensor Array for Classification of Beef and Rat Meat Using Backpropagation Artificial Neural Network Method Diana Rusjayanti; Indri Yanti; Muh Pauzan
International Journal of Engineering Continuity Vol. 3 No. 1 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i1.241

Abstract

The differentiation of beef and rat meat is crucial for food safety and consumer protection. This research aims to create a tool to distinguish between beef and rat meat and to analyze the training data patterns for both types of meat. A sensor array consisting of three gas sensors—TGS822, TGS2602, and TGS2610—was used to detect the presence of Metal Oxide Semiconductor (MOS) gases in the meat samples. The classification method employed was a backpropagation artificial neural network (ANN). Results indicate that the classification tool performs well in differentiating beef from rat meat, with distinct patterns observed in the training data for each type of meat. The model achieved a precision of 100%, a recall (sensitivity) of 80%, and an accuracy of 90%. However, the TGS2610 sensor did not show a significant difference between beef and rat meat, suggesting no variance in the gas content detected by this sensor. These findings highlight the potential of using such sensors in practical applications for meat detection and underscore the need for further refinement in sensor selection and system integration to improve classification performance.
Co-Authors Indri Yanti Muh Pauzan Taufik Hidayat Tion Sutiyono