Pramuditha Shinta Dewi Puspitasari
Politeknik Negeri Jember

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The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Pramuditha Shinta Dewi Puspitasari; Prawidya Destarianto; Dyah Laksito Rukmi; Arizal Mujibtamala Nanda Imron
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i02.p03


Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.
A Smart Greenhouse Production System Utilizes an IoT Technology Choirul Huda; Bety Etikasari; Pramuditha Shinta Dewi Puspitasari
JUITA : Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.16191


Food is an essential need for every living creature. Choosing the wrong food leads to serious problems e.g. indigestion, obesity, diabetes mellitus, stroke, including heart disease that causes death. To prevent those diseases from harming the body, people should be concerned about food consumption, for example by consuming organic food. Organic food is obtained by cultivating plants in a greenhouse to increase production, minimize risk, prevent disease, and be safer against environmental risk. However, some obstacles faced by farmers such as disease or pests, water supply, temperature, and so on.  Based on some previous research, the problem is dominated by soil moisture since the farmer has to water all plants manually. It has affected crop yields directly. If this phenomenon is not handled properly, farmers are threatened with losses so organic farming becomes a catastrophe. Therefore, in this research, an IoT technology is proposed to increase soil moisture in real time. The proposed system is also equipped with a Web-based information system to expose the cultivation phase, and market crops, as well as a tool for buyers as interaction media through the feedback provided. In the end, the proposed system is adequate to increase the productivity of vegetable cultivation grown in a greenhouse. Based on some experiments that have been done, the proposed method is capable to work optimally and effectively meet user needs by 95.55%.