SUNANDI, (2022) RANCANG BANGUN APLIKASI PENGHITUNG KOLONI PLANKTON PADA CITRA DENGAN LIBRARY OPENCV. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
ABSTRAK Sistem penghitung Plankton secara otomatis berbasis pengolahan citra untuk mendeteksi jumlah koloni Plankton pada media sedgewick rafter cell di laboratorium. Sistem penghitungan berisi algoritma menghitung jumlah plankton berbasis bentuk. Koloni dianggap sebagai kumpulan dari suatu jenis object dan diklasifikasikan sebagai kelompok Plankton sehubungan dengan rasio kekompakannya. Analisa dan perhitungan terhadap koloni Plankton bagi para peneliti atau praktisi mahasiswa yang sedang melakukan praktikum. Dengan demikian sangat penting untuk melakukan analisis bagi seorang peneliti dalam melakukan penelitian dengan mikrobiologi yang akan diteliti. Pada penelitian yang kami lakukan terkait menghitung objek plankton dengan hasil analisa dan pengujian yang telah dilakukan, dengan hasil dimana tingkat keakuratan sistem membaca objek berada pada range -2,72 % - 51,1 % dengan rata-rata kecepatan membaca objek pada 0.289196 detik, disimpulkan dari hasil ini bahwa perhitungan melalui sistem ini cukup dapat membaca objek dan menghitung objek dengan tingkat akurasi yang cukup baik pada tingkat pembesaran 400x. Kata Kunci: OpenCV, Plankton, Python, aplikasi Penghitung, Image Processing ABSTRACT Plankton counting system automatically based on image processing to detect the number of Plankton colonies on sedgewick rafter cell media in the laboratory. The counting system contains an algorithm to calculate the amount of plankton based on shape. Colonies are considered as collections of one type of object and are classified as plankton groups due to their compactness ratio. Analysis and calculation of Plankton colonies for researchers or student practitioners who are doing practicum. Thus it is very important to conduct an analysis for a researcher in conducting research with microbiology to be studied. In the research we did related to counting plankton objects with the results of the analysis and testing that had been done, with the results where the accuracy of the object reading system was in the range of -2.72% - 51.1% with an average object reading speed of 0.289196 seconds, it can be concluded from these results that the calculation through this system is sufficient to read objects and count objects with a fairly good level of accuracy at a 400x magnification level. Keywords: OpenCV, Plankton, Python, application counting, Image Processing
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FT/ELK 22 003 |
| NIM/NIDN Creators: | 41416310071 |
| Uncontrolled Keywords: | Kata Kunci: OpenCV, Plankton, Python, aplikasi Penghitung, Image Processing |
| Subjects: | 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan > 621.3 Electrical Engineering, Lighting, Superconductivity, Magnetic Engineering, Applied Optics, Paraphotic Technology, Electronics Communications Engineering, Computers/Teknik Elektro, Pencahayaan, Superkonduktivitas, Teknik Magnetik, Optik Terapan, Tekn |
| Divisions: | Fakultas Ilmu Komunikasi > Hubungan Masyarakat |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 20 Dec 2022 08:12 |
| Last Modified: | 20 Dec 2022 08:12 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/72624 |
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