Optical Character Recognition Seven Segment Pada Timbangan Berat Badan Menggunakan Metode Canny Edge Detection

Timbangan badan merupakan alat pengukuran yang digunakan untuk menentukan berat atau massa badan, terdapat dua jenis timbangan badan yaitu timbangan manual dan digital. Timbangan yang di produksi oleh PT. X merupakan jenis timbangan berat badan digital. Dalam proses produksi timbangan berat badan terdapat beberapa tahapan yang harus dipenuhi agar dapat dipasarkan salah satunya proses pengecekan quality control (QC). Proses QC pada PT. X menggunakan Lembar Kerja Pengujian (LKP) yang dilakukan masih secara manual, pertama saat administrasi memberikan LKP kosong ke operator dan kedua setelah operator selesai memasukkan data hasil QC, administrasi akan memasukkan data yang diserahkan operator ke komputer. LKP yang diperlukan sebanyak dua lembar dalam satu produk saat proses QC. Hal tersebut juga menyebabkan terjadinya human error dari operator seperti kehilangan LKP, kesalahan penulisan karena tulisan tangan tidak dapat dibaca dengan jelas. Oleh karena itu, proyek akhir ini dibangun menggunakan Optical Character Recognition (OCR) sebagai proses deteksi citra seven segment pada display timbangan berat badan. Hasil dari proses OCR akan disimpan dalam database, penyimpanan di database ini digunakan untuk menyimpan data hasil deteksi citra seven segment secara otomatis pada LKP, sehingga dapat mengurangi dampak kehilangan LKP serta mengurangi kesalahan perhitungan rata-rata proses validasi produk OK atau Reject karena semua proses perhitungan sudah dilakukan oleh sistem. Pada penelitian ini sistem dapat mendeteksi citra seven segment pada timbangan berat badan secara optimal menggunakan metode canny edge detection memiliki tingkat akurasi sebesar 88,82% dibandingkan dengan metode sobel edge detection yang memiliki tingkat akurasi sebesar 71,04% dan metode prewitt edge detection memiliki tingkat akurasi sebesar 57,72%. Untuk metode canny edge detection berdasarkan tingkat presisi lebih baik dibanding metode deteksi tepi lainnya dengan memiliki tingkat presisi sebesar 100% sedangkan metode sobel edge detection memiliki tingkat presisi sebesar 94,1% dan metode prewitt edge detection memiliki tingkat presisi sebesar 89,60%. Kemudian untuk metode canny edge detection proses deteksinya lebih efisien waktu yaitu rata-rata deteksi 0,73 detik dibanding dengan metode sobel edge detection yang memiliki waktu rata-rata deteksi 9,89 detik dan metode prewitt edge detection memiliki waktu rata-rata deteksi 16,75 detik. ================================================================================================================================= Body scales are measurement tools used to determine weight or body mass, there are two types of body scales, namely manual and digital scales. QC Process at PT. X uses the test worksheet which is done manually, first when the administration gives a blank test worksheet to the operator and second after the operator finishes. Therefore, this final project is proposed to use Optical Character Recognition (OCR) as a seven-segment image detection process on weight scale displays. The results of the OCR process will be stored in the database, storage in this database is used to store data on the results of seven segment image detection automatically on the test worksheet, so as to reduce the impact of test worksheet loss and reduce the average calculation error of the OK or Reject product validation process because all calculation processes have been carried out by the system. In this study, the system can optimally detect seven segment images on weight scales using the canny edge detection method which has an accuracy rate of 88.82% compared to the sobel edge detection method which has an accuracy rate of 71.04% and the prewitt edge detection method has an accuracy rate of 57.72%. For the canny edge detection method based on a better level of precision than other edge detection methods with a level of precision of 100%, while the sobel edge detection method has a level of precision of 94.1% and the prewitt edge detection method has a precision level of 89.60%. Then for the canny edge detection method, the detection process is more time efficient, which is an average detection of 0.73 seconds compared to the sobel edge detection method which has an average detection time of 9.89 seconds and the prewitt edge detection method has an average detection time of 16.75 seconds

The Pemetaan Tingkat Bahaya Erosi dengan Pemanfaatan Teknologi Drone di DTA Cipaheut Sub DAS Cikapundung Hulu

Technology in the field of mapping is constantly evolving to provide convenience for human work. One of the innovations that are developing in today’s modern era is drone or Unmanned Aerial Vehicle (UAV). Erosion mapping and erosion measurements are carried out to determine the potential risk of erosion in certain areas. The erosion risk map shows the distribution of erosion hazard levels in an area. The objective of this research is to determine erosion prediction and erosion hazard levels spatially based on USLE (Universal Soil Loss Equation) method using drone. The used of UAV for collecting data to generate soil erosion risk map at detail scale based on USLE method is relatively infrequently done in Indonesia. The research was conducted in Cipaheut Watershed, Cikapundung Hulu Sub-watershed, Cimenyan, Cimenyan, Bandung Regency, West Java. The results of the study show that the role of drone is very helpful in mapping general conditions and land use spatially. In addition, drones are able to provide actual data sources to identify the physical factors needed in the USLE method of erosion calculations such as LS, C and P factors. Erosion prediction and erosion hazard levels can be calculated using data acquired from drones. There are 8 SPLs with a total land area of ​​225.10 Ha. SPL 5 with the use of dry land and steep slope have the highest erosion prediction values ​​of 703.1207 tons/ha/year and an erosion hazard index of 87.8901 tons/ha/year with a very high level of erosion hazard.&nbsp