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Journal : Buana Information Technology and Computer Sciences (BIT and CS)

Detection of Diseases and Pests on The Leaves of Sweet Potato Plants sing Yolov4 nisti, Melita; Yuniar Rahman, Aviv; Marisa, Fitri
Buana Information Technology and Computer Sciences (BIT and CS) Vol 5 No 1 (2024): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v5i1.6065

Abstract

Sweet potato (Ipomea batats) is a root plant that can live in all weather, in mountainous areas and on the coast.. This plant is one of the important food crops in Indonesia, and makes Indonesia the second largest sweet potato producer after China. However, according to data from the Central Statistics Agency (BPS), sweet potato production in Indonesia in 2018 decreased by 5.63% when compared to production in 2017 which reached 1,914,244 tons (Gultom, 2021). Based on these data, it is important to conduct research on pest and disease detection in plants. Therefore, the author conducted a study related to this problem entitled Detection of Diseases and Pests on the Leaves of Sweet Potato Plants using Yolov4 with the aim of helping educate farmers in recognizing diseases on the leaves of sweet potato plants and how to overcome them. In this study the dataset was sweet potato leaves with a total of 1500 data divided into three classes, namely aspidomorpha, yellow spot and normal leaves with 4000 iterations. The best training results on 1500 data with 75% accuracy. The Yolov4 algorithm produces high accuracy in detecting diseases in the leaves of sweet potato plants.
A Detection of Malacca Woven Fabric Motifs Using the YOLOv4 Method Neno, Adi; Yuniar Rahman, Aviv; Marisa, Fitri
Buana Information Technology and Computer Sciences (BIT and CS) Vol 5 No 1 (2024): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v5i1.6081

Abstract

Malacca is one of the districts that has a weaving culture and also produces woven cloth in East NusaTenggara. The large number of types of woven cloth from each Malacca tribe means that outsiders andeven native Malacca people are not yet familiar with typical Malacca motifs, therefore a system isneeded that can help make it easier for people to recognize the types of woven fabric motifs. Malaccawoven fabric in this study was used to detect the types of woven fabric motifs in Malacca district usingthe YOLOv4 method. The results of detecting Malacca woven fabric motifs correspond to each type ofwoven fabric. Apart from that, the Malacca woven fabric motif detection system with YOLOv4technology is an effective and efficient solution in recognizing Malacca woven fabric motifs. Malaccawoven fabric is classified into four classes with an impressive mAP score of 100%.
Identification of Socio Economic Registration Data Using OCR Based Tesseract and Google Cloud Vision Ursaputra Pratama, Lionardi; Yuniar Rahman, Aviv; Pahlevi Putra, Rangga
Buana Information Technology and Computer Sciences (BIT and CS) Vol 5 No 2 (2024): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v5i2.6258

Abstract

The Indonesian government program, called Socio-Economic Registration (Regsosek), aims to measure and monitor the socio-economic conditions of low-income people. One of the relevant data used for research is Regsosek. This method is used to analyze the influence of economic and social infrastructure on economic growth, analyze the socio-economic determinants of ownership of work accident insurance for informal workers, create a women's socio-economic vulnerability index (IKSEP), and study intercultural literacy from a social, economic and political perspective. The success of the government's Socio-Economic Registration program depends on the role of data collection officers or surveyors, who directly interact with the community to obtain information about Socio-Economic Registration (Regsosek) data collection. This method also has other obstacles that significantly affect the overall results of the survey, where the survey results must be entered manually by the surveyor from a form with handwritten data, after which it is entered into the website. This method is vulnerable to human error, where the handwriting is difficult to read, and mistakes are made during the data input. The technology that can be used to handle this problem is implementing the OCR method, where writing that was initially handwritten manually can be identified and converted into digital text that can be edited (editable text) and processed automatically. This research shows that the proposed method has good accuracy, with an Accuracy of 96.45%, CER 0.3%, and WER 4.30%.