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Penerapan Model Waterfall pada Sistem Manajemen Jasa Tenaga Kerja Bangunan Berbasis Android As'ari, Abdul Haris; Suhendar, Agus
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4549

Abstract

The purpose of this research is to develop a system that can assist the community, especially in the Pemalang region, in facilitating the management of construction workforce services and reducing unemployment in the surrounding areas. The specific focus of this study will be on the community in the Pemalang region. Currently, in Pemalang, the majority of the population still seeks construction labor services through manual methods, such as visiting service providers at their residences to offer jobs. Most people rely solely on personal connections. Consequently, if a service provider is already engaged in another project, customers may struggle to find an alternative. Meanwhile, there are many unemployed service providers outside the community seeking job opportunities. Additionally, some individuals feel that the wages for these workers are either insufficient or too high, necessitating negotiations for mutually agreed-upon compensation. The proposed construction workforce management system aims to create an application that assists the community in searching for services, negotiating terms, and estimating incurred expenses. For service searches, the system will enable users to search based on skills and location, allowing customers to find service providers according to the required expertise and in nearby areas to minimize transportation costs. Negotiations can be conducted remotely to determine prices and required departure times. Customers can also calculate costs to estimate their future expenditures. Service providers can upload their services for visibility, facilitating job acquisition and ultimately reducing unemployment.
Removal Technique of Penetrating Nail in Head: A Case Report Suhendar, Agus; Effendy, Effendy
International Journal of Integrated Health Sciences Vol 11, No 1 (2023)
Publisher : Faculty of Medicine Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15850/ijihs.v11n1.3150

Abstract

Objective: To present a unique case involving a 44-year-old man who sustained a penetrating head injury after nailing his head with a hammer. Despite the severity of his injury, the patient underwent successful surgical treatment and experienced a good recovery.Methods: Clinical and imagery review  was performed on a cranial puncture trauma caused by a metal nail, which penetrated the cranium, dura mater, right parietal cerebral parenchyma, and right ventricle. The nail was lodging next to midline without damaging the superior sagittal sinus. The patient underwent craniotomy nail removal and debridement with normal saline and metronidazole antibiotics.Results: Craniotomy, careful nail extraction, wound debridement, and duraplasty remain the treatment standard for penetrating nail injury in the head. Patient in this case study did not exhibit any signs of neurologic deficit or infection.Conclusion: Proper diagnosis and treatment are required in patients with penetrating brain trauma, with head x-rays and CT scans help in evaluating vascular depth and damage. Craniotomy and debridement are the main treatments for this type of trauma.
Application of Yolov11 for Corn Plant Disease Detection Based on Leaf Images Shinta, Velen; Suhendar, Agus
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 4 (2025): Vol. 4 No. 4 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i4.977

Abstract

This study develops a corn leaf disease detection system using the YOLOv11 algorithm to overcome the limitations of manual identification, which is often subjective and slow. The dataset from Roboflow was converted to the object detection format with four classes (Leaf Spot, Blight, Rust, Healthy), annotated with bounding boxes, split in a 70:20:10 proportion, and optimized through preprocessing and data augmentation. The model was trained for 150 epochs, yielding an average precision of 0.785, a recall of 0.662, and an mAP@0.5 of 0.717 from 80 test images. The Healthy class performed superiorly (mAP 0.988), while the Leaf Spot class was the lowest (mAP 0.471) due to the variation of complex lesions. The confusion matrix confirmed prediction consistency. The main advantage is the detection of specific disease locations via bounding boxes, complementing previous classification approaches. This system has the potential to support automatic diagnosis and effective precision agriculture management.