Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Informatik : Jurnal Ilmu Komputer East Asian Journal of Multidisciplinary Research (EAJMR)
Jimmy Robot
Universitas Sam Ratulangi
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Application Development Android-based Tourism Case Study of North Sulawesi Province Jimmy Robot
East Asian Journal of Multidisciplinary Research Vol. 2 No. 5 (2023): May, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/eajmr.v2i5.4410

Abstract

North Sulawesi Province, which is often called North Sulawesi, is a province located at the northern tip of Sulawesi Island. North Sulawesi has 11 regencies and 4 cities and Manado City is the capital of this province. Based on Law Number 13 of 1964, on August 14, 1959 the North Sulawesi Province was formed so that day was determined as the anniversary of the Province. Some areas in Sulawesi North is also a barometer of national tourism development. The tourism sector has become a mainstay sector not only for the provincial government, but also for the participation of all layers of society. Technological developments that are increasing rapidly can now cover all fields, one of which is in the field of Information Systems such as the Android application which has been used to display tourist information and tourism support facilities such as hotels, transportation, souvenir shops, and other things. To make things easier, Android also provides access and integration with the Google Maps service. The process of making this application uses the Rapid Application Development (RAD) method which consists of 3 phases, namely Requirements Planning, RAD Design Workshop, and Implementation. For the continued development of the Android-based Tourism Location Based Service application in North Sulawesi Province by adding other features that are more supportive.
Comparative Analysis of Computer Vision Models for Detecting Nilam Plant Diseases: A Case Study of MobileNet vs. YOLO Jimmy Robot; Nancy Tuturoong; Ade Yusupa
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12800

Abstract

Nilam Plant (Patchouli plant) located in Minahasa Regency can be affected by several diseases that can significantly reduce their essential oil capacity. The common practice of monitoring crops for diagnosis relies on labor-intensive methods that can be variable in accuracy, depending on previous experience of the tended. The main goal of the study is to develop and to compare model performance using Deep Learning computer vision in monitoring conditions of patchouli plants with respect to different conditions (healthy, bacterial wilt, viral, and budok). This study assess and compare MobileNet (a lightweight classifier) against Deep Learning based object detectors (YOLOv5, v8, v11) using an underlying dataset that was created unimpeachably in a natural patchouli field setting, consisting of 3,000 images which contain 3,820 annotated bounding boxes, across 4 classes (Healthy, Bacterial Wilt, Viral, Budok). Evaluation reveals a clear trade-off between the two models. MobileNet finds (nearly perfect) classification accuracy of 94.7% (F1-score>0.90 for all classes), while the faster YOLOv8l yields an 88.2% mAP50. Both models had the hardest time dealing with the "Viral" class due to visual similarities it shared with the healthy class (F1: 0.90, mAP: 0.81). MobileNet produced better accuracy (94.7%) but had slower inference time (3.0s). YOLOv8l provided real-time detection (1.4s) but lower mAP (88.2%). Our recommendation is a hybrid 2-stage system (YOLO-drone scan; MobileNet-farmer confirmation) as an operational approach for Precision Agriculture in Patchouli farming. Overall, the main takeaway is that: MobileNet is intended for diagnostic application and YOLOv8 superior for real-time video-based field monitoring.
Co-Authors Ade Yusupa Nancy Tuturoong