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All Journal Informatik : Jurnal Ilmu Komputer Target : Jurnal Manajemen Bisnis
Nancy Tuturoong
Universitas Sam Ratulangi
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Penguatan Daya Saing Komoditas Kelapa Sulawesi Utara dalam Pasar Asia Pasifik melalui Unit Bisnis Strategis Pengeloaan Kelapa Terpadu Skala Industri Pedesaan Jimmy Reagen Robot; Nancy Tuturoong
Target : Jurnal Manajemen Bisnis Vol 5 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/target.v5i1.2886

Abstract

This study is a strategic analysis of the current condition of empowering the coconut industry in North Sulawesi which is reflected in the performance of national exports. The hypothesis that has been developed refers to the comparative strength of the competitiveness of countries in the Southeast Asian region in facing the Asia Pacific free trade era as a turbolence effect of world free trade openness which has caused international market competition to become increasingly extreme due to the rapid advancement of information technology. Vibration of the rural economy has significance for the resilience of a country's national economy. On the other hand, open access to world information that has reached rural areas is not fragmented by the touch of technological advances in the coconut processing industry in rural areas and is still trapped by its traditional stigma. The analysis of this paper elaborates on the implementation of value engineering innovations in downstream integrated coconut processing on a rural industrial scale as a breakthrough in efforts to accelerate the distribution of skills and welfare of rural farming communities in improving the final quality of downstreaming people's coconut products, as a contribution to bridging the reality of inequality in economic empowerment and applied technology in rural North Sulawesi.
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 Jimmy Reagen Robot Jimmy Robot