Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Biologi Tropis JRIIN :Jurnal Riset Informatika dan Inovasi
Ardiansyah, Muhammad Ilham
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Immunology & microbiology

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

Found 2 Documents
Search

 1 
Identification of Types of Pests and Diseases of Cauliflower Plants (Brassica oleracea var. botrytis L) in Gonaman Village, Koripan, Matesih District, Karanganyar Hanik, Nur Rokhimah; Armania, Vallery; Ardiansyah, Muhammad Ilham; Marta, Fadhilah; Hidayad, Muhammad Nur; Saputra, Dian Andhi; Mardyah, Muthia; Pangestu, Prestiani Yulia; Yoshia, Selumiel; Sarima, Sarima
Jurnal Biologi Tropis Vol. 25 No. 3 (2025): Juli-September
Publisher : Biology Education Study Program, Faculty of Teacher Training and Education, University of Mataram, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbt.v25i3.9221

Abstract

Cauliflower (Brassica oleracea var. botrytis L.) during its development period needs to be controlled by pests and diseases, because at that time the flowers or krobs come out and will determine the quality and quantity of its production. This research is conducted to characterize and differentiate various types of pests and diseases found in cauliflower plants in the gardens of Gonaman Village, Koripan, Matesih District, Karanganyar. This research employs a methodology based on descriptive qualitative analysis. The tools utilized in this study include a mobile phone, writing instruments, and observation sheets. The research subjects consist of various pests and diseases identified in the garden area. Data collection by observation of the garden and direct interviews with garden owners. For data analysis and validation, qualitative descriptive techniques are used with additional interview activities with cauliflower farmers and literature studies. Based on the results of the study, eight pests have been found, namely; Whitefly / Cabuk (Aphis brassicae), Thrips (Thrips tabasi), Whitefly (Aleyrodidae sp), Crop Caterpillar (Crocidolomia binotalis), Ladybug (Illeis galbula), Brown grasshopper (Valanga nigricornis), Leaf beetle (Aluacphora sp), Leaf caterpillar (Plutella xylostella). And four diseases were found; Alternaria brassicae fungus, bacterial rot by Pectobacterium carotovorum, Erwinnia carotovora bacteria, and Xanthomonas camprestis bacteria. The visible symptoms are damage to the leaves, yellow, brownish to blackish leaves, rotten and wilted flowers so that they can reduce the harvest. Pest and disease control can be done by spraying pesticides, fungicides once a week, maintaining land cleanliness, maintaining planting distance, selecting healthy seeds, and providing lime and boren. For further research, it should be done for several days, in the morning and evening when insects are active.
Penerapan Deep Learning CNN (Convolution Neural Network) untuk Deteksi Objek dengan Konsep YOLO Radjartha, Shevcenko; Rudin, Naza; Tsubaisa, Maulana Royyan; Suhayah, Yayah; Ardiansyah, Muhammad Ilham; Yanti, Fitri
Jurnal Riset Informatika dan Inovasi Vol 3 No 8 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penerapan Deep Learning berbasis Convolutional Neural Network (CNN) telah membawa perubahan besar dalam bidang computer vision, terutama untuk deteksi objek secara otomatis. Salah satu pendekatan yang sangat populer adalah You Only Look Once (YOLO), yang mampu melakukan deteksi objek secara real-time. Penelitian ini membahas penerapan YOLO versi terbaru (YOLOv8) untuk mendeteksi berbagai jenis kendaraan menggunakan dataset dari Kaggle. Model dilatih menggunakan GPU Google Colab selama 50 epoch dan dievaluasi dengan metrik Mean Average Precision (mAP). Hasil penelitian menunjukkan bahwa integrasi CNN dan YOLO mampu menghasilkan sistem deteksi objek yang akurat, cepat, dan efisien untuk berbagai kebutuhan industri.
Co-Authors Armania, Vallery Fitri Yanti Hidayad, Muhammad Nur Mardyah, Muthia Marta, Fadhilah Nur Rokhimah Hanik Pangestu, Prestiani Yulia Radjartha, Shevcenko Rudin, Naza Saputra, Dian Andhi Sarima, Sarima Tsubaisa, Maulana Royyan Yayah Suhayah, Yayah Yoshia, Selumiel