Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Engineering and Technological Sciences Jurnal Kesehatan
Herdiana, Dina
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Nursing

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

Found 2 Documents
Search

 1 
Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8 Chitraningrum, Nidya; Banowati, Lies; Herdiana, Dina; Mulyati, Budi; Sakti, Indra; Fudholi, Ahmad; Saputra, Huzair; Farishi, Salman; Muchtar, Kahlil; Andria, Agus
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.1.5

Abstract

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO-v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations.
PENGARUH SENAM HAMIL TERHADAP DURASI TIDUR IBU HAMIL TRIMESTER III Susanti, Evi; Herdiana, Dina
Jurnal Kesehatan - STIKes Prima Nusantara Vol 10 No 3 (2019): JURNAL KESEHATAN
Publisher : LPPM Universitas Prima Nusantara Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35730/jk.v10i3.915

Abstract

Gangguan pola tidur pada ibu hamil sering dirasakan saat kehamilan trimester III, hal tersebut terjadi karena perubahan adaptasi fisiologis dan psikologis, perubahan fisiologis yang dialami ibu hamil, dikarenakan bertambahnya usia kehamilan seperti pembesaran perut, perubahan anatomis dan perubahan hormonal.Penelitian ini bertujuan untuk mengetahui pengaruh senam hamil terhadap durasi tidur ibu hamil trimester III di wilayah kerja Puskesmas Tigo Baleh Kota Bukittinggi. Desain penelitian ini adalah praeksperiment pre test dan post test menggunakan rancangan one group pre test post test. Sampel yang diambil dalam penelitian ini adalah 10 responden dari 46 populasi dengan menggunakan Accidental sampling. Pengumpulandata dengan menggunakan lembar observasi kemudian analisis dengan wilcoxon.Instrumen yang digunakan adalah lembar Observasi, waktu penelitian dilakukan pada tanggal 20 agustus 2019 s/d 8 september 2019. Hasil penelitian menunjukkan bahwa sebelum diberikan senam hamil dengan ratarata 4.3 dan setelah diberikan senam hamil dengan rata-rata 5.7 . Pada uji wilcoxon didapatkan hasil bahwa p=0,004, dimana p<0,05. Sehingga dapat disimpulkan bahwa adanya pengaruh senam hamil terhadap durasi tidur ibu hamil trimester III di wilayah kerja Puskesmas Tigo Baleh Kota Bukittinggi tahun 2019. Disarankan pada peneliti selanjutnya agar dapat meneliti tentang faktor –faktor lain yang dapat mempengaruhi peningkatan durasi tidur pada ibu hamil trimester III.
Co-Authors Ahmad Fudholi Andria, Agus Banowati, Lies Budi Mulyati, Budi Chitraningrum, Nidya Evi Susanti Farishi, Salman Indra Sakti Muchtar, Kahlil Saputra, Huzair