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All Journal Jurnal Hilirisasi IPTEKS (JHI) CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking)
Harzikra, Rafki
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Dentistry Electrical & Electronics Engineering Engineering Health Professions Public Health Veterinary Other

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PENINGKATAN KAPASITAS DIGITAL SUSI SEPRAI DAN BEDCOVER MELAUI PENDAMPINGAN DAN PEMBUATAN KONTEN VISUAL DI KECAMATAN LUBUK BEGALUNG KOTA PADANG Komalasari, Sanda Patrisia; Rahman, Annisaa; Kartika, Rayna; Triani, Laura Amelia; Aprilita, Vivi; Alamsyah, Adithya; Alfiqri, Muhammad Rozaq; Tri, Diwah Ganda; Hadelina, Rizka; Mardiah, Ainul; Harzikra, Rafki
Jurnal Hilirisasi IPTEKS Vol 7 No 4 (2024)
Publisher : LPPM (Lembaga Penelitian dan Pengabdian kepada Masyarakat) Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jhi.v7i4.836

Abstract

This community service activity aims to enhance the digital marketing capabilities of the Susi Seprai and Bedcover MSME by providing assistance and creating visual content in the form of product photos and videos. With high-quality visual content, it is expected to increase product appeal and broaden online marketing reach. The activity is carried out in two phases: a product photography training on August 15, 2024, and visual content creation assistance from August 1, 2024, to October 1, 2024, at the Susi Seprai and Bedcover MSME location in Padang City, West Sumatra. The methods used in this activity include technical training in product photography, an introduction to and use of photo editing applications such as Canva and Photoshop, and video editing applications like Premiere Pro. This training is designed to provide practical knowledge to MSME owners on how to take attractive product photos and edit them for a more professional look. The results of this activity show an improvement in the MSME owner's ability to take and edit photos and videos of their products and to use editing applications like Canva for digital marketing purposes. Additionally, photos and videos have been produced as promotional content. The conclusion of this activity is that training and assistance in visual content creation are effective in enhancing MSME digital marketing capabilities. The recommendation is for further training on other digital marketing techniques and deeper exploration of editing applications to optimize visual content outcomes.
PENINGKATAN KAPASITAS DIGITAL SUSI SEPRAI DAN BEDCOVER MELAUI PENDAMPINGAN DAN PEMBUATAN KONTEN VISUAL DI KECAMATAN LUBUK BEGALUNG KOTA PADANG Komalasari, Sanda Patrisia; Rahman, Annisaa; Kartika, Rayna; Triani, Laura Amelia; Aprilita, Vivi; Alamsyah, Adithya; Alfiqri, Muhammad Rozaq; Tri, Diwah Ganda; Hadelina, Rizka; Mardiah, Ainul; Harzikra, Rafki
Jurnal Hilirisasi IPTEKS Vol. 7 No. 4 (2024)
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jhi.v7i4.836

Abstract

This community service activity aims to enhance the digital marketing capabilities of the Susi Seprai and Bedcover MSME by providing assistance and creating visual content in the form of product photos and videos. With high-quality visual content, it is expected to increase product appeal and broaden online marketing reach. The activity is carried out in two phases: a product photography training on August 15, 2024, and visual content creation assistance from August 1, 2024, to October 1, 2024, at the Susi Seprai and Bedcover MSME location in Padang City, West Sumatra. The methods used in this activity include technical training in product photography, an introduction to and use of photo editing applications such as Canva and Photoshop, and video editing applications like Premiere Pro. This training is designed to provide practical knowledge to MSME owners on how to take attractive product photos and edit them for a more professional look. The results of this activity show an improvement in the MSME owner's ability to take and edit photos and videos of their products and to use editing applications like Canva for digital marketing purposes. Additionally, photos and videos have been produced as promotional content. The conclusion of this activity is that training and assistance in visual content creation are effective in enhancing MSME digital marketing capabilities. The recommendation is for further training on other digital marketing techniques and deeper exploration of editing applications to optimize visual content outcomes.
Identification and Prediction System of Tomato Leaf Diseases Based on YOLO and Random Forest Harzikra, Rafki; Hersyah, Mohammad Hafiz; Pramawahyudi, Pramawahyudi
CHIPSET Vol. 7 No. 02 (2026): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/chipset.7.02.126-131.2026

Abstract

Tomato crop failure in Indonesia caused by leaf disease can reach 78%, and manual visual monitoring by farmers is often delayed and subjective. This study designs and evaluates a portable, IoT-based diagnostic and prediction system for tomato leaf disease that can operate independently in the field. The system integrates a Raspberry Pi 4 single-board computer with a Raspberry Pi Camera V2, a DHT22 air temperature/humidity sensor, and a capacitive soil-moisture sensor. Two computational models run on-device: a YOLOv5 convolutional neural network that classifies leaf images into healthy, early blight, late blight, and septoria leaf spot; and a Random Forest classifier that predicts disease potential from air temperature, air humidity, and soil moisture. Results are transmitted via a 4G module to a Firebase cloud database and displayed on a real-time dashboard. Functional and field tests validated camera resolution, sensor accuracy, model accuracy, processing latency, and transmission delay. The YOLOv5-small model classified leaf images with 85.4% accuracy, while the Random Forest model predicted disease potential with 97% accuracy. On-device dual-model inference averaged 7.26 seconds and cloud transmission delay averaged 8.8 seconds, both within the 10-second design target. Field validation on a working tomato farm produced an average diagnostic correctness of 85.53%. The dual-model, off-grid-capable IoT system is technically feasible for real-time, field-deployable tomato leaf disease diagnosis and can help farmers act earlier on emerging infections, reducing potential crop loss.
Co-Authors Ainul Mardiah Alamsyah, Adithya Alfiqri, Muhammad Rozaq Aprilita, Vivi Hadelina, Rizka Hersyah, Mohammad Hafiz PRAMAWAHYUDI, PRAMAWAHYUDI Rayna Kartika, Rayna Sanda Patrisia Komalasari Tri, Diwah Ganda Triani, Laura Amelia