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All Journal Jurnal EECCIS Andalas Journal of Electrical and Electronic Engineering Technology
Irman Idris
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Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Implanted Layer Characterization Adharul Muttaqin; Irman Idris
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 1 No. 1 (2007)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (304.007 KB) | DOI: 10.21776/jeeccis.v1i1.351

Abstract

In modern semiconductor process technology, ion implantation has become the most important technique to introduce dopant atoms into semiconductor materials. The main advantage of ion implantation technique is its high controllability of process parameters, which influencing dopant distribution profile. This research was intended to characterize the product of ion implantation machine NV-3204.Ion implantation characterization successfully produced and evaluated pn-junction diode characteristics. PN-junction diode was fabricated using 100 keV energy and 5x1013 cm-3 dose of phosphorus on a silicon wafer type N<111>. For all measured area, pnjunction diode has junction depth Xj = 1 um, breakdown voltage -45V, built-in voltage 0.8V, and dopant concentration 5x1018 cm-3. Comparing the simulation, this result exhibited that output of ion implantation machine was well controlled.
IoT-Based Plant Growth Chamber with YOLOv8 for Anthracnose Disease Severity Classification in Chili Pepper Sutoyo, Mochammad Rizky Abadi; Irman Idris; Gilang Mardian Kartiwa; Muhammad Adli Rizqulloh; Faisal Asadi Nasution
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 5 No. 2 (2025): November 2025
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v5i2.109

Abstract

Plant growth chambers provide controlled environments for agricultural research, enabling precise monitoring of crop diseases under optimal microclimate conditions. This paper presents an integrated IoT-based smart plant growth chamber system utilizing YOLOv8 machine learning for the automated classification of anthracnose disease severity in chili peppers (Capsicum annuum L.). The system integrates multiple subsystems, including environmental control, a robotic camera with 2-axis movement, a gateway for data communication, and remote monitoring capabilities through a cloud server and a web interface. Dataset labeling was performed using LabelImg and Roboflow, with data augmentation increasing training samples from 70% to 86%. Three YOLOv8 models were evaluated: YOLOv8L (150 epochs), YOLOv8N (100 epochs), and YOLOv8N (398 epochs). Based on our test so far, the YOLOv8L model achieved the best performance with mAP of 67.4% and successfully detected 44 out of 102 test samples (43% detection rate) across multiple disease severity scores (0-9). The system enables both onsite and remote access, automatic data logging, real-time image capture with PyQt5-based GUI, and environmental parameter control (temperature: 5-50°C, humidity: 40-90%RH, light: 0-15,000 lux), which can be manually set and automatically set based on the requirements of the user. This integrated approach demonstrates practical deployment of edge AI and IoT technologies for precision agriculture and disease monitoring applications.
Co-Authors Adharul Muttaqin Faisal Asadi Nasution Gilang Mardian Kartiwa Muhammad Adli Rizqulloh Sutoyo, Mochammad Rizky Abadi