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All Journal G-Tech : Jurnal Teknologi Terapan
Afu Ichsan Pradana
Universitas Duta Bangsa Surakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Identifikasi Jenis Kelamin Otomatis Berdasarkan Mata Manusia Menggunakan Convolutional Neural Network (CNN) dan Haar Cascade Classifier Afu Ichsan Pradana; Wijiyanto Wijiyanto
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3814

Abstract

In forensics and security, it is necessary to determine a person's gender. Gender identification utilizing several types of identification, such as pictures of faces, voices, or handwriting, has been extensively studied in recent years. But a lot of offenders are hard to spot on surveillance tape because they cover their heads or have masks on that only show particular eye shapes. In this article, we explore the usage of a CNN with Relu activation for each hidden layer and the Haar Cascade Classifier Algorithm to detect objects of the human eye to recognize the human eye using deep learning. 11.525 Images of male and female eyes were used as the study's data. Utilizing Adam's optimization (Adaptive Moment Estimation), the training procedure lasts for 20 epochs. This study's findings have a 92% accuracy rate for automatically identifying gender. The performance evaluation matrix was used in this investigation, and it produced an overall F1-Score of 93%.
Alat Pendeteksi Kebocoran Gas LPG Pada Resto Ayam Bakar dan Goreng Kremes Tata Berbasis Internet Of Things Tri Endah Purnamawati; Afu Ichsan Pradana; Joni Maulindar
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3815

Abstract

LPG (Liquefied Petroleum gas) is one of the most commonly used gases for household purposes, comprising two components: propane (C3H8) and butane (C4H10). However, LPG gas has a drawback, namely the danger associated with gas leaks. LPG gas leaks pose a highly hazardous risk that can impact human safety and the surrounding environment. In this journal, the author discusses the development of a system for monitoring the detection of LPG gas leaks using an MQ-2 sensor and the ESP32 microcontroller. Subsequently, the system transmits LPG gas data to the Blynk application, providing users with quick information through sound and messages. The research test results indicate that the MQ-2 sensor is effective in rapidly detecting gas concentrations ranging from 300 to 1000 ppm.
Pengembangan Sistem Deteksi Penyakit Tanaman Tomat Melalui Citra Daun dengan Metode You Only Look Once (YOLO) Berbasis Android Bagus Erwanto; Afu Ichsan Pradana; Dwi Hartanti
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4327

Abstract

Agriculture plays a crucial role in Indonesia's economy, particularly in horticultural sub-sectors like fruit and vegetable production. Cultivation of tomatoes (Lycopersicum esculentum Mill) is one of the flagship commodities, but leaf disease attacks pose a major challenge that can reduce yields. Various studies have highlighted the need for computer vision-based plant disease detection solutions for tomatoes. This research focuses on developing a leaf disease detection application for tomato images on Android using the You Only Look Once (YOLO) method. Model evaluation was conducted using a confusion matrix and metrics such as precision, recall, and mAP (mean Average Precision). The results demonstrate high accuracy in classifying various diseases on tomato leaves. The model showed good performance in classifying different types of tomato leaf diseases, achieving an mAP of 96.6% and recall of 92.2% across all disease classes. Black box testing of the application indicated strong detection capabilities. This application has been successfully developed and released as 'Plantify' on Apkpure.
Pengembangan Alat Monitoring Kanopi Pada Sistem Smart Home Berbasis IoT (Internet of Things) Bagus Adi Nugroho; Rudi Susanto; Afu Ichsan Pradana
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4533

Abstract

The main problem if using a regular canopy, if the room is used for activities such as drying clothes, sunlight cannot enter the area and the canopy becomes less effective in terms of design or benefits. This research aims to develop a tool to monitor and control the canopy automatically using the prototyping method starting from Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, and Deployment Delivery & Feedback. This research produces innovative solutions to increase the effectiveness of canopies, especially if they are on limited land, using microcontrollers combined with Raindrop, DHT22, BH1750 sensors. The addition of the DHT22 humidity sensor and the BH1750 light sensor can help the Raindrop sensor in validating rainy or sunny conditions, making the system more accurate. The test results of the three sensors based on the rules / regulations obtained a system that can carry out the task as expected. Based on Black Box testing on the IoT System, the results show that the system can be integrated with the Blynk platform properly.
Co-Authors Bagus Adi Nugroho Bagus Erwanto Dwi Hartanti Joni Maulindar Rudi Susanto Tri Endah Purnamawati Wijiyanto