Article Per Year (5 Year)
p-Index From 2021 - 2026
2.884
P-Index
This Author published in this journals
All Journal Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika Journal of Physics and Its Applications Martabe : Jurnal Pengabdian Kepada Masyarakat Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Tsaqofah: Jurnal Penelitian Guru Indonesia YASIN: Jurnal Pendidikan dan Sosial Budaya MASALIQ: Jurnal Pendidikan dan Sains Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia BULETIN FISIKA
AZWAR ANNAS, MUHAMAD
Universitas Muhammadiyah Lamongan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Physics Social Sciences Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : BULETIN FISIKA

 1 
Rancang Bangun Sistem Deteksi Dini Kanker Payudara Berdasarkan Indikator Warna Pada Kulit Berbasis ESP32-Cam Terintegrasi Edge Impulse Hana Dwi Cahyani; Muhamad Azwar Annas; Uswatun Chasanah; Muktamar Cholifah Aisiyah
BULETIN FISIKA Vol. 26 No. 2 (2025): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2025.v26.i02.p10

Abstract

Deteksi dini kanker payudara merupakan langkah penting untuk meningkatkan peluang kesembuhan dan menekan angka kematian. Penelitian ini merancang sistem deteksi dini berbasis citra digital dengan memanfaatkan indikator visual berupa warna kemerahan pada kulit. Sistem dikembangkan menggunakan ESP32-Cam yang terintegrasi dengan Edge Impulse untuk pelatihan dan inferensi model klasifikasi berbasis Artificial Intelligence (AI). Deteksi dilakukan melalui pengolahan citra warna dan metode machine learning (ML) yang bekerja berdasarkan prinsip fisika optik dan sensorika, khususnya interaksi cahaya dengan permukaan kulit, serta merupakan bentuk penerapan prinsip-prinsip fisika dalam pengembangan teknologi deteksi visual modern. Dataset dikumpulkan dari phantom payudara dengan variasi tingkat kemerahan, dua jenis warna kulit (kuning langsat dan sawo matang), serta pencahayaan berbeda (600 lux, 800 lux, dan 1000 lux). Gambar diambil dari jarak 20 cm dengan sudut 90° dan 45° menggunakan kamera handphone. Model dilatih menggunakan metode CNN dan diintegrasikan ke ESP32-Cam, namun proses inferensi masih memerlukan koneksi internet untuk dapat dijalankan. Hasil menunjukkan bahwa pencahayaan 800 lux memberikan visual optimal, dan berhasil menjalankan klasifikasi dengan akurasi rata-rata sebesar 90,9%. Evaluasi menggunakan confusion matrix menunjukkan bahwa sistem ini baik dan akurat. Kata kunci: Deteksi dini; edge impulse; ESP32-cam; fisika optik; kanker payudara.
Characteristics and Performance Test of the MQ-135 Sensor in the Organophosphate Pesticide Residue Detection System in Fruits and Vegetables Ma'rufah, Asmaul; Annas, Muhamad Azwar
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p12

Abstract

Organophosphates are compounds that we often find in vegetables and fruits. Organophosphates have several health impacts if consumed excessively and over a long period of time. This study aims to test the characteristics and performance of the MQ-135 sensor in the organophosphate pesticide residue detection system in fruits and vegetables. The MQ-135 sensor was chosen because of its sensitivity to various gaseous compounds, including volatile compounds resulting from pesticide degradation. The research method includes testing the sensor response to variations in the concentration of organophosphate pesticide solutions with 3 frequently used brands, namely kanon, diazinon and curacron. First, the MQ-135 was calibrated in clean air for 24 hours to obtain a baseline. After obtaining the baseline, the sensor was tested with concentration variations of 0-100 ppm in multiples of 10. Based on the research results, overall, the MQ-135 sensor showed good detection capabilities for the three pesticides, with the highest sensitivity in pesticide A, namely 325.645, where pesticide B has a value of 21.836 and pesticide C is 24.436. The best linearity value was obtained for pesticide B, which was 0.9969, while pesticides A and c were 0.9443 and 0.9933, respectively. However, at low concentrations, the sensor still showed instability, especially for pesticide A. This character indicates that the MQ-135 is suitable for use as a rapid screening tool (rapid test) to detect the presence of organophosphate pesticide residues, but requires additional calibration and further validation for quantitative precision measurements.
Co-Authors Abelia Arianingsih Agistina Agus Salim, Alfi Tranggono Aris Widodo Aris Widodo Asmaul Lutfi Marufah Badiyah, Rohmatul Bagus Dwi Jauhari Cahyani, Hana Dwi Dian Nur Faizah Dini Makrufah Dwi Indah Setyo Pratiwi Febrianti, Denisa Eka Hana Dwi Cahyani Hariono Hariono Henny Dwi Bhakti Ma'rufah, Asmaul Maida Nanda Widiatiningsih Makrufah, Asmaul Lutfi Makrufah, Dini Muktamar Cholifah Aisiyah Musfiana, Masria Musfiana, Masria Ningrum, Izza Eka Sandi, Aris Satria Praja, Tatag Suyatno Suyatno Tatag Satria Praja Tegar Sabilliah Uswatun Chasanah Wida Yuliar Rezika, Wida Yuliar