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INDONESIA
Jurnal Fisika Unand
Published by Universitas Andalas
ISSN : 23028491     EISSN : 26862433     DOI : https://doi.org/10.25077/jfu
Core Subject : Science, Engineering,
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics
Makalah yang dapat dipublikasikan dalam jurnal ini adalah makalah dalam bidang Fisika meliputi Fisika Atmosfir, Fisika Bumi, Fisika Intrumentasi, Fisika Material, Fisika Nuklir, Fisika Radiasi, Fisika Komputasi, Fisika Teori, Biofisika, ataupun bidang lain yang masih ada kaitannya dengan ilmu fisika.
Arjuna Subject : Fisika dan Astronomi - Fisika Nuklir dan Molekuler, dan Ooptik
Articles 11 Documents
 1   2 
Search results for , issue "vol 15 no 4 (2026)" : 11 Documents clear
Quality Control dan Analisis Korelasi Mean Glandular Dose Terhadap Parameter Fisik pada Pesawat Mamografi Di Instalasi Radiologi RSUP Dr. M. Djamil Padang Tsamiatul Dini Aulia; Dian Milvita
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.355-362.2026

Abstract

Penelitian tentang Quality Control dan analisis korelasi Mean Glandular Dose (MGD) terhadap parameter fisik pada pesawat mamografi telah dilakukan di Instalasi Radiologi RSUP Dr. M. Djamil Padang. Tujuan penelitian adalah memastikan generator dan tabung sinar-X serta sistem Automatic Exposure Control (AEC) memenuhi Peraturan BAPETEN No. 2 Tahun 2022 serta bertujuan untuk menganalisis korelasi MGD terhadap parameter fisik meliputi tegangan, arus waktu dan tebal fantom menggunakan analisis korelasi linear. Penelitian dilakukan menggunakan pesawat mamografi merek Siemens, RaySafe X2, plat Pb dan fantom polymethylmethacrylate (PMMA) dengan ketebalan 2 cm, 3 cm, 4 cm, 5 cm, dan 6 cm. Parameter Quality Control (QC) yang diuji meliputi akurasi tegangan, reproduksibilitas, linearitas keluaran radiasi, timer darurat, reproduksibilitas AEC, dan waktu eksposi. Hasil penelitian menunjukkan semua parameter QC yang diuji masih memenuhi Peraturan BAPETEN No. 2 Tahun 2022 dengan kategori andal sehingga aman digunakan bagi pasien. Korelasi MGD terhadap tegangan menunjukkan hubungan yang sangat kuat dan bersifat positif. Korelasi MGD terhadap arus waktu menunjukkan hubungan yang sempurna dan bersifat positif. Korelasi MGD terhadap tebal fantom menunjukkan hubungan yang sangat kuat namun bernilai negatif.
Rancang Bangun Alat Penyimpan dan Pendeteksi Kematangan Pisang Ambon (Musa paradisiaca var. sapientum) Menggunakan Sensor TCS34725 Berbasis Machine learning Nur Aisya Zakila; Nini Firmawati
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.397-404.2026

Abstract

Banana is one of the most widely produced fruits in Indonesia; however, it is highly perishable after harvest. One environmental factor that needs to be considered to maintain post-harvest fruit quality is storage temperature. This study aims to design a prototype banana storage system with automatic temperature control and banana ripeness detection using a TCS34725 sensor implemented with machine learning. The system is equipped with a DHT22 sensor to monitor temperature and humidity, an MQ3 sensor to detect alcohol content, and a Peltier element to control the storage room temperature. The ripeness level classification uses a machine learning model trained with the SVM method with 94% accuracy. The test results show that the system is capable of maintaining the storage temperature in the range of 20°C to 25°C. The testwas conducted using 5 bananas placed inside the system and 5 bananas outside the system. The bananas stored in the system ripened on the 10th day, while the bananas stored outside the system ripened on the 8th day. The bananas in the system had a shelf life of up to 23 days before rotting, while the bananas stored outside the system rotted on the 19th day. The bananas stored in the system experienced a 31% weight loss, while the bananas stored outside the system experienced a 38% weight loss. The system is capable of displaying real-time banana conditions through an LCD interface.
Identification of Weak Zones with Resistivity Values Using Electrical Resistivity Imaging (ERI) in Bentiring Subdistrict, Bengkulu Adrian Daniel; Halauddin Halauddin; Suhendra Suhendra; Muhammad Taufik Apriadi; Agung Wijaya; Lulu Indah Setyawati; Hana Raihana
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.363-371.2026

Abstract

Bentiring Subdistrict is dominated by alluvial rock with a low water table depth, causing several roads and houses to experience subsidence and tilting, which may indicate the presence of a weak zone. The purpose of this study was to identify the presence of a weak zone based on resistivity values using the electrical resistivity imaging (ERI) method. This study utilised the ERI method with a Wenner Schlumberger configuration. The results showed that there was a weak zone on track 1 at a depth of 10.3-78.8 metres, with resistivity values of 12.3-28.2 Ωm. On track 2, subsidence was suspected at a depth of 3-39.6 metres, on track 3, the subsidence is located at a depth of 2.50-57.3 and 2.50-45.5 metres, on track 4, the subsidence is located at a depth of 10.8-67.3 meters, and on track 5, the subsidence is located at a depth of 10.3-57.3 metres. Weak zones are found on track 1 at a depth of 10.3-78.8 metres, where the constituent rock layer is sandy claystone. On tracks 2, 3, 4, and 5, there are subsidence areas with varying resistivity values ranging from 8.78 to 96 Ωm at depths ranging from 2.7 to 78.8 meters.
Pembuatan Nanopartikel Zinc Oxide melalui Metode Sintesis Hijau menggunakan Ekstrak Daun Kersen (Muntingia calabura L.) Adinda Mahesa Putri; Sri Handani; Afdhal Muttaqin
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.389-396.2026

Abstract

This study aims to synthesize ZnO nanoparticles through a green synthesis approach utilizing Muntingia calabura L. leaf extract as a natural bioreducing and stabilizing agent. The green synthesis route was chosen for its environmental friendliness and sustainability compared to conventional chemical methods that rely on toxic reagents and high energy consumption. The Muntingia calabura leaf extract, rich in secondary metabolites such as flavonoids, polyphenols, and tannins, acted as reducing agents to convert Zn²⁺ ions into ZnO nanoparticles. The synthesis involved mixing various extract concentrations (5%, 10%, 15%) with 0.05 M zinc acetate dihydrate, followed by calcination at 600°C. UV-Vis spectra showed an absorption peak around 370 nm, confirming the formation of ZnO nanoparticles. XRD analysis indicated a hexagonal wurtzite crystal structure with an average crystallite size of 20–40 nm, while SEM revealed nearly spherical morphology with uniform distribution. FTIR confirmed the presence of –OH and C=O functional groups from phenolic compounds that acted as capping agents. These findings highlight the promising role of Muntingia calabura leaf extract as a sustainable biogenic source for ZnO nanoparticle synthesis.
Deteksi Landmark pada Citra Sefalogram Lateral Menggunakan YOLOv11 Ica Dewi Monica; Sri Oktamuliani; Wulandani Liza Putri
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.382-388.2026

Abstract

Landmark identification on lateral cephalogram images plays a crucial role in orthodontic diagnosis and treatment planning because it forms the basis for analyzing the skeletal relationship between facial structures and teeth in the oral cavity. However, manual landmark identification is time-consuming and potentially leads to subjective errors. Therefore, Artificial Intelligence (AI)-based technology is a potential solution to improve the efficiency and consistency of analysis. This study aims to develop an automatic anatomical landmark detection system on lateral cephalogram images using the YOLOv11 algorithm. The dataset used consisted of 50 lateral cephalogram images obtained from the Radiology Installation of RSGM Andalas University and annotated according to the American Board of Orthodontics (ABO) standards. Then, augmentation was performed to obtain a total of 110 images. The model training and testing process was carried out using the YOLOv11 variant “yolo11s-pose”. The evaluation results showed an accuracy, precision, recall, and F-score of 1.0, with a mean Average Precision (mAP) of 0.995. Overall, this model shows good potential in improving the efficiency of cephalogram landmark identification, but it requires increasing the amount and variety of data for more reliable performance in clinical applications.
Komparasi Deep Learning Convolutional Neural Network (CNN) dan Deep Learning CNN- Support Vector Machine (SVM) untuk Identifikasi Tumor Otak dan Payudara Siti Nur Khalisha; Pandji Triadyaksa; Ngurah Ayu Ketut Umiati
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.372-381.2026

Abstract

The identification of brain and breast tumor MRI images is an important aspect in the development of an accurate and reliable computer-aided diagnosis (CAD) system. This study compares the performance of a single CNN model based on Res-Net 50 and a hybrid CNN-SVM model in classifying different type of tumor from MRI images. The research methods include acquiring brain and breast tumor MRI image dataset from the online repository Kaggle and processing it through image preprocessing steps such as resizing, converting grayscale images to RGB, and performing data augmentation on the training data. In the single CNN approach, Res-Net-50 is used as an end-to-end classifier, while in the hybrid CNN-SVM model, features are extracted from the global pooling layer and classified using SVM. Performance evaluation is carried out using a confusion matrix and chart comparing performance metrics. The research results show that both models achieved 99,16 % accuracy in multi-class brain tumor classification and 98,02 % accuracy in binary breast tumor classification. The CNN-SVM model demonstrated more stable performance across all performance metrics.
Deteksi Gempa Bumi Real-Time Menggunakan Sensor MPU6050 dengan Koreksi Drift Adaptif dan Sistem ESP32 Berbasis IoT Nadila Gusriyani; Maria Evita
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.414-422.2026

Abstract

Indonesia is located in the most seismically active region in the world, creating an urgent need for an affordable earthquake monitoring system. This study presents a real-time earthquake event detection system based on an MPU6050 MEMS (Micro-Electro-Mechanical System) accelerometer with adaptive drift correction integrated with an ESP32 microcontroller and Internet of Things (IoT)-based data transmission. The system acquires three-axis ground acceleration in real time, adaptive bias compensation, drift suppression using zero-velocity update (ZUPT), and event detection based on an adaptive threshold relative to a local noise baseline. Key seismic parameters, including Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV), are calculated in real time, while frequency domain characterization is performed offline using Fast Fourier Transform (FFT) analysis. Long-term stationary measurements over approximately 60 minutes showed stable noise characteristics, with root mean square (RMS) noise levels of 0.0067 m/s², 0.0039 m/s², and 0.0027 m/s² in the X, Y, and Z axes, respectively. The system demonstrated excellent baseline stability, with a PGA variation of only 0.7% and a measured drift rate of 0.00088 m/s² per hour. Comparative validation against a smartphone-based reference sensor demonstrated a 100% event detection rate, and a PGA estimation error of ±2%, with linearity exceeding a coefficient of determination of 0.99.
Analisis Respons Dosimetri Phantom Berbasis PMMA dengan Variasi Media Air dan Nylon Menggunakan Sumber Cs-147 Berdasarkan Standar ISO 4037 Widya Rachma Wulan; Primasari Cahya Wardhani; Fajar Timur; Wahyu Dwi Lestari; Arie Chintya Martania
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.405-413.2026

Abstract

This study analyzes the dosimetric response of Polymethyl Methacrylate (PMMA)-based phantoms with water and nylon as alternative media for radiation dose measurement.. Cylindrical phantoms resembling human extremities were fabricated using 3D printing according to the ISO 4037 standard. Dose measurements used a Thermoluminescent Dosimeter (TLD) type Hp(10) with a Cesium-137 gamma source (662 keV) at angles of 0°, 40°, and 235°. The results show that the dose in nylon consistently produced higher dose values than water at all angles. The average dose ranged from (31,95–32,20) μGy (absorbed dose) and (31,95–32,20) μSv (equivalent dose), while the nylon medium ranged from. (33,03–33,45) μGy (absorbed dose) and (33,03–33,45) μSv The percentage difference between the two media was in the range of 2,61%–3,88%, which is close to the 5% dosimetry tolerance limit by the IAEA. This variation is influenced by the scattering characteristics of the nylon material, which increase the probability of radiation interaction, particularly Compton scattering. Additionally, standard deviation values indicate that the dose distribution in water is more homogeneous than in nylon. Nevertheless, the consistency of the dose values suggests that nylon possesses good radiological equivalence to water. Therefore, nylon has the potential to be used as an alternative material in dosimetry phantom fabrication, especially in conditions where the use of water is limited. 
Analisis Struktur Bawah Permukaan menggunakan Data Satelit Gravitasi GGMPlus di Pulau Enggano, Bengkulu Utara Provinsi Bengkulu Muhammad Deni Apriyanto; Refrizon Refrizon; Arif Ismul Hadi; Laura Nurul Aprilia Simamora; Siti Herfina Anjani
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.345-354.2026

Abstract

This study aims to identify subsurface structures and rock type in Enggano Island, North Bengkulu, using GGMPlus satellite graity data. The methods applied include the calculation of ABL with SRTM2gravity topographic correction, spectral analysis to estimate anomaly depth, and separation of regional and residual anomalies using the moving average method. Furthermore, two-dimensional modelling was conducted to obtain subsurface density distribution. The results show that the ABL values range from -20 mGal to 80 mGal, with low anomalies dominating the northern part and gradually increasing towards the south. Spectral analysis indicates that the average depth of regional anomalies is approximately 1875.7 m, while residual anomalies are about 123.27 m. The interpretation reveals that the northern area is dominated by low-density rocks, whereas the central to Southern parts are characterized by high-density rocks. The 2D modelling suggest the presence of sedimentary rocks, limestone, and igneous rocks (mafic intrusions), as well as fault structures or basin boundaries in the study area.
Rancang Bangun Alat Deteksi Bahan Baku Gelatin Menggunakan Multisensor Gas Berbasis Machine learning Febby Trishe Ananda; Harmadi Harmadi; Wildan Panji Tresna; Aditya Eka Mulyono
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.423-429.2026

Abstract

In The demand for gelatin for halal food ingredients in Indonesia is very high. Pork-based gelatin, which is difficult to identify after undergoing the production process, is still widely used in the food and pharmaceutical industries. Analytical methods such as PCR and FTIR offer high accuracy, but they require high operational costs and specialized laboratory facilities. This study develops a machine learning-based electronic nose (e-nose) system to detect and classify gelatin types based on their raw material sources. This system integrates eight types of gas sensors to detect volatile organic compounds (VOCs) produced by each type of gelatin, which are then classified by PCA and predicted by LDA. The study will use gelatin with raw materials sourced from cows, pigs, and fish with varying gelatin concentrations of 1%, 3%, 5%, and 7%. Testing will be conducted for 30 minutes for each sample. The results of the testing prove that the device is capable of detecting and distinguishing gelatin based on its raw materials with a detection accuracy of 95%.

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