cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dr. rer.nat. Muldarisnur
Contact Email
-
Phone
+6282387463421
Journal Mail Official
jfu@sci.unand.ac.id
Editorial Address
Jurusan Fisika, FMIPA, Universitas Andalas ,Kampus Unand Limau Manis Padang 25163
Location
Kota padang,
Sumatera barat
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 1,828 Documents
 179   180   181   182   183 
Analisis Pengaruh Madden-Julian Oscillation (MJO) Terhadap Curah Hujan Ekstrem di Wilayah Kepulauan Kei Sebagai Upaya Mitigasi Bencana Hidrometeorologi Devika Meilona A Mannu; Jasruddin Jasruddin; Pariabti Palloan
Jurnal Fisika Unand Vol 15 No 3 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.3.289-299.2026

Abstract

The research has been done with the purpose to examine the impact of MJO propagation on extreme rainfall as a reference for weather forecasting and analysis, especially on extreme rainfall as an efforts to mitigate hydrometeorological disasters in the Kei Islands region. The analysis of MJO activity on extreme rainfall in this study uses the MJO data of phases 4, 5, and 6 with amplitude ≥ 1, and rainfall data in the period of 1997 to 2023, which is processed using the 98th percentile method to obtain extreme rainfall thresholds which are then used to determine trends in the frequency of extreme rain events and the influence of MJO activity. The results show that the trend in the frequency of extreme rain events has a negative value, meaning that extreme rain events will decrease in the future. MJO activity influences ±  30% of extreme rainfall events in the period 1997 to 2023, the other 70% is influenced by other factors. The use of the MJO index as an early warning for mitigation plan on hydrometeorological disaster in Kei Islands could be done by utilizing MJO monitoring and forecasting data accompanied by analysing the atmospheric dynamic conditions
Peningkatan Ketahanan Baja SS-304 terhadap Korosi Menggunakan Lapisan Tembaga dan Inhibitor dari Tanin Ekstrak Daun Sirsak dengan Metode Elektrodeposisi Letmi Syara; Dahyunir Dahlan
Jurnal Fisika Unand Vol 15 No 3 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.3.282-288.2026

Abstract

Research has been conducted on improving the corrosion resistance of SS-304 stainless steel using copper coating and an inhibitor derived from soursop leaf extract via the electrodeposition method. The main objective of the research was to determine the optimal concentration of soursop leaf extract as an inhibitor for protecting the steel from the corrosion. The inhibitor concentrations used were 0%; 1%; 2%; 3%; 4% and 5%. Layer characterization was performed using an optical microscope to observe the surface of the electrodeposited coating, while X-ray diffraction (XRD) was used to identify the phase and crystal structure of the formed protective layer. The corrosion rate was calculated based on mass change after immersion in a corrosive medium. The result showed that the most optimal corrosion inhibition was achieved at an inhibitor concentration of 4%, with an inhibition efficiency of 94%. At this concentration, the lowest corrosion rate was recorded, and the surface morphology appeared the smoothest compared to the other prepared samples. Crystal phase analysis revealed that before immersion, the protective layer consisted of the copper (Cu) phase, whereas after immersion, a new phase was formed, namely copper oxide (CuO).
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.
Karakterisasi Komposit Serat Pelepah Pisang dan Serbuk Cangkang Kelapa Sawit Jumatul Annisa; Dwi Puryanti; Sri Handani
Jurnal Fisika Unand Vol 15 No 3 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.3.269-275.2026

Abstract

Research on themanufacture of composites reinforced with banana stem fiber and palm kernel shells aims to analyze the effect of variation in the volume fractionof banana stem fiber and palm kernel shells on the mechanical and physical properties of the composite. The composite was fabricated using the hand lay-up method with volume fraction variations (matrix: fiber: shell) of 40%:50%:10%, 40%:40%:20%, 40%:30%:30%, 40%:20%:40%, and 40%:10%:50%. Mechanical test include Charpy impact test, compressive strength test using a Universal Testing Machine (UTM),Vickers hardness test, and water absorption test. The results showed that the highest impact strength of 0,0947 J/mm2 was obtained at 40%:20%:40% composition, the highest compressive strength of 7,69 MPa at 40%:50%:10%, and the highest hardness value of 18,68 HV at 40%:40%:20%. The lowest water absorption value of 14,28% at 40%:10%:50%. These result indicate that the composition of 40%:20%:40% is the optimum composition for automotive dashboard applications, as it meets the mechanical standards required for dashboard materials.
Pemetaan Daerah Semburan Lumpur Sebagai Kajian Awal Dampak Bencana Menggunakan Metode Self-Potential Di Desa Napan-NTT. Yanti Boimau; Hilary F. Lipikuni; Anastasia K. D. Lestary
Jurnal Fisika Unand Vol 15 No 3 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.3.276-281.2026

Abstract

Research has been conducted in the mudflow area of Napan village, Kefamenanu-NTT. The aim of this study is to identify fluid flow or mudflow in the research area using the Self-potential method. Data acquisition In this study, a closed loop system was used. Based on the distribution of potential values on the isopotential map, the research location shows low anomalies which indicate that the area is conductive. The low potential anomaly value in the research area is caused by the presence of subsurface mudflow along the measurement path. The direction of the mudflow fluid flow in Napan village is predicted to be in the measurement area of the research location. Based on the results of the interpretation of the measurement area, it has the potential to become a weak zone which has the potential to become a location for the emergence of new mudflow sources if there is a change in hydrogeological conditions.
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.

Page 182 of 183 | Total Record : 1828

 179   180   181   182   183 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15 No 4 (2026) Vol 15 No 3 (2026) Vol 15 No 2 (2026) Vol 15 No 1 (2026) Vol 14 No 6 (2025) Vol 14 No 5 (2025) Vol 14 No 4 (2025) Vol 14 No 3 (2025) Vol 14 No 2 (2025) Vol 14 No 1 (2025) Vol 13 No 6 (2024) Vol 13, No 5 (2024) Vol 13 No 5 (2024) Vol 13 No 4 (2024) Vol 13 No 3 (2024) Vol 13 No 2 (2024) Vol 13 No 1 (2024) Vol 12 No 4 (2023) Vol 12 No 3 (2023) Vol 12 No 2 (2023) Vol 12 No 1 (2023) Vol 11 No 4 (2022) Vol 11 No 3 (2022) Vol 11 No 2 (2022) Vol 11 No 1 (2022) Vol 10 No 4 (2021) Vol 10 No 3 (2021) Vol 10 No 2 (2021) Vol 10 No 1 (2021) Vol 9 No 4 (2020) Vol 9 No 3 (2020) Vol 9 No 2 (2020) Vol 9 No 1 (2020) Vol 8 No 4 (2019) Vol 8 No 3 (2019) Vol 8 No 2 (2019) Vol 8 No 1 (2019) Vol 7 No 4 (2018) Vol 7 No 3 (2018) Vol 7 No 2 (2018) Vol 7, No 1 (2018) Vol 7 No 1 (2018) Vol 6 No 4 (2017) Vol 6 No 3 (2017) Vol 6 No 2 (2017) Vol 6 No 1 (2017) Vol 5 No 4 (2016) Vol 5 No 3 (2016) Vol 5 No 2 (2016) Vol 5, No 1 (2016) Vol 5 No 1 (2016) Vol 4 No 4: Oktober 2015 Vol 4 No 3: Juli 2015 Vol 4 No 2: April 2015 Vol 4 No 1: Januari 2015 Vol 3 No 4: Oktober 2014 Vol 3 No 3: Juli 2014 Vol 3 No 2: April 2014 Vol 3 No 1: Januari 2014 Vol 2 No 4: Oktober 2013 Vol 2 No 3: Juli 2013 Vol 2 No 2: April 2013 Vol 2 No 1: Januari 2013 Vol 1 No 1: Oktober 2012 More Issue