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All Journal International Journal of Electrical and Computer Engineering Medical Journal of Indonesia REKAYASA International Journal of New Media Technology Jurnal ULTIMA Computing Indonesian Journal of Electrical Engineering and Computer Science Indonesian Community Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Warta LPM
Shabrina, Nabila Husna
Universitas Multimedia Nusantara, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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3D FDTD Method for Modeling of Seismo-Electromagnetics Disturbance on Crustal Earth Shabrina, Nabila Husna; Hobara, Yasuhide; Munir, Achmad
Makara Journal of Technology Vol. 23, No. 2
Publisher : UI Scholars Hub

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Abstract

The paper deals with the modelling of seismo-electromagnetics disturbance on the crustal earth by use of threedimensional (3D) finite-difference time-domain (FDTD) method. The model is built up by discretizing the frontier geographical region between Java Island and Sumatra Island in a cylindrical coordinate system-based 3D object. The proposed method is applied to compute and analyze electromagnetics (EM) fields of the observed very low frequency (VLF) wave used for the investigation. Boundary condition of uniaxial perfectly matched layer (UPML) are applied surrounding the area of computation for truncating the object of simulation. The investigation are focused on the propagation time of observed VLF wave and its amplitude variation between the observation point and disturbance pulse. The result shows that the propagation time is significantly affected by the distance of observation point and the permittivity of propagation medium. Meanwhile, the addition pulse associated with the earthquake influences the amplitude of observed VLF wave instead of its frequency.
Faster region-based convolutional neural network for plant-parasitic and non-parasitic nematode detection Natalia Angeline; Nabila Husna Shabrina; Siwi Indarti
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp316-324

Abstract

Nematodes represent very abundant and the largest species diversity in the world. Nematodes, which live in a soil environment, possess several functions in agricultural systems. There are two huge groups of soil nematodes, a non-parasitic nematode, which contributes positively to ecological processes, and a plant-parasitic nematode, which cause various disease and reduces yield losses in the agricultural system. Early detection and classification in the agricultural area infected with plant-parasitic nematode and interpreting the soil level condition in this area required a fast and reliable detection system. However, nematode identification is challenging and time-consuming due to their similar morphology. This study applied a pre-trained faster region-based convolutional neural network (RCNN) for plant-parasitic and non-parasitic nematodes detection. These deep learning-based object detection models gave satisfactory results as the accuracy reached 87.5%.
Penggunaan Metode FDTD untuk Analisis Gelombang pada Struktur Berbasis Kartesian dan Silinder Nabila Husna Shabrina; Hardi Nusantara; Achmad Munir
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

In this paper, the comparison of wave characteristics between Cartesian and cylindrical coordinate system–based structures was analyzed using finite-difference time-domain (FDTD) method. The use of FDTD method wasconsidered due to its advantage in solving electromagnetics (EM) problems in wide spectrum of frequency and geometry shapes. The analysis was undertaken for three-dimensions (3D) Cartesian and cylindrical coordinate system–based structures with dimension of x = 600 mm, y = 300 mm, z = 1.200 mm, dan ???? = 600 mm, ???? =1 °, z = 1.200 mm, respectively. A transverse electric (TE) mode excitation of sine wave modulated Gaussian pulse with frequency of 1 GHz was applied for exciting both structures with the direction of propagation wave assumed in z–axis. Some scenarios were applied for both structures conditioned with free space, dielectric, and conductive medium. The attenuation rate obtained from three modelling scenarios in Cartesian coordinate system structures were 0.35 Np/m, 0.24 Np/m, and 0.62 Np/m, respectively. Meanwhile the attenuation rates for cylindrical coordinate system structure were 0.35 Np/m, 0.21 Np/m, and 0.40 Np/m. The simulation result for resonant frequency in Cartesian and cylindrical coordinate systemstructure conditioned with free space were 558.706 MHz and 498.466 MHz, respectively. The resonant frequency obtainedfrom simulation result in Cartesian and cylindrical coordinatesystem structure conditioned with dielectric medium was similar with the one from theoretical calculation in which the highesterror were 2.03% and 0.73%, respectively.
Assistance and Development of Website Profile in Curug Sangereng Village Nabila Husna Shabrina; Gideon K.F.H. Hutapea; Irmawati; Monica Pratiwi; Ardiles Akyuwen; Aditya Satyagraha; Imamul Masyhudi; Suwito Casande; Kanza Amanda
I-Com: Indonesian Community Journal Vol 3 No 4 (2023): I-Com: Indonesian Community Journal (Desember 2023)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v3i4.3531

Abstract

Curug Sangereng Village, located in the independent city of Gading Serpong, Tangerang Regency, boasts a range of complete infrastructure, including offices, health facilities, educational institutions, places of worship, shopping places, and family recreation areas. However, the village presently lacks a comprehensive village profile that can serve as a means of socializing and disseminating information to its community. The objective of this community service activity, therefore, is to develop a website profile for Curug Sangereng Village that will provide information on the village's profile, public services, MSMEs, facilities, routine activities, and news about important events. In addition to developing the website, the activity will also implement a website usage assistance program to introduce and promote the use of the developed website. The assistance and development of the website for Curug Sangereng Village are part of the community service activities undertaken by Universitas Multimedia Nusantara.
Project-Based Learning Training for Teachers at Madrasah Aliyah Raudhlatul Irfan, Lengkong Kulon Village Melissa Indah Fianty; Nabila Husna Shabrina; Fahmy Rinanda Saputri
I-Com: Indonesian Community Journal Vol 4 No 1 (2024): I-Com: Indonesian Community Journal (Maret 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i1.3922

Abstract

Madrasah Aliyah Raudlatul Irfan faces challenges in the learning process as it still adopts conventional models such as lectures and assignments. Consequently, the learning experience becomes monotonous, lacking support for individual students' exploration of their abilities. Changes and increased creativity are necessary to make the learning process more engaging and to facilitate the diversity of learning abilities. The introduction of the Project-Based Learning method is expected to create a more dynamic learning environment, allowing students to develop critical thinking skills. Through projects, students have the opportunity to produce tangible evidence of their mastery of the material, while teachers have improved their skills through training. The training methods involve lectures, Q&A sessions, discussions, and demonstrations, positively impacting teachers' knowledge of Project-Based Learning. Evaluation results indicate an improvement in teachers' abilities, suggesting that this training has successfully enhanced their understanding of this innovative learning model.
A deep learning-based mobile app system for visual identification of tomato plant disease Wang, Aurelius Ryo; Shabrina, Nabila Husna
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6992-7004

Abstract

Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.
Plastic Waste Identification using ResNet-50: A Deep Learning Approach Bakti, Akmal Nusa; Shabrina, Nabila Husna
Rekayasa Vol 17, No 3: Desember, 2024
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v17i3.26456

Abstract

Plastic waste is a significant environmental concern, constituting a major portion of the global waste stream. Improper disposal and accumulation have led to severe environmental challenges, including pollution, harm to marine life, and contributions to climate change. Effective waste management strategies are essential to mitigate these issues. However, manual sorting methods are both time-consuming and costly, requiring substantial human effort and financial investment. To address these limitations, automated solutions utilizing advanced technologies like artificial intelligence have gained increasing attention. Deep learning-based method can automatically identify and classify various types of plastic waste using computer-captured image patterns. This study explores the application of ResNet50, a state-of-the-art deep learning model, for the classification of plastic and non-plastic waste. A robust dataset comprising 4,000 diverse images of waste materials was employed for model training and validation. ResNet50, with its advanced architecture designed for image recognition tasks, demonstrated exceptional performance, achieving an accuracy, precision, recall, and F1-score of 0.99. These results highlight the model’s ability to precisely and reliably differentiate between plastic and non-plastic waste categories. The findings of this research underscore the potential of deep learning-based approaches in revolutionizing waste management practices. By leveraging automated classification methods, waste sorting can become significantly faster, more accurate, and cost-effective. This has far-reaching implications reducing environmental harm and fostering a more sustainable future. The results demonstrate that integrating AI technologies into waste management systems can lead to efficient and environmentally friendly solutions for tackling plastic waste challenges.
Artificial intelligence for enhanced diagnostic precision of prostate cancer Hamid, Agus Rizal Ardy Hariandy; Harahap, Agnes Stephanie; Miranda, Monik Ediana; Gibran, Kahlil; Shabrina, Nabila Husna
Medical Journal of Indonesia Vol. 34 No. 3 (2025): September
Publisher : Faculty of Medicine Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13181/mji.oa.258312

Abstract

BACKGROUND Accurate diagnosis and grading of prostate cancer are essential for treatment planning. The role of artificial intelligence in prostate cancer intervention and diagnosis (RAPID) is a study aimed at developing artificial intelligence (AI) models to enhance diagnostic precision in prostate cancer by distinguishing malignant from non-cancerous histopathological findings. METHODS Histopathological images were collected between 2023 and 2024 at the Department of Anatomical Pathology, Faculty of Medicine, Universitas Indonesia. The dataset included benign prostatic hyperplasia and prostate cancer cases. All slides were digitized and manually annotated by pathologists. Patch-based classification was performed using convolutional neural network and transformer-based models to differentiate malignant from non-malignant tissues. RESULTS A total of 529 whole-slide images were processed, yielding 26,418 image patches for model training and testing. Deep learning models achieved strong performance in classification. Architectures including EfficientNetV2B0, Xception, ConvNeXt-Tiny, and Vision Transformer (ViT) achieved near-perfect classification outcomes. EfficientNetV2B0 reached an AUC of 1.00 (95% CI: 1.00–1.00), sensitivity 0.99 (95% CI: 0.99–1.00), and specificity 1.00 (95% CI: 1.00–1.00). Xception and ConvNeXt-Tiny both achieved AUC 1.00 (95% CI: 1.00–1.00) with sensitivity and specificity of 1.00 (95% CI: 1.00–1.00). ViT performed strongly with AUC 0.999 (95% CI: 0.99–1.00), sensitivity 0.99 (95% CI: 0.99–0.99), and specificity 0.99 (95% CI: 0.99–0.99). CONCLUSIONS RAPID demonstrated high potential as an AI-based diagnostic tool for prostate cancer, showing excellent accuracy in histopathological classification using the Indonesian dataset. These findings highlight the feasibility of deploying deep learning models to support diagnostic decision-making in clinical practice.
Pengembangan Sistem Pelayanan Posyandu Berbasis Website dan Aplikasi Mobile di Desa Curug Sangereng Pratiwi, Monica; Irmawati; Shabrina, Nabila Husna; Kristiyanti, Dinar Ajeng; Johan, Monika Evelin
Warta LPM WARTA LPM, Vol. 29, No. 1, Maret 2026
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Pos Pelayanan Terpadu (Posyandu) is a form of Upaya Kesehatan Bersumberdaya Masyarakat (UKBM) managed by and for the community to facilitate access to basic health services. Desa Curug Sangereng, located in Kecamatan Kelapa Dua, Tangerang, Banten, has nine posyandu that serve and monitor the health of 14,311 residents. This activity is supported by posyandu members who act as health promoters and educators to encourage a clean and healthy lifestyle while recording residents' health data. In addition to on-site recording at the posyandu, members also conduct home visits for residents who are absent during service hours or undergoing outpatient care. To improve the efficiency of data recording, a digital platform is needed that can be accessed anytime and can present data in summary and graphical formats to facilitate health analysis and evaluation. The E-Posyandu of Desa Curug Sangereng is designed as a website-based and mobile application integrated with the village’s main website. The features in the E-Posyandu application were developed using the waterfall method. The available features include data input, health record history, and data export in various file formats. After the development and deployment process, socialization and training were conducted for posyandu members to familiarize them with the application, ensuring optimal use by relevant stakeholders. User Acceptance Testing (UAT) was also conducted for the E-Posyandu web and mobile applications by distributing questionnaires. Based on UAT results from 35 repondents, it was concluded that the application has an easy-to-understand interface, comprehensive features, runs smoothly without bugs, and allows easy access to recorded data.
Co-Authors Achmad Munir Aditya Satyagraha Agnes Stephanie Harahap Agus Rizal Ardy Hariandy Hamid, Agus Rizal Ardy Hariandy Akmal Nusa Bhakti Akrom, Akrom Andre Wijaya Ardiles Akyuwen Arsheldy Alvin Aurelius Ryo Axel Patria Bakti, Akmal Nusa Christopher Leonard Cindy Cornelia Dinar Ajeng Kristiyanti, Dinar Ajeng Edgar Christian Fahmy Rinanda Saputri gibran, kahlil Gideon K.F.H. Hutapea Hardi Nusantara Hobara, Yasuhide Imamul Masyhudi Irmawati Irmawati Jason Adrian Mahalim Jeremy Pratama Wijaya Johan, Monika Evelin John Christopher Julando Omar Kanza Amanda Markus Aminius Gilbert Melania Regina Ao Melissa Indah Fianty Miranda, Monik Ediana Monica Pratiwi Muhamad Aliefian Rahmatulloh Muhamad Rizky Febrianto Nadya Laurencya Natalia Angeline Nusantara, Hardi Pratiwi, Monica Samuel Samuel Siwi Indarti Suwito Casande Vanessa Lee Wang, Aurelius Ryo Yoel Zakhary