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All Journal CommIT (Communication & Information Technology) Jurnal Informatika Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Indonesian Journal of Aerospace
Iskandar Fitri, Iskandar
Bina Nusantara University Master of Information Technology
Aerospace Engineering Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy

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Development of ResNet-18 architecture to lesion identification in breast ultrasound images Andini, Silfia; Sumijan, Sumijan; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1236-1248

Abstract

Breast ultrasound (USG) is widely used for early breast cancer detection, but challenges such as noise, low contrast, and resolution limitations hinder accurate lesion identification. This study proposes a modified residual network-18 (ResNet-18) architecture for breast lesion segmentation, aimed at improving detection accuracy. The methodology involves preprocessing steps including red green blue (RGB) to Grayscale conversion, contrast stretching, and median filtering to enhance image quality. The modified ResNet-18 model introduces additional convolutional layers to refine feature extraction. The proposed model was trained and validated on 30 breast ultrasound images, with evaluation metrics including accuracy, sensitivity, and specificity. Experimental results indicate that the modified architecture outperforms the baseline model, achieving an average accuracy of 0.97093, sensitivity of 0.90056, and specificity of 0.97705. Validation by a radiology specialist confirms the model’s clinical relevance. These findings suggest that the enhanced ResNet-18 model has the potential to assist radiologists in more accurately identifying breast lesions. Future research should focus on expanding the dataset, integrating multi-modal imaging, and optimizing model generalizability for real-time clinical applications. The study contributes to advancing artificial intelligence (AI)-driven breast cancer diagnostics, supporting early detection, and improving patient outcomes.
Development of Apple Fruit Classification System using Convolutional Neural Network (CNN) MobileNet Architecture on Android Platform Masparudin, Masparudin; Fitri, Iskandar; Sumijan, Sumijan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3533

Abstract

In the current digital era, image classification of fruits, particularly apples, has become crucial for various applications, ranging from agriculture to retail. This research focuses on the utilization of Convolutional Neural Network (CNN) with the MobileNet architecture to classify apple fruit images. Using the Python programming language, three models were successfully trained: Model 1 for apple fruit types, Model 2 for apple fruit diseases, and Model 3 for apple fruit ripeness levels. All three models underwent training and validation, with the final results at epoch 10: Model 1 for apple types achieved an accuracy of 100% and a loss of 0.0046, Model 2 for apple diseases achieved an accuracy of 100% and a loss of 0.0075, while Model 3 for apple ripeness levels achieved an accuracy of 99.76% and a loss of 0.0439. Subsequently, these models were tested on an Android device, and there were two testing scenarios. In the first scenario, each model was tested with 15 images individually. The results showed 100% accuracy for Models 1 and 2, while Model 3 achieved a lower accuracy of 86.67%. In the second scenario, all three models were tested simultaneously using 30 test images, resulting in an accuracy of 55.55%. Several factors, such as limitations in the apple image dataset, particularly in the ripeness dataset, object backgrounds, image capture distances, color and texture similarities, as well as lighting quality, influenced the classification outcomes. To enhance future performance, improved data preprocessing and a combination of detection and classification techniques are needed. This research provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.
OPTIMALIZATION OF MICROSTRIP SLOT ARRAY ANTENNAS FOR MULTI-WIDEBAND Fitri, Iskandar
Indonesian Journal of Aerospace Vol. 5 No. 1 (2007): Vol 5, No.1 Juni (2007)
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Microstrip slot antenna fed by matching network of microstrip line to increase very wide-bandwidth and multiband is proposed. The microstrip line composed of multi tuning stubs is used to control slots antenna real impedance to match with impwdance cahracteristic of feeding line so that it could increase the bandwidth. The design are achieves good input impedance in the ranges frequency of 1.3 - 5.1 GHz for single slot and 1.1 - 6.4 GHz for two slots. The bandwidths of antennas become very wide if the slots made in array configuration. The measured return loss S11 agrees well with the simulation results for single slot as axample. Keywords: microstrip slot antenna, array configuration, network impedance, multi tuning stub.
Implementation of Augmented Reality for Introduction To Android Based Mammalian Animals Using The Marker Based Tracking Method Kristian, Mikhael; Fitri, Iskandar; Gunaryati, Aris
JISA(Jurnal Informatika dan Sains) Vol 3, No 1 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i1.623

Abstract

Augmented Reality is a technology on 2-dimensional and 3-dimensional virtual objects that are combined into the real environment that is around us. With the ability of Augmented Reality that is able to change the atmosphere of children’s learning that can be used as a medium of learning in the introduction of mammals for kindergarten children. This AR can provide interesting facilities such as displaying 3-dimensional objects of these mammals along with animal sounds and animations using Smartphones, so that children can interact and be more creative in recognizing these mammals, because children experience their golden age at the age of 4 to 7 years which is a time when children begin to receive stimuli, so that children will be faster to receive and catch on learning from the introduction of these mammals by using Augmented Reality Technology. The results of testing on the Vuforia plugin and making the AR application on Unity can provide a good information result, where the use of AR can bring up mammalian objects by pointing the Smartphone at the marker, so that all mammals can be recognized properly. This shows that children’s interest around 85% in terms of UI/UX appearance, and 70% of children have no problems in running the Mammal Animal AR application
Co-Authors Abdul Aziz Abdullah, Mohammad Aldinugroho Abdullah, Syamsun Nahar Agus Iskandar Albaar Rubhasy Aldisa, Rima Tamara Alhadi, Alhadi Andrianingsih Ardiansyah, Aldi Aris Gunaryati, Aris Arundy, Vicky Agnes Azamta, Firnas Benrahman Burhani, Hanif Rahman Cahyadi, Iman Deny Hidayatullah Endah Tri Esti Handayani Ernawati Ernawati Fauziah Fauziah Fauziah, F Finny Fitry Yani Furqaansyah, Yoga Gunaryarti, Aris Gunaryati, Agus Haryono, Muklis Hasbulloh, Hasbulloh Herlan, Agus Humansyah, Hanggita Ahmad Iriandi, Aditya Nuryudha Irsandi, Johar Saputra Jayanti, Riza Dwi Khuzaifi, Ahmad Kristian, Mikhael Lesmana, Delles Mardia, Ahmad Muslih Mardiani, Eri Masparudin, Masparudin Muchlis, Muhammad Muttaqin Muhammad Khairul Muhammad Ridwan Muhammad, Fauzan Nehe, Kurniadin Novi Dian Nathasia Nurhayati Nurhayati Oka Saputra Pangestu, Danang Aji Prayogi, Bayu Setyo Putra, Muhammad Aldi Aditia Putra, Wisnu Andika Rahman, Ben Retno Devita Rini Nuraini, Rini Rohadi, Chairil Sari Ningsih Sari Ningsih Shofi, Niam Sholihatih, Ira Diana Silfia Andini Situmorang, Daud Sopiyan, Muhamad Sumarhadi, Sumarhadi Sumijan Sumijan Suryadi Suryadi Syafii, Muhammad Faiz Syafrika Deni Rizki Syiamtoni, Eky Pambudi Takyudin, Takyudin Taslim, Denis Utomo, Budi Tri Wilianto Wilianto Yauma, Alfath Yudhi Setiawan Yuhandri Yuhandri, Yuhandri Zayana, Muhammad Rafiq Zikrullah, Alhadi Putra