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All Journal Building of Informatics, Technology and Science Indonesian Journal of Informatic Research and Software Engineering PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Mulya, Anggi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Rancang Bangun Sistem Informasi Peminjaman Infocus Berbasis Web Pada Fakultas Sains dan Teknologi: Design and Development of a Web-Based Infocus Lending Information System at the Faculty of Science and Technology Mulya, Anggi; Baihaqi, Fajrul Falakh; Novita, Rita
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 3 No. 2 (2023): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v3i2.963

Abstract

Mahasiswa saat ini memerlukan sistem pelayanan akademik dalam peminjaman infocus yang efisien dan efektif dalam rangka memenuhi kebutuhan akademik. Proses layanan peminjaman infocus pada FST UIN SUSKA Riau masih menggunakan proses manual dimana mahasiswa harus datang terlebih dahulu ke bagian umum untuk memastikan infocus masih tersedia dan kemudian melakukan pencatatan peminjaman data secara manual menggunakan kertas yang saya rasa kurang efektif dan efisien. Salah satu langkah yang dapat diambil dan sesuai dengan tujuan penelitian ini yaitu dengan merancang bangun suatu sistem informasi berbasis web guna membantu efektifitas pelayanan peminjaman infocus. Penelitian ini menggunakan metode waterfall. Penelitian ini menghasilkan suatu sistem informasi peminjaman infocus berbasis web yang dapat meningkatan efisiensi, akurasi, dan aksesibilitas dalam proses peminjaman infocus.      
Klasifikasi Citra CT Scan Kanker Paru-Paru Menggunakan Pendekatan Deep Learning Mulya, Anggi; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6528

Abstract

This research aims to develop a reliable deep learning model for classifying CT-scan images of lung cancer. This research has the advantage of evaluating the performance of several Convolutional Neural Networks (CNN) architectures including DenseNet121, InceptionResNetV2, InceptionV3 and ResNet152V2 to compare their performance in classification accuracy. The dataset consists of 1,561 CT scan images obtained from Kaggle and the dataset is categorized into malignant cancer, benign cancer and normal. Through a combination of innovative data pre-processing techniques, such as augmentation with random rotation and normalization, division of the dataset using the hold-out method with ratios of 70:30, 80:20, and 90:10, and model training using Adam's optimizer and SGDM, researchers achieved very high classification accuracy. The evaluation results showed that InceptionV3 with SGDM optimizer at 90:10 ratio achieved performed very well with an accuracy of 99.38% while InceptionResNetV2 with Adam optimizer at 80:20 hold-out the highest performance, with an accuracy of 99.40%. These promising results indicate great potential in supporting the early discovery of lung cancer, thereby improving the accuracy of diagnosis and the chances of patient recovery. This research opens up opportunities for further development, such as the application of fine-tuning, ensemble learning, or integration with clinical decision support systems for medical applications.
Deep Learning for Pneumonia Detection in Chest X-Rays using Different Algorithms and Transfer Learning Architectures Lestari, Danur; Mulya, Anggi; Tatamara, Aghnia; Haiban, Ryando Rama; Khalifah, Habibah Dian
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.1553

Abstract

Pneumonia is one of the lung conditions brought on by bacterial infections. An accurate diagnosis is necessary for successful treatment. A radiologist can typically diagnose the condition based on images from a chest X-ray. The diagnosis may be arbitrary for a variety of reasons, such as the indistinctness of certain diseases on chest X-ray pictures or the possibility of the illness being mistaken for another. Consequently, clinicians require guidance from computer-aided diagnosis tools. We diagnosed pneumonia using two algorithms CNN and GAN, as well as two architectures ResNet50V2 and InceptionV3. The test results show that the ResNet50V2 architecture is superior to the InceptionV3 architecture on the CNN algorithm with an accuracy of 94% versus 93%. In addition, the test results on the GANs algorithm show that the ResNet50V2 architecture is superior to the InceptionV3 architecture with an accuracy of 96%, while the InceptionV3 architecture achieves an accuracy of 92%.
Co-Authors Baihaqi, Fajrul Falakh Haiban, Ryando Rama Khalifah, Habibah Dian Lestari, Danur Rice Novita Rita Novita Tatamara, Aghnia