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Gilas Adi Saputra
Universitas Mercu Buana
Computer Science & IT Engineering

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Construction and Management of a Cosmos-Based Operating System Using Visual Studio Development and VMware Virtualization Technology Mohamad Yusuf; Zidane Fahrezi; Rafif Syari Hidayah; Yudha Andika Istanto; Gilas Adi Saputra
Journal Collabits Vol. 1 No. 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25559

Abstract

Operating systems play an important role in bridging hardware and software on various computing devices. This research focuses on building an operating system based on Cosmos, an open source project that allows the creation of operating system kernels quickly and efficiently. In the process, we leverage Visual Studio development tools to develop and maintain the kernel, while VMware virtualization technology is used to test and manage development. This research contributes to further understanding of the development of Cosmos-based operating systems with optimal use of Visual Studio development tools and VMware virtualization technology
Using Tensorflow for Clean and Messy Room Image Classification with Python Gilas Adi Saputra; Damar Pratama Ristadias Hariyanto
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.27274

Abstract

Image classification is a fundamental computer vision task that can support automated visual monitoring in domestic, educational, and workplace environments. This study develops a transparent baseline pipeline using TensorFlow 2.x, Keras, and Python to distinguish clean and messy room images. The dataset contains 192 training images, with 96 images in each class, and 20 validation images, with 10 images in each class. All images are resized to 150 x 150 pixels and normalized to a 0-1 range. Rotation, horizontal flipping, and shearing are applied only to the training data, while validation images are normalized without random transformation. The sequential convolutional neural network contains four convolution-pooling blocks, a fully connected layer, and a sigmoid output for binary classification. Qualitative testing with two external images produced labels that were consistent with visual observation: the cluttered room was classified as messy and the organized room as clean. These demonstrations confirm that the pipeline operates from image input to class prediction, but they do not establish broad generalization or perfect accuracy. The main contribution is a reproducible small-data workflow that documents dataset distribution, preprocessing, augmentation, model parameters, validation procedures, and prediction thresholds. The study is limited by the small validation set, the absence of a large independent test set, and the lack of direct comparison with pretrained models. Future studies should evaluate transfer learning, larger datasets, repeated trials, and metrics such as precision, recall, F1-score, and confusion matrices.
Co-Authors Damar Pratama Ristadias Hariyanto Mohamad Yusuf Rafif Syari Hidayah Yudha Andika Istanto Zidane Fahrezi