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All Journal Indonesian Journal of Informatic Research and Software Engineering Indonesian Journal of Applied Technology and Innovation Science
Afriyanto, Rahmat
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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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Perancangan Sistem Informasi Bisnis Laundry Berbasis Website Pada Bahagia Laundry Pekanbaru: Design of Information System for Business Bahagia Laundry Pekanbaru Stevani, Stevani; Anrahvi, Rifka; Aqeil, Ahmeid; Afriyanto, Rahmat
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 4 No. 2 (2024): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v4i2.1819

Abstract

Bahagia Laundry beroperasi sejak November 2019. pertama kali dibuka di jalan Belimbing dan pada awalnya tidak memiliki pegawai. Beberapa proses administrasi di Bahagia Laundry masih menggunakan proses manual, seperti pencatatan di buku besar, perhitungan dengan kalkulator, dan lainnya, yang masih rentan terhadap kesalahan manusia. Untuk mengatasi masalah tersebut, peneliti membuat sistem informasi berbasis web yang membantu proses laundry menggunakan metode pengembangan waterfall yang mana dapat mempermudah dan mempercepat pelayanan laundry. Analisis kebutuhan, perancangan, pemodelan, penerapan, pengujian, dan pemeliharaan sistem informasi adalah langkah-langkah dalam pendekatan perangkat lunak yang dikenal sebagai metode Waterfall. Penelitian ini menghasilkan suatu sistem informasi bisnis laundry berbasis web yang dapat meningkatkan akurasi, efisiensi, dan aksesibilitas proses laundry. Berdasarkan pengujian blackbox diperoleh kesimpulan bahwa semua fitur yang ada telah sesuai sehingga layak untuk digunakan.
Comparison of Deep Neural Network and Convolutional Neural Network Algorithms for Bone Fracture Aqeil, Ahmeid; Afriyanto, Rahmat; Adzhar, Arif Haikal Bin Shamsul Kamarul
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2271

Abstract

Bone fracture is a common medical condition that often affects elderly populations or individuals with degenerative diseases such as osteoporosis. Manual classification of fractures from X-ray images presents diagnostic challenges due to visual complexity and interobserver variability. In this study, we implemented and compared Deep Neural Network (DNN) and Convolutional Neural Network (CNN) architectures to classify bone fractures from radiographic images. The dataset consisted of 4099 X-ray images divided into fractured and non-fractured categories. Each model was trained using preprocessed and augmented data and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that the CNN model achieved better classification performance, with an accuracy of 80% and balanced class scores. In contrast, the DNN model showed poor generalization and strong bias toward the fractured class, yielding only 51% accuracy. This study concludes that CNN are more suitable for bone fracture classification tasks due to their superior ability to extract spatial features and generalize across categories.
Co-Authors Adzhar, Arif Haikal Bin Shamsul Kamarul Anrahvi, Rifka Aqeil, Ahmeid Stevani, Stevani