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All Journal Jurnal Ilmiah KOMPUTASI Engineering, Mathematics and Computer Science Journal (EMACS)
Maria Susan Anggreainy
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Tasks Management: Approach to Problem Solving and its Relation to the Scrum and Agile Software Development Method Maria Susan Anggreainy; Alvin Putra Sulaiman; Calvin Mathew; Kezia Eka Tirta
Jurnal Ilmiah Komputasi Vol. 20 No. 4 (2021): Jurnal Ilmiah Komputasi Volume: 20 No. 4, Desember 2021
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.20.4.2871

Abstract

Manajemen tugas adalah aktivitas atau proses mengidentifikasi, merencanakan, memvisualisas- ikan, memantau, dan mengevaluasi pekerjaan selama periode waktu tertentu. Biasanya dalam melakukan kegiatan tersebut, Anda akan terlebih dahulu mengidentifikasi pekerjaan mana yang pent- ing dan mendesak. Setelah direncanakan, divisualisasikan, dan dikerjakan. Setiap pekerjaan dipantau sejauh mana pekerjaan telah dilakukan atau kemajuan pekerjaan. Studi ini mengeksplorasi cara un- tuk mengelola tugas-tugas tersebut dalam proyek, dengan metode pengembangan perangkat lunak scrum dan tangkas. Aplikasi mobile yang dibuat memiliki beberapa fitur untuk membantu peng- guna mengelola tugas dengan baik dan membantu mengimplementasikan metode scrum dengan lebih mudah.
Training CNN-based Model on Low Resource Hardware and Small Dataset for Early Prediction of Melanoma from Skin Lesion Images Ivan Halim Parmonangan; Marsella Marsella; Doharfen Frans Rino Pardede; Katarina Prisca Rijanto; Stephanie Stephanie; Kreshna Adhitya Chandra Kesuma; Valentina Tiara Cahyaningtyas; Maria Susan Anggreainy
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9904

Abstract

Melanoma is a kind of rare skin cancer that can spread quickly to the other skin layers and the organs beneath. Melanoma is known to be curable only if it is diagnosed at an early stage. This poses a challenge for accurate prediction to cut the number of deaths caused by melanoma. Deep learning methods have recently shown promising performance in classifying images accurately. However, it requires a lot of samples to generalize well, while the number of melanoma sample images is limited. To solve this issue, transfer learning has widely adapted to transfer the knowledge of the pretrained model to another domain or new dataset which has lesser samples or different tasks. This study is aimed to find which method is better to achieve this for early melanoma prediction from skin lesion images. We investigated three pretrained and one non-pretrained image classification models. Specifically, we choose the pretrained models which are efficient to train on small training sample and low hardware resource. The result shows that using limited sample images and low hardware resource, pretrained image models yield better overall accuracy and recall compared to the non-pretrained model. This suggests that pretrained models are more suitable in this task with constrained data and hardware resource.
Co-Authors Alvin Putra Sulaiman Calvin Mathew Doharfen Frans Rino Pardede Ivan Halim Parmonangan Katarina Prisca Rijanto Kezia Eka Tirta Kreshna Adhitya Chandra Kesuma Marsella Marsella Stephanie Stephanie Valentina Tiara Cahyaningtyas