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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Universitas Pamulang KOMPUTIKA - Jurnal Sistem Komputer JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Repositor
Christian Sri Kusuma Aditya
Universitas Muhammadiyah Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Public Health Social Sciences

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Game Design for Mobile App-Based IoT Introduction Education in STEM Learning Indra Puja Laksana; Evi Dwi Wahyuni; Christian Sri Kusuma Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.5007

Abstract

STEM education has received considerable attention in recent years. However, developing valid and reliable assessments in interdisciplinary learning in STEM has been a challenge. Therefore, many students ranging from junior high school to university students are only familiar with the Internet of Things (IoT) from social media but do not know its concept and function in STEM learning. This is also supported by the absence of educational applications about IoT. This research aims to introduce IoT by using mobile applications. This research refers to the multimedia development method according. The data collection method in this study was carried out by means of observation and interviews randomly to high school students to university students. This data collection was carried out using the experimental method of application testing to analyze user needs from several aspects such as features, images, and fonts. This research is also supported by the existence of literature studies derived from several journals. The results show that the functions in the application can operate as expected. Based on the survey results of the application, 75.37% of respondents rated this application in the very good category and gave positive responses so that this application could be well received by users
MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture Novia Adelia Ujilast; Nuris Sabila Firdausita; Christian Sri Kusuma Aditya; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5457

Abstract

Alzheimer's disease is a neurodegenerative disorder or a condition characterized by degeneration and damage to the nervous system. This leads to a decline in cognitive abilities such as memory, thinking, and focus, which can impact daily activities. In the medical field, a technology called Magnetic Resonance Imaging (MRI) can be used for the initial diagnosis of Alzheimer's disease through image procedures-based recognition methods. The development of this detection system aims to assist medical professionals, including doctors and radiologists, in diagnosing, treating, and monitoring patients with Alzheimer's disease. This study also aims to classify different types of Alzheimer's disease into four distinct classes using the convolutional neural network method with the EfficientNet-B0 and EfficientNet-B3 architectures. This study used 6400 images that encompass four classes, namely mild demented, moderate demented, non-demented, and very mild demented. After conducting testing for both scenarios, the exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the exactness was 97.00%.
Co-Authors Abdul Hadiy Dyo Fatra Adam Novrisal Ahmad Akbar Maulana Ahmad Rizky Habibullah Asmaul Lailiyah Chandranegara, Didih Rizki Chastine Fatichah Dati Nafa Alfiana Diana Purwitasari Dzur Rifqi Aziz Evi Dwi Wahyuni Fatahillah Arsyad Fatimah Defina Setiti Alhamdani Galih Wasis Wicaksono Gian Bagus Sadewa Gita Indah Marthasari Hardianto Wibowo Hasbi Atsqalani Hilman Abi Sarwan Heryawan Ilyas Nuryasin Indra Puja Laksana Muhammad Abizar Muhammad Ferry Septian Ihzanor Syahputra Muhammad Robby Fathuda Nisrina Arintia Maghfiroh Novia Adelia Ujilast Nur Hayatin Nuris Sabila Firdausita Qori Raditya Damayanti Raffi Ainul Afif Roni Hadi Wijaya S, Vinna Rahmayanti Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Trifebi Shina Sabrila Vera Suryani Wicaksono, Galih Wasis Wildan Suharso Wisnu Prayogo Kusumo Yufis Azhar