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All Journal Jurnal Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JUTEKIN (Jurnal Manajemen Informatika) Computer Science and Information Technologies Jurnal Computer Science and Information Technology (CoSciTech) Kalbiscientia Jurnal Sains dan Teknologi SisInfo : Jurnal Sistem Informasi dan Informatika
Deni Setiana
Fakultas MIPA, Universitas Padjadjaran
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Program Kontrol Objek Beroda Menggunakan Neurosky Asep Sholahuddin; Deni Setiana
SisInfo Vol 1 No 2 (2019): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.429 KB)

Abstract

Headset NeuroSky MindWave Mobile adalah perangkat yang dapat mendeteksi gelombangotak dengan proses sederhana dan Arduino adalah mikrokontroler yang dapat dihubungkan denganperalatan lain. Kami menerapkan MindWave untuk memungkinkan pergerakan objek menggunakanroda. Sistem ini akan menerima dan menjalankan perintah dasar yang dihasilkan dari dua kondisiotak Di MindWave: Perhatian eSense Attention dan Poor Signal Quality . Kondisi otak ditentukan olehgelombang otak yang dikenal sebagai gelombang alfa, beta, dan gamma. Gelombang otak terdeteksimenggunakan MindWave, terhubung dengan Komputer di objek beroda dan mengirim transmisidata ke komputer melalui koneksi Bluetooth. Program menggunakan C# untuk menganalisis datadan mengirim empat perintah sederhana ke objek yang bergerak menggunakan roda. Kamimelakukan percobaan sebanyak sepuluh kali, dan memperoleh akurasi 70%. Hasil ini menunjukkanbahwa perintah gelombang otak dapat diproses dengan sukses untuk jalan terus, stop, belok kiri,dan belok kanan.
Rancangan radio frequency identification (rfid) smart door lock system berbasis internet of things untuk manajemen membership fitness center Azhar Jauharul Umam; Deni Setiana; Aditya Pradana
Computer Science and Information Technology Vol 4 No 2 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i2.5126

Abstract

The Fitness Center is a sports facility that provides a variety of sports equipment. Typically, fitness centers offer membership packages to their visitors and manually check the membership status of visitors each time they come to the fitness center. This is the basic motivation for this research, to build a system where membership packages are automatically checked. The Internet of Things (IoT) facilitates fitness center owners in managing member data and controlling who is allowed to enter the fitness area. This research utilizes RFID technology with the MFRC522 module as an access control for the fitness room, and Raspberry Pi as a microprocessor to process RFID data using Python. Member data can be managed in a web-based application using the MERN stack, as it is a popular combination for modern web application development. The research method used is Research and Development, where extensive research is initially conducted on the requirements and design of this system. Then, the implementation process is carried out both from the application side and the devices used. The research resulted in an IoT-based smart door lock system that can automatically check the membership status of visitors using RFID technology as access control. This system is IoT-based, allowing member data to be managed anywhere through the application as long as the Raspberry Pi and the device running the application are connected to the internet. The response time of this device in processing data on RFID cards/tags is an average of 177,44ms from the 60 cards/tags tested. Based on these results, it can be concluded that the built RFID smart door lock system is functioning well.
Klasifikasi Citra Gerakan Olahraga Dalam Gym Menggunakan Graph Convolutional Network Kurniadi, Affan Rifqy; Akmal; Deni Setiana
KALBISCIENTIA Jurnal Sains dan Teknologi Vol. 12 No. 01 (2025): Jurnal Sains dan Teknologi
Publisher : Research and Community Service UNIVERSITAS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbiscientia.v12i01.4400

Abstract

The participation rate in sports activities among Indonesians remains low, with the Sport Development Index (SDI) in 2022 recording only 30.93%, a decline from 32.80% in the previous year. Simple kind of sport can be followed is gym. This study aims to introduce and promote basic gym movements such as bench press, squat, and deadlift to encourage greater engagement in sports activities. This research utilizes Deep Learning technology based on Graph Convolutional Network (GCN) to classify gym movement images into three classes: benchpress, squat, and deadlift. The study focuses on comparing various hyperparameters, including model type, batch size, and dropout, to determine the optimal configuration with the best performance.The results indicate that the GCN model achieved an F1 Score of 0.8667, demonstrating strong performance in classifying gym movement images. A simple web-based application was developed as an implementation to facilitate automatic gym movement classification.
Klasifikasi Citra Gerakan Olahraga Dalam Gym Menggunakan Graph Convolutional Network Kurniadi, Affan Rifqy; Akmal; Deni Setiana
KALBISCIENTIA Jurnal Sains dan Teknologi Vol. 12 No. 01 (2025): Jurnal Sains dan Teknologi
Publisher : Research and Community Service UNIVERSITAS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbiscientia.v12i01.4400

Abstract

The participation rate in sports activities among Indonesians remains low, with the Sport Development Index (SDI) in 2022 recording only 30.93%, a decline from 32.80% in the previous year. Simple kind of sport can be followed is gym. This study aims to introduce and promote basic gym movements such as bench press, squat, and deadlift to encourage greater engagement in sports activities. This research utilizes Deep Learning technology based on Graph Convolutional Network (GCN) to classify gym movement images into three classes: benchpress, squat, and deadlift. The study focuses on comparing various hyperparameters, including model type, batch size, and dropout, to determine the optimal configuration with the best performance.The results indicate that the GCN model achieved an F1 Score of 0.8667, demonstrating strong performance in classifying gym movement images. A simple web-based application was developed as an implementation to facilitate automatic gym movement classification.
Co-Authors Aditya Pradana Aditya Pradana Aditya Pradana, Aditya Akik Hidayat Akmal Asep Sholahuddin Azhar Jauharul Umam Billal Hadian Billal Hadian, Billal Dessy Novita Erick Paulus Faishal Wahiduddin Faishal Wahiduddin, Faishal Juli Rejito Khairil Azmi Azhari Kurniadi, Affan Rifqy Rudi Rosyadi Setiawan Hadi