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Penerapan Teknologi Real Time Computer Vision pada Robot Cerdas Penyeleksi dan Pemungut Bola Berdasarkan Warna Hasanah, Fitria Nur; Dzikrillah, Ahmad Rizal; Wiranata, Ade Davy; Rahardjo, Rafi Diandra Dani
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 16, No 1 (2025): JURNAL SIMETRIS VOLUME 16 NO 1 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i1.13722

Abstract

Visi komputer merupakan salah satu teknologi terbaru dalam kecerdasan buatan yang banyak digunakan dalam bidang otomasi, deteksi objek, dan robotika. Penelitian ini bertujuan untuk mengembangkan robot cerdas berbasis visi komputer real-time yang mampu mendeteksi, memilah, dan memungut bola berdasarkan warna pada kontes ABU Robocon 2024. Sistem robot ini dirancang dengan menggunakan kamera Pixy CMUCam5 yang dilengkapi algoritma Color Connected Component (CCC) untuk mendeteksi objek berwarna merah dan biru sesuai dengan standar bola kontes. Robot beroda yang dikendalikan oleh mikrokontroler Arduino Uno, memanfaatkan roda mechanum untuk manuver, sementara mekanisme pemungut bola menggunakan silinder karet yang digerakkan motor DC. Pengujian dilakukan melalui dua skenario: deteksi bola yang bergerak dan pengambilan bola yang diam di beberapa jarak. Hasil penelitian menunjukkan bahwa robot mampu mendeteksi dan merespon objek berwarna dalam jangkauan tertentu. Namun, kamera Pixy memiliki keterbatasan dalam mendeteksi bentuk atau pola objek, hanya mendasarkan seleksi pada warna. Penelitian ini berhasil menunjukkan implementasi algoritma deteksi warna berbasis visi komputer pada robot cerdas, meskipun masih perlu peningkatan dalam kemampuan pengenalan bentuk.Computer vision is one of the latest technologies in artificial intelligence, widely used in automation, object detection, and robotics. This study aims to develop an intelligent robot based on real-time computer vision that can detect, sort, and pick up balls based on color for the ABU Robocon 2024 contest. The robot system is designed using the Pixy CMUCam5 camera, equipped with a Color Connected Component (CCC) algorithm to detect red and blue objects according to the contest's ball standards. The wheeled robot, controlled by an Arduino Uno microcontroller, utilizes mechanum wheels for maneuvering, while the ball-picking mechanism uses rubber cylinders driven by DC motors. Testing was conducted through two scenarios: detecting moving balls and picking up stationary balls at various distances. The results indicate that the robot can detect and respond to colored objects within a certain range. However, the Pixy camera has limitations in detecting the shape or pattern of objects, only basing selection on color. This study successfully demonstrates the implementation of a color-detection algorithm using computer vision in an intelligent robot, although improvements in shape recognition are still needed.
Impelementasi Algoritma LSTM Dan SVR Untuk Prediksi Harga Bitcoin Menggunakan Data Yahoo Finance: Indonesia Riziq, Ihsannur Fathan; Dzikrillah, Ahmad Rizal
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/90ypad22

Abstract

Technological developments in the financial sector have facilitated the emergence of various digital investment instruments, one of which is cryptocurrency. Bitcoin and Ethereum are digital assets with the largest market capitalization, while the USD remains a significant player in global trade. The high price volatility of these three assets demands accurate and adaptive prediction methods. This study aims to apply the Long Short-Term Memory (LSTM) learning algorithm to predict Bitcoin, Ethereum, and USD prices based on historical data from Yahoo Finance from 2019 to 2024. Preprocessing includes data normalization with a Min-Max Scaler and feature engineering in the form of daily returns. Model evaluation was conducted using the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics. The results showed that the LSTM model performed best, with the lowest MAE value of 1,320.41 and an MSE of 3,464,596.53 for the highest price prediction. These findings demonstrate that LSTM excels in consistently handling complex and fluctuating data patterns. This research is expected to serve as a reference in the development of a machine learning-based digital asset price prediction system, particularly for assets with high volatility.