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All Journal Teknik: Jurnal Ilmu Teknik dan Informatika Reputasi: Jurnal Rekayasa Perangkat Lunak
M. Zikry Sahendra Chan
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Computer Science & IT

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Development of a Computer Vision-Based Auto-Scroll Guitar Chord Application Using Motion Detection M. Zikry Sahendra Chan; Qatrunnada Athirah Herdiyanto; Chelsea Febiola Sebayang; Debi Yandra Niska
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Mei 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v7i1.12681

Abstract

The development of computer vision technology enables the creation of a more natural human-computer interaction system through the use of body movements as a control medium. One of the problems often faced by users of digital guitar chord applications is the process of navigating the chord display which is still done manually so that it can interfere with guitar playing activities. This study aims to design and implement a computer vision-based AutoChord application that utilizes head movement detection as a control medium to automatically auto-scroll the guitar chord display. The research method used is the Waterfall software development method which includes the stages of system requirements analysis, system design, implementation, and system testing. The system was developed using webcam-based facial detection technology to read changes in the user's head orientation in real-time. The results of the study show that the AutoChord application is able to automatically run the chord display navigation function through the user's head movements so that it can increase the comfort and efficiency of user interaction when playing the guitar. Thus, the developed system can be an alternative solution in supporting guitar playing activities based on computer vision interaction
Analisis Komparatif XGBoost dan Temporal Fusion Transformer (TFT) pada Time Series Forecasting Harga Solana Qatrunnada Athirah Herdiyanto; Juhraini Helfiana Lexa; M. Zikry Sahendra Chan
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1138

Abstract

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.
Co-Authors Chelsea Febiola Sebayang Juhraini Helfiana Lexa Niska, Debi Yandra Qatrunnada Athirah Herdiyanto Qatrunnada Athirah Herdiyanto