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All Journal Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Saturnus: Jurnal Teknologi dan Sistem Informasi
Asrorul Faradis
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Computer Science & IT

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Implementasi Metode Optical Character Recognition (OCR) untuk Deteksi Karakter pada Citra Plat Nomor Kendaraan Bermotor Mohammad Ridwan Bayu Pratama; Asrorul Faradis; Soffiana Agustin
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 4 (2025): Juli : Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i4.938

Abstract

Manual collection of vehicle license plates is often inefficient and prone to errors, so an automatic identification system is needed. This research aims to implement and evaluate the performance of a license plate character detection system, focusing on the accuracy comparison between black and white base plates in Indonesia. The method used is Optical Character Recognition (OCR) with image preprocessing workflow including Grayscale, Gaussian Blur, and edge detection implemented in Google Colab. The system was tested using 100 primary data samples consisting of 50 black base plates and 50 white base plates. The findings showed that the system achieved a combined average accuracy of 84.36%. Specifically, it was found that the accuracy on the black base plate (85.40%) was slightly superior to that on the white base plate (83.32%). The implication of this study is that the change in license plate standards has a measurable technical impact on the ANPR system, where the findings can serve as a foundation for developers to calibrate the system to be reliable on both plate types during the transition period.
Peramalan Harga Bitcoin Menggunakan Metode Moving Average Asrorul Faradis; Raditya Thabroni Romadhon; Soffiana Agustin
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 3 No. 3 (2025): Juli : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v3i3.952

Abstract

Bitcoin is one of the most prominent digital assets in the modern financial era due to its high volatility and huge profit potential. However, its extreme price volatility also makes it a high-risk asset, so a reliable forecasting approach is needed to help investors make more rational decisions. This study aims to forecast Bitcoin price using the Moving Average (MA) method, specifically MA3, by utilizing monthly historical data of Bitcoin price in USD currency obtained from investing.com website. The MA3 method was chosen for its ability to smooth out short-term fluctuations and identify the direction of price trends. The forecasting process is performed by calculating the average of the last three months' prices for each point in time and compared to the actual price to evaluate its accuracy. The evaluation is done using various prediction error metrics, namely Error, Absolute Error, Squared Error, and Percentage Error. The results of the analysis show that the MA method provides a fairly representative picture of price trends and can be used as an early indicator in short-term investment strategies. Thus, the Moving Average method proves to be a simple but effective prediction tool, especially for novice investors in the dynamic crypto asset market.
Co-Authors Mohammad Ridwan Bayu Pratama Raditya Thabroni Romadhon Soffiana Agustin, Soffiana