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All Journal Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Sistem Informasi dan Ilmu Komputer
Alifya Meirza
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Arts Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media

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Penerapan Algoritma Decision Tree Dalam Melakukan Analisis Klasifikasi Harga Handphone Ahmad Taufiq Ramadhan; Faishal Hilmy F. G; Nadya Rafaela Puteri; Alifya Meirza
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 1 No. 4 (2023): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v1i4.1861

Abstract

The use of the Decision Tree method in smartphone price classification is the focus of this study. By using the 10 most relevant features and data normalization to achieve scale consistency, the Decision Tree algorithm delivers an average accuracy of 81%. Although some false positives and false negatives occur, the model is able to classify smartphone prices well, especially in identifying low and high prices. These results provide important insights into the features that affect smartphone prices. While there is still room for improvement, this model provides a solid foundation for the smartphone industry to determine prices based on certain specifications. The importance of relevant feature selection and data normalization was revealed in this study. Despite the accuracy reaching 81%, improvements in the classification of medium and high price classes are still possible to reduce prediction errors. This method provides an important basis for the smartphone industry to set prices based on specifications, and data mining techniques such as Decision Tree can be improved to improve the accuracy of future price predictions.
Implementasi Metode YOLOV5 dan Tesseract OCR untuk Deteksi Plat Nomor Kendaraan Nadya Rafaela Puteri; Alifya Meirza
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 9 No 1 (2024): Jurnal Ilmu Komputer dan Desain Komunikasi Visual
Publisher : Fakultas Ilmu Komputer Universitas Nahdlatul Ulama Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/jikdiskomvis.v9i`1.1288

Abstract

Campus security and convenience are crucial factors in supporting the advancement of higher education institutions. Conventional security systems that involve manual vehicle inspections through ownership identification often consume time and cause vehicle queues, leading to traffic congestion. To address this issue, this research proposes a solution in the form of an automatic license plate recognition system based on You Only Look Once (YOLO) and character extraction using Tesseract Optical Character Recognition (OCR) technology. This system enables quick and efficient vehicle license plate recognition, optimizing traffic flow, saving time, and enhancing convenience for all vehicle users on campus. The research methodology involves training a YOLO model with a vehicle license plate Dataset to detect and recognize license plates, followed by character extraction to accurately identify plate numbers. The research results show the system's accuracy reaching 70%, indicating its effectiveness in detecting vehicle plates in various situations. It is hoped that this system can be widely implemented on campuses to improve security, convenience, and access efficiency for the entire academic community.
Implementasi Metode YOLOV5 dan Tesseract OCR untuk Deteksi Plat Nomor Kendaraan Nadya Rafaela Puteri; Alifya Meirza
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 9 No 1 (2024): Jurnal Ilmu Komputer dan Desain Komunikasi Visual
Publisher : Fakultas Ilmu Komputer Universitas Nahdlatul Ulama Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/jikdiskomvis.v9i`1.1288

Abstract

Campus security and convenience are crucial factors in supporting the advancement of higher education institutions. Conventional security systems that involve manual vehicle inspections through ownership identification often consume time and cause vehicle queues, leading to traffic congestion. To address this issue, this research proposes a solution in the form of an automatic license plate recognition system based on You Only Look Once (YOLO) and character extraction using Tesseract Optical Character Recognition (OCR) technology. This system enables quick and efficient vehicle license plate recognition, optimizing traffic flow, saving time, and enhancing convenience for all vehicle users on campus. The research methodology involves training a YOLO model with a vehicle license plate Dataset to detect and recognize license plates, followed by character extraction to accurately identify plate numbers. The research results show the system's accuracy reaching 70%, indicating its effectiveness in detecting vehicle plates in various situations. It is hoped that this system can be widely implemented on campuses to improve security, convenience, and access efficiency for the entire academic community.
Co-Authors Ahmad Taufiq Ramadhan Faishal Hilmy F. G Nadya Rafaela Puteri