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All Journal Jurnal Ilmiah Dan Karya Mahasiswa
Yohanna Permata Putri
Universitas Negeri Medan
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media

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Implementasi Model Machine Learning dalam Mengklasifikasi Kualitas Air Stacyana Jesika; Suci Ramadhani; Yohanna Permata Putri
Jurnal Ilmiah Dan Karya Mahasiswa Vol. 1 No. 6 (2023): DESEMBER : JURNAL ILMIAH DAN KARYA MAHASISWA
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jikma.v1i6.1162

Abstract

Water quality is an important factor in maintaining human health and environmental sustainability. Water pollution is a major problem in Indonesia, so it is important to monitor and classify water quality effectively. Implementation of machine learning models in classifying water quality can provide important benefits in the environmental and health fields. This research uses two machine learning algorithms, namely KNN and SVM, to classify water quality. The water quality data used comes from the website www.kaggle.com, which was uploaded by MsSmartyPants in 2021 with the title "Water Quality (Dataset for water quality classification)". Implementation of this machine learning model involves the steps of data collection, data pre-processing, selection of relevant attributes, algorithm selection, model training, evaluation, and model implementation for real-time water quality classification.
Sistem Pendeteksi Kecepatan Kendaraan dengan Menggunakan Metode Deep Learning Samuel Anaya Zai; Sindy Fitriani Margaret; Yohanna Permata Putri
Jurnal Ilmiah Dan Karya Mahasiswa Vol. 1 No. 6 (2023): DESEMBER : JURNAL ILMIAH DAN KARYA MAHASISWA
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jikma.v1i6.1163

Abstract

Object detection based on digital image processing in vehicles is important to be applied in building surveillance systems or as an alternative method of collecting statistical data for effective technical decision making in traffic engineering. This review describes the implementation of vehicle speed detection and measurement using OpenCV. This method involves the use of a cascade classifier to detect cars and a dlib correlation tracker to track objects in traffic video footage. Car detection is performed on each image using a Cascade Classifier, while object tracking uses a correlation tracker to track the location and identity of the vehicle. Each time the position changes between images, the program calculates the car's speed based on the difference in position. Speed ​​is measured in kilometers per hour and is displayed above each passing vehicle. Experimental results demonstrate the program's success in effectively detecting and tracking vehicles, providing clear and accurate visualization of vehicle movement and speed. The program is also capable of measuring performance in frames per second (fps). The conclusions of this journal highlight the potential for further development, optimization, and maintenance as measures to improve system performance in various contexts of use. This journal contributes to the literature in the field of automated traffic monitoring and can be a useful reference for developers of security monitoring and traffic management systems.
Co-Authors Samuel Anaya Zai Sindy Fitriani Margaret Stacyana Jesika Suci Ramadhani