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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Compiler CCIT (Creative Communication and Innovative Technology) Journal Journal of Soft Computing Exploration
pambudi, elindra ambar
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Computer Science & IT

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Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods Damarsiwi, Dyah Kartika; Pambudi, Elindra Ambar; Fitriani, Maulida Ayu; Wibowo, Feri
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13556

Abstract

Face detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The SIFT method looks for keypoint descriptors in images that can be used as a reference in comparing different images. After the keypoint descriptor is found with SIFT, the Haar Cascade Classifier method is used to detect faces in the image. Haar Cascade Classifier is a practical algorithm for object detection in images. After facial features are extracted with these two methods, the results are compared with the K-Nearest Neighbor (KNN) approach. This research involves the introduction of 28 color images with complex backgrounds. The results of combining these two methods produce an accuracy of 81.75%. This shows that combining these two methods effectively overcomes complex background challenges in face detection.
Rainfall forecasting using triple exponential smoothing for rice cultivation in lamongan, jawa timur Widyantri, Shafrila; Hakim, Dimara Kusuma; Pambudi, Elindra Ambar; Fitriani, Maulida Ayu
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.519

Abstract

Rice cultivation is a major agricultural activity that is heavily influenced by weather conditions. Extreme weather events, such as heavy rainfall, can cause farmers' productivity to decline. Rainfall forecasts are important for farmers to help them make the right decisions in managing their farming businesses. This research aims to predict rainfall in Lamongan Regency, East Java province, and provide valuable information to rice farmers to plan the optimal planting season. The method used in this study is Triple Exponential Smoothing (TES), an effective forecasting technique for processing time series data with seasonal patterns. Monthly rainfall data for the last five years formed the basis of the forecast, with data sourced from NASA's Power Data Access Viewer. The analysis results include a Mean Absolute Percentage Error (MAPE) value of 97.559% for rainfall. This rainfall forecast can assist farmers in increasing rice productivity and minimizing the risk of crop failure due to unpredictable weather conditions. With the rainfall weather forecast, farmers are expected to know the suitable months for rice cultivation so that productivity increases
Impact of Wolf Thresholding on Background Subtraction for Human Motion Detection Pambudi, Elindra Ambar; Nurhidayat, Muhammad Ivan
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2116

Abstract

Series of motion detection based on background subtraction there is an image segmentation stage. Thresholding is a common technique used for the segmentation process. There are two types that can be used in thresholding techniques namely local and global. This research intends to implement local adaptive wolf thresholding as the threshold value of the background subtraction method to detect motion objects. The proposed method consists of the reading frame, background and foreground initialization of each frame, preprocessing, background subtraction, wolf thresholding, providing a bounding box, and running frame sequentially. Based on MSE and PSNR obtained on four videos, it has shown that wolf thresholding has succeeded in outperforming of global threshold.
Fine-Tuning GMM and Total Pixel-Based Drowsiness Detection: A Strategy for Detection Open and Closed Eye Pambudi, Elindra Ambar; Romodhon, Dion; Wijaya, Ermadi Satriya
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3614

Abstract

Fatigue driving represents a substantial and often unrecognized risk in traffic accidents. A technique that may be employed involves the detection of open and closed eyes. The research on open and closed eye identification use approaches based on haar cascade and complete pixel analysis. Our proposed method employs an adaptive thresholding technique is implemented right before total pixel process. The processing steps involve the application of haar cascade, adaptive thresholding, fine-tuning of Gaussian Mixture Models (GMM), and the calculation of the total pixel count in the image that is utilized to identify the state of the eye using thresholding. The results from Fine-Tuning GMM thresholding for the left and right eyes are as follows: MSE values of 7.02 and 7.96, and PSNR values of 39.24 and 39.21, respectively. The results derived from fine-tuning are comparable to those obtained using Otsu's method.
Co-Authors Damarsiwi, Dyah Kartika Dimara Kusuma Hakim Ermadi Satriya Wijaya, Ermadi Satriya Feri Wibowo Fitriani, Maulida Ayu Nurhidayat, Muhammad Ivan Romodhon, Dion Widyantri, Shafrila