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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 Jurnal Pendidikan dan Teknologi Indonesia Eduvest - Journal of Universal Studies
pambudi, elindra ambar
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Aerospace Engineering Computer Science & IT Education Engineering Health Professions Neuroscience Social Sciences

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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.
Perbandingan Kinerja Algoritma Naïve Bayes, Decision Tree, dan Support Vector Machine dalam Deteksi Serangan Siber Berdasarkan Log Sistem di Universitas Muhammadiyah Purwokerto Aysha, Aulya Alyana; Aji, Mukhlis Prasetyo; Wijaya, Ermadi Satriya; Pambudi, Elindra Ambar
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 12 (2025): JPTI - Desember 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1196

Abstract

Keamanan sistem informasi merupakan aspek vital dalam era digital, terutama bagi institusi pendidikan yang sangat bergantung pada infrastruktur teknologi dan rentan terhadap serangan siber. Salah satu faktor penyebab lemahnya pertahanan siber adalah kurangnya pemanfaatan data log sistem sebagai alat deteksi dini terhadap potensi ancaman. Penelitian ini bertujuan untuk mengevaluasi efektivitas tiga algoritma klasifikasi machine learning—Naive Bayes, Decision Tree, dan Support Vector Machine—dalam mendeteksi serangan siber menggunakan data log sistem dari Biro Sistem Informasi Universitas Muhammadiyah Purwokerto. Metode penelitian meliputi preprocessing data, pemisahan data menjadi data latih dan uji, pelatihan model, serta evaluasi kinerja menggunakan metrik akurasi, precision, recall, dan f1-score. Hasil pengujian menunjukkan bahwa algoritma Decision Tree memberikan performa terbaik dengan akurasi 99,50% dan nilai evaluasi sebesar 0,9983 pada seluruh metrik. Sementara itu, Naive Bayes memperoleh akurasi terendah sebesar 67,50%, dan Support Vector Machine mencapai 77,25% dengan nilai evaluasi 0,9200. Berdasarkan temuan ini, Decision Tree direkomendasikan sebagai algoritma utama dalam pengembangan sistem deteksi dini untuk meningkatkan keamanan dan ketahanan infrastruktur teknologi informasi di lingkungan perguruan tinggi.
Deteksi dan Klasifikasi Ancaman pada Log Serangan Siber Menggunakan Algoritma K-Nearest Neighbor (KNN) dan Random Forest (RF) Ningrum, Aissyah Wahyu; Aji, Mukhlis Prasetyo; Wijaya, Ermadi Satriya; Pambudi, Elindra Ambar
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 12 (2025): JPTI - Desember 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1197

Abstract

Ancaman siber yang semakin kompleks dan terus berkembang menuntut sistem keamanan yang mampu mendeteksi serangan secara cepat dan akurat. Pesatnya perkembangan serangan siber menuntut sistem deteksi yang cerdas dan adaptif untuk mengamankan jaringan informasi. Penelitian ini bertujuan untuk menerapkan dan mengevaluasi kinerja algoritma K-Nearest Neighbor (KNN) dan Random Forest (RF) dalam mendeteksi serta mengklasifikasikan ancaman berdasarkan log serangan siber. Data yang digunakan diperoleh dari Biro Sistem Informasi Universitas Muhammadiyah Purwokerto, berjumlah 500 entri dengan 25 atribut, yang kemudian diproses melalui tahap pra-pemrosesan seperti parsing, imputasi nilai hilang, dan encoding atribut kategorikal. Model KNN dan RF dibangun dan diuji menggunakan metrik evaluasi akurasi, precision, recall, dan f1-score. Hasil menunjukkan bahwa algoritma RF memiliki kinerja yang lebih unggul dengan akurasi 94,87% dibandingkan KNN yang mencapai 89,32%. Selain itu, RF menunjukkan konsistensi tinggi dalam precision dan recall pada kedua kelas, menjadikannya lebih efektif dalam mendeteksi variasi serangan. Dengan demikian, RF direkomendasikan sebagai algoritma utama dalam pengembangan sistem deteksi ancaman siber berbasis pembelajaran mesin.
Application of Adaptive Camera Zoom Using the Kalaman Filter Algorithm for Low Light Conditions Arrosyid, Hannafi; Pambudi, Elindra Ambar; Harjono, Harjono; Prinandita, Tito
Eduvest - Journal of Universal Studies Vol. 5 No. 11 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i11.52300

Abstract

This study aims to implement the Kalman Filter algorithm in an adaptive zoom camera system to improve image quality in low-light conditions. The main problem faced by conventional cameras is the instability of light intensity, which affects image sharpness and contrast. To that end, experiments were conducted using three different Android devices, namely Infinix Hot Play 11, Oppo Reno 6, and Oppo Reno 11, with shooting distances of 30 cm and 60 cm, respectively. Each device was tested using a Kalman Filter-based camera application and compared with actual measurements using a lux meter. The results of the study show that the Kalman Filter-based adaptive camera system is capable of providing light intensity estimates that are close to the actual values with a deviation of less than 7%. This algorithm works predictively through a process of dynamic estimation and updating of lighting values, enabling it to simultaneously adjust camera exposure and focus settings. This results in sharper, more stable, and more realistic images even in low-light environments
Co-Authors Arrosyid, Hannafi Aysha, Aulya Alyana Damarsiwi, Dyah Kartika Dimara Kusuma Hakim Ermadi Satriya Wijaya, Ermadi Satriya Feri Wibowo Fitriani, Maulida Ayu Harjono Harjono Mukhlis Prasetyo Aji Ningrum, Aissyah Wahyu Nurhidayat, Muhammad Ivan Prinandita, Tito Romodhon, Dion Widyantri, Shafrila