Garuda - Garba Rujukan Digital

SISTEM INFORMASI ABSENSI DENGAN MENGGUNAKAN FINGERPRINT Devita, Retno
Jurnal Ekobistek Vol 2, No 2 (2013): Jurnal Ekobistek UPI "YPTK" Padang
Publisher : Universitas Putra Indonesia YPTK Padang

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Abstrak - Sistem absensi guru sangat diperlukan dalam menentukan tingkat kehadiran guru di sekolah-sekolah. Absensi yang selama ini dilakukan secara manual, dapat dilakukan perubahan dengan adanya perkembangan teknologi sekarang ini yaitu sistem absensi yang menggunakan fingerprint (sidik jari) masing-masing guru, sehingga tidak akan terjadi kecurangan seperti pemalsuan sidik jari ataupun titip absen. Ini dikarenakan sidik jari setiap manusia berbeda-beda dan memiliki keunikkan tersendiri. Kata kunci : absensi, manual, teknologi, fingerprint (sidik jari)

Safe Security System Using Face Recognition Based on IoT Putra, Ondra Eka; Devita, Retno; Wahyudi, Niko
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12231

Face recognition is widely used in various applications, especially in the field of surveillance and security systems. This study aims to design and build a safe security system using face recognition via camera based on internet of things. This system uses the Raspberry Pi 3B and the OpenCV library as face recognition data processing which produces output on the Selenoid to open and close the safe, LCD 16x2 to display system status, IoT-based email delivery when smugglers occur. This study performs face recognition through the face detection stage using the Viola Jones method, feature extraction using the PCA (Principal Component Analysis) method and face recognition, then matched with the existing profile data in the directory. The results of this study indicate that the safe is open when a face is detected and will send a face capture to the e-mail address of the owner’s safe if the detected face is not recognized. Tests carried out on the safe security system using face recognition based on IoT build reach validity 90,25%.

Development and modification Sobel edge detection in tuberculosis X-ray images Devita, Retno; Fitri, Iskandar; Yuhandri, Yuhandri; Yani, Finny Fitry
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1191-1200

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.

PERBANDINGAN KERNEL PENAJAMAN, GAUSSIAN BLUR DAN DETEKSI TEPI PADA CITRA OTAK Devita, Retno; Putra, Ondra Eka; Rianti, Eva
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 7, No 4 (2024): November 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v7i4.2271

Citra otak merupakan gambar yang didapat dari proses pencitraan otak melalui teknologi medis seperti MRI (Magnetic Resonance Imaging), CT scan (Computed Tomography), atau PET scan (Positron Emission Tomography). Citra ini memberikan visualisasi dari struktur otak secara terperinci dan digunakan untuk mendeteksi atau mendiagnosa kondisi otak. Citra otak yang digunakan pada penelitian ini sebanyak 5 citra otak yang diproses menjadi 30 citra otak. Penelitian ini membandingkan kinerja kernel 7x7 dan 9x9 pada tiga jenis operasi utama dalam pengolahan citra otak yaitu penajaman, gaussian blur, dan deteksi tepi. Kernel penajaman diterapkan untuk memperjelas struktur halus dari citra, gaussian blur digunakan untuk mereduksi noise citra dan deteksi tepi bertujuan mengidentifikasi batas anatomi otak. Perbandingan dilakukan dengan mengevaluasi hasil dari dua ukuran kernel terhadap kualitas visual, tingkat detail, dan keberhasilan dalam mengidentifikasi fitur penting otak. Nilai tertinggi dari 5 citra yang didapat adalah kernel penajaman 7x7 pada citra 5 dengan MSE 4.832.932.323, RMSE 69.519.295 dan PSNR 11.288696 dB dan nilai terendahnya adalah kernel gaussian blur 9x9 pada citra 1 dengan MSE 16.747.259.747, RMSE 129.411.204 dan PSNR 5.891366 dB. Kesimpulannya, hasil terbaik pada penelitian ini adalah kernel 7x7 dilihat dari nilai PSNR.

EXPLANATION OF FEATURE EXTRACTION IN FACE RECOGNITION USING VIOLA JONES ALGORITHM Devita, Retno; Rianti, Eva; Yuhandri, Muhammad Habib; Putra, Ondra Eka
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3844

Face recognition has become a common thing used in the field of surveillance and security in computer technology and image devices. This study aims to identify the usefulness of a person's face on 3 test images. This study examines the methods of cropping techniques, image enhancement through intensity measurement, and histogram analysis to improve the contrast and distribution of image intensity. In addition, the Viola-Jones algorithm is used to detect key facial features such as eyes, nose, and mouth. The results of the analysis are then applied in the feature evaluation stage, where usually between facial features are applied to measure the ratio of facial proportions. Furthermore, the comparison of proportional ratios of several images was analyzed using bar graphs and line graphs to evaluate the trend and stability of facial proportions. The results showed the best ratio stability with a smaller variation of the on-off ratio of image 2 which is 0.4762 pixels to 0.4983 pixels. Image 2 is the most ideal for face measurement systems based on geometric ratios because it provides more consistent and visible results.

Improved feature extraction method and K-means clustering for soil fertility identification based on soil image Ramadhanu, Agung; Hendri, Halifia; Enggari, Sofika; Andini, Silfia; Devita, Retno; Rianti, Eva
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2001-2011

This research is conducting analysis of digital land images using digital image processing techniques. The main purpose of the research is to classify soil fertility based on two-dimensional RGB colored digital soil images. The research is done by extracting features and shapes from the soil image. The research uses methods of segmentation, extraction, and identification against digital soil images. This research is carried out in three stages. The first phase of this research is image pre-processing which begins with the conversion of RGB color image to Grayscale then color conversion to binary which subsequently performs noise reduction with the method Three-layer median filter. The second stage is a process that is divided into the first two stages, namely the process of segmentation by grouping RGB color images into L*a*b which is continued by clustering using the K-means clustering method. The second is the extraction of characteristics of the soil image which is characteristic of shape and texture. The final stage is the identification of soil images that are clustered into two types: fertile soils and unfertile soil. The study achieved an accuracy of 85% which could accurately identify 20 images while inaccurately classifying 5 images out of a total of 25 input images.

CANNY EDGE DETECTION AND IMAGE SEGMENTATION FOR PRECISION FACE RECOGNITION SYSTEM Devita, Retno; Sumijan, Sumijan
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 2 (2024): Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.3059

Abstract: Facial recognition is widely used in areas such as video surveillance and database management. Facial images have been used as a preferred biometric feature in many identity recognition systems to obtain good image results in image segmentation. A good image must pay attention to several factors, namely high resolution, good contrast, image sharpness, consistent colors, lack of noise and appropriate lighting conditions. In this face recognition research, using canny edge detection method for 10 original images paired with 10 other images. The original faces taken are male and female. Canny edge detection has a low error rate in image segmentation compared to other edge detections. The purpose of this study is to determine the edge of the image in I-rat and can display the results of a good segmentation of facial images. The results of the test data with data stored in the database in the study is 1 face image produces 67.69% accuracy and 26.92% and 8 other face images produce 100% accuracy. The average success rate of 10 experiments using image segmentation is 89.461%. In conclusion, the canny edge detection method can provide accurate results in the face recognition process. Keywords: accuracy; canny edge detection; face recognition; image; segmentation Abstrak : Pengenalan wajah banyak digunakan dalam diberbagai bidang seperti pengawasan video dan manajemen basis data. Gambar wajah telah digunakan sebagai ciri biometrik yang disukai di banyak sistem pengenalan identitas untuk mendapatkan hasil citra yang bagus dalam segmentasi citra. Citra yang baik harus memperhatikan beberapa faktor yaitu resolusi tinggi, kontras yang baik, ketajaman citra, warna yang konsisten, kurangnya noise dan kondisi pencahayaan yang sesuai. Pada penelitian pengenalan wajah ini, menggunakan metode deteksi tepi canny untuk 10 citra asli yang dipasangkan dengan 10 citra lainnya. Wajah asli yang diambil berjenis kelamin laki-laki dan perempuan. Deteksi tepi canny memiliki tingkat kesalahan rendah dalam segmentasi citra dibandingkan dengan deteksi tepi lainnya. Tujuan dari penelitian ini adalah menentukan tepi gambar secara akurat dan dapat menampilkan hasil segmentasi citra wajah yang baik. Hasil dari data uji dengan data yang tersimpan di database dalam penelitian adalah 1 citra wajah menghasilkan akurasi 67,69% dan 26,92% dan 8 citra wajah lainnya menghasilkan akurasi 100%. Rata-rata tingkat keberhasilan dari 10 kali percobaan dengan menggunakan segmentasi citra adalah 89,461%. Kesimpulan, metode deteksi tepi canny dapat memberikan hasil yang akurat dalam proses pengenalan wajah. Kata Kunci : akurasi; deteksi tepi canny; citra; pengenalan wajah; segmentasi

EXPLANATION OF FEATURE EXTRACTION IN FACE RECOGNITION USING VIOLA JONES ALGORITHM Devita, Retno; Rianti, Eva; Yuhandri, Muhammad Habib; Putra, Ondra Eka
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3844

Face recognition has become a common thing used in the field of surveillance and security in computer technology and image devices. This study aims to identify the usefulness of a person's face on 3 test images. This study examines the methods of cropping techniques, image enhancement through intensity measurement, and histogram analysis to improve the contrast and distribution of image intensity. In addition, the Viola-Jones algorithm is used to detect key facial features such as eyes, nose, and mouth. The results of the analysis are then applied in the feature evaluation stage, where usually between facial features are applied to measure the ratio of facial proportions. Furthermore, the comparison of proportional ratios of several images was analyzed using bar graphs and line graphs to evaluate the trend and stability of facial proportions. The results showed the best ratio stability with a smaller variation of the on-off ratio of image 2 which is 0.4762 pixels to 0.4983 pixels. Image 2 is the most ideal for face measurement systems based on geometric ratios because it provides more consistent and visible results.

Accurately Determining Labor Test Results Using the Rough Set Method Devita, Retno; Defit, Sarjon
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7069

An exam is something that must be done to test a person's ability or intelligence. The laboratory exam in the Computer Systems study program at Putra Indonesia University "YPTK" Padang consists of a digital systems exam, a fuzzy logic control exam, and a tool presentation. The Labor Exam must be passed by students who will take the comprehensive exam. In this study, laboratory exam data was taken for 20 students. So far, processing of student laboratory exam results has been done manually so it takes a long time to make decisions. To overcome this problem, a Rough Set method is used to determine laboratory test results. The Rough Set method is part of machine learning. This research produces 29 rules as knowledge, namely {Digital System} Or {A} = 3 rules, {Fuzzy Logic} Or {B} = 3 rules, {Tool Presentation} Or {C} = 3 rules, {Fuzzy Logic, Tool Percentage} Or {BC} = 6 rules, {Digital System, Fuzzy Logic} Or {AB} = 6 rules and {Digital System, Tool Percentage} Or {AC} = 8 rules. The Rough Set method can determine student laboratory exam results (pass or fail) accurately.

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