Garuda - Garba Rujukan Digital

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.

PENGUATAN DAN PENERAPAN LITERASI DIGITAL BAGI KELOMPOK TANI DALAM MENINGKATKAN PRODUKTIVITAS KOMODITAS UNGGULAN DAERAH Ondra Eka Putra; Muhammad Fikri Ramadhan
JMM (Jurnal Masyarakat Mandiri) Vol 10, No 3 (2026): Juni
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v10i3.38498

Abstrak: Varietas padi Cisokan Kubang merupakan komoditas unggulan daerah Nagari Kubang yang memiliki potensi ekonomi tinggi, namun pemanfaatan teknologi digital oleh petani masih terbatas, terutama dalam akses informasi pertanian, pemasaran, dan pengelolaan usaha tani secara modern. Harga komoditas pertanian yang cenderung fluktuatif, keterbatasan akses ke pasar yang lebih luas juga menjadi kendala, di mana hasil pertanian hanya dipasarkan di sekitar daerah tanpa ada pengembangan distribusi yang lebih besar. Tujuan dilakukannya pengabdian ini yaitu meningkatkan literasi digital, kemampuan branding dan pemasaran produk secara online melalui sosialisasi dan pelatihan kepada kelompok tani yang terdiri dari 10 anggota dalam meningkatkan produktivitas hasil komoditas unggulan daerah. Metode pelaksanaan meliputi pelatihan penggunaan smartphone secara efektif, pemanfaatan internet untuk memperoleh informasi pertanian, pembuatan konten promosi digital, serta pengenalan marketplace dan media sosial sebagai sarana pemasaran. Evaluasi dilakukan menggunakan angket dan wawancara pasca kegiatan. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan dan keterampilan petani menggunakan literasi digital untuk meningkatkan produktivitas sebesar 85%. Program ini menunjukan peningkatan kemampuan kelompok tani dalam menerapkan teknologi digital.Abstract: The Cisokan Kubang rice variety is a superior commodity in the Kubang Nagari region with high economic potential. However, farmers' use of digital technology is still limited, especially in accessing agricultural information, marketing, and modern farming management. Fluctuating agricultural commodity prices and limited access to wider markets are also obstacles, where agricultural products are only marketed within the surrounding area without any development of wider distribution. The purpose of this community service is to improve digital literacy, branding skills, and online product marketing through outreach and training for farmer groups consisting of 10 members in increasing the productivity of the region's superior commodities. Implementation methods include training in effective smartphone use, utilizing the internet to obtain agricultural information, creating digital promotional content, and introducing marketplaces and social media as marketing tools. Evaluation was conducted using questionnaires and post-activity interviews. The results of the activity showed an increase in farmers' knowledge and skills in using digital literacy to increase productivity by 85%. This program demonstrated an increase in the ability of farmer groups to apply digital technology.

Implementasi Computer Vision Dalam Deteksi Dan Klasifikasi Sampah Otomatis Pada Sistem Pengolahan Limbah Perkotaan Akbar Lusman; Retno Devita; Ondra Eka Putra; Eva Rianti; Fajrul Islami
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.956

Waste is a very serious environmental problem commonly faced by Indonesians. According to data from the National Waste Management Information System (SIPSN), Indonesia's waste volume reached 20.02 million tons in 2022. In Indonesia, the amount of waste generated reached 65 million tons per day in 2016 and increased to 66.5 million tons in 2018. The amount of waste in Indonesia continues to increase annually. In large cities, waste management is an increasingly pressing challenge, given the negative impacts caused by improper management, such as waste accumulation in landfills (TPA), water and air pollution, and public health issues. This study aims to design and implement an automatic waste classification system based on Computer Vision technologies as a solution for urban waste management. The system utilizes an Arduino Mega 2560, camera, ultrasonic sensor, servo motor, and conveyor to detect and classify five main types of waste: plastic, paper, glass, metal, and organic materials in real time. The camera captures images of waste, which are then analyzed using a Computer Vision model, while sensors and actuators control the flow and physical sorting process. This research seeks to improve waste processing efficiency by reducing human involvement in hazardous tasks and to promote the application of intelligent technologies in supporting sustainable recycling systems and reducing the burden on final disposal sites (landfills). The system created can detect and classify waste types well.

Rancang Bangun Permainan Simon Says Untuk Meningkatkan Daya Ingat & Respon Siswa Sekolah Dasar Ibnu Michael Jasman; Retno Devita; Ondra Eka Putra
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Perkembangan kecerdasan pada siswa sekolah dasar terutama dalam aspek daya ingat dan kecepatan respons merupakan fondasi penting dalam proses pembelajaran. Namun, metode konvensional yang bersifat monoton dan kurang melibatkan aspek sensorimotor seringkali menurunkan motivasi dan minat belajar siswa. Untuk itu, penelitian ini merancang dan membangun sebuah permainan interaktif “Simon Says” berbasis mikrokontroler yang dirancang khusus untuk meningkatkan daya ingat dan kecepatan respons siswa SD. Mode pertama berfokus pada peningkatan respon dan fokus melalui rangkaian perintah yang harus dilakukan. Mode kedua menitik beratkan pada peningkatan daya ingat. Terakhir mode 3 menggabungkan focus dan daya ingat. Diharapkan inovasi ini tidak hanya meningkatkan aspek kognitif siswa meliputi working memory dan reaksi sensorimotor tetapi juga memacu minat belajar anak yang selama ini rendah akibat metode tradisional yang membosankan

PERBANDINGAN KERNEL PENAJAMAN, GAUSSIAN BLUR DAN DETEKSI TEPI PADA CITRA OTAK Retno Devita; Ondra Eka Putra; Eva Rianti
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.

ANALISIS VISUALISASI DATA PASIEN GIGI DAN MULUT DENGAN ALGORITMA K-MEANS BERBASIS WEB Putri Salma; Eva Rianti; Liga Mayola; Retno Devita; Ondra Eka Putra
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6579

RSGM Baiturrahmah serves as a medical institution that generates a high volume of daily patient records. However, this wealth of data has not been optimally utilized by management as a primary consideration for strategic decision-making. The identified core problem is the absence of comprehensive patient characteristic mapping, which often leads to an uneven distribution of medical resources. To address this critical issue, this study applies advanced data mining techniques using the K-Means Clustering algorithm to group 1,708 dental and oral disease patient records. The clustering process was conducted by determining three main clusters based on three crucial attributes patient age, the total number of diagnoses received, and the duration of medical service provided. The results of this study successfully classify all patients into three specific service categories, namely Basic Service, Intermediate Service, and Intensive Service. This research also produced a comprehensive web-based decision support system developed using the Python programming language and MySQL database. The system is specifically designed to assist the management of RSGM Baiturrahmah in accurately visualizing the characteristics of each patient group. With the successful implementation of this system, hospital management can be more effective in formulating highly personalized and targeted service strategies for every patient group.

Implementasi Computer Vision Dalam Deteksi Dan Klasifikasi Sampah Otomatis Pada Sistem Pengolahan Limbah Perkotaan Akbar Lusman; Retno Devita; Ondra Eka Putra; Eva Rianti; Fajrul Islami
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.956

Waste is a very serious environmental problem commonly faced by Indonesians. According to data from the National Waste Management Information System (SIPSN), Indonesia's waste volume reached 20.02 million tons in 2022. In Indonesia, the amount of waste generated reached 65 million tons per day in 2016 and increased to 66.5 million tons in 2018. The amount of waste in Indonesia continues to increase annually. In large cities, waste management is an increasingly pressing challenge, given the negative impacts caused by improper management, such as waste accumulation in landfills (TPA), water and air pollution, and public health issues. This study aims to design and implement an automatic waste classification system based on Computer Vision technologies as a solution for urban waste management. The system utilizes an Arduino Mega 2560, camera, ultrasonic sensor, servo motor, and conveyor to detect and classify five main types of waste: plastic, paper, glass, metal, and organic materials in real time. The camera captures images of waste, which are then analyzed using a Computer Vision model, while sensors and actuators control the flow and physical sorting process. This research seeks to improve waste processing efficiency by reducing human involvement in hazardous tasks and to promote the application of intelligent technologies in supporting sustainable recycling systems and reducing the burden on final disposal sites (landfills). The system created can detect and classify waste types well.

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