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All Journal Jurnal Edukasi Elektro TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JURNAL ELEKTRO ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology CogITo Smart Journal TESLA: Jurnal Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science Jurnal Bakti Masyarakat Indonesia Jurnal Industrial Galuh Sewagati Jurnal Pengabdian Masyarakat Charitas Jurnal Informatika: Jurnal Pengembangan IT
Nova Eka Budiyanta
Program Studi Teknik Elektro, Universitas Katolik Indonesia Atma Jaya
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Sistem Deteksi Kemurnian Beras berbasis Computer Vision dengan Pendekatan Algoritma YOLO Nova Eka Budiyanta; Melisa Mulyadi; Harlianto Tanudjaja
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 1 (2021): JPIT, Januari 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i1.2309

Abstract

Penelitian ini bertujuan untuk menerapkan sistem deteksi kemurnian beras terhadap campuran kotoran untuk dapat digunakan sebagai parameter nilai untuk mensortir kotoran yang terdeteksi pada proses kontrol kualitas beras. Sistem yang dikembangkan pada penelitian ini berbasis computer vision menggunakan kamera sebagai sensor. Data citra yang didapat dari kamera selanjutnya diproses untuk mengenali objek beras yang murni dan objek kotoran yang tercampur pada kumpulan beras. Penelitian ini berfokus pada algoritma deteksi objek batu atau kerikil (gravel) pada proses produksi beras. Proses deteksi objek pada penelitian ini menggunakan metode You Only Look Once (YOLO) v3. Secara keseluruhan sistem deteksi objek pada penelitian ini berjalan baik. Proses pelatihan model berhasil meminimalisir loss secara signifikan dengan nilai loss sebesar 1.89 di iterasi ke 1000 menjadi 0.16 di iterasi ke 15000. Seiring dengan keberhasilan proses pelatihan model, pengujian model pada penerapan proses deteksi juga berjalan baik yang ditunjukkan dengan nilai rerata akurasi sebesar 86.11%.
Rancang Bangun Mesin Presensi berbasis Metode Pengenalan Wajah HoG berbantuan Proses Klasifikasi Linear SVM Samuel Matthew; Ferry Rippun Gideon Manalu; Nova Eka Budiyanta
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 1 (2022): JPIT, Januari 2022
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i1.2843

Abstract

Di dalam daerah perkantoran, salah satu benda yang sering disentuh adalah alat presensi/pencatat kehadiran. Studi ini bertujuan untuk menerapkan sistem presensi berbasis metode pengenalan wajah menggunakan Raspberry Pi dan kamera dilengkapi dengan sensor pendeteksi suhu tubuh berbantuan aplikasi web. Pendekatan pengenalan wajah yang dilakukan dalam studi ini adalah Histogram of Oriented Gradients (HoG) didukung dengan linear Support Vector Machine (SVM). Hasil yang didapatkan dalam studi ini berupa sebuah mesin presensi yang mampu mengenali wajah pengguna yang sudah terdaftar dengan tingkat akurasi sebsar 98% pada 43,02 frame per detik (idle) dan 2,90 frame per detik (aktif) dengan menggunakan dataset berisikan 20 data tiap wajah (20 data x 20 wajah = 400 data). Sensor yang digunakan dapat mengukur suhu dengan akurasi ±0,5°C. Daftar kehadiran dapat diakses oleh pihak yang berkepentingan melalui situs web yang menampilkan data dari basis data. Untuk menanggulangi kesalahan sistem pada saat uji coba, presensi secara manual dapat dilakukan melalui aplikasi berbasis web.
Pengenalan Sistem Aquaponik Cerdas untuk Memfasilitasi Kemampuan Mandiri pada Siswa Berkebutuhan Khusus di SLB Anggraeni, Wiwik; Risqiwati, Diah; Husniah, Lailatul; Sugiyanto; Aulia Sidharta, Hanugra; Budiyanta, Nova Eka; Djunaidy, Arif; Tyasnurita, Raras; Ali, Achmad Holil Noor; Divka, Princessa Sissy; Rahmanisa, Fathia
Sewagati Vol 8 No 2 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i2.967

Abstract

SLB ABCD Bakti Sosial merupakan sebuah sekolah luar biasa yang melayani siswa dengan kebutuhan khusus, terletak di Kecamatan Simo, Kabupaten Boyolali, Jawa Tengah.SLB tersebut memiliki 28 siswa SD, 18 siswa SMP, dan 17 siswa SMA, yang dibimbing oleh 11 guru. Berdasarkan diskusi dengan para guru, diketahui bahwa tempat tinggal siswa memiliki radius hingga 25 kilometer, karena jumlah SLB di Jawa Tengah yang belum sebanding dengan jumlah siswa. Metode pengajaran di SLB ini berbeda dengan sekolah umum, dengan kurikulum tahun 2013 yang mengalokasikan 40% pembelajaran pada materi teori dan 60\% pada keterampilan praktis. Untuk meningkatkan keterampilan siswa, Tim Pengabdian Masyarakat ITS mengusulkan penerapan sistem smart-aquaponic. Sistem ini tidak hanya menjadi alat latihan, tetapi juga sesuai dengan latar belakang pertanian masyarakat Boyolali, yang merupakan mata pencaharian utama di daerah tersebut. Materi video dan modul pendukung diperkenalkan untuk membantu para guru dalam membimbing siswa berkebutuhan khusus. Pengajaran ini bertujuan untuk meningkatkan keterampilan siswa dengan memperkenalkan integrasi budidaya ikan dan tanaman melalui otomatisasi sederhana. Otomatisasi ini meliputi pemberian pakan ikan secara terjadwal dan aliran air yang dapat disesuaikan berdasarkan kebutuhan tanaman. Dengan memperkenalkan smart-aquaponic di SLB ABCD Bakti Sosial, harapannya adalah guru dan siswa dapat mengembangkan keterampilan aquaponik dan menyesuaikannya dengan kebutuhan daerah sekitar.
Efektivitas ROS Development Studio sebagai Simulator Pendukung Pengembangan Algoritma Platform Robotika Effendy, Kevin Julianto; Budiyanta, Nova Eka
Jurnal Elektro Vol 13 No 2 (2020): Jurnal Elektro: Oktober 2020
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v13i2.1972

Abstract

This study aims to analyze robotics simulation tools that can be used to support algorithm development in the robot modelling process. There are several simulation tools for robot algorithm modelling that are popularly used by robotics developers. This study focuses on analyzing the effectiveness based on the features of the Robot Operating System Development Studio (ROSDS) and MATLAB as a development simulation platform using the comparative method. The results of this study claim that ROSDS is more effective than MATLAB in terms supporting the development of robotics simulation because ROS is used as a middleware in ROSDS that connects the communication between the program with the robot, so that makes it easier for making robotics simulation and ROSDS is easier to access than MATLAB. The trial case study of developing trajectory algorithm in the robot arm was carried out in this research to support the statement above, and it worked out well.
Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data Mulyanto, Edy; Yuniarno, Eko Mulyanto; Hafidz, Isa; Budiyanta, Nova Eka; Priyadi, Ardyono; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.832

Abstract

Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation.
Predicting Stock Market Trends Based on Moving Average Using LSTM Algorithm Permana, Rizki Surya; Mahyastuty, Veronica Windha; Budiyanta, Nova Eka; Bachri, Karel Octavianus; Kartawidjaja, Maria Angela
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.648.486-495

Abstract

Prediction of the stock market is highly needed to assist traders in making decisions. Many methods are used by traders to predict this such as technical analysis and moving averages. Moving averages predict stock trends based on the past data of the stock. The disadvantage of using a moving average analysis is the delay in crossover signals. As a solution, a deep learning technique known as LSTM is applied to the moving average strategy in this paper. In this research, the BBCA stock dataset spanning from 2010 to 2018 was utilized. The data was segmented into two parts: 2010-2017 for training data and 2018 for testing data. The training process employed Long Short-Term Memory (LSTM) networks, with the subsequent results being combined with moving average crossover techniques. Validation results indicate that BBCA shows a relatively minimal error. BBCA's average MAPE is 1.1%, and its RMSE is 65.402, classifying it within the "Highly Accurate Forecasting" category. Various combinations of moving average crossovers were tested during model training, with the combination of SMA05 and SMA50 for BBCA yielding the highest profit potential. Stocks that exhibit a downward trend are more likely to incur substantial losses. The model can predict the reversal of trends by predicting the trading signal given by the moving averages.
Pupilometri Dinamis untuk Mengukur Respons Pupil sebagai Pendeteksi Dini Demensia pada Lansia WAHYUDI, CELINE GABRIELLA; LUKAS, LUKAS; BUDIYANTA, NOVA EKA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 3: Published July 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i3.553

Abstract

ABSTRAKPupilometri merupakan metode pengukuran respons pupil terhadap stimulus. Kemampuan pupil mata dalam merespons cahaya diamati melalui pupillary light response (PLR). Penelitian mendapati PLR pasien demensia berbeda dengan pasien normal. Penelitian ini bertujuan merancang algoritma computer vision yang dapat mendeteksi pupil secara akurat, menampilkan respons pupil terhadap cahaya dalam bentuk grafik dan PLR pada sebuah aplikasi desktop, yang mengendalikan goggles berisi rangkaian kamera, pencahayaan, dan sensor jarak VL53L0X. Rekaman diproses dengan Local Binary Pattern (LBP) dan deteksi kontur untuk mendeteksi pupil. Data pengukuran diproses dan disimpan pada basis data lokal dan aplikasi web, sehingga tenaga medis dapat menentukan ada atau tidaknya gejala demensia pada pasien lansia. Tingkat ketelitian algoritma pengukuran pupil sebesar 73,33% yang didapatkan dari 30 kali pengujian.Kata kunci: computer vision, demensia, deteksi dini, pupillary light response, pupilometri ABSTRACTPupillometry is a method of measuring the pupil’s response towards stimulus. Pupil response to light is observed through pupillary light response (PLR). Research found that PLR values of patients suffering from dementia differ from that of normal patients. This study implements a computer vision algorithm that accurately detects the pupil, calculates, and shows its response towards light in graphs and PLR values on a desktop application which controls goggles that contain a camera, lighting setup, and the VL53L0X distance sensor. Video is processed using Local Binary Pattern (LBP) and contour detection to detect the pupil. Results are processed and saved in the local and web database, so experts can determine the presence of dementia symptoms in the elderly patient. The accuracy of the pupil detection algorithm is 73,33%, as obtained from 30 tests.Keywords: computer vision, dementia, early detection, pupillary light response, pupillometry
Implementasi CMS WordPress dalam Pengembangan website Sekolah SLB ABCD Bakti Sosial Anggraeni, Wiwik; Purnama, I Putu Adhitya Pratama Mangku; Risqiwati, Diah; Sugiyanto, Sugiyanto; Sidharta, Hanugra Aulia; Budiyanta, Nova Eka; Djunaidy, Arif; Vinarti, Retno Aulia; Rikasakomara, Edwin; Mahananto, Faizal; Kusumawardhani, Renny Pradina; Meilani, Maulidiya
Sewagati Vol 9 No 1 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i1.2321

Abstract

Pengabdian masyarakat ini bertujuan untuk mengembangkan Sistem Informasi Sekolah Luar Biasa (SLB) ABCD Bakti Sosial berbasis website dengan menggunakan Content Management System (CMS) WordPress. Metode yang digunakan adalah Agile Software Engineering, yang terdiri dari lima tahapan, yaitu perencanaan, perancangan, pengembangan, pengujian, serta publikasi dan evaluasi. Melalui Focus Group Discussion (FGD), dilakukan pengumpulan kebutuhan dari pihak sekolah, diikuti dengan perancangan desain prototipe website. Sistem informasi dikembangkan menggunakan CMS WordPress dan plugin Elementor, yang memungkinkan sekolah untuk mengelola konten pada website tanpa keahlian pemograman secara khusus. Pengujian dilakukan bersama pihak sekolah untuk memastikan kesesuaian fungsi dengan kebutuhan yang ada. Hasilnya adalah website yang dapat diakses publik melalui https://slbbaktisosial.com dan sebuah modul teknis untuk mengelola website. Luaran yang dihasilkan ini dapat mendukung akselerasi program digitalisasi sekolah, meningkatkan visibilitas sekolah, dan efisiensi manajemen data.
Enhancing Practical AI Competency with YOLO 2D Detector Object Localization Technology Budiyanta, Nova Eka; Sidharta, Hanugra Aulia; Kristiana, Stefani Prima Dias; Risqiwati, Diah
Jurnal Pengabdian Masyarakat Charitas Vol. 5 No. 01 (2025): Jurnal Pengabdian Masyarakat Charitas
Publisher : Program Studi Teknik Industri, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/charitas.v5i01.6754

Abstract

Object locatization is one of important aspect of computer vision, which refers to a system's ability to detect and determine the position of objects within an image. However, general audience practical understanding of object localization is remains limited. To address this issue as a community service team, and organized a workshop focused on YOLO (You Only Look Once)-based object localization. This workshop was conducted free of charge online via the Google Colab platform. The event was successfully carried out and received positive feedback from the participants. This workshop are providing a real studycase through brain tumor detection from image-based approaches, aiming to provide an in-depth experience in object localization while also offering the latest updates on artificial intellegent technology trends based on digital image processing. Based on evaluation results indicated that the majority of participants, who previously had no experience in object detection, were able to understand the fundamental concepts of object localization and apply them directly using the cloud platform. This workshop demonstrates that cloud-based learning approaches utilizing Google Colab and Roboflow are highly effective in bridging the gap between theory and practice in object localization.
Design and Implementation of a Vision-based Wheeled Mobile Robot Using HSV Color Segmentation and P-D Control Aditama, Wira; Herianto, David; Fernando, Nico; Henry, Carolus; Budiyanta, Nova Eka
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6892

Abstract

This study presents the design and implementation of a wheeled mobile robot capable of detecting and tracking a ping-pong ball using vision-based processing. The system integrates a Raspberry Pi 3 Model B+ as the main controller, a Raspberry Pi Camera Rev 1.3 for visual input, and DC motors driven by an L298N motor driver for actuation. Object detection is achieved through color segmentation in the HSV color space using the OpenCV library, followed by morphological filtering and contour analysis. A proportional-derivative (PD) control algorithm is employed to adjust motor speeds dynamically based on the ball's horizontal position in the frame. The experimental results demonstrate that the robot can successfully detect and follow a ping-pong ball, although it exhibits limitations in processing speed and motion stability. The average frame rate during operation was 5 FPS, which is sufficient for basic tracking tasks but suboptimal for high-speed applications. This project highlights the feasibility of vision based robotic systems for simple object tracking tasks.
Co-Authors Achmad Holil Noor Ali Aditama, Wira Ardyono Priyadi Arif Djunaidy Aulia Sidharta, Hanugra Bachri, Karel Octavianus Basuki, Widodo Widjaja Catherine Olivia Sereati Darmawan, Ilham Akbar Darwin Darwin Diah Risqiwati Divka, Princessa Sissy Doli Ramli W Effendy, Kevin Julianto Eko Mulyanto Yuniarno Evans Karlin Faizal Mahananto Fernando, Nico Ferry Rippun Gideon Manalu Harlianto Tanudjaja Henry, Carolus Herianto, David Isa Hafidz Kartadinata, Budi Kevin Julianto Effendy Kristiana, Stefani Prima Dias Kusumawardhani, Renny Pradina Lailatul Husniah Lukas Lukas Lukas Lukas Lukas, Lukas Maria Angela Kartawidjaja Mauridhi Hery Purnomo Mega Cynthia Wishnu Meilani, Maulidiya Melisa Mulyadi Mulyanto, Edy Nugroho, Y. Prasetyo Adi Purnama, I Putu Adhitya Pratama Mangku Purwaningsih, Agustina Ekwin Rahmanisa, Fathia Raras Tyasnurita Retno Aulia Vinarti Rikasakomara, Edwin Rizki Surya Permana Samuel Matthew Sidharta, Hanugra Aulia SUGIYANTO Sugiyanto - Veronica Windha Mahyastuty Vivi Triyanti WAHYUDI, CELINE GABRIELLA Wijayanti, Linda Wiwik Anggraeni