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All Journal Jurnal Ilmu Sosial dan Ilmu Politik (JISIP) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Humanities and Social Studies Jatilima : Jurnal Multimedia Dan Teknologi Informasi REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Bulletin of Information Technology (BIT) INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS Jurnal Nasional Teknologi Komputer Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Jurnal Ekonomi dan Bisnis
Nasution, Darmeli
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : METHOMIKA: Jurnal Manajemen Informatika

 1 
PENERAPAN DEEP LEARNING YOLO UNTUK PENGUKURAN JARAK OBJEK MENGGUNAKAN MONO KAMERA Herdianto, Herdianto; Nasution, Darmeli; Atmaja, Niko Surya; Ramadhan, Syahrul
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp51-56

Abstract

Mobile robots are a type of robot that can move from one place to another. Therefore, this type of robot has been given the ability to detect objects and measure estimated distances to objects around it to then carry out actions to turn left and right, forward, backward or even stop to avoid collisions. In general, to measure the distance of objects on mobile robots, ultrasonic sensors such as the HC-SR04 are used and some also use cameras, although they can be used to measure distances, but the use of these sensors has disadvantages, such as the maximum distance that can be measured is 4 meters. Given this deficiency, the research that will be carried out will try a YOLO deep learning method that can detect objects and then measure the distance of objects around them. From the results of the tests that have been carried out, it is known that the distance to the object that can be measured is 31,400 mm with the actual object height being 1750 mm.
Implementasi Metode Yolo pada Deteksi Objek Manusia Herdianto, Herdianto; Hafni, Hafni; Nasution, Darmeli; Ramadhan, Syahrul
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp234-240

Abstract

Until now, the problem of theft of motorbikes and livestock in North Sumatra is still quite high.  Locations for motorbike theft can occur in many places such as schools, homes, parking lots, offices and so on, while for livestock it can occur on pastures and in pens during the day or night and the perpetrators are men. To make this theft a success, various modes are used in varying human positions, from sitting, squatting to standing. To help overcome this, several object detection methods have been developed such as Background Subtraction, Template Matching, Histogram Oriented Gradient (HOG), Deformable Part-based Model (DPM) and Viola Jones (VJ).   Of the many methods that have been used, there are still shortcomings, namely in terms of time, accuracy and various human positions.  For this reason, research was carried out with the aim of improving the time and level of accuracy in detecting human objects using the YOLO method. The research stages carried out in this research include literature study, collecting data, determining training and test data, creating programs, training, and testing. From the trials carried out, it is known that YOLO can detect humans in various positions with a mAP value of 0.99 and an average detection time of 810.01 ms.
Co-Authors Ahmad Akbar, Ahmad Akbar, Muhammad Caesar Amren S, Hairul Amril, M. Andi Ernawati Andysah Putera Utama Siahaan Atmaja, Niko Surya Barutu, Sipra Devina, Annisa Farta wijaya, Rian Hadi Prayitno Hafni Hafni Haralayya, Bhadrappa Herdianto Herdianto, Herdianto Heri Kurniawan, Heri Hermawan, Bagus Indrayani, Maida IQBAL , MUHAMMAD Iqbal Wiranata Siregar, Jimmy Iswandi Idris Jabar, Ami Abdul Khairul Khairul Khairul, Khairul Kurniawan, Fahmi Leni Marlina Liber Tommy Hutabarat Lubis, Darma Putra Marsya, Alviona Mentari, Risca Sri Muhammad Iqbal Muhammad Muttaqin Nasywa, Khairun Pane, Danang Putra Panjaitan, Albert Parhusip, Nelviony Permata, Dinda Suci Aliya Pranoto, Sugeng Pulungan, Ahmad Fakhrizal Ramatika, Desy Rian Farta Wijaya Rian Putra, Randi Rizaldy Khair Rizky Rinaldi Romani, Daniel D. Sahputra, Fajar Sari, Ayu Ofta SARIFUDIN Sebayang, Ayu Nuriana Septiadi, Fahri Siburian, Ramli S Simanjuntak, Yosei Ht Simorangkir, Elsya Sabrina Asmita Sirait, Donna Nurhaida Masdiana Sitorus, Mhd Arfan Sitorus, Zulham Situkkir, Meiarni Sri Wahyuni Sutiono, Sulis Syahputra, Afandi Syahrul R, Syahrul Tuti Andriani Usman Usman Wadly, Fachrid Wahyudi Sihombing, Ridho Wardani, Ivana Wijaya, Rian Farta Yahya, Susilawati Yusman, Yanti Zalukhu, Anzas Ibezato