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All Journal KOMPUTIKA - Jurnal Sistem Komputer Journal Social Science And Technology For Community Service
Rahman Taufiq
Universitas Lampung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media

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Pelatihan Pembuatan Website Untuk Peningkatan Publikasi Desa Wisata Di Desa Gebang, Kec. Padang Cermin, Kab. Pesawaran Ridho Sholehurrohman; Igit Sabda Ilman; Yunda Heningtyas; Muhaqiqin Muhaqiqin; Rahman Taufiq; Irwan Adi Pribadi
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 4, No 1 (2023): Volume 4, Nomor 1, March 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v4i1.2533

Abstract

Tourism is one of the rapidly growing industries in Indonesia and has proven to contribute a large income. Tourism will continue to grow in line with the development of industrialization and changes in people's lifestyles, who increasingly have the ability to travel and have more time to do so, especially in Indonesia. Gebang village is one of the villages located in Padang Cermin district, Pesawaran regency. Gebang village is a village with a large tourism object and mangrove conservation. This condition is supported by the availability of observation tracks along the coast and even entering the mangrove forest area, making this area one of the important natural/educational tourism destinations in Lampung province. By combining the use of technology and information systems, it is expected that Gebang village can display public information that serves as a promotional medium to introduce the profile and objects of Gebang village tourism. The village website can also be used as a portal for an integrated database system. Based on the assessment of the Pesawaran Regency government, Gebang village has sufficient resource readiness to utilize technology and information systems such as a website. This PKM (Community Service Program) activity aims to transfer knowledge in implementing and managing the Gebang Village Tourism website.
Analisis Metode Kalman Filter, Particle Filter dan Correlation Filter Untuk Pelacakan Objek Ridho Sholehurrohman; Mochammad Reza Habibi; Igit Sabda Ilman; Rahman Taufiq; Muhaqiqin Muhaqiqin
Komputika : Jurnal Sistem Komputer Vol 12 No 2 (2023): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v12i2.9567

Abstract

Object tracking is a challenging in computer vision. Object tracking is divided into two, which can be one object or several objects, depending on the object being observed. The process of tracking an object in the form of one object is to estimate the target in the next sequence based on information from the first frame given. In object tracking in the form of single object tracking, there are five steps that are often used in discriminatory methods, including motion models, feature extraction, observation models, model updates and integration methods. Although various algorithms of object tracking are proposed, there are still failures in the object tracking process caused by occlusion, non-rigid target deformation, and other factors. This study proposes the implementation of the Kalman filter, particle filter, and correlation filter methods for object tracking in video data. The results of the implementation of the three methods can track objects in traffic video data and the script circuit video. In object tracking calculations and method analysis, the kalman filter gets 96.89% where the kalman method is better in terms of accuracy compared to other methods. Meanwhile, in the average performance of computation time, the correlation method gets 26.69 FPS, where the correlation method is superior compared to other competitor methods. Keywords – Kalman Filter; Particle Filter; Correlation Filter; Object Tracking; Object Tracking in Video
Co-Authors Heningtyas, Yunda Igit Sabda Ilman Irwan Adi Pribadi Mochammad Reza Habibi Muhaqiqin Muhaqiqin Ridho Sholehurrohman