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Jurnal Teknologi dan Sistem Komputer
Published by Universitas Diponegoro
ISSN : 26204002     EISSN : 23380403     DOI : -
Jurnal Teknologi dan Sistem Komputer (JTSiskom, e-ISSN: 2338-0403) adalah terbitan berkala online nasional yang diterbitkan oleh Departemen Teknik Sistem Komputer, Universitas Diponegoro, Indonesia. JTSiskom menyediakan media untuk mendiseminasikan hasil-hasil penelitian, pengembangan dan penerapannya di bidang teknologi dan sistem komputer, meliputi sistem embedded, robotika, rekayasa perangkat lunak dan jaringan komputer. Lihat fokus dan ruang lingkup JTSiskom. JTSiskom terbit 4 (empat) nomor dalam satu tahun, yaitu bulan Januari, April, Juli dan Oktober (lihat Tanggal Penting). Artikel yang dikirimkan ke jurnal ini akan ditelaah setidaknya oleh 2 (dua) orang reviewer. Pengecekan plagiasi artikel dilakukan dengan Google Scholar dan Turnitin. Artikel yang telah dinyatakan diterima akan diterbitkan dalam nomor In-Press sebelum nomor regular terbit. JTSiskom telah terindeks DOAJ, BASE, Google Scholar dan OneSearch.id Perpusnas. Lihat daftar pengindeks. Artikel yang dikirimkan harus sesuai dengan Petunjuk Penulisan JTSiskom. JTSiskom menganjurkan Penulis menggunakan aplikasi manajemen referensi, seperti Mendeley, Endnote atau lainnya. Penulis harus register ke jurnal atau jika telah teregister, dapat langsung log in dan melakukan lima langkah submisi artikel. Penulis harus mengupload Pernyataan Pengalihan Hak Cipta saat submisi. Artikel yang terbit di JTSiskom akan diberikan nomer identifier unik (DOI/Digital Object Identifier) dan tersedia serta bebas diunduh dari portal JTSiskom ini. Penulis tidak dipungut biaya baik untuk pengiriman artikel maupun pemrosesan artikel (lihat APC/Article Processing Charge). Jurnal ini mengimplementasikan sistem LOCKSS untuk pengarsipan secara terdistribusi di jaringan LOCKSS privat.
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Articles 12 Documents
Search results for , issue "Volume 8, Issue 3, Year 2020 (July 2020)" : 12 Documents clear
Penghitung orang pada video CCTV menggunakan metode histogram of oriented gradient dan filter Kalman Faisal Dharma Adhinata; Muhammad Ikhsan; Wahyono Wahyono
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13660

Abstract

CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.
Optimalisasi model prediksi kesesuaian lahan kelapa sawit menggunakan algoritme pohon keputusan spasial Andi Nurkholis; Imas Sukaesih Sitanggang
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (14.805 KB) | DOI: 10.14710/jtsiskom.2020.13657

Abstract

Land suitability evaluation has a vital role in land use planning aimed to increase food production effectiveness. Palm oil is a leading and strategic commodity for Indonesian people, which is predicted consumption will exceed production in the future. This study aims to evaluate palm oil land suitability using a spatial decision tree algorithm that is conventional decision tree modification for spatial data classification with adding spatial join relation. The spatial dataset consists of eight explanatory layers (soil nature and characteristics), and a target layer (palm oil land suitability) in Bogor District, Indonesia. This study produced three models, where the best model was obtained based on optimizing accuracy (98.18 %) and modeling time (1.291 seconds). The best model has 23 rules, soil texture as the root node, two variables (drainage and cation exchange capacity) are uninvolved, with land suitability visualization obtains percentage S2 (29.94 %), S3 (53.16 %), N (16.57 %), and water body (0.33 %).

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