cover
Contact Name
Wayan Ordiyasa
Contact Email
wayanordi@gmail.com
Phone
+6281226465721
Journal Mail Official
ijicom@respati.ac.id
Editorial Address
Department of Informatics, University of Respati Yogyakarta
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Informatics and Computation
ISSN : 26858711     EISSN : 27145263     DOI : -
International Journal of Informatics and Computation (IJICOM) is an international, peer-reviewed, open-access journal, which publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of Informatics includes technologies of information and communication as well as the social, linguistic and cultural changes that initiate, accompany and complicate their development. IJICOM aims to be an international platform to exchange novel research results in simulation-based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices. Scope of this journal including IoT, 5G, Artificial Intelligence, sensor networks, and high-resolution imaging techniques. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods
Articles 2 Documents
Search results for , issue "Vol 2 No 2 (2020): International Journal of Informatics and Computation" : 2 Documents clear
COMPARISON OF HOP COUNT ON WIRELESS MESH NETWORK Eliza Staviana; Hizbul Wathan
International Journal of Informatics and Computation Vol 2 No 2 (2020): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v2i2.29

Abstract

Wireless Mesh Network (MWN) is a self-configured and self-organized network that can typically be implemented on 802.11 hardware. It consists of several nodes that make up the network backbone in a multi-story and sealed room, in contrast to building a hall or a place without bulkheads. This experiment uses an odd and even number scheme with a maximum number of routers of 8 pieces. In a sealed room, the performance of the method of installation of the number of strange Hops is better than the number of even Hops, with throughput calculation of 2665.19 KB, delay 0.25 s, data lost 0.60 %, and jitter 0.01 s and the best scheme that is with the number of Hops as much as five pieces, with the calculation of the number of throughput 7001.88 KB, delay 0.51s, data lost 0.47%, and jitter 0.002 s. In the free spaces, it can produce the better performance of the even hop count calculation scheme than the odd hop count by building throughput 16709.8 KB, delay 0.2 s, data lost 0.08 %, and jitter 0.03 s. and the best scheme that is with the number of throughput 68975,2 KB, wait for 0.0148 s, data lost 0 %, and jitter 0.0014 s. WMN performance in unshared space is more maximized than the version in a sealed area, with throughput values of 11786.82 kbps, delay of 2.08 ms, and data lost by 0.08 %, and jitter 0.03 s.it can produce the better performance of the even hop count calculation scheme than the odd hop count by producing throughput 16709.8 KB, delay 0.2 s, data lost 0.08 %, and jitter 0.03 s. and the best scheme that is with the number of throughput 68975,2 KB, wait for 0.0148 s, data lost 0 %, and jitter 0.0014 s. WMN performance in unshared space is more maximized than the version in sealed space, with throughput values of 11786.82 kbps, delay of 2.08 ms, and data lost by 0.08 %, and jitter 0.03 s. and data lost by 0.08%, and jitter 0.03s.
Implementation of CNN for Plant Leaf Classification Mohammad Diqi; Sri Hasta Mulyani
International Journal of Informatics and Computation Vol 2 No 2 (2020): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v2i2.28

Abstract

Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.

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