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Rahmad Hidayat
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Jl. Medan - Banda Aceh No.Km. 280 3, RW.Buketrata, Mesjid Punteut, Kec. Blang Mangat, Kota Lhokseumawe, Aceh 24301
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Jurnal Teknologi Rekayasa Informasi dan Komputer
Published by Politeknik Negeri Lhokseumawe
ISSN : 25812882     EISSN : 27971724     DOI : http://dx.doi.org/10.30811/jtrik.v8i1
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi Rekayasa Informasi dan Komputer (JTRIK) merupakan media publikasi hasil penelitian yang diterbitkan oleh Politeknik Negeri Lhokseumawe. JTRIK dipublikasikan setiap 2 bulan yaitu maret dan september baik secara print dan online. Scope jurnal ini meliputi bidang ilmu komputer, pemrosesan citra, jaringan komputer, keamanan komputer, multimedia, pengembangan perangkat lunak dan internet of things
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 134 Documents
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Design of Monitoring and Control Devices for Bird Repellents in Rice Fields Based on IoT Irfan, Muhammad; Nasir, Muhammad; Syahputra, Guntur
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8700

Abstract

Technological developments are advancing rapidly every year, such as the technology developed in various aspects of life, one of them is the Internet of Things, one of which can be applied in agriculture. The role of IoT in agriculture: In general, farmers have problems with rice quality due to pests in rice fields, which is a serious problem, such as bird pests. This results in a decline in rice quality. In this study, the author came up with an idea to solve this problem by designing an IoT-based bird repellent system. The author also hopes that this research can help the community or farmers in repelling birds from rice fields in a way that can be controlled or monitored using an IoT application. This study uses the QoS method, which functions as a measurement of network performance. The results of network measurement using the QoS method showed an average throughput of 75 kbps, packet loss of 0.1%, and delay of 61 ms. Based on the testing, the rice pest repellent showed performance capable of detecting bird pests at the distances of 80 cm, and was also able to detect grasshoppers at a distances of 20 cm. On the other hand, this device was also able to detect humans at a distances of 500 cm. During operation, when bird pests are detected, the gearbox motor will move automatically, and the DC siren will activate automatically, indicating the presence of bird pests.
Design And Development of A Flood-Prone Area Mapping System Using The K-Means Clustering Method Based On Web (Case Study: Lhoksukon District) Khadafi, Muhammad; Suana, Nurmaisuri; mulyadi, mulyadi
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8756

Abstract

Natural disasters are a form of natural events that result in a major impact on human populations. The natural disaster that frequently occurs in Lhoksukon District is flooding. Flooding is caused by continuous rainfall. Based on data from BPS (Central Statistics Agency) of North Aceh, the rainfall height in 2021 averaged 152.19 mm/month. High rainfall has caused many areas to be affected by floods and experience many losses, including disrupted road access, submerged houses, a paralyzed economy, and even loss of life. Based on data from BPBD (Regional Disaster Management Agency), in 2018 there were 19 villages affected by floods out of 75 existing villages, while in 2022 there were 54 villages affected by floods out of 75 villages in Lhoksukon District. In this research, a flood-prone area mapping system was created using the K-Means Clustering method, where the K-Means Clustering method is used to cluster villages affected by floods using 5 variables: duration of water inundation, water height, watershed (DAS - Daerah Aliran Sungai), elevation, and land cover. Based on the test results that have been conducted, there are 2 villages in the green cluster, 52 villages in the yellow cluster, and 21 villages in the red cluster. The results of this clustering are digitalized into a Geographic Information System using the Mapbox API. The digital map displayed shows the area in Lhoksukon District divided by village with green zone identification for non-vulnerable level, yellow zone for vulnerable level, and red zone for highly vulnerable level.
Monitoring Networks Based on Simple Network Management Protocol With Cacti Application and Telegram Notifications on Linux Ubuntu Zikra, Fauzan; Anwar, Anwar; Safar, Ilham
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8701

Abstract

This study discusses the implementation of SNMP-based network monitoring with tools such as Cacti, Telegram, and The Dude. A common problem in networks is damage to router interfaces. In this study, notifications of problematic interfaces and bandwidth usage are sent via Telegram. The main objectives of this network monitoring are to monitor network stability, bandwidth usage, remote device monitoring, and to detect problems with interfaces. Cacti is used to monitor and analyze network performance. Telegram plays a role in receiving notifications related to problematic interfaces and bandwidth usage. Meanwhile, The Dude is used to automatically monitor and manage network devices by detecting connected devices. This study uses four routers and one server in i ts implementation. This monitoring focuses on interfaces that manage network distribution and data traffic. The results of this study show that on August 14, 2023, from 14:00 to 17:20, bandwidth measurements from the Cacti and Graphing charts. For inbound traffic, the maximum value on ether1 (CORE) with the Cacti application was 10.27 Mbps and on Graphing it was 11.56 Mbps with a difference of 1.29 Mbps. In comparing the maximum values for inbound traffic, the difference between the data obtained from Cacti and Graphing ranged from only 3.2% to 12.9%. This demonstrates the accuracy of the monitoring tools used in this study.
Deep Learning–Based Phenotype Classification of Arabidopsis thaliana from Top-View Imagery Syauqina, Zata Hilya; Azima, Fauzan; Farhan, Muhammad
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8865

Abstract

The development of digital image processing and machine learning enables automated and objective plant phenotyping, reducing reliance on manual observations that are time-consuming and subjective. This study aims to classify Arabidopsis thaliana leaf conditions into three classes, namely Healthy, Senescent, and Anthocyanin-Rich, using a Convolutional Neural Network (CNN) based on top-view images from the public Quantitative Plant and Zenodo datasets. A total of 1,500 images were used, representing diverse variations in leaf color, pigmentation levels, and visual conditions. The images were processed through several preprocessing stages, including resizing, pixel normalization, data augmentation, and stratified dataset splitting to maintain class balance. A custom CNN model was developed and trained to automatically extract visual features from leaf images, and its performance was evaluated using accuracy, confusion matrix, precision, recall, and F1-score metrics. Experimental results indicate that the model achieved an overall accuracy of 82%, with the best performance observed in the Healthy and Senescent classes. However, the Anthocyanin-Rich class still exhibited classification errors due to visual similarities with other classes. These findings demonstrate the potential of CNN-based approaches to support automated plant phenotyping, although further improvements are required to enhance model generalization and classification accuracy for visually similar classes.

Page 14 of 14 | Total Record : 134

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