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All Journal Justek : Jurnal Sains Dan Teknologi Insearch: Information System Research Journal Journal of Informatics and Information Security
Indra Sari Kusuma Wardhana
STMIK Jakarta STI&K
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Social Sciences Other

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Analisis Pengiriman Data Sensor dengan Jaringan Wireless Meggunakan Metode Quality of Service (QoS) Indra Sari Kusuma Wardhana; Bheta Agus Wardjiono
Justek : Jurnal Sains dan Teknologi Vol 5, No 2 (2022): November
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v%vi%i.11869

Abstract

Abstract:  Hydroponic plants are currently being cultivated, but on a large scale, the Internet of Things approach to hydroponic plant management systems can support and get optimal results. The existing management uses the GSM network to send sensor data to the web database and on some locations that are not covered by the GSM network are often found, thus experiencing problems in sending sensor data to the web database. The purpose of this study is to analyze the performance of a wireless network as an alternative to using GSM networks for sending sensor data, using the Quality of Service method in a hydroponic plant management system using Internet of Things technology. The research method used is to perform a simulation of testing data transmission on a wireless network with the help of the Wireshark application, while the calculations are assisted by Microsoft Excel. The results of the observations show that the performance of the wireless network is good and very good so that it can be used as a substitute for a GSM connection for sending sensor data to a web database.Abstrak: Tanaman hidroponik saat ini sedang marak dibudidayakan, namun untuk skala besar, pendekatan Internet of Things pada system pengelolaan tanaman hidroponik dapat mendukung dan mendapatkan hasil yang optimal. Pengelolaan yang ada menggunakan jaringan GSM untuk mengirimkan data hasil sensor ke webdatabase dan sering ditemukan lokasi yang tidak tercakup jaringan GSM, sehingga mengalami kendala dalam pengiriman data hasil sensor ke web database. Tujuan dari penelitian ini adalah menganalisis performa wireless network sebagai alternative penggunaan jaringan GSM untuk pengiriman data hasil sensor, dengan metode Quality of Service pada sistem pengelolaan tanaman hidroponik dengan menggunakan teknologi Internet of Things. Metode penelitian yang digunakan dengan melakukan simulasi pengujian pengiriman data pada wireless network dengan bantuan aplikasi Wireshark sedangkan perhitungannya dibantu dengan Microsoft Excel. Hasil dari pengamatan menunjukkan bahwa performa wireless network baik dan baik sekali sehingga dapat digunakan sebagai pengganti koneksi GSM untuk pengiriman data hasil sensor ke web database.
Brain Tumor Classification Using Four Versions of EfficientNet Widi Hastomo; Adhitio Satyo Bayangkari Karno; Dody Arif; Indra Sari Kusuma Wardhana; Nada Kamilia; Rudy Yulianto; Aji Digdoyo; Tri Surawan
Insearch: Information System Research Journal Vol 3, No 01 (2023): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v3i01.5810

Abstract

Medical image processing approaches for detecting brain cancers are still primarily done manually, with low accuracy and taking a long period. Furthermore, this task can only be done by professionals with a high degree of medical competence, and the number of experts is obviously restricted in comparison to the large number of patients who need to be treated. With the growth of artificial intelligence and the rapid development of computers in terms of processing speed and storage capacity, it is feasible to assist doctors in classifying the existence of tumors in the head. This study employs four variations of the EfficientNet architecture to train a model on a variety of MRI imaging data. The model version B1 was shown to be the best in this investigation, with 98% accuracy, 99% precision, 95% recall, and 97% f1 score from versions B0 to B3 (4 versions). These results are excellent, but they do not rule out additional study utilizing various forms of design.
Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet Adhitio Satyo Bayangkari Karno; Dodi Arif; Indra Sari Kusuma Wardhana; Eka Sally Moreta
Journal of Informatic and Information Security Vol. 2 No. 1 (2021): Juni 2021
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/abbs9m42

Abstract

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%).
Co-Authors Adhitio Satyo Bayangkari Karno Aji Digdoyo Bheta Agus Wardijono Dodi Arif Dody Arif Eka Sally Moreta Nada Kamilia Rudy Yulianto Tri Surawan Widi Hastomo