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All Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Ranti Holiyanti
Universitas Singaperbangsa Karawang
Computer Science & IT Mathematics

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Implementasi Metode Dempster Shafer Berbasis Web untuk Mendiagnosa Kerusakan Jaringan LAN Sukmawati Sukmawati; Rifky Maulana; Ranti Holiyanti; Betha Nurina Sari
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 7, No 1 (2022)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v7i1.13548

Abstract

Local Area Network (LAN) is a type of computer network that covers a local area and is widely used in various aspects.In places that provide LAN connections there are those using Wi-Fi technology which are usually called hotspots. Damage to the LAN, especially Wi-fi itself, is often experienced by the community.It is also inconvenient for internet network users due to lack of knowledge about the damage. Therefore, it is necessary to create a system that can be used to collect knowledge data from network experts and store it. The result of this research is an expert system that diagnoses LAN network damage. The system automatically provides diagnostic results by displaying the type of glitch and its solution based on the symptoms of the glitches experienced. The conclusion of this study is that an expert system by using the Dempster Shafer method to handle data uncertainty when diagnosing LAN service disruptions is very helpful in overcoming the problem of declining service quality.
Pendeteksi Sampah Metal untuk Daur Ulang Menggunakan Metode Convolutional Neural Network Ranti Holiyanti; Sukma Wati; Ikbal Fahmi; Chaerur Rozikin
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4492

Abstract

Waste is part material that has no value within the scope of production. If you no longer need it, metal cans can take about 80 to 200 years to decompose. CNN is part of the supervised learning method that exists in deep learning, where those who have expertise in representing images or images from several categories increase recognition, namely in classifying objects, doing scene recognition, and detecting object detection. In this study, using the CNN method as a development model and applying the ResNet 50 network design, which includes the type Convolutional Neural Network (CNN) that operates by way of working, namely receive an input in the form of an image or images. The input will be carried out by training that is set using the CNN architecture so that later it will produce an output that can recognize objects as expected in knowing the types of cardboard and glass waste. The implementation of this research uses the Python programming language, Anvil, and the TensorFlow and Keras libraries. The system has succeeded in detecting the type of metal waste from general waste and assisting third parties, namely implementing it through the website using Anvil. The input shape for CNN modeling in this study is 512x384 pixels, which has a value of 100 eras, and the data set used contains images of metal waste and general waste found 547 images, resulting in an accuracy of 96%.
Co-Authors Chaerur Rozikin Ikbal Fahmi Rifky Maulana Sukma Wati Sukmawati Sukmawati