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All Journal Kilat Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Informatika: Jurnal Pengembangan IT
Muhammad Nur
STMIK Bani Saleh
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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SELF ADAPTIVE SOFTWARE DENGAN TEKNIK CASE-BASED REASONING UNTUK MENINGKATKAN PERFORMA APPLICATION SERVER Muhamad Nur; Domo Pranowo Kuswandono
KILAT Vol 7 No 2 (2018): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2560.561 KB) | DOI: 10.33322/kilat.v7i2.355

Abstract

Performance of application server is an important attribute that must be maintained quality and stability, because application server operated in dynamic environment with fluctuating user loads and resource levels, its potentially caused unpredictable errors. In order application server performance still optimal, it is need maintains configuration application parameters. But, do that process need deep observation each environment condition changed and of course the cost and time. The approach that can be used is self-adaptive (autonomic computing). Self-adaptive can configure application parameters automatically. Best combination application parameters configuration can be stored on a repository and used as reference when decide new decision for configuration of application server parameters when similar condition is occurred. Cased-based reasoning can do the process. Best approach will be used is self-adaptive with case-based reasoning. This research implements the approach on an experiment application that deployed on glassfish application server. The results show that self-adaptive with case-based reasoning can improve application server performance with significant improvement.
PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328 Rudi Budi Agung; Muhammad Nur; Didi Sukayadi
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 5 No 1 (2019): JOURNAL CERITA
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (549.362 KB) | DOI: 10.33050/cerita.v5i1.235

Abstract

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.
Identifikasi Visual Cacat Produk Menggunakan Neural Network Model Backpropagation (Studi Kasus: PT. Panasonic Gobel Eco Solution) Muhammad Nur; Sjaeful Irwan; Danang Santosa
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i2-2.1865

Abstract

Product defects are common in the production process. Visual identification of product defects is first carried out when the product is produced. Identification of vague defects in very small shapes with different sizes and positions is difficult to do with ordinary eye sight, so that often results in decisions about the status of the product that is not right. Product defects in visual form can be identified by patterns such as shape, size and position on the product image. In this study, we will apply a neural network with the backpropagation model as a classification of the pattern. Product images will be processed using image processing by converting the RGB pixel value of the image into a numeric value. Data in numerical form will be input for training values in the backpropagation model. Training results are used to identify identified product defects and produce product status decisions. The results show that the backpropagation neural network model is able to recognize product patterns with an accuracy of 99.24% and based on simulation test data with the final weight and bias of training results, able to identify product defects with success up to 91%.
Co-Authors Danang Santosa Didi Sukayadi Domo Pranowo Kuswandono Rudi Budi Agung Sjaeful Irwan