Indonesia Conference Directory

Corresponding Author
Budi dwi satoto

Institutions
(a) S3 Student of Applied Mathematics, Airlangga University, Surabaya, Indonesia
*budids[at]trunojoyo.ac.id; budids[at]gmail.com
(b) Mathematic department, Airlangga University, Surabaya, Indonesia
(c) Physic department, Airlangga University, Surabaya, Indonesia

Abstract
Gram-negative bacteria are one of the causes of nosocomial in Indonesia. This bacterium is a cause of resistance so that the disease is difficult to cure with antibiotic treatment. In the process of antibiotic therapy, these nosocomial bacteria must be removed first before handling the main bacteria that cause disease. This bacterial observation is carried out using image processing to replace visual observation. The process consists of four stages, namely pre-processing, segmentation, feature extraction, and identification. At the segmentation stage, the bacterial image object is selected that best suits the expert representation, in this case, the medical analyst. Feature extraction is done to get pixel information to be processed. At the classification stage, the use of Extreme learning machine is chosen because the training process time is shorter than other algorithms based on artificial neural networks. At the stage of modeling, 2 different bacteria were used, namely nosocomial bacterium and Gram-negative bacterium. In this research trial, each consists of 420 images of training data, validation and testing so that the total amount of data used is 2520 images with a pixel size of 256x256. Accuracy results obtained at 97% of the training process

Keywords
Gram-negative bacteria; nosocomial; shape segmentation; artificial neural networks; extreme learning machine

Topic
Information System and Technology

Link: https://ifory.id/abstract/ghr9JBQHKY7C

Corresponding Author
Masud Effendi

Institutions
Department of Agroindustrial Technology, Faculty of Agricultural Technology, Universitas Brawijaya
Veteran street, Malang, Indonesia
* mas.ud[at]ub.ac.id

Abstract
Sugar cane (Saccharum officianarum) is an important commodity because it is widely used as raw material for sugar and MSG. Data on sugarcane production has not been used optimally, except for administrative purposes. The data, if used appropriately, can be used to predict the yield of sugar cane which can be utilized by cooperatives and farmers. This research was conducted to design an information system that can be used to forecast sugarcane yields in the working area of "KUD Subur Malang". The information system design process is carried out by implementing Machine Learning. The results of sugarcane yield forecasting using machine learning implementation in KUD Subur Malang showed the best results using the gradient boosting algorithm with 75% model accuracy. Website-based yield forecasting information system can be used as a production forecasting tool for KUD Subur to improve its business processes. Sugarcane forecasting information system can be well received by users.

Keywords
machine learning, sugarcane, gradient boosting

Topic
Information System and Technology

Link: https://ifory.id/abstract/PXpbq9Ktx7f8

Corresponding Author
Lailyn Puad

Institutions
STMIK Nurdin Hamzah

Abstract
Higher education and industry are the sectors most demanded by changes with the development of the Industrial Revolution 4.0 issue. One of the basic things is about security, data integration and reducing paper usage. This system can be used as one of the answers to some of these demands, with the ability to produce digital documents integrated with one account. In addition, the authenticity of documents is a guarantee, with the standalone authentication feature owned by the application, so that not everyone can duplicate it. Utilizing the Qrcode that is integrated with the Codeigniter framework can produce an application that is able to provide convenience to each of its users in a variety of things, from organizing digital documents with adequate security guarantees, reducing the use of paper to the integration of accessible data.

Keywords
Industrial Revolution;Integration;Authentication;Independent;Digital Documents.

Topic
Information System and Technology

Link: https://ifory.id/abstract/7agLPBQ6mD2u