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Vol. 7 No. 2 (2018): JITA - APEIRON

Latifah Nurbaiti, Kudang Boro Seminar, Nugraha Edhi Suyatma ć

WEB BASED DECISION SUPPORT SYSTEM YO DETERMINE YHE APPROPRIATE PACKAGING OF ETHNIC AND TRADITIONAL INDONESIAN FOODS

Original scientific paper

DOI: https://doi.org/10.7251/JIT1702115N

Abstract

Culinary efforts, especially ethnic and traditional snacks attract many people to Indonesia. Maintaining the quality of snacks for consumers requires a good packaging technique. Food packaging consists of a wide variety of packaging options that match the characteristics of each snack; this is no easy task. Decision support systems can help to facilitate decisions made regarding selection of the right packaging. This paper focuses on identifying snacks, types of packaging and active packaging parameters to build a decision support system in order to determine appropriate packaging. Types of packaging are determined using fuzzy Sugeno 4 parameters: fat, water activity, shelf-life and price. Active packaging of the snacks is done using the if-else rule with parameterised types of packaging, preservatives, oxygen barriers and water vapour barriers. The end result of this research is a web-based decision support system, which recommends types of packaging and active packaging for snacks.

Keywords: :active packaging; snacks; fuzzy logic.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.