Sugiono

The Eco-Innovation Variables which Influence the Performance of Creative Industries Center of Natural Stone Crafts

This study investigates the eco-innovation variable which has the significant effect on creative industries center’s performance of marble and natural stone craft sector in Tulungagung, Indonesia. The object of the study is the creative industries center with the non-renewable raw material. Mostly, the companies are in form of small and medium-sized enterprises (SMEs) which is ‘passive eco-innovator’ and their eco-innovation variables have not been investigated before in terms of their influence on their performance. The respondents were 81 craftsmen taken from the population. The data were collected through questionnaires which were tested, processed and analyzed by using Consistent Partial Least Square (PLSc). The eco-innovation variables which significantly effect on innovative performance are eco-organizational innovation and eco-product innovation. Eco-process innovation and eco-marketing innovation don’t directly affect on innovative performance, but its significant effect on eco-product innovation may influence innovative performance. Improving innovative performance will impact on financial performance through improvement of production performance, but market performance does not significantly affect financial performance. The findings of this study could be a reference for creative industries center’s of marble and natural stone craft sector to prioritize which type of eco-innovation should be improved so that its impact on performance is more significant.

The model relationship of wastes for parameter design with green lean production of fresh water

A fresh water industry, serving the region of Banjarbaru and Banjar district with 62,205 customers in total by the end of 2015, has a service scope about 55% under the national target, which is 68%, and also the UN MDGs (Millennium Development Goals) target, that is 80% with NRW (non revenue water) average of 29%. The result of sedimentation is small water particles mixed with mud that is expelled 15 m3·day–1 as a result of production process with the capacity of 250 l·s–1. By seeing the condition above, an upgrade of production and service for customer by repairing/minimizing waste in the production process is needed. Identifying waste requires certain model that can ease and simplify the problem search process and it can be done by waste relationship matrix (WRM). The advantages of this model are the simplicity of its matrix and the questionnaire that is able to cover many things and give contribution to gain an accurate result in identifying the root cause of waste Rawabdeh (2005). Lean is a continual effort to eliminate waste and increase the value adding by cutting unnecessary things so that it can give good customer value and make the process become fl exible (easy to change). Researchers observed that lean manufacturing is a must and an integral part of the manufacture principles around the world, including fresh water industry over the past few decades. In this part, lean is production principles applied most by companies that allows Scientifi c Review – Engineering and Environmental Sciences (2017), 26 (4), 481–488 Sci. Rev. Eng. Env. Sci. (2017), 26 (4) Przegląd Naukowy – Inżynieria i Kształtowanie Środowiska (2017), 26 (4), 481–488 Prz. Nauk. Inż. Kszt. Środ. (2017), 26 (4) http://iks.pn.sggw.pl DOI 10.22630/PNIKS.2017.26.4.46

Analysis the relationship of physiological, environmental, and cow milk productivity using AI

The dairy cattle productivity is very depending on the quality of their environment and physiological aspect. Hence, the purpose of the paper is to looking for the relationship model of physiological, environmental and milk productivity by using artificial intelligence (AI). The model will be useful for the user to decide the best cow treatment in order to gain the best milk production. The research is started with literature review and early survey of cattle physiological, environment factors and milk productivity. The next step is measuring the environment data (temperature, wind speed, and relative humidity) and measuring physiological aspect (heart rate, body temperature) correlated with milk productivity in 500 pairs of data. All the data are collected and stored into the database and then trained and validated using Back Propagation Neural Network (BPNN) with Genetic Algorithm (GA) optimization. The initial BPNN architectures are selected in 2 hidden layer, delta bar delta learning rule, sigmoid transfer function and epoch 10000. As a result, the system successfully developed an intelligent tool to predict milk production in any levels of environment and physical condition. Based on sensitivity analysis, the relative humidity, heart rate, environment and cow body temperature are categorized in strong impact, beside that are in weak impact on milk production.