Industrial zones require careful and meticulous planning because industry can have a major impact on the surrounding environment. The research location is the northern part of West Java Province which is a gold triangle area named Rebana Triangle Area. The purpose of this study is to measure the weight of the research variables in determining industrial zones from the results of fuzzy analytical hierarchy process (F-AHP) analysis, assessing the location of industrial zones in the research area based on important variables in determining industrial zones. The result of this study is the weight of the research variables in determining the industrial zone from the results of the fuzzy analytical hierarchy process (F-AHP) analysis obtained is the availability of electrical infrastructure with an influence weight of 15.00%. The second most influential factor is the availability of telecommunications infrastructure with an effect of 13.02%, the distance of land to roads and access of 11.76%, land use of 11.21%, distance of land to public facilities of 9.99%, labour cost work is 9.60%, the distance of land to the river is 8.19%, the price of land is 7.97%, the slope is 6.79%, and the type of soil is 6.43%. This GIS analysis model can be a reference model for the government in determining the potential of industrial zones in other regions in Indonesia. A total of 4822.41 Ha or the equivalent of 3.50% of the total area of 6 (six) regencies/cities research areas which are very suitable to be used as industrial zones. The district that has the largest area of potential industrial zone is Majalengka, while Cirebon does not have a location that has the potential for industrial zone locations. Based on the results of the analysis of 10 (ten) variables for determining industrial zones from expert opinion, a draft policy proposal for the government can be proposed, among others. These 10 (ten) variables are variables that are expected to be mandatory variables in planning and determining the location of potential industrial areas.

Modern agricultural production technologies based on the widespread use of pesticides and mineral fertilizers have largely solved the problem of providing the population with food, and at the same time have generated multiple ecological, medical and environmental problems, problems of environmentally friendly and biologically valuable food products, land rehabilitation, restoration of their fertility, etc. Therefore, the emergence of new classes of pesticides with different mechanisms of action, high selectivity and low toxicity for warm-blooded animals is very modern. Currently, the development and application of new plant protection products that are not toxic to humans and animals is of global importance. Priority is given to research aimed at creating plant protection products based on microorganisms and their metabolites, as well as the search for plant substances with potential pesticide activity. In this regard, the question arose of finding new safe fertilizers that can also be economically profitable for production on an industrial scale. One of the current trends in this industry is the use of green microalgae. In this regard, the purpose of our research is the possibility of cultivating green microalgae on phosphorus production waste. During the work, traditional and modern research methods in biology were used. As a result of the work, several problems can be solved, such as the disposal of industrial waste and the production of safe biological fertilizer.

There is a large literature on public-private-partnership, covering many different areas and aspects. This article deals with a specific but important aspect: the decision-making mechanisms to choose the management of PPP enterprises. In this sector, a suitable choice of managers is of particular importance because the persons chosen must balance the public and private interests. This is often difficult to achieve. Two new procedures are discussed, “Directed Random Choice” and “Rotating CEOs”. In each case, the advantages and disadvantages of the procedure of choosing the managers of PPP enterprises are discussed and evaluated. It is concluded that the two novel mechanisms should be seriously considered when choosing the managers of PPP enterprises.

Kampar Regency, as the largest pineapple producer in Riau Province, has yet to provide significant added value for the surrounding SMEs. The limitations in technology and innovation, infrastructure support, and market access have prevented this potential from being optimally utilized. A Technopark can provide the necessary facilities and infrastructure to enhance production efficiency, innovation, and product quality, thus driving local economic growth. The objective of this study is to identify and determine potential locations for the development of a pineapple-based Technopark in Kampar Regency. This study is crucial as a fundamental consideration in selecting the technopark location and assessing the effectiveness and success of the technopark area. The method used in this study is AHP-GIS to analyze relevant parameters in the site selection process for the technopark area. Parameters considered in this study include slope, land use, availability of raw materials, accessibility of roads, access to water resources, proximity to universities, market access, population density, and landfill. The analysis results indicate that the percentage of land highly suitable for the technopark location is 0.78%, covering an area of 8943 hectares. Based on the analysis, it is recommended that potential locations for the development of a pineapple SMEs-based technopark in Kampar Regency are dispersed in Tambang District, encompassing three villages: Rimbo Panjang, Kualu Nenas and Tarai Bangun. The findings of this study align with the spatial planning of Kampar Regency.

The artificial intelligence (AI)-based architect's profile's selection (simply iSelection) uses a polymathic mathematical model and AI-subdomains' integration for enabling automated and optimized human resources (HR) processes and activities. HR-related processes and activities in the selection, support, problem-solving, and just-in-time evaluation of a transformation manager's or key team members' polymathic profile (TPProfile). Where a TPProfile can be a classical business manager, transformation manager, project manager, or an enterprise architect. iSelection-related selection processes use many types of artifacts, like critical success factors (CSF), AI-subdomain' integration environments, and an enterprise-wide decision-making system (DMS). iSelection focuses on TPProfiles for various kinds of transformation projects, like the case of the transformation of enterprises' HRs (EHR) processes, activities, and related fields, like enterprise resources planning (ERP) environments, financial systems, human factors (HF) evolution, and AI-subdomains. The iSelection tries to offer a well-defined (or specific) TPProfile, which includes HF's original-authentic capabilities, education, affinities, and possible polymathical characteristics. Such a profile can also be influenced by educational or training curriculum (ETC), which also takes into account transformation projects’ acquired experiences. Knowing that selected TPProfiles are supported by an internal (or external) transformation framework (TF), which can support standard transformation activities, and solving various types of iSelection’s problems. Enterprise transformation projects (simply projects) face extremely high failure rates (XHFR) of about 95%, which makes EHR selection processes very complex.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.