This study updates Humanitarian Logistics Digital Business Ecosystem framework coupled with the development of a proposed integrated CCSD-CoCoSo MCDM method to rank factors used in assessing humanitarian and business logistics actor’s propensity to use, diffuse, and adopt a collaborative digital business ecosystem platform for their future operational use. Employing nine criteria derived from technology innovation theories and institutional theory, and 28 experts comprising our decision matrix. The findings report perceived relative advantage, perceived safety and security, and infrastructure and expertise as the top three vital criteria that experts believe when addressed in an ecosystem platform for humanitarian and business logistics actors it would encourage a collaboration for their sustainable future operations. With organisational culture and structure as the least prioritised criteria. The study concludes that the CCSD-CoCoSo obtained results are objective, validating, and that this model is useful and suitable for MCDM analysis and policy making.

Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.

The purpose of this study is to analyse the simultaneous spatial relationship between economic growth and e-commerce as well as the spillover effect between the two variables in East Java at the beginning of the Covid-19 pandemic in 2020. To answer the research objectives, spatial simultaneous modeling is used with the Spatial Autoregressive Generalized Spatial Three Model. Stage Least Square (SAR-GS3SLS) using rook contiguity. Based on the results of the SAR-GS3SLS, it can be concluded that at the beginning of the Covid-19 pandemic in 2020 in East Java, economic growth and e-commerce were simultaneously spatially interconnected. Variables that affect East Java's economic growth are e-commerce activities, the number of villages that have Base Transceiver Stations (BTS) and the spatial lag of economic growth (ρ1) while the open unemployment rate (TPT) and the Gini ratio have no significant effect on growth. economy. Variables that affect e-commerce are economic growth, internet banking users, percentage of population who have cellphones, number of millennials, number of villages that have ATMs and spatial lag of e-commerce (ρ2) while the number of villages with 4G/LTE signals has no effect on e-commerce. commerce. Regencies/cities that provide the highest spillover of economic growth and e-commerce in East Java are Malang, Mojokerto and Madiun Regencies. The three districts were able to provide a positive net spillover.

Experience plays a vital role in the decision-making (DM) process. In this paper simulation, modeling, and analysis of past experience over DM has been done using the Iowa gambling task (IGT). The Human DM process is very complex and difficult to model through computational methods because it is a subjective type of process and varies person-to-person. Therefore, this study is an attempt to simulate a DM model similar to the human DM process. For this collection of real data was done and was provided as input to the developed eight Reinforcement Learning (RL) models. The result shows that the performance of the model based on Prospect Utility (PU) learned with Decay Reinforcement Rule (DRI) and Trial Dependency Choice (TDC) is better as compared to other models. It is observed from the analysis of data and also validated that simulation and models output that the experienced group performs better than inexperienced.

From 2000 to 2019, trade between the People's Republic of China and the Republic of Peru grew at an average annual rate of 22%, however, income and wealth inequality in Peru remained the same. The aim of this study is to understand the effect of trade between Peru and China on the inclusive economic growth of Peru from 2000 to 2019. The method used was the correlation of variables, and a linear regression between Peru and China trade and several indicators of inclusive economic growth in the Peruvian economy was performed using the Ordinary Least Squares model. The results suggest that there is sufficient statistical evidence to support that inclusive economic growth may depend on increased trade between Peru and China; the study show that if trade growth between Peru and China fluctuates by $1 million per year, labor income will increase by $10.3 per capita in the Economically Active Population (EAP). Moreover, for every 1% increase in trade between Peru and China, GDP per capita increases by 0.1057% and labor productivity increases by 0.0681740%. The variables poverty, vulnerable employment, GINI index and life expectancy at birth were not significant factors.

The purpose of the study is to evaluate the efficiency of Islamic Rural Banks (BPRS) and analyze the factors that determine them using a two-stage approach to Data Envelopment Analysis (DEA). DEA in this study focuses on the production, intermediation, and inefficiency causes. This research was done on BPRS across Indonesia. The data were taken from a financial report for the 2013-2021 period. The source of the data was a publication from the Financial Services Authority of Indonesia. The data were analyzed using the non-parametric approach with a two-stage DEA method. The input variables were personnel costs, fixed assets, and third-party funds. The result shows that Revenue Sharing, ROA, and Growth have a significant positive effect on DEA. BOPO and inflation have a positive but insignificant effect on DEA. While NPF and FDR have negative but insignificant effects on DEA. Then CAR has a negative and not significant effect on DEA. It also shows that the variables of Revenue Sharing, NPF, ROA, CAR, FDR, BOPO growth, and inflation have a simultaneous effect on DEA.

Fires are devastating risky events in forests, having a negative effect on resources, biodiversity, economics, animal life, and putting people in danger. The goal of this study is to use geospatial techniques to identify areas in Jordan that are at risk of forest fires. The research area extends 50 kilometers north and 15 kilometers east from the Dead Sea. The forest fire risk zones map was developed using six factors: land cover class, aspect, proximity to settlements, elevation, slope, and proximity to roads. All of the factors have been selected based on their fire sensitivity or capacity to cause fire. In this study, a Turkish model with fuzzy logic and Analytical hierarchy analysis (AHP) was utilized to classify the area into five categories of risk ranging from very low to very high. According to the findings, approximately 12.12% of the study area is classified as very low risk, 25.54 % is classified as medium risk, while 12.84% is classified as very high risk. Over the last ten years, the map has been confirmed by prior fire occurrences using data from civil defense archives. This conclusion was very useful in gaining an understanding of the geographical distribution of fire-vulnerable zones. The research found that the GIS approach combined with AHP and fuzzy logic is a useful tool for estimating such kinds of maps.

Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.

A pine tree, namely Pinus merkusii is an indigenous species from Indonesia which grows extensively in the Island of Java, Sumatera, and Sulawesi. This plant produces both timber and non-timber forest products (TFP and NTFP). Resin or oleum pine resin, as the main NTFP of Pinus merkusii, becomes the raw material for the gum rosin and turpentine oil industry. Globally, Indonesia is ranked 3rd as a producer of pine products after China and Brazil, in which Perhutani as a State Owned Forestry Enterprise plays a major role in this industry. On average, Perhutani manufactures 65,000 tons of gum rosin and 14,000 turpentine oil per year. Entire volume of both pine products is produced by nine factories with various maximum capacities. Therefore, this research aims to measure efficiency and/or inefficiency score of each factory using data envelopment analysis (DEA) method, which is then complemented by a single bootstrap technique with 2.000 iterations to eliminate bias scores. Cost of raw material, labour, energy, and general affairs are employed as input variables, while the output variables are total revenue and production volume. As result, 27.3% inefficiency (efficiency score = 72.7%) is generally found in all Perhutani’s pine chemical factories. To resolve this inefficiency issue, analytical hierarchy process (AHP) pairwise comparison questionnaire is distributed to 13 expert respondents to determine prioritized operational capability to focus on in optimizing efficiency of production performance. Dimensions of Cost, Quality, Flexibility, Innovation, and Sustainability are selected to construct the AHP questionnaires.

Stock is one of the investment assets that has its charm for investors. It is very liquid and has a high rate of return, but it has a high risk. The strategy commonly used to minimize investment risk is to diversify through portfolio formation. A good allocation of funds must be determined in forming an optimal portfolio. In addition, the method of stock selection needs to be considered so the stocks are well diversified and the portfolio developed has good performance. This study aims to compare stock selection between K-Means and Average Linkage clustering approaches in forming an investment portfolio. Clustering analysis is used to group IDX80 stocks based on their attributes. In forming a portfolio with the Mean-VaR model, the stock selection decision criteria used are by selecting stocks with the highest positive returns from each cluster. As a result, the two clustering techniques show the superiority of the Silhouette score for a certain number of clusters, but there are still more advantages in Average Linkage. The portfolio approached by Average Linkage resulted in a better performance than the portfolio approached by K-Means. Therefore, Average Linkage clustering can be used as a better recommendation in decision-making to select stocks so as to produce optimal portfolio performance.