In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.

Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.

A new metaheuristic algorithm is proposed. The algorithm integrates the information sharing as well as the evolution operators in the swarm intelligence

Sentiment analysis or opinion mining is a method to group opinions or reviews into positive or negative. It is important sources for decision making and can be extracted, identified as well as evaluated from online sentiments reviews. This research discussed sentiment analysis in law enforcement on a law case in Indonesia. The analysis uses Support Vector Machine and K-Nearest Neighbor (KNN) for data classification integrated with Particle Swam Optimization (PSO) to increase their performance. The experiment results show that PSO increase the performance of both algorithm of the paper is PSO method make value SVM with PSO where value C = 1.0 and Epsilon = 1.0 accuracy 100% while for algorithm KNN with PSO 93.08%. This result show SVM algorithm more accurate than KNN algorithm by using PSO optimization. The performance of law enforcers in the trial case get more positive responses from the people of Indonesia in accordance with their comments or tweets in social media.