A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems

Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel and effective criterion for uncertainties modeling of the RESs as well as system loads. Four sorting stages are applied for the proposed uncertainty cases reduction. Added to that, it proposes three different uncertainty reduction strategies for obtaining different accuracy and speed options. The proposed reduction strategies are tested on medium and large scale distribution systems; IEEE 69-bus and 118-bus systems. The obtained results verify the effectiveness of the proposed criterion in uncertainties modeling in distribution systems with acceptable level of accuracy.

Reduction of Communication Uncertainty for COVID-19 Community Service Program Students Universitas Sebelas Maret

The COVID-19 pandemic that has hit the world since the beginning of 2020 has certainly had a major impact on human interaction. The world of education is one of those affected by restrictions on the learning process and also other policies, including community service activities carried out by students, namely Kuliah Kerja Nyata(KKN). Universitas Sebelas Maret is a state university which at the beginning of the COVID-19 Pandemic was a pioneer in holding the COVID-19 community service program. Student KKN in its work program holds activities related to preventing the transmission of COVID-19 in the community as well as food and economic security. What is different from KKN is usually the COVID-19 KKN, students have to go out on their own in the community without being in groups. They hold KKN work programs in their respective neighborhoods while maintaining health protocols. This study will review the reduction of communication uncertainty that occurs to students of the COVID-19 Community service program at Universitas Sebelas Maret where they have to build relationships independently with the surrounding community where they feel that they have left their homes for a long time while studying in Solo. There is a sense of communication uncertainty that occurs in students when they have to interact and build communication with the community, therefore, this study uses the Uncertainty Reduction Theory (URT) perspective which was pioneered by Charler Berger and Richard Calabrese. This research is a qualitative research using a descriptive type of research and with the conclusion of an active, passive, and interactive description of the strategy for reducing communication uncertainty for COVID-19 students.

Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

Iterative uncertainty reduction in multi-actor smart service innovation

Smart service innovation is the process of reconfiguring resources, structures, and value co-creation processes in service systems that result in novel data-driven service offerings. The nature of such offerings requires the involvement of multiple actors, which has been investigated by a few studies only. In particular, little is known about the multiple actors’ efforts to manage uncertainty in the process of establishing smart service systems. Empirically grounded in data from 25 interviews with industry experts, we explore how organizations act and interact in smart service innovation processes. For our data analysis, we adopt a microfoundational view to derive a theoretical model that conceptualizes actor engagement as a microfoundation for iterative uncertainty reduction in the actor-to-actor network of the smart service system. Our study contributes to information systems research on service systems engineering and digital transformation by explaining smart service innovation from both a multi-actor and a multi-level perspective, drawing on service-dominant (S-D) logic and microfoundations as well-established theoretical lenses.

Alternative learning media post-covid-19: uncertainty reduction theory perspective

This study aims to determine how users respond to the use of social media during online learning after the COVID-19 pandemic from the perspective of uncertainty reduction theory. This study uses a phenomenological research method, where data collection is carried out by means of Focus Group Discussion (FGD), which was conducted twice on 15 users of online learning applications. Through this research, it is known that the user's response from the compulsion in using the online learning model is that the subject tends to feel mostly bored, difficult to discipline, tendency to be lazy, and has the proclivity to be dishonest. In addition, in terms of emotional connection, teachers and students do not have a good bond, which is far different when using the offline learning modality. The majority of users prefer to utilize the offline learning system when the pandemic ends. One person prefers to use a hybrid system which is a combination of online and offline learning method.