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All Journal Jurnal CoreIT Journal of Innovation, Business and Entrepreneurship
Wasesa, Meditya
School of Business and Management, Institut Teknologi Bandung
Computer Science & IT Decision Sciences, Operations Research & Management

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Business Process Improvement of Fixed Wing UAV Assembly Process: Imperia Dirgantara Case Ajibaskoro, Akbar; Wasesa, Meditya
Journal of Innovation, Business and Entrepreneurship Vol 4, No 2 (2019)
Publisher : Journal of Innovation, Business and Entrepreneurship

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Abstract

Abstract. Imperia Dirgantara is a company engaged in providing aerial survey solutions using their own university-based research Unmanned Aerial Vehicle (UAV) technology and data processing systems. To anticipate an increasing demand of the aerial survey, the UAV has to be prepared in a short preparation time. Currently, Imperia Dirgantara is facing an issue of lengthy assembly process of the UAV which may lead to lost sales opportunity. In response, this research focuses on providing proposition improvements for the UAV assembly business process, All of data source directly from Imperia Dirgantara internal archives. This business process improvement research is initiated with thorough analysis of bottleneck process in the existing UAV assembly process. Following the bottleneck process identification, we conduct root cause analyses and propose the corresponding business process improvement propositions. Finally we assess the impact of the solution propositions, define the required resources and provide a plan to implement the solution proposition. This research resulting time reduce in processing time as much 12.66% or equal to 10 days. The suggestion of keep improving the current business process instead picking up another option is stated in this research’s report.Keywords: Business Process Improvement; Business Process Model; Bottleneck Analysis; Unmanned Aerial Vehicle; UAV Assembly Process.
Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company Jayanti, Luh Putu Saraswati Devia; Wasesa, Meditya
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.211 KB) | DOI: 10.24014/coreit.v8i1.16915

Abstract

Industry 4.0 refers to the increasing tendency towards automation and data exchange in technologies like Big Data and AI. The existence of technology means telecommunication companies have to adapt. Therefore, it takes great people so that the company can continue to survive. The problem that companies often face in hiring great people is that it costs a lot and takes a long time to recruit. Predictive analysis can assist in identifying system issues and solutions. This study aims to develop predictive analytics that can improve recruitment screening based on CVs and find the best predictive model for the company to reduce costs and long recruitment cycles using technology. The authors built an analytical prediction model in four stages: data collection, data preprocessing, model building, and model evaluation. This technique uses Random Forest and Naive Bayes classification algorithms. Both systems properly predicted more data sets with 70% accuracy, 70% precision, and a recall rate above 80%. Compared between the two techniques, Random Forest outperforms Naive Bayes for this predictive model. A lot of people are talking about predictive analytics for hiring, but there aren't many data mining frameworks that can help to find rules based on the CVs of people who have worked for companies before.Keywords: Recruitment, Human Resource, HR Analytic, Predictive Analytic, Random Forest, Naïve Bayes
Co-Authors Ajibaskoro, Akbar Jayanti, Luh Putu Saraswati Devia