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All Journal CommIT (Communication & Information Technology) Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Applied Data Sciences
Utama, Ditdit Nugeraha
Bina Nusantara University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Materials Science & Nanotechnology Physics

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Journal : Journal of Applied Data Sciences

 1 
Performance Fuzzy Decision Model for Evaluating Employees’ Work-from-Home Performance Immanuel, Christiawan; Utama, Ditdit Nugeraha
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.269

Abstract

This study aims to identify key workplace environmental parameters and develop a Decision Support Model (DSM) to evaluate the performance of work-from-home (WFH). The methods utilized include Tsukamoto Fuzzy Logic and conventional techniques. Key parameters incorporated into the DSM-WFHP model include room temperature, internet speed, number of children, virtual office setup, and physical activity (sport). The research culminates in the DSM-WFHP model, which provides accurate assessments of WFH employee performance. Findings indicate that variations in these parameters significantly impact performance, with specific quantitative results demonstrating that optimal room temperature, high internet speed, fewer children present, an effective virtual office setup, and regular physical activity correlate with higher performance scores. Thus, this research concludes that the DSM-WFHP model effectively offers precise performance evaluation guidance for remote employees, making a valuable contribution to remote work management. With regards to the novelty of this study, this is the first time that the synergetic effect of multiple environmental factors has been incorporated into a comprehensive DSM.
Fuzzy TOPSIS-Based Group Decision Model for Selecting IT Employees Vania, Abigail; Utama, Ditdit Nugeraha
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.511

Abstract

In the era of digitalization, the demand for competent IT employees is growing rapidly. However, the IT employee selection process often faces various challenges, such as biased selection criteria, many applicants, and difficulty in objective assessment. These challenges can lead to inaccurate selection decisions and have a negative impact on company performance. This research aims to develop a Group Decision Support Model (GDSM) for IT Employee Selection using the Fuzzy TOPSIS method to enhance objectivity and reliability in decision-making. This GDSM combines assessments from HRD and User IT groups by considering the weight of each criterion. The proposed model overcomes bias, uncertainty, and subjectivity in judgments from both groups. The GDSM is constructed with 8 parameters/sub-criteria (2 criteria) from the HRD group and 12 parameters (5 criteria) from the User IT group from interviews and research. Thus, the total is 20 assessment parameters, consisting of coding test, education, certification, computer literacy, openness to experience, conscientiousness, extroversion, agreeableness, neuroticism, verbal, numerical, ability to learn, appearance attitude, work experience, communication skills, time management, job knowledge, motivation to apply, decision making, and service orientation. The methodology involves determining parameters, weights, fuzzification and this GDSM was tested through a limited simulation of IT employee selection using 11 respondents from Computer Science students for evaluation of the model. The result of this model is a ranking of the candidates. The best candidate is Cand. 8, with a closeness coefficient (CC) value of 0.896. The worst candidate is Cand. 3, with CC 0.241. The model is acceptable because it has no difference value between coding and manual for all candidates. This study contributes to increasing objectivity in IT employee selection and offers an implementation model for companies that want to improve the effectiveness of the recruitment process.
Fuzzy SAW Based Decision Model for Determining the Priority Scale of ICT Handling in Public Sector Organizations Yulanda, Rissa; Utama, Ditdit Nugeraha
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.419

Abstract

Determining the priority of handling information and communication technology (ICT) infrastructure in public sector organizations can help them take the right actions in maximizing limited budgets, to handle technical maintenance, improve human resource (HR) capabilities and governance of ICT infrastructure. The purpose of this research is to develop a decision-making model that is able to determine the priority of handling ICT, especially in public sector organizations. Decision support modeling (DSM) with Fuzzy Simple Additive Weighting (Fuzzy SAW) method is used to build a computer model that supports decision making in this case. The study consists of four stages, which are an integral part of the Fuzzy SAW-based DSM process. These stages include analyzing the case, determining parameters, collecting data and building the model. This study produces a Fuzzy SAW-based DSM consisting of 14 parameters, namely governance, number of internet users, number of ICT managers, work experience of ICT managers, bandwidth service capacity, router device age, educational background of ICT managers, network firewalls, network maintenance, server room availability, Network Attached Storage (NAS) storage devices, neatly organized cable devices, adequate electrical resources and internet connection backup networks, to determine the priority ranking of 34 existing alternatives. The final result of this research is a Fuzzy SAW-based DSM that is able to provide a priority score for handling ICT infrastructure in Public Sector Organizations. The findings in this model show that the parameter weights affect the final score of the model. Thus, the conclusion of this research is that the model has been successfully implemented, making a significant contribution in providing guidance on determining accurate ICT infrastructure handling for public sector organizations.
Decision Support Model for Determining Fuel in Boiler Machines Widyanto, Jeremia; Utama, Ditdit Nugeraha
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.520

Abstract

This investigation seeks to formulate a Decision Support Model (DSM) aimed at identifying the most suitable fuel for boiler systems utilized in industrial contexts, encompassing three distinct fuel categories: natural gas, industrial diesel oil, and coal. The assessment is predicated on four fundamental criteria: cost, calorific value, safety, and emissions. Employing a synergistic methodology that combines Analytic Hierarchy Process (AHP) and Fuzzy Logic, AHP allocates weights to each criterion (cost: 0.503, calorific value: 0.273, safety: 0.145, emissions: 0.079). The Fuzzy Logic approach is utilized to effectively address uncertainty and process subjective assessments. The findings indicate that cost constitutes the paramount determinant, exhibiting the highest weight, succeeded by calorific value, safety, and emissions. In accordance with these weighted criteria, the fuels are ordered as follows: coal (0.794), natural gas (0.653), and industrial diesel oil (0.456). These results underscore that cost remains the predominant factor in fuel selection for industrial boilers, whilst safety and environmental ramifications concurrently exert significant influence. The originality of this inquiry is manifested in its implementation of an all-encompassing DSM for fuel selection, marking a pioneering effort within this domain, which integrates both AHP and Fuzzy Logic to furnish a versatile and resilient decision-making framework. The implications of this research are substantial, as it offers a transparent and systematic approach for fuel selection in industrial environments, providing valuable insights into the optimization of energy resources while taking into account economic, environmental, and safety considerations. Subsequent investigations could further examine the incorporation of renewable energy sources and the ramifications of advancing environmental policies on fuel selection.
Co-Authors Ahmad Nurul Fajar Diyapradana, Taruna Gunawan Wang, Gunawan Hartono, John Reigton Immanuel, Christiawan Vania, Abigail Widayat, Muhamad Akbar Bin Widyanto, Jeremia Yulanda, Rissa