DATA WAREHOUSE MODEL BASED ON KIMBALL METHODOLOGY TO SUPPORT DECISION MAKING IN ASSET MAINTENANCE

ITSM e-Prime is an ICT service management application based on ITSM framework owned by Pusintek that includes service desk, incident management, problem management, change management, release management, and configuration management processes. Currently there is a problem in determining the number of devices that will be included in the device maintenance contract or determining the number of devices that need to be replaced in a given year. The objective of this research is to build an asset management data warehouse so that it can be utilized by the Data Analysis and Presentation Team to produce a dashboard that presents data on network infrastructure assets that need to be maintained or replaced for budget planning needs. This descriptive verification analysis research used nine out of ninety tables from the ITSM e-Prime application and applied dimensional modeling Kimball to build a data warehouse because this methodology offers high query performance and understandable by end-user. The resulting data warehouse were tables in the form of star-schema. The tests were carried out by qualitative methods, namely quality testing by users (user acceptance test and blackbox testing) and quantitative method, namely comparing the number of infrastructure devices included in the maintenance contract in 2022. The final result of this research is a data warehouse consisting of fact table F_infrastructure and dimension table D_Merk, D_Area, D_Kategori, D_EoS, D_Garansi, and D_StatusPemeliharaan with acceptance percentage of 95% based on the test results

DECISION SUPPORT SYSTEM FOR PREDICTING EMPLOYEE LEAVE USING THE LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) AND K-MEANS ALGORITHM

Nowadays, decision support systems have gained wide popularity not only in private companies but also in government sectors. These systems play a crucial role in assisting leaders during the decision-making process. The effective functioning of the government heavily relies on employee performance, which requires discipline in carrying out their duties and responsibilities. Employee discipline is closely linked to their attendance, including leave-taking. Therefore, analyzing employee leave data can reveal trends and interrelationships, providing leaders with valuable information and insights for determining employee leave policies. To address this issue, data mining applications such as the Light Gradient Boosting Machine (LightGBM) regression prediction model can be utilized. This model takes into account factors like gender, age, and the starting year of leave to predict the number of employees who take annual leave simultaneously with holidays. Additionally, clustering algorithms like K-Means can be employed to group reasons for leave into clusters, identifying common leave patterns among employees. In this study, employee leave application data from January 2018 to July 2022 was collected from the Leave module within the HRIS (Human Resource Information System) application. The research outcomes encompass a dashboard visualization presenting descriptive analysis and modeling using LightGBM. The modeling results yielded reasonably accurate predictions, as evidenced by model testing that showed a difference of only 1 employee. Additionally, K-Means clustering formed 4 clusters of leave reasons, with the majority being family-related, illness, childcare, and elderly care. The dashboard can be used by management as a consideration for approving employee leaves, ensuring well-planned leave scheduling for the following year and minimizing disruption to work execution in each department

SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PADA UDANG WINDU ( Penaeus Monodon)

Desain aplikasi sistem pakar ini dibuat dengan bahasa pemrograman PHP dan menggunakan editor Dreamweaver 8, Apache sebagai Application server, serta

Employee Education and Training Recommendations using the Apriori Algorithm

The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data of education and training implementation in 2022 at the Central ICT Department in MoF, only 27.35% of employees participated in education and training according to proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or the substitute participants that is not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the apriori algorithm to analyze historical data of employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resource Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance