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All Journal International Journal of Electrical and Computer Engineering Conference SENATIK STT Adisutjipto Yogyakarta
Hutajulu, Kristina
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Development of Good Procurement Information System Web Based at PT. XYZ Hutajulu, Kristina; Ariyanto, Sisia Dika; Rascalia, Radix
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN XXX-XXX-XXXXX-
Publisher : Sekolah Tinggi Teknologi Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.357

Abstract

PT. XYZ is one of the companies engaged in the production of motorcycle spare parts. In carrying out its operational activities, PT. XYZ has a procurement process. Generally in applying of good procurement, employees must submit a document submission of goods in the printed form and must meet their supervisor directly to ask for approval in a signature. This condition requires quite a long time until the submission document reaches the Director because they have to wait for the signature of each related section. That is quite an obstacle for the employee who submits because the longer the approval process, the longer the procurement process. To overcome this obstacle, a good procurement information system web based was built using phased development methodology. The information system uses SQL Server as database and ASP.Net as programming language. Good procurement information system web-based can accelerate the procurement process.
Development of Good Procurement Information System Web Based at PT. XYZ Hutajulu, Kristina; Ariyanto, Sisia Dika; Rascalia, Radix
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN 978-602-52742-
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.357

Abstract

PT. XYZ is one of the companies engaged in the production of motorcycle spare parts. In carrying out its operational activities, PT. XYZ has a procurement process. Generally in applying of good procurement, employees must submit a document submission of goods in the printed form and must meet their supervisor directly to ask for approval in a signature. This condition requires quite a long time until the submission document reaches the Director because they have to wait for the signature of each related section. That is quite an obstacle for the employee who submits because the longer the approval process, the longer the procurement process. To overcome this obstacle, a good procurement information system web based was built using phased development methodology. The information system uses SQL Server as database and ASP.Net as programming language. Good procurement information system web-based can accelerate the procurement process.
Prediction of student performance at polytechnic using machine learning approach Hutajulu, Kristina; Wulandhari, Lili Ayu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5356-5365

Abstract

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and non-informatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI.
Co-Authors Ariyanto, Sisia Dika Arti, Shindy Radix Rascalia, Radix Ratwastuti, Ning Wulandhari, Lili Ayu