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All Journal Jurnal Ilmiah Matrik JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Loekito, Jimmy
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Computer Science & IT

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Perancangan Sistem Informasi Kerja Paruh Waktu Mahasiswa dengan Lingkup Internal Program Studi X Chandra, Jonathan; Loekito, Jimmy
Jurnal Ilmiah Matrik Vol. 27 No. 1 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/878yk946

Abstract

The information system in the form of part-time job vacancies in the scope of study programme X is one of the containers that is needed both from employers (lecturers) and job seekers (students). The information system regarding part-time work in study programme X is able to provide convenience for employers in finding workers who are in accordance with what they need and also provide opportunities in the form of experience for job seekers in study programme X. This research work begins with a system design design with predetermined limits based on observations of the X study programme which is used as a benchmark. The development of this information system is determined using the prototype development method because of the short development time and must be able to be used while the development itself takes place. After obtaining the limitations from the observation results, a database system design will be carried out for the purposes of this research information system application which will be applied to the system. The next step is the information system design process which is broken down into two parts, namely the front-end and back-end parts. For testing this information system itself using the black box testing method with the main target, namely the original user as the respondent
Studi Perbandingan Evaluasi Kinerja Metode Pembelajaran Eager Learning versus Lazy Learning Lukman, Selvi; Loekito, Jimmy; Yapinus, Pin Panji
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9197

Abstract

The major revenue in banking sector is generated long term deposits from customers. Many marketing strategies are implemented to target potential customers by examining their impacted characteristics for decision making. Therefore, machine learning as a scientific computing has drawn many interest in finding best potential customers especially in predicting whether a long term deposit is subscribed or not. In this research, lazy and eager learning of K-Nearest Neighbours (KNN) and Random Forest (RF) is compared. The computation procedure of the prediction makes a sharp distinction between them and accordingly, RF is proven to be more superior than KNN in the term of Accuracy as much as 96%, Precision 93% and F1 score 0.97. Therefore, the ultimate performance of RF relies on the ability to handle non-linearities and its resistance to overfitting makes RF a suitable choice for many predictive applications. Keywords— Classification; Easy learning; Lazy Learning, Term Deposit
Co-Authors Chandra, Jonathan Lukman, Selvi Pin Panji Yapinus, Pin Panji