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Journal : Bulletin of Computer Science Research

Penerapan Metode MOORA dan ROC Dalam Pemilihan Oli Mesin Terbaik Untuk Sepeda Motor Matic Serdina Feria Sidabutar; Rima Tamara Aldisa; Geofani Pasaribu
Bulletin of Computer Science Research Vol. 4 No. 2 (2024): Februari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i2.329

Abstract

As the use of automatic type of motorbike vehicles increases, the problem also increases in the automotive world because there are many users of automatic type motorcycle vehicles who do not understand how to care for and maintain the durability of their automatic motorbikes, especially the most important part of the motorbike, namely the the engine components. Machine maintenance is inseparable from lubricating oil which has a function in lubricating engine components so that they are maintained, durable, & away from rusting or even chipping because of friction between components one with another which results in damage to the motorbike engine so that their favorite motorbike has to be make repairs to the workshop. Moreover, the motorbike used is of the automatic type, where maintenance is much more complicated than other motorbikes, which are of the gear or clutch type. In determining the best engine oil for the best automatic motorbikes, starting from the contents of the package which are at least 1 liter, has a non-concentrated odor, and is able to keep the engine cool when the motorbike is used. Combination of the MOORA and ROC Methods in the selection of the best engine oil for the best automatic type motorcycle based on predetermined criteria for the “Evalube” brand alternative S5 oil with a preference value of 0.019 as the best engine lubricating oil for automatic type motorcycles. The application of the MOORA Method has a simple and easy-to-understand concept in selecting the best engine oil for automatic type motorcycles. The process of selecting the best engine oil for automatic type motorbikes using the MOORA and ROC methods, starts from determining the weight value of each criterion and then ranking the largest preference value as a consideration and aid in decision making.
Penerapan Metode Simple Additive Weighting (SAW) dan Rank Order Centroid (ROC) dalam Keputusan Pemberian Kredit Sepeda Motor Dwika Asrani; Rima Tamara Aldisa; Gunawan Siburian; Jannus Manik
Bulletin of Computer Science Research Vol. 4 No. 2 (2024): Februari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i2.330

Abstract

Motorcycle credit is a method of borrowing money from a financial institution, such as a bank or finance company, to buy a motorcycle. Naira Finance, as one of the leading finance companies in Indonesia, provides convenience for customers to own vehicles (both new and used motorcycles) of various brands. The job of a motorcycle loan manager is to decide whether or not to provide motorcycle loans to customers. To reduce the possibility of customer negligence in paying credit bills in the future, it is important to provide credit on time. As a supporting effort, credit managers should use a computer-based application known as a Decision Support System (DSS) to make credit granting decisions more accurate. A computer-based system known as SPK can assist managers in making both structured and unstructured decisions. Researchers try to combine the Rank Order Centroid (ROC) method and the Simple Additive Weighting (SAW) method when deciding to give credit or not to customers. In preference to the SAW method of calculation, it is intended that the weighting results of the ROC method are significant. the results of the decision from the application of the ROC and SAWt methods, there are 5 alternatives that are accepted to receive credit because they are considered feasible and meet the requirements criteria specified as customers who are entitled to receive credit while the other 5 alternatives are rejected so that they are declared unable to receive credit because they do not meet the customer requirements criteria.
Sistem Pendukung Keputusan Penilaian Kinerja Pada Siswa Magang dengan Metode Simple Additive Weighting (SAW) Nita Noptapia Sihombing; Rima Tamara Aldisa; Yudika Parulian Simatupang
Bulletin of Computer Science Research Vol. 4 No. 2 (2024): Februari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i2.331

Abstract

The apprenticeship program is a learning activity in the field that aims to introduce and develop students' skills in a real work environment. Students who will take part in internships need to prepare as well as possible, not just focusing on the academic competencies they learn at school. On the other hand, they must also have experience, knowledge, and insight into a wide world of work. So far, appraising the performance of apprentice students is primarily based on disciplinary criteria. However, there are actually several other criteria that can also be taken into consideration in assessing the performance of apprentice students, such as Discipline, Performance, Creativity, Teamwork, Adaptation and Mastery of Work Materials. To conduct a more comprehensive assessment of the performance of apprentice students, a decision support system is needed. In this study, the method used is Simple Additive Weighting (SAW). The choice of this method is due to its ability to handle assessments by considering the priority values or weights that have been assigned to each criterion. The results of this study indicate that Alternative A4, represented by "Rezka," is the best alternative with a Vi value of 0.9472.