JLICS: Journal of Lasalle Informatics and Computer Science

Sistem Pendukung Keputusan Seleksi Pemenang Lomba Kolintang Menggunakan Metode Technique for Order Preference by Similarity to Ideal Solution

Billy Tuerah — 2023-12-20

The Kolintang competition is one of the traditional music festival competitions held in Indonesia. The competition's winners can come from various groups of people who are determined based on the jury's decision. In general, the group that won the competition was the group that got the best score in performing kolintang music. Implementing the kolintang competition cannot be separated from the involvement of the jury, who is the decision maker for the competition's winner. The problem in deciding the winner of the match was that there needed to be a fixed criterion in judging the playing of kolintang music, which led to a lengthy discussion process from the jury. This research provides a solution by building a Decision Support System for the Selection of Kolintang Winners Using the Technique for Order Preference by Similarity to the Ideal Solution Method to solve complex problems by providing comparisons of alternatives that are ideal solutions. This system was built using the Rational Unified Process methodology as a development guide and Unified Modeling Language modeling as a system design. The programming language used is object-based programming, namely Hypertext Preprocessor. This research results in a Decision Support System that can assist the jury in deciding the winner of the Kolintang competition according to the criteria given.

Aplikasi Klasifikasi Peminatan Studi Lanjut Dengan Menggunakan Metode K-Nearest Neighbor

Claudio Untu — 2023-12-20

Specialization in advanced studies (Strata-2) is the ideal or goal of some people who want to deepen their knowledge and skills according to their interests and talents. However, many undergraduate graduates are still confused about what field of study to pursue. Students only follow friends in selecting courses each semester when taking undergraduate education. Choosing the wrong major that does not match your interests and talents results in a mismatch between work and talent. The K-Nearest Neighbor (KNN) method is a method that uses a supervised algorithm where the results of the new query instance are classified based on the majority of the class labels in the KNN. The purpose of the KNN algorithm is to classify new objects based on attributes and training data. Based on the tests that have been carried out, determining the classification of further study specialization using the KNN method with a value of k = 5 and training data totaling 148 actual data, with an accuracy level of 86.486%, a recall of 86.486% and an error rate of 13.513%.

Sistem Pendukung Pengambilan Keputusan Penentuan Lokasi Pembukaan Usaha Kafe Menggunakan Metode Simple Additive Weighting

Nathania The — 2023-12-20

Cafes are one of the places to relax that are most in demand by various groups of people. Therefore, many entrepreneurs have opened cafe businesses, and some plan to do so. There is still a lot of location selection to open a cafe business that is not following the supporting criteria for success, so frequent decision making errors occur, which lead to the closure of the business. This, of course, results in losses for the cafe owner. In this study, the Decision Support System for Determining the Location of Opening a Cafe Business was created using the Simple Additive Weighting (SAW) method. The SAW method can select the best alternative from many existing options because of the ranking process after determining the weight of each attribute. The choice in question is the best location based on existing criteria, namely the selection of an area with access so that it is easy to visit, visibility (easily visible), the size of the parking lot, the distance to competition, and the intensity of congestion. The modeling tools used are Flowcharts and Entity Relationship Diagrams (ERD), as well as the eXtreme Programming (XP) system development methodology that can solve problems in application development. The results of this study are to produce recommendations for strategic locations to open a cafe business according to the criteria and weights entered.

Evaluasi Penggunaan Aplikasi Pengenalan Model Rumah Berbasis Virtual Reality Technology Acceptance Model

Aiderman Makakombo — 2023-12-20

The housing marketing process has been highly developed, and developers are competing to promote their housing in the most attractive way possible. Fairs make an excellent place to encourage model homes. In exhibitions, there are usually brochures that are often distributed by those who market housing. Virtual Reality (VR) is a medium that allows users to experience real life even though they are only in cyberspace. In this research, a VR-based home recognition application will be built to promote the house model. Then, most of the focus of this research refers to testing the attitude of user acceptance of the application to be made. Satisfaction with the application will be analyzed using the Technology Acceptance Model (TAM) approach based on the relationship between PEOU, PU, ATU, and BI. The results are that the level of public acceptance of the VR-based home model recognition application is very high.

Pengelompokan Data Mahasiswa menggunakan Algoritma K-Means

Jonathan Tontong — 2023-12-20

New Student Registration at Universitas Katolik De La Salle Manado (UKDLSM) is open every year starting in February. Most prospective students come from within the North Sulawesi province; many even come from outside the province, such as Gorontalo, South Sulawesi, Papua, and Maluku. This student data will later be useful and produce much information if processed properly. Through this data, you can see the distribution of students who enter this university from which areas, the type of high school or vocational school equivalent, and many more. Therefore, this research develops a data mining-based application to cluster student data at UKDLSM using the K-Means Clustering algorithm. The data parameters processed include the district/city of origin, study program, school of origin, and department at the school of origin. The clustering process is done by selecting the value K=3 according to student data of the 3 study programs with the highest number of students. It produces the amount of data in each cluster, namely cluster 1 with 408 data, 2 with 19 data, and cluster 3 with 58 data, with several iteration processes: 7 iterations and Silhouette Score: 0.56.