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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) KLIK: Kajian Ilmiah Informatika dan Komputer
Jhiro Faran
Universitas Nasional, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids Jhiro Faran; Rima Tamara Aldisa
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4313

Abstract

Class assignments are carried out to focus students on the subjects that will be studied during Senior High School (SMA). Class majors are generally carried out in class of all the main values used in the class majoring process. This is a problem with the class majoring process, where mistakes often occur in the class majoring process. Mistakes regarding class majors made by students will have quite a fatal impact on the student, apart from not being able to change classes, it will also have a laziness effect on the student because it does not match the student's abilities. Solving this problem can be done using a technique called data mining. The solution to this problem is done using clustering. The K-Medoids algorithm is the algorithm used to solve the problems in this research. The process of grouping or forming clusters in the K-Medoids algorithm is based on calculating the closest distance to each object, calculating the closest distance is based on determining the centeroid value first. The K-Medoids algorithm can form 2 (two) clusters according to existing class majors. The results obtained show that there are 3 (three) alternatives included in cluster 1 and also 12 (twelve) alternatives included in cluster 2.
Analisis Data Mining dalam Komparasi Average Linkage AHC dan K-Means Clustering untuk Dataset Facebook Live Sellers Jhiro Faran; Rima Tamara Aldisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6892

Abstract

Facebook Live is a social media platform owned by Facebook that allows users to broadcast videos directly or live stream via the internet. Users can share moments in real-time with friends, followers, or members of certain groups. The platform allows anyone with a Facebook account to create live video broadcasts from a mobile device or computer equipped with a webcam. Many Micro, Small and Medium Enterprises (MSMEs) use Facebook Live as a tool to sell products or services directly to their audience. This strategy is increasingly popular in direct marketing on social media, especially in countries such as China and Thailand. Sellers on Facebook Live, known as Facebook Live Sellers, broadcast live on the platform to introduce products or services. They explain all the features offered, answer questions from viewers, and encourage them to make a purchase immediately. To increase buyer interest, they often offer special offers or discounts. Facebook Live Sellers can also be considered a form of influencer marketing, where individuals or businesses build a loyal following and use their influence to promote products and services. Despite the potential benefits, Facebook Live Sellers also face challenges. They interact directly with potential buyers, who may sometimes be dissatisfied with the product offered or the way the seller promotes it. Therefore, evaluations such as comments, reactions (such as like, unlike, angry), and other interactions during broadcasts are important. This research aims to group potential buyers' reactions during Facebook Live broadcasts as a strategy to overcome several problems in direct sales via this platform. In addition, grouping by the number of likes and comments can help sellers identify the most active groups of buyers and have the potential to become loyal customers. The number of data samples was determined using the Solvin method so that the dataset that became the data sample was 341 data. The methods used for grouping are K-Means and AHC (Average linkage) with the final results showing that the amount of data grouped into three clusters by both methods is the same, with most of the data being in Cluster 0, namely 98.5% of the total data sample. . Cluster 1 has a small amount of data, namely 0.6%, while Cluster 2 has 0.9% of the data sample.
Implementasi Metode MAUT dengan Menerapkan Pembobotan ROC Dalam Pemilihan Ketua Himpunan Mahasiswa Jhiro Faran; Rima Tamara Aldisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6471

Abstract

The election of the head of the student association is an important process in maintaining the sustainability and progress of the organization. However, determining the best association chairperson can be a complex and subjective task. Therefore, we need an effective decision support system to assist in the selection process. This study aims to develop a Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT) method with ROC weighting in selecting the best association chairman. The MAUT method is one of the methods used to overcome complexity in decision making by considering several relevant attributes. The results of this study are expected to provide objective recommendations in the process of selecting the best chairman of the association. The results of this study using the MAUT method are with a value of 0.775 as the highest alternative chosen as chairman of the association. By utilizing the MAUT method, this system can assist the relevant election committee in obtaining better and more informed decisions. This proposed decision support system can also be used as a guide for other schools facing similar challenges in selecting association leaders
Sistem Pendukung Keputusan untuk Penentuan Jurusan dengan Metode Simple Additive Weighting (SAW) dan Pembobotan ROC Jhiro Faran; Rima Tamara Aldisa
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1541

Abstract

When someone chooses a major, they will focus on a particular area of study throughout their learning period. Usually, each department has a curriculum that has been specially prepared to provide in-depth understanding, specific skills and knowledge in that discipline. Decisions in choosing a major are often based on personal interests, talents, environmental influences, including parental views and personal career goals. Some courses may have specific entry requirements or may involve developing specific skills over the course of your studies. Apart from that, choosing a major can also influence the direction and career you will follow after graduating. Therefore, it is very important to make careful decisions and obtain the views of educational advisors or career counselors according to personal educational and career goals. This research discusses studies at a high school, where there are only two major choices, namely science and social studies. In the process of selecting majors, schools use special selection methods other than the general test route. The problem that arises is the need for flexibility and accuracy in decision making. To overcome this problem, researchers used a decision support system method by combining the ROC and SAW methods. ROC is used to calculate criteria weights, while SAW is used to assess preferences. The results of applying these two methods show that the initial major choices made by students are often different from the majors determined using these two methods. A total of five students were assigned to major in science, including alternatives A2, A3, A7, A8 and A10, while five other students were assigned to major in social sciences, namely alternatives A1, A4, A5, A6 and A9.
Penerapan Algoritma K-Means Data Mining untuk Clustering Kinerja Karyawan Koperasi Jhiro Faran; Agung Triayudi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1728

Abstract

Employees are people who are the main element in every organization/company. An employee is someone who can carry out work and provide the results of their work to the employer or agency where the employee works, where the results of their work are in accordance with the profession or work based on their expertise. The role of employees in cooperatives is the same as the role of employees in general in every other organization/company. Giving rewards to employees is a form of company appreciation for its employees. Reward or recognition is a form of gratitude from the company for the dedication and performance of employees, namely those who have good quality work and have met the criteria for employees with good performance. The problem faced is that currently there is no process that has been carried out to group employee performance. Grouping employee performance is a fairly important problem and must be resolved immediately by the company. The solution to this problem can be solved by paying attention to patterns based on processes or data that occurred in the past. Data mining is the right way to solve this problem. Data mining is a process of processing data and extracting data to get information back from a collection of data. Clustering is a process of grouping data contained in a dataset. Grouping data in a dataset using clustering is done based on the similarity values or characteristics of each data. The K-Means algorithm is part of clustering data mining, where the K-Means algorithm can be used to form new groups of data. The results obtained from the research are that the formation of new groups/clusters is based on a total of 15 data, so there are 2 (two) clusters where in cluster 1 there is 7 data and cluster 2 there is 8 data
Sistem Pendukung Keputusan Rekomendasi Aplikasi Pembuat Kuis Edukasi Untuk Pembelajaran Menerapkan Metode OCRA dan Pembobotan ROC Jhiro Faran; Rima Tamara Aldisa
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4045

Abstract

Education quizzes are a form of interactive test or game designed to support the learning process in a fun and interactive way. Despite the availability of many applications for creating educational quizzes, users often face difficulties in selecting the ones that suit their learning needs and objectives. Therefore, this research suggests the use of a Decision Support System by implementing the OCRA and Weighted Rank Order Centroid (ROC) methods to recommend educational quiz maker applications. The ROC method is utilized to determine the weights or relative values of predefined criteria, thus facilitating the ranking process based on the importance of each criterion. Furthermore, the OCRA method is employed to analyze the operational competitiveness level of various alternative educational quiz maker applications. The research findings indicate that out of the 5 evaluated alternatives, ProProfs Quiz Maker scored the highest with a value of 1.515, making it the top choice for online learning media. Thus, the Decision Support System based on OCRA and ROC provides accurate recommendations for selecting educational quiz maker applications that align with learning needs and objectives.
Co-Authors Agung Triayudi Rima Tamara Aldisa