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INDONESIA
Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 432 Documents
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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.
Penerapan Algoritma K-Means Clustering untuk Daerah Penyebaran Sampah Kelurahan Yantria Gusta Nugraha; Maimunah Maimunah; Pristi Sukmasetya
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.v5i2.4158

Abstract

Waste in Indonesia, especially in Magelang City, has become a serious problem due to rapid population growth. Waste management issues, including landfills and collection, need effective handling. Data mining methods, such as K-Means clustering, can help identify areas with the highest levels of waste generation. This approach provides insights for the development of a more focused and efficient waste management strategy, a significant contribution to the improvement of Magelang City. By identifying the areas with the highest waste generation, waste management measures can be directed more efficiently and effectively. This includes increasing the transparency, capacity, and role of waste banks, as well as other efforts to reduce the negative impact of waste on the environment and human health. After clustering, the waste in Magelang City was grouped into 3 clusters according to the supplier area and the volume of waste. Then after the evaluation stage with the silhouette score displays a value of 0.79 which is a good value because it is close to the value of 1.0. With this method, it is expected that the city government in handling waste in Magelang city can be done optimally, efficiently, and on target

Page 44 of 44 | Total Record : 432

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