Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informatika
Sardo Pardingotan Sipayung
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Katolik Santo Thomas Medan
Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Pola Cuaca di Provinsi Sumatera Utara Menggunakan Metode Clustering K-Means ester manalu; Efrans Surbakti; Sardo Pardingotan Sipayung
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Weather is an important factor that influences various sectors of life, such as agriculture, transportation, and community activities. North Sumatra Province has diverse weather characteristics due to differences in geographical conditions; therefore, analytical methods are required to identify weather patterns based on historical data. This study aims to analyze weather patterns in North Sumatra Province using the K-Means clustering method. The data used consist of 50 daily weather records, including air temperature, humidity, and rainfall parameters.The research stages include data collection, data preprocessing, determination of the number of clusters, implementation of the K-Means algorithm, and analysis of the clustering results. The number of clusters used is K = 3 to represent different weather patterns. The clustering results indicate that the cluster representing clear to partly cloudy weather with low rainfall is the dominant cluster, accounting for 40% of the data, followed by the cluster representing humid weather with relatively lower temperatures at 36%, and the cluster representing rainy weather with high humidity at 24%. These results demonstrate that the K-Means algorithm can effectively group weather data based on the similarity of their characteristics. The information generated is expected to support decision-making related to activity planning and weather analysis in North Sumatra Province.
Analisis Pengelompokan Minat Belajar Mahasiswa Menggunakan Algoritma K-Means yoel simbolon; Aritonang Giovani; Sardo Pardingotan Sipayung
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the main elements affecting students' academic success in higher education is their interest in learning. However, direct observation is frequently used to subjectively identify differences in students' learning interests, which could result in inaccurate assessments. Therefore, in order to objectively classify students according to their learning characteristics, a data-driven approach is needed. The purpose of this study is to analyze and categorize students' learning interest levels using the K-Means clustering algorithm. Thirty university students filled out a learning interest questionnaire with a Likert scale of 1 to 5. Attendance at lectures, classroom activity, timely completion of assignments, level of independent study, and interest in the course are among the variables examined. Three clusters—representing high, medium, and low learning interest levels—were created using the K-Means algorithm. Based on the final cluster centroids, the results show that the K-Means algorithm successfully divided the students into three clusters: 11 students with high learning interest, 12 students with moderate learning interest, and 7 students with low learning interest. These results offer an unbiased summary of students' learning environments and can be used as a foundation for creating more focused and efficient teaching methods in higher education.
Penerapan Algoritma K-Means dalam Pengelompokan Indeks Harga Perdagangan Besar (IHPB)Produk Logam,Mesin, dan Perlengkapannya Tahun 2025 Vio Br LumbanBatu; Virzinia Napitupulu; Sardo Pardingotan Sipayung
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Wholesale Price Index (WPI) is an important economic indicator used to measure price changes at the wholesale level. The metal, machinery, and equipment product group plays a strategic role in supporting the industrial and national development sectors. Price fluctuations in this product group need to be analyzed systematically to identify their movement patterns. This study aims to classify the Wholesale Price Index (WPI) of metal, machinery, and equipment products in 2025 using the K-Means clustering algorithm. The data used in this study consist of annual WPI values obtained from the official publications of Statistics Indonesia (BPS). The research stages include data collection, data preprocessing, data normalization using the Min-Max method, determination of the optimal number of clusters, application of the K-Means algorithm, and analysis of clustering results. The number of clusters used is K = 3, representing low, medium, and high price index groups. The results show that the K-Means algorithm is effective in grouping WPI data based on the similarity of price index values. The clustering results provide a clearer overview of price movement patterns and can be used to support economic analysis, price monitoring, and policy decision-making in the industrial sector.
Co-Authors Aritonang Giovani Efrans Surbakti ester manalu Vio Br LumbanBatu Virzinia Napitupulu yoel simbolon