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All Journal Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Kendali Teknik dan Sains
Rizki Riandi
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Computer Science & IT

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Sistem Temu Balik Informasi Data Penerima Bantuan PIP pada Sekolah Menengah Pertama Menggunakan Metode TF-IDF Achmad Faris Fadhlulah; Dika Arif Sihombing; Muhammad Fahri Rinanda; Rizki Riandi; Sotar Ferdinand Hutabarat
Jurnal Kendali Teknik dan Sains Vol. 4 No. 1 (2026): Januari: Jurnal Kendali Teknik dan Sains
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jkts-widyakarya.v4i1.5875

Abstract

The Indonesia Smart Program (Program Indonesia Pintar/PIP) is a government initiative aimed at ensuring equal access to education for students from underprivileged families, including those at the junior high school (SMP) level. However, at the school level, the management of PIP recipient data still faces several challenges, particularly in data searching and utilization, due to the increasing volume of data and the use of simple or manual search methods. These conditions can lead to delays in obtaining information and reduce the accuracy of decision-making. Therefore, an effective information retrieval system is needed to manage and search PIP recipient data efficiently. This study aims to design and develop an Information Retrieval System for PIP recipient data at the junior high school level using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The TF-IDF method is applied to assign weights to terms in each document, enabling the system to identify and rank documents based on their relevance to user queries. The test results show that the system is able to measure document relevance accurately, where documents D3 and D4 obtain the highest similarity value of 0.099586089 and are classified as highly relevant, while other documents show lower similarity values down to zero. These results are also supported by graphical visualization, which helps users compare relevance levels more clearly. Thus, the implementation of the TF-IDF method has proven to be effective in supporting accurate, efficient, and systematic searching and management of PIP recipient data at the junior high school level.
Data Mining Pengelompokkan Jenis Penyakit pada Balita Berdasarkan Usia Menggunakan Metode Clustering Achmad Faris Fadhlullah; Dika Arif Sihombing; Rizki Riandi; Suri Handayani
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 3 No. 4 (2025): JNovember :urnal Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v3i4.5837

Abstract

Toddlers are a vulnerable age group to various types of diseases due to their immune systems that are still developing. Limited utilization of medical record data and the lack of structured information regarding disease patterns in toddlers based on age and causative factors have resulted in suboptimal prevention and treatment efforts. Therefore, an approach is needed to systematically classify toddler disease data. This study aims to apply data mining techniques using the clustering method with the K-Means algorithm to group types of diseases in toddlers based on age and causative factors. The variables used in this study include toddler age, type of disease, and causative factors. The data were obtained from RSUD Dr. R. M. Djoelham Binjai and processed using MATLAB software with three clusters. The results show that the K-Means algorithm successfully groups toddler disease data into three clusters with different characteristics. The first cluster is dominated by toddlers aged 0–11 months with appendicitis caused by genetic factors. The second cluster is dominated by toddlers aged 1–3 years with diarrhea caused by environmental factors and has the largest number of members. Meanwhile, the third cluster is dominated by toddlers aged 0–11 months with sore throat caused by environmental factors. The clustering results indicate a relationship between toddler age, disease type, and causative factors, which can be used as supporting information for decision-making in the prevention and treatment of toddler diseases.
Co-Authors Achmad Faris Fadhlulah Achmad Faris Fadhlullah Dika Arif Sihombing Muhammad Fahri Rinanda Sotar Ferdinand Hutabarat Suri Handayani