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All Journal Jurnal Riset Informatika Journal of Informatics and Advanced Computing
Desti Fitriati
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Computer Science & IT

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Journal : Jurnal Riset Informatika

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PREDICTION OF PIP RECIPIENTS USING K-NEAREST NEIGHBOR AT MI NURUL QOLBI Ningrum, Dea Fitra; Desti Fitriati
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.321

Abstract

Education is a key foundation in the development of quality human resources. However, the rising cost of education makes some children unable to attend school due to their parents' financial limitations. To address this problem, the government launched the Indonesia Smart Program (PIP) which provides education funding assistance to eligible students. This research aims to develop an Information System that can predict the eligibility of students to receive PIP assistance using the K-Nearest Neighbors (KNN) algorithm. The data used comes from all students of Madrasah Ibtidaiyah (MI) Nurul Qolbi in the 2022-2023 school year. This research methodology involves testing with a value of k=13 and model evaluation is done using split ratio and cross-validation techniques. The results showed an accuracy of 98.98% from various split ratios (10:90, 20:80, 30:70, 40:60) and an accuracy of 99.24% using the 10-fold cross-validation technique. The accuracy results show excellent performance and provide important significance in the development of prediction systems to help the selection process of aid recipients more efficiently and reduce the administrative burden for schools. However, its application on a wider scale still requires further research, especially to test its consistency and effectiveness in different contexts and with more diverse datasets.
Application of Data Mining Using Methods K-Means Clustering for Clustering Baby Goods Rental Patterns (Case Study: Baby Kha House Store) Roja' Putri Cintani; Shafa Aurelia Putri; Desti Fitriati
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i2.265

Abstract

A baby item rental business is a practical option for parents who want to fulfill their baby's needs without buying them. Babykhahouse is one of the stores that offer rental services for various kinds of mother, baby, and child equipment. As the volume of data related to rental transactions increases, it is also increasingly difficult to know and understand the rental patterns found at the Babykhahouse store. This research aims to get a rental pattern that can later be a consideration for the store in determining promos and adding stock items. In handling these problems, data mining methods, especially clustering, are applied to group data and classify it based on certain groups. The clustering method used in this research is K-Means Clustering, which generates clusters to find similar rental patterns. In this study, 2 (two) types of clusters were formed, where, based on the 2 (two) clusters, it will be known which products have high and low rental rates. Based on the research, the results are 100 data in cluster 0, or the unsold cluster, and 64 in cluster 1, or the sold cluster. Products included in cluster 1 or in-demand clusters are products with a high level of sales.
Co-Authors Adinda Ayu Azzahra Andiani Asfian, Faeqal Hafidh Muhammad Bayu Surya Arafah Hastanto Boing Dwiatmojo Hidayahningtyas Rahmawati Hidayahningtyas Rahmawati Indrawan, Bintang M Zaidani Anwar Maylida Dwi Chairunnisa Maylida Dwi Chairunnisa Muhammad Shevarizky Muhammad Shevarizky Ningrum, Dea Fitra Paundra Nabillah Priangga Sotya W Roja' Putri Cintani Shafa Aurelia Putri Shela Pratiwi Tasya Ilmelia Sabarwati Sianturi Thoriq Ilhami Zaidan Thoriq Ilhami Zaidan Valiant Benvenuto Gianzuriell Yuvan Feberian