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All Journal Jurnal Riset Informatika Journal of Informatics and Advanced Computing
Dea Fitra Ningrum
Universitas Pancasila
Computer Science & IT

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Prediksi Harga Smartphone berdasarkan Spesifikasi menggunakan K-Nearest Neighbors Dea Fitra Ningrum; Shabrina Putri Ramadhani; Iman Paryudi; Ionia Veritawati; Sri Rezeki Candra Nursari
Journal of Informatics and Advanced Computing (JIAC) Vol 4 No 2 (2023): Journal of Informatics and Advanced Computing (JIAC)
Publisher : Teknik Informatika Universitas Pancasila

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Abstract

Di era teknologi informasi yang terus berkembang, pasar ponsel pintar menjadi salah satu pasar konsumen yang paling dinamis dan beragam. Pembeli seringkali dihadapkan pada banyak pilihan dalam memilih smartphone baru yang sesuai dengan kebutuhan dan budgetnya. Penelitian ini bertujuan untuk memprediksi harga smartphone berdasarkan spesifikasi. Metodologi yang digunakan adalah algoritma K-Nearest Neighbor dengan menggunakan Euclidean distance, membagi dataset menjadi 70% data latih dan 30% data uji. Model ini telah diuji sebanyak 2 kali, pengujian pertama menggunakan k sebesar 1 dan menghasilkan akurasi sebesar 57%, sedangkan pengujian kedua menggunakan nilai k sebesar 3 dan memperoleh akurasi sebesar 65%.
PREDICTION OF PIP RECIPIENTS USING K-NEAREST NEIGHBOR AT MI NURUL QOLBI Dea Fitra Ningrum; 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.
Co-Authors Desti Fitriati Iman Paryudi Ionia Veritawati Shabrina Putri Ramadhani