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All Journal Jurnal Riset Informatika Innotech
Rika Astuti
Universitas Cyber
Computer Science & IT

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Naive Bayes and Decision Tree Algorithms for BRI Life Sharia Insurance Product Classification Rika Astuti
Jurnal Riset Informatika Vol. 5 No. 4 (2023): September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (622.697 KB) | DOI: 10.34288/jri.v5i4.246

Abstract

Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are eligible for KIP scholarships, it is necessary to classify scholarship recipients correctly using data mining classification techniques. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.
Identifikasi Perbandingan Prediksi Harga Saham pada PT XYZ Menggunakan Teknik Algoritma FB Prophet dan Random Forest pada Metode CRISP-DM Rika Astuti; Dwi Chandra Bagaskoro Setyoko
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 3 No 1 (2026): Innotech Issue Januari 2026
Publisher : Universitas Siber Indonesia

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Abstract

This research aims to compare the effectiveness of two stock price prediction algorithms, namely FB Prophet and Random Forest, using the CRISP-DM method. The main focus of the study is on the stocks of PT XYZ, with data taken from the period of March 1, 2019, to March 1, 2024. Stock price prediction is a significant topic in the field of finance as it can help investors make better decisions. Both algorithms are evaluated based on error rates measured using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). This study demonstrates that both FB Prophet and Random Forest algorithms have their respective advantages in predicting stock prices. This research is also expected to contribute to the scientific literature in the fields of data mining and stock price prediction analysis.
Co-Authors Dwi Chandra Bagaskoro Setyoko