Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Information Technology (BIT) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
Nur Azizah Harahap
Universitas Pembangunan Panca Budi
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analysis of Inpatient Data Using Cluster Analysis on Simulation Dataset Andysah Putera Utama Siahaan; Nur Azizah Harahap; Rahma Yuni Simanullang; Khairunnisa; Puspita Wanny; Utari
Bulletin of Information Technology (BIT) Vol 6 No 1: Maret 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i1.1830

Abstract

This study aims to analyze inpatient data using the K-Means Clustering method on a simulated dataset. The dataset includes various patient-related attributes such as age, billing amount, length of stay, medical condition, and type of admission. Several preprocessing steps were applied, including date conversion, duration calculation, numerical normalization, and one-hot encoding for categorical attributes. The Elbow Method was used to determine the optimal number of clusters, and clustering quality was evaluated using both the Silhouette Score and Davies-Bouldin Index. The analysis results show that the patients can be segmented into three major clusters, each exhibiting distinct characteristics—for example, younger patients with short and low-cost stays, and elderly patients with prolonged and more expensive hospitalizations. The resulting Silhouette Score of 0.14 and Davies-Bouldin Index of 1.74 reflect a moderate clustering performance, yet the model remains informative and meaningful. These clusters provide actionable insights that hospitals can use to optimize their service strategies, improve resource allocation, and enhance operational efficiency. Moreover, the study illustrates the practical application of unsupervised learning techniques in healthcare settings, contributing to data-driven decision-making practices and offering a foundation for further research into patient segmentation.
Perbandingan Kinerja Algoritma Decision Tree dan Random Forest dalam Prediksi Kelulusan Siswa Nur Azizah Harahap; Andika Syahdewa; Fadhlan Ihsan Lubis; Hengki Gunawan; Marsini Sibuea; Darma Juang; Muhammad Amin
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12469

Abstract

Kelulusan siswa merupakan salah satu indikator penting dalam evaluasi proses pembelajaran di bidang pendidikan. Pemanfaatan teknik data mining dapat membantu memprediksi kelulusan siswa secara lebih objektif berdasarkan data akademik dan non-akademik. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Decision Tree dan Random Forest dalam memprediksi kelulusan siswa. Dataset yang digunakan adalah Student Performance Dataset yang diperoleh dari UCI Machine Learning Repository dengan jumlah 649 data dan 33 variabel. Proses penelitian meliputi tahap prapemrosesan data, pembentukan model klasifikasi, serta evaluasi kinerja model menggunakan metrik accuracy, precision, recall, dan confusion matrix. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki nilai akurasi dan precision yang lebih tinggi dibandingkan Decision Tree, sedangkan Decision Tree menunjukkan nilai recall yang lebih baik dalam mendeteksi siswa yang tidak lulus. Temuan ini mengindikasikan bahwa Random Forest lebih unggul dalam menghasilkan prediksi yang tepat, sementara Decision Tree lebih efektif dalam mengenali seluruh kasus ketidaklulusan. Dengan demikian, pemilihan algoritma terbaik perlu disesuaikan dengan kebutuhan analisis dan tujuan penerapan sistem prediksi kelulusan siswa.
Co-Authors Amin, Muhammad Andika Syahdewa Andysah Putera Utama Siahaan Darma Juang Fadhlan Ihsan Lubis Hengki Gunawan Khairunnisa Marsini Sibuea Puspita Wanny Rahma Yuni Simanullang Utari