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All Journal Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Nasional Teknologi Komputer
Fadhlan Ihsan Lubis
Universitas Pembangunan Panca Budi
Computer Science & IT

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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.
SEGMENTASI PORTOFOLIO PRODUK BERBASIS PROFITABILITAS MENGGUNAKAN K-MEANS DAN ATURAN ASOSIASI UNTUK PERANCANGAN STRATEGI BUNDLING PADA KEDAI KOPI Fadhlan Ihsan Lubis
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

high-volume products do not necessarily yield proportionate profit contributions if their cost of goods sold is also high. This study aims to develop a product portfolio segmentation approach that integrates profitability dimensions into clustering and association rule mining processes. The dataset consists of 13,159 transactions and 22,185 itemized rows from Jalan Cerita Kopi & Space over a 180-day period from January to June 2026, supplemented by cost of goods sold data for all 59 products. The methodology employs K-Means clustering utilizing six derived features capturing sales volume, profit margin, total profit contribution, and purchasing behavior. Validation is performed via Elbow, Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index, and validated against Agglomerative Hierarchical Clustering. Association rule mining is executed using the FP-Growth algorithm, after which each rule is re-evaluated using a profit-based utility metric. The results yield four distinct product clusters with a Silhouette score of 0.457, a Davies-Bouldin Index of 0.873, and an agreement level of 0.883 with Agglomerative Clustering measured by Adjusted Rand Index. The primary finding reveals that association rule rankings based on lift and profit utility are virtually uncorrelated, with a Spearman correlation coefficient of only 0.168 and zero overlap among the top ten rules. The rule with the highest lift generated Rp 1,367,000 in profit, whereas the rule with the highest utility yielded Rp 3,309,000 despite a modest lift of 1.08. These findings demonstrate that bundling strategies designed solely on frequency metrics risk guiding business owners toward product combinations that are frequently purchased yet financially sub-optimal.
Co-Authors Amin, Muhammad Andika Syahdewa Darma Juang Hengki Gunawan Marsini Sibuea Nur Azizah Harahap