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All Journal Telcomatics Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan
Ahmad Budi Trisnawan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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PENENTUAN KELULUSAN SISWA YAYASAN CERDAS BAKTI PERTIWI DENGAN MENGGUNAKAN ALGHORITMA NAÏVE BAYES DAN CROSS VALIDATION Elkin Rilvani; Ahmad Budi Trisnawan; Priasnyomo Prima Santoso
Jurnal Pelita Teknologi Vol 14 No 2 (2019): September 2019
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (390.023 KB) | DOI: 10.37366/pelitatekno.v14i2.240

Abstract

Yayasan Cerdas Bakti Pertiwi sebagai unit pelaksana pendidikan non formal dalam mencapai tujuan pendidikan dan pelatihan menyiapkan peserta didik untuk kedinasan bintara yang bisa menjadi penerus bangsa untuk dapat menjawab tantangan zaman. Dalam kegiatan operasionalnya siswa dituntut agar lulus namun untuk mencapai kelulusan banyak faktor yang menjadi tantangan hambatan siswa. Pada penelitian ini akan memecahkan permasalahan faktor hambatan kelulusan dengan data mining untuk menentukan kelulusan siswa. Teknik data mining adalah klasifikasi dengan metode Naïve Bayes dan Cross Validation, maka didapatkan hasil penentuan kelulusan siswa dengan persentase keakuratan sebesar 99,4 %.Dalam penelitian menggunakan data sebanyak 500 siswa yang terdiri dari 443 siswa laki-laki dan 57 siswa perempuan.
Analisis Efektivitas Algoritma Komputasi pada Sistem Pendukung Keputusan Ahmad Budi Trisnawan
Telcomatics Vol. 10 No. 1 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i1.11022

Abstract

Decision Support Systems (DSS) play a crucial role in assisting decision-makers by analyzing large and complex datasets to generate actionable insights. The core performance of a DSS relies heavily on the computational algorithms embedded within its structure, which are responsible for data processing, pattern recognition, and prediction. This study aims to evaluate the effectiveness of three commonly used algorithms Decision Tree (C4.5), Naive Bayes, and K-Nearest Neighbor (K-NN) in supporting decision-making processes using healthcare-related data. The analysis focuses on three performance metrics: classification accuracy, computational speed, and memory usage. A benchmark dataset on heart disease from the UCI Machine Learning Repository was utilized for empirical testing. Results indicate that the Decision Tree algorithm achieved the highest accuracy (92%) and interpretability, making it well-suited for transparent decision-making contexts. Naive Bayes demonstrated the fastest processing time and lowest memory consumption, making it ideal for real-time or resource-constrained systems. Meanwhile, K-NN showed moderate performance but was sensitive to parameter tuning and data volume. These findings suggest that algorithm selection should be aligned with system requirements and resource availability. The study contributes to the development of more efficient and tailored decision support systems by providing empirical evidence of algorithmic strengths and limitations across multiple evaluation dimensions.
Co-Authors Elkin Rilvani Priasnyomo Prima Santoso