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All Journal Telcomatics Jurnal UNITEK Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan International Journal of Applied Mathematics and Computing.
Ahmad Budi Trisnawan
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Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics

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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.
Algorithmic Simulation for Optimization in Combinatorial Mathematics Using Heuristic Techniques Ahmad Budi Trisnawan; Syed Asif Ali; Erlita Sulistiati
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.274

Abstract

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.
Pemanfaatan Machine Learning untuk Peningkatan Akurasi Sistem Pendukung Keputusan Prediktif Ahmad Budi Trisnawan; Tuti Susilawati
JURNAL UNITEK Vol. 18 No. 2 (2025): Juli-Desember 2025
Publisher : Sekolah Tinggi Teknologi Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/unitek.v18i2.1702

Abstract

The rapid development of information technology and the increasing availability of large-scale data have driven the need for decision-making systems that are more intelligent, faster, and more accurate. Conventional Decision Support Systems (DSS) generally rely on rule-based approaches or simple statistical analyses, which have limitations in recognizing complex patterns and are less adaptive to changes in data. Therefore, the integration of machine learning technology represents a strategic solution to enhance the predictive capability and decision quality produced by DSS. This study aims to analyze the utilization of machine learning algorithms in improving the accuracy of predictive decision support systems. The method employed is a comparative experimental approach involving three algorithms, namely Decision Tree, Random Forest, and Support Vector Machine. The dataset used consists of historical decision outcomes along with their determining variables derived from a case study. The research stages include data cleaning, normalization, training–testing set splitting, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of machine learning significantly improves DSS accuracy compared to conventional methods. Random Forest achieved the best performance with an accuracy of 91%, followed by Support Vector Machine at 87% and Decision Tree at 84%. In addition to improving accuracy, the integration of machine learning also enhances data processing efficiency and decision-making speed. These findings demonstrate that artificial intelligence–based DSS has great potential for application across various domains, such as business, healthcare, education, and government.
Co-Authors Elkin Rilvani Erlita Sulistiati Priasnyomo Prima Santoso Syed Asif Ali TUTI SUSILAWATI