Article Per Year (5 Year)
p-Index From 2021 - 2026
1.006
P-Index
This Author published in this journals
All Journal Telcomatics Jurnal UNITEK Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan International Journal of Applied Mathematics and Computing. International Journal of Mechanical, Industrial and Control Systems Engineering Green Engineering: Journal of Engineering and Applied Science Global Science: Journal of Information Technology and Computer Science
Ahmad Budi Trisnawan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
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.
Adaptive Edge-AI Framework for Real-Time Cyber-Physical Systems in Smart Cities with Resource-Constrained IoT Devices Benny Martha Dinata; Ahmad Budi Trisnawan; Eram Abbasi
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.170

Abstract

This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effectiveness of this framework in real-world smart city applications such as traffic monitoring, environmental sensing, and smart utilities management. The proposed method integrates lightweight AI models, edge computing, and adaptive resource management techniques, including Federated Learning and Neural Architecture Search, to ensure optimal performance while addressing hardware constraints. The main findings reveal that the framework significantly improves real-time inference speed, reduces energy consumption of IoT devices, and enhances CPS reliability by minimizing communication delays and ensuring continuous system operation despite network disruptions. The application of this framework to smart transportation and urban utilities further demonstrates its potential to optimize city management processes. The study concludes that the Adaptive Edge-AI framework offers a promising solution for smart cities, enhancing operational efficiency, sustainability, and resilience. It is recommended for integration into smart city infrastructures to enable better resource management and decision-making in real-time applications.
Sustainable Precision Agriculture Irrigation System Using Edge Computing and Renewable Energy Integration for Water Conservation and Climate Adaptation Agus Wantoro; Ferly Ardhy; Fahlul Rizki; Ahmad Budi Trisnawan; Yulaikha Mar’atullatifah; Rachmat Setiabudi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.288

Abstract

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery Suyahman Suyahman; Deny Prasetyo; Ahmad Budi Trisnawan; Ardy Wicaksono; Muhamad Furqon
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.402

Abstract

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.
Co-Authors Agus Wantoro Ardy Wicaksono Benny Martha Dinata Deny Prasetyo Elkin Rilvani Eram Abbasi Erlita Sulistiati Fahlul Rizki Ferly Ardhy Muhamad Furqon Priasnyomo Prima Santoso Rachmat Setiabudi Suyahman Suyahman Syed Asif Ali TUTI SUSILAWATI Yulaikha Mar’atullatifah