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helfy susilawati
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FAKULTAS TEKNIK, UNIVERSITAS GARUT Jalan Jati No. 42B Tarogong Kaler Kab. Garut 44151 Jawabarat, Indonesia telp. 0262-540007 fax. 0262-540181 email : jurnal.fuse@uniga.ac.id
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INDONESIA
Fuse-teknik Elektro
Published by Universitas Garut
ISSN : 27978745     EISSN : 2797815X     DOI : http://dx.doi.org/10.52434/jft.v2i2
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Teknik Elektro, teknik tenaga listrik, mesin-mesin listrik dan sistem konversi energi, elektronika dan aplikasi, teknik komputer, teknologi informasi dan sistem kontrol, telekomunikasi dan teknik biomedik.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 132 Documents
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Klasifikasi Kelayakan Air Minum Berbasis Pembelajaran Mesin Menggunakan Artificial Neural Network dan Random Forest Fega Yudistira; DILLA RESTU AGUSTHIANI; EKI AHMAD ZAKI HAMIDI; EDI MULYANA
Fuse-teknik Elektro Vol 6 No 1 (2026): Fuse-teknik Elektro
Publisher : Fakultas Teknik Universitas Garut

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

Abstract

Drinking water quality is an important factor affecting public health. Accurate water potability classification is essential to ensure safe water consumption. This study aims to compare the performance of Artificial Neural Network (ANN) and Random Forest algorithms for drinking water potability classification using the Water Potability dataset. The dataset consists of 3,276 samples with nine water quality parameters, including pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. Data preprocessing involved missing value handling, normalization using MinMaxScaler, and train-test splitting with 70:30 and 80:20 scenarios. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the 80:20 train-test split produced the best performance for both models. Under this scenario, ANN achieved an accuracy of 0.66, precision of 0.67, recall of 0.88, and F1-score of 0.76, while Random Forest achieved an accuracy of 0.66, precision of 0.66, recall of 0.87, and F1-score of 0.75. The results showed that ANN achieved slightly higher precision, recall, and F1-score values ​​than Random Forest. However, the performance difference between the two models was relatively small, so both models can be considered competitive in classifying drinking water quality.
Optimasi Sudut Terminal Rudal Menggunakan Simulasi Monte Carlo Probabilistik Shilfa Nisa Agustin; Y. H. Yogaswara; Robertus Heru Thirarjanto; Larasmoyo Nugroho
Fuse-teknik Elektro Vol 6 No 1 (2026): Fuse-teknik Elektro
Publisher : Fakultas Teknik Universitas Garut

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

Abstract

Modern missiles, loitering munitions, and glide bombs are increasingly utilized due to their high-precision strike capabilities. However, in urban and complex environments where targets are often hidden behind obstacles, these systems frequently fail to achieve optimal terminal impact angles. This study addresses the limitations of conventional GPS/INS systems by optimizing the terminal angle using a numerical statistical approach based on Monte Carlo simulation. The simulation was conducted on an online cloud platform with 10,000 iterations to handle operational uncertainties. The results demonstrated a reduction in vertical angle error of up to 31.6%, an increase in altitude error of 10.7%, and an increase in hit probability to 84.8% compared to conventional methods. This approach enables missiles to achieve a more effective balance between penetration capability and impact accuracy, which is critical in contested and cluttered battlefields. This study concludes that integrating Monte Carlo simulation provides a more robust solution for improving attack effectiveness in complex environments. Future research is recommended to include hardware-in-the-loop testing and artificial intelligence integration.  

Page 14 of 14 | Total Record : 132

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