Tinjauan Pustaka Sistematis: Penerapan Metode Machine Learning untuk Deteksi Bencana Banjir

Faiza, Intan Mayla; Gunawan, Gunawan; Andriani, Wresti

doi:10.33395/jmp.v11i2.11657

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…Algorithms such as SVM, K-NN, Naive Bayes, ANN, Hierarchical Clustering, Decision Tree and Random Forest are machine learning algorithms that are commonly used for classification [10]. However, the machine learning algorithms AdaBoost, XGBoost and CatBoost also show quite good results in water quality classification [15], [16].…”

Section: Related Workmentioning

confidence: 99%

“…Machine Learning is a branch of artificial intelligence that can overcome this. Machine Learning can focus on utilizing data and algorithms to imitate the human learning process with the aim of increasing accuracy and the level of intelligence [9], [10]. Machine Learning can be used in various contexts to solve various problems by analyzing existing data and executing specific tasks [11], [12].…”

Section: Introduction (Capital 10pt Bold)mentioning

confidence: 99%

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Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Elmeftahi,

Rakhman,

Rahmatulloh

2024

iiroi

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Water, a vital natural resource essential for human existence, is a fundamental human right, indispensable for a dignified life. Despite its significance, the quality of water is often compromised by a myriad of harmful substances, minerals, and contaminants stemming from various sectors like industry, agriculture, residential, and energy. Traditional methods such as WQI and STORET, relying on manual inspection, prove time-consuming. Thus, the integration of machine learning emerges as a pivotal solution to swiftly assess water quality.Numerous studies have explored this challenge using various algorithms; however, a definitive comparison is elusive due to the abundance of existing methods. In response, this research undertakes a meticulous evaluation of seven algorithms to ascertain the optimal approach for water quality classification, employing metric values as benchmarks. Notably, the Random Forest algorithm emerges as the most effective, achieving an impressive accuracy of approximately 84.8%. Following closely are the XGBoost and CatBoost algorithms, showcasing commendable performance with accuracies of 82.9% and 80.2%, respectively. Subsequent rankings include the Decision Tree algorithm at 77.3%, SVM at 72.3%, K-NN at 70.6%, and AdaBoost with the lowest accuracy at 63.33%. This comparative analysis contributes valuable insights for informed decision-making in water quality assessment.

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Section: Related Workmentioning

confidence: 99%

Section: Introduction (Capital 10pt Bold)mentioning

confidence: 99%

Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Elmeftahi,

Rakhman,

Rahmatulloh

2024

iiroi

View full text Add to dashboard Cite

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“…Machine learning is a part of AI that aims to create artificial intelligence. Machine learning also refers to a branch of computer science that focuses on the use of data and algorithms to understand how humans learn and gradually improve its accuracy (Faiza et al, 2022) (Muniroh & Agus Priatno, 2022). Classification is the process of finding patterns intended to estimate the class of unknown objects.…”

Section: Introductionmentioning

confidence: 99%

Classification of Drinking Water Potability With Artificial Neural Network Algorithm

Darmawan,

Muhammad Fatchan,

Andri Firmansyah

2024

IJIST

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Having safe water for consumption is essential for public health in every region. However, water quality is declining in some places, especially to meet human needs for drinking water. There are many efforts to maintain water potability, such as checking to see if there are bacteria or diseases in the water. This research classifies water potability using the Artificial Neural Network method, a technique in the field of machine learning. This research classifies water quality using a python library to analyze data and perform classification. Data is processed through stages such as data cleaning and data division into training and testing. In testing, the data is divided into 20% for testing and 80% for training. The results of the ANN algorithm show 70% accuracy. in conclusion, the ANN model has moderate performance in classifying the feasibility of drinking water. Model improvement is needed to improve accuracy and prediction, including the use of larger and more diverse datasets.

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“…There are two techniques in machine learning: supervised learning and unsupervised learning. [2]. Supervised learning is a machine learning technique for polarized data.…”

Section: Introductionmentioning

confidence: 99%

Application of the K-Means Algorithm for Clustering Production of Capture Fisheries in Maluku Province

Matdoan,

Purnamasari,

Laamena

2023

Pattimura Int. J. Math.

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Maluku Province has large natural resources with various potentials, from the ocean floor to the mainland. Capture fishery products are one of the leading sectors that contribute greatly to the GRDP of Maluku Province. The K-Means clustering algorithm is a suitable algorithm for grouping data objects that have the same identity. The purpose of this study is to cluster districts/cities in Maluku Province based on capture fishery products. The type of data in this study is secondary data sourced from the Maluku Province Central Bureau of Statistics (BPS) Publication in 2022. The result is that there are 3 districts/cities clusters in Maluku Province based on capture fishery products. Cluster 1 with the category of sufficient capture fisheries products, namely the Districts of Tanimbar Islands, Buru, East Seram, West Seram, South Buru, Southwest Maluku, Ambon City and Tual City. Furthermore, Cluster 2 with the category of many capture fishery products, namely the Aru Islands Regency and Southeast Maluku Regency. Furthermore, for Cluster 3, the category of capture fishery products is very large, namely Central Maluku Regency.

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Tinjauan Pustaka Sistematis: Penerapan Metode Machine Learning untuk Deteksi Bencana Banjir

Cited by 6 publications

References 3 publications

Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Classification of Drinking Water Potability With Artificial Neural Network Algorithm

Application of the K-Means Algorithm for Clustering Production of Capture Fisheries in Maluku Province

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