Anomaly detection on displacement rates and deformation pattern features using tree-based algorithm in Japan and Indonesia

Wibowo, Adi; Purnama, Satriawan Rasyid; Pratama, Cecep; Heliani, Leni Sophia; Sahara, David P.; Wibowo, Sidik T.

doi:10.1016/j.geog.2022.07.003

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…To select relevant features from the network traffic features shown in Table I, the author uses the feature importance approach [16,17]. Feature importance is the influence that a feature has in predicting the classification results [18].…”

Section: Methodsmentioning

confidence: 99%

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Sanmorino

Gustriansyah

Alie

2022

JUITA

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In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.

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Section: Methodsmentioning

confidence: 99%

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Sanmorino

Gustriansyah

Alie

2022

JUITA

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“…In Indonesia, natural events such as earthquakes occur quite frequently on several islands, including Jawa, (Supendi et al, 2022), Sulawesi (Jena et al, 2020), Kalimantan, Maluku (Sianipar et al, 2022), and other smaller islands (Ramdani et al, 2019). This is due to Indonesia's location surrounded by three tectonic plates: the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate (Wibowo et al, 2023). Various methods have been developed to create sensor systems that detect underwater earthquakes and swiftly transmit information to land (Z.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Fiber Bragg Grating Accelerometer for Underwater Vibration Detection

Gani,

Purnamaningsih,

Rahardjo

2024

jppipa, pendidikan ipa, fisika, biologi, kimia

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In this paper, the results of the characterization of a Fiber Bragg Grating (FBG) Accelerometer for detecting vibrations underwater are reported. The FBG Accelerometer, consisting of three FBGs, is utilized to detect underwater vibrations in three-dimensional directions. A water pump, with positions varied from 0 to 10 cm, is employed as the vibration source. Furthermore, the experimental results are presented in the form of the peak wavelength shift reflected by the FBG (ΔλB) and frequency. From the experiment results, it is shown that with increasing distance, ΔλB decreases linearly with successive gradients of 0.0058 nm/cm; 0.0059 nm/cm; and 0.0045 nm/cm. for FBG X, Y, and Z. It is also shown that with increasing distance, there is a decrease in frequency from 50 Hz for FBGs X, Y, and Z to 39 Hz; 38 Hz; and 40 Hz for FBGs X, Y, and Z respectively.

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Multi-Class Imbalance Classification of Diabetes Cases Using Light Gradient Boosting Machine

Manfaati Nur,

Rosadi,

Abdurakhman

2024

ITM Web Conf.

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Diabetes is the third leading cause of death in Indonesia. Diabetes is considered a silent killer because it kills slowly and triggers various complications of chronic diseases in the body of the sufferer. Early detection of diabetes is very important to reduce the risk of more serious health problems and reduce the country's socio-economic losses in diabetes management. Machine learning classification is an alternative method that can be used for early detection of diabetes by predicting category labels from observed data. This study aims to classify diabetes using the Light Gradient Boosting Machine (LGBM) method with Synthetic Minority Oversampling Technique of Nominal and Continuous (SMOTENC). The SMOTENC oversampling method is used to handle the imbalance problem in the dataset used, while the LGBM method is used for multi-class classification of diabetes. The results showed that by applying the SMOTENC technique, a more balanced data distribution was obtained, so that when used in the classification process using LGBM, it resulted in high model performance. Based on the confusion matrix, the accuracy value is 90%.

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Anomaly detection on displacement rates and deformation pattern features using tree-based algorithm in Japan and Indonesia

Cited by 4 publications

References 23 publications

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Characterization of Fiber Bragg Grating Accelerometer for Underwater Vibration Detection

Multi-Class Imbalance Classification of Diabetes Cases Using Light Gradient Boosting Machine

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