Machine learning-based approaches for tomato pest classification

Pattnaik, Gayatri; Parvathy, Kodimala

doi:10.12928/telkomnika.v20i2.19740

Cited by 9 publications

(7 citation statements)

References 19 publications

(24 reference statements)

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“…Research on factory activities [30] to extract knowledge in the form of sound. Even for precision performance, SVM is best for knowledge extraction activities in the form of tomato fruit textures [31]. Optimizing problem-solving with SVM using (5).…”

Section: Svm Methodsmentioning

confidence: 99%

A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

Abidin,

Othman,

Hassan

et al. 2024

Int. J. Adv. Intell. Informatics

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Knowledge Extraction Model (KEM) is a system that extracts knowledge through an IoT-based smart waste bin emptying scheduling classification. Classification is a difficult problem and requires an efficient classification method. This research contributes in the form of the KEM system in the classification of scheduling for emptying waste bins with the best performance of the Machine Learning method. The research aims to compare the performance of Machine Learning methods in the form of Decision Tree, Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Multi-Layer Perceptron, which will be recommended in the KEM system. Performance testing was performed on accuracy, recall, precision, F-Measure, and ROCS curves using the cross-validation method with ten observations. The experimental results show that the Decision Tree performs best for accuracy, recall, precision, and ROCS curve. In contrast, the K-NN method obtains the highest F-measure performance. KEM can be implemented to extract knowledge from data sets created in various other IoT-based systems.

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Section: Svm Methodsmentioning

confidence: 99%

A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

Abidin,

Othman,

Hassan

et al. 2024

Int. J. Adv. Intell. Informatics

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show abstract

“…n this study the decision tree method was used as a method for data classification. Classification is the process of identifying an object and can be in the form of data (Pattnaik & Parvathi, 2022) (Elmannai & Al-Garni, 2021). The classification that will be carried out is by grouping data based on the class of each data (Ali, Yusro, Hitam, & Ikhwanuddin, 2021).…”

Section: Methodsmentioning

confidence: 99%

Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method

Cantika,

Yanris,

Hasibuan

2023

SinkrOn

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SIM card (Subscriber Identification Module) card is a physical electronic device that is the integrated circuit of the internet. Sim cards are used by the public as a place to store quotas for internet, phone calls and SMS. There are many types of SIM cards that are used by the public, such as Telkomsel cards, XL cards, Exis cards and Smartfren cards. There are some people who are interested and use Telkomsel cards, because the network is good. But there are some people who don't use Telkomsel cards, because the quota price is quite expensive. Therefore, the Penlus will make research about people's interest in Telkomsel cards. This study aims to determine the amount of public interest in the Telkomsel card. To conduct this research, the authors used 42 community data which would be classified using the decision tree method. The data used by the author was obtained by distributing a questionnaire to the public. After classifying using the decision tree method, the result is that the people who are interested in the Telkomsel card are 33 people who are interested in the Telkomsel card (for the representation results it is 78.5%) and the results obtained are that the people who are not interested in the Telkomsel card are 9 people (for its representation results of 21.4%). From the results of the study, many people are interested in Telkomsel cards, even though the internet, call and SMS quota prices are quite expensive.

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“…The K-Nearest Neighbor (kNN) method is a technique used in data mining and machine learning to carry out classification and regression based on observations of the nearest neighbors of data (Waliyansyah & Fitriyah, 2019) (Sanjaya & Fitriyani, 2019) (Fitri et al, 2021). The K-Nearest Neighbor (kNN) method is a very simple but effective method for predicting or grouping data based on similarities to data that already exists in the dataset (Pattnaik & Parvathi, 2022) (Kurniadi, Mulyani, & Muliana, 2021) (Nugraha & Herlina, 2021). The K-Nearest Neighbor (kNN) method is a method with a classification model that is easy to understand and realize (Prasetio, 2020) (Supriyadi, Safitri, Amriza, & Kristiyanto, 2022).…”

Section: Metode K-nearest Neighbor (Knn)mentioning

confidence: 99%

Analysis of the Level of Public Satisfaction on the Tiktok Application as an E-Commerce

Irmayani,

Sinaga,

Masrizal

2023

SinkrOn

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Online shopping has become one of the alternatives used by society today. This happens because doing online shopping saves a lot of time. There are many online shops in Indonesia and are used by many people, such as Shopee, Lazada and Tokopedia. But now there is an application that initially only acted as a social media platform, but now also doubles as an E-commerce application, namely TikTok. TikTok has now become an E-commerce application. The prices given are also very cheap and there are lots of promotions given to customers. But there are still some people who don't want to shop online on TikTok on the grounds that the goods are not good. So from these 2 things, research needs to be made to determine the level of public satisfaction with the TikTok application as an E-commerce. The aim of this research is to see how many people are satisfied shopping on TikTok. This research was carried out using a classification model in data mining using the K-Nearest Neighbor (kNN) method and the Decision Tree method. The classification results obtained were 119 community data (for representation of 96.74%) and for people who were dissatisfied with the TikTok application as an E-commerce it was 4 community data (for representation of 3.25%). These results provide the conclusion that many people are satisfied shopping on the TikTok application as E-commerce

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Machine learning-based approaches for tomato pest classification

Cited by 9 publications

References 19 publications

A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method

Analysis of the Level of Public Satisfaction on the Tiktok Application as an E-Commerce

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