Identifikasi Sampah Berdasarkan Tekstur Dengan Metode GLCM dan GLRLM Menggunakan Improved KNN

Telaumbanua, Kristian; Sudarto, Sudarto; Butar-Butar, Fitri; Bilqis, Putri Shania

doi:10.47065/explorer.v1i2.94

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Penelitian ini menggunakan metode Improved KNN. Improved K-Nearest Neighbor merupakan pengembangan dari metode K-Nearest Neighbor (K-NN), yang mana perbedaannya terdapat pada pembobotan nilai-k. Pada Improved KNN dilakukan perhitungan bobot pada tiap data latih berdasarkan data uji dan data latih lainnya, serta kelas dari data uji ditentukan oleh bobot data latih yang terbesar [17]. Untuk menghitung kemiripan antara dua dokumen, digunakan metode Cosine Similarity (CosSim).…”

Section: Klasifikasi Improved Knnunclassified

Sentiment Analysis Pada Masyarakat Terhadap LRT Kota Palembang Menggunakan Metode Improved K-Nearest Neighbor

Arafah

Fathoni²

2022

mib

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The LRT is a sustainable fast transportation system, which was built to overcome the congestion problem in the city of Palembang. In order to attract people's interest to switch to using public transportation compared to private transportation, one of them is by improving the quality of services provided. Sentiment analysis is used to classify positive and negative opinions on users of Palembang City LRT transportation services. In addition to retrieving data through crawling data on tweet data, the researchers also distributed questionnaires. In conducting the classification process of sentiment analysis, this study uses the Improved K-Nearest Neighbor method which is a modification of the K-Nearest Neighbor method. The results of this research are testing and training data on 1617 data records and the highest accuracy of 74.07% on 90% training data and 10% testing data, with 70% precision, 56% recall and 59% f-1 score, while the lowest accuracy with an accuracy of 63.04% on 50% training data and 50% testing data, with 44% precision, 42% recall and 42% f-1 score

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Section: Klasifikasi Improved Knnunclassified

Sentiment Analysis Pada Masyarakat Terhadap LRT Kota Palembang Menggunakan Metode Improved K-Nearest Neighbor

Arafah

Fathoni²

2022

mib

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“…Evaluating the performance of classification algorithms generally uses the overall results on the test dataset. A matrix of predictions that will be compared with the original class of input or in other words contains information on the actual and predicted values of the classification [13] .…”

Section: Confusion Matrixmentioning

confidence: 99%

The Classification of Mushroom Types Using Naïve Bayes and Principal Component Analysis

Rianasari

Triana

Dewi

et al. 2022

JISA

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Indonesia is one of tropical countries with high humidity which makes it possible for various plants and microorganisms to grow properly. One of the microorganisms that shall grow well in Indonesia is be considered as fungi or mushrooms. They have several types including poisonous and edible mushrooms that shall be consumed by human beings. The purpose of this research is to make it easier to classify between the types of poisonous mushrooms and edible mushrooms which can be consumed by using the Naïve Bayes algorithm to get the accurate classification results. In this research, the Naive Bayes algorithm is used to classify the types of mushrooms by utilizing the Principal Component Analyst technique which serves to reduce the number of features applied in the dataset. The data collection technique used in the research is by documenting the official website of the UCI Machine Learning Repository whereas the Mushrooms dataset consists of 22 features and 1 class are applied. After classifying using Naïve Bayes with Principal Component Analyst, then the researcher is evaluating using the 10-Fold Cross Validation technique whereas the results obtained are pc = 10 and the classification result is be considered as 84%.

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“…Adapun penelitian yang menggunakan metode Metode GLCM dan GLRLM Menggunakan Improved KNN tentang sampah dengan jumlah citra 50 menghasilkan akurasi 90,4% pada 5 jenis sampah (Telaumbanua, Butar-butar, & Bilqis, 2021). Dan penelitian yang dilakukan oleh febian putra maulana menggunakan CNN tentang klasifikasi citra buah dengan akurasi 100% dan nilai loss 0,012, pada proses pengujian menggunakan 45 sample menghasilkan akurasi 91,42% (Maulana & Rochmawati, 2019).…”

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Optimasi Image Classification Pada Jenis Sampah Dengan Data Augmentation Dan Convolutional Neural Network

Permana¹,

Saldu²,

Maulana³

2022

j.sist.inf.inform.

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Garbage is useless goods/materials used normally or specifically in production, goods damaged during production or useless materials which mainly come from households. Moreover, inorganic waste is very difficult and takes a longer time to be decomposed by the soil. The lack of public knowledge about the classification of types of waste and how to process it causes a very serious problem in Indonesia. Therefore, this research creates a waste type recognition program using the Convolutional Neural Network (CNN) algorithm, which can be used to detect and recognize objects in an image. CNN is a technique inspired by the way mammals, humans, produce visual perception. CNN is included in the type of deep neural network because of its high network depth and widely applied to imagery. 2 Types of waste classification, namely inorganic waste and organic waste. The implementation of garbage image recognition uses 2 test models, Sequential and on top VGG16 which runs on the Google Collaboratory application, and Keras. After carrying out the Augmentation process, the number of test data in this study was 1489 images on the training data and 182 on the testing data resulting in an evaluation value with an accuracy of 90.97% and a loss value of 0.307 on the Sequential model, and an accuracy value of 97.99% with a loss value of 0.069 on the on top model. VGG16.

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Identifikasi Sampah Berdasarkan Tekstur Dengan Metode GLCM dan GLRLM Menggunakan Improved KNN

Cited by 3 publications

References 3 publications

Sentiment Analysis Pada Masyarakat Terhadap LRT Kota Palembang Menggunakan Metode Improved K-Nearest Neighbor

Sentiment Analysis Pada Masyarakat Terhadap LRT Kota Palembang Menggunakan Metode Improved K-Nearest Neighbor

The Classification of Mushroom Types Using Naïve Bayes and Principal Component Analysis

Optimasi Image Classification Pada Jenis Sampah Dengan Data Augmentation Dan Convolutional Neural Network

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