Implementation of Gain Ratio and K-Nearest Neighbor  for Classification of Student Performance

Setiyorini, Tyas; Asmono, Rizky Tri

doi:10.33480/pilar.v16i1.813

Cited by 5 publications

(3 citation statements)

References 24 publications

(36 reference statements)

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“…K-NN uses data from several classes to predict the classification of a new data point. However, k-NN will suffer a slow prediction stage (high computation time), if the dataset is large and the dimension of features is high [56]. Other than that, K-NN is also sensitive to noise in the dataset, and any irrelevant features such as missing values and outliers, need to be manually imputed and removed, respectively [57].…”

Section: Tehmentioning

confidence: 99%

The Study of Malay’s Prosodic Features Impact on Classical Arabic Accents Recognition

et al. 2023

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Modeling individual's variation in speech pattern can be challenging in Automatic Speech Recognition (ASR). In Classical Arabic (CA) language, 20 Quranic accents are permitted for Quranic recitation. An ASR system for CA with accent detection requires a modeling method that can capture speech pattern changes. Here, we study the accentual influences on Malay speakers' pronunciation and its prosodic impacts towards ASR system for CA language with seven Quranic accents identification. The proposed ASR system was developed over three stages. First, a dataset of Surah Al-Fatihah recitation was recorded from 14 Malay speakers in seven Quranic accents, forming a total of 5,684 words. Second, various spectral and prosodic features are extracted from the dataset for further classification process. The final stage includes training and testing the classification model. The existing ASR systems are often enabled by Gaussian Mixture Models (GMM) because of its capability to represent a wide range of sample distributions. However, GMM is susceptible to overfitting when the model complexity is high, due to the presence of singularities. To support identification of seven Quranic accents, Universal Background Model (UBM) is adapted to GMM using Maximum A Posteriori (MAP) estimation method. The UBM models were trained over each of Quranic accents, and combined to establish final UBM with 512 mixture components. The proposed ASR system utilizing the GMM-UBM outperformed k-NN, GMM, and GMM-iVector in identifying Al-Fatihah recitation to the corresponding Quranic accents. The GMM-UBM yields a testing accuracy of 86.148%, which is an increment of 4.435% from utilizing GMM alone.INDEX TERMS Automatic speech recognition (ASR), Gaussian mixture model-universal background model (GMM-UBM), Malay speakers, Quranic accents

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Section: Tehmentioning

confidence: 99%

The Study of Malay’s Prosodic Features Impact on Classical Arabic Accents Recognition

et al. 2023

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“…Pada penelitian ini proses klasifikasi yang digunakan adalah metode KNN (K-Nearest Neighbor), cara kerja metode ini yaitu dengan cara menemukan jarak terdekat dari suatu data dengan sejumlah k data terdekat, kemudian dilihat data apa yang paling dominan dari antar data yang memiliki jarak terdekat [19]. Dalam penelitian ini nilai k yang digunakan adalah angka ganjil 1-30, yang dimana dilakukannya pencarian nilai k terbaik di masing masing percobaan menggunakan Hyperparameter Tunning dengan teknik Grid Search CV sebelum dilakukan prediksi terhadap dataset test.…”

Section: K-nearest Neighborunclassified

Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN

2022

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As the times move forward, technologies are following to develop with the times, especially internet technology. Nowadays, people can find out information about shopping center via the Internet. One of the best-known shopping centers in Bandung is Paris van Java resort lifestyle place. The person's consideration of going to the shopping center is based on the location and opinions of the person who once visited that shop. With a huge amount of opinion and an underlying amount of information that is of great value causes the information to become dangerous if the person misunderstood it. With all these problems, sentiment analysis one of the solution that can prevent the problems of these misunderstanding, in which sentiment analysis works to analyze each text to determine the level of positive or negative sentiment values. In this study, the sentiment analysis dataset goes first to the preprocessing stage, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature then classified using the K-Nearest Neighbor method, The K-Nearest Neighbor method was chosen because this method is one method that can classify text and data, besides K-Nearest neighbor method has a good classification accuracy where the classification process is easy and quite simple in its implementation. With a system that built using TF-IDF Unigram and Euclidean Distance, the best accuracy value is 88.29%.

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“…Penelitian dari [10] yang melakukan kombinasi K-Nearest Neighbor dengan Gini Index untuk klasifikasi tingkat kognitif soal pada Taksonomi Bloom dengan hasil yang diperoleh yaitu Gini Index mampu mengurangi dimensi fitur yang tinggi, sehingga meningkatkan kinerja KNN dan meningkatkan tingkat akurasi klasifikasi tingkat kognitif soal pada Taksonomi Bloom dari akurasi 59.97 % menjadi 68.37 %. Penelitian dari [11] melakukan reduksi atribut dengan Gini Index pada K-Nearest Neighbor pada klasifikasi kinerja siswa dengan hasil yang diperoleh yaitu peningkatan akurasi K-NN setelah reduksi atribut dari 74.068 % menjadi 76.516 %. Penelitian dari [12] melakukan kombinasi K-Means dengan Bee Colony Optimization (BCO) pada pengelompokan data dengan hasil yang diperoleh yaitu peningkatan performa K-Means setelah dikombinasikan dengan BCO yaitu dari 83.09 % menjadi 83.30%.…”

Section: Pendahuluanunclassified

Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index

Zarkasyi

Mawengkang

Sitompul

2022

bits

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In K-Means Clustering, the number of attributes of a data can affect the number of iterations generated in the data grouping process. One of the solutions to overcome these problems is by using a reduction technique on the dimensions of the dataset. In this study, the authors apply the Gini Index to perform attribute reduction on the data set to reduce attributes that have no effect on the dataset before clustering with K-Means Clustering. The dataset used to be tested as a testing instrument in this research is Absenteeism at work obtained from the UCI Machine Learning Repository, with 20 attributes, 740 data records and 4 attribute classes. The results of the tests in this research indicate that the number of iterations obtained from the comparison of tests using the K-Means in a Conversional (Without Attribute Reduction) is obtained by the number of 9 iterations, while the K-Means with attribute reduction with the Gini Index obtains the number of iterations totaling 6 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering in a Conversional (Without Attribute Reduction) is 1391.613, while in K-Means Clustering with attribute reduction with a Gini Index, it is 440.912. From the results of the proposed method, it is able to reduce the percentage of errors and minimize the number of iterations in K-Means Clustering by reducing the dimensions of the dataset using the Gini Index

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Implementation of Gain Ratio and K-Nearest Neighbor for Classification of Student Performance

Cited by 5 publications

References 24 publications

The Study of Malay’s Prosodic Features Impact on Classical Arabic Accents Recognition

The Study of Malay’s Prosodic Features Impact on Classical Arabic Accents Recognition

Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN

Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index

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