Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm

Sirajuddin, Haerany; Suriamihardja, Dadang Ahmad; Imran, A. M.; Thaha, Muhammad Arsyad

doi:10.24843/ijeet.2017.v02.i01.p11

Cited by 36 publications

(13 citation statements)

References 1 publication

(1 reference statement)

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“…Naive Bayes is a simple probabilistic classification that calculates a set of discrete values by adding up the frequency and combination of values from a given dataset [14]. The algorithm uses the Bayes theorem and assumes all the independent or non-interdependent given by values to class variables [15].…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, we convert the number data into string with coding in RStudio to get the data which presented in Table 7. Table 7 Grouping based on classification tweets with Bayes [1] -1.00 0.40 0.25 0.00 0.50 -1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.75 [14] 0.00 0.40 0.40 0.30 0.80 0.80 0.80 0.00 -0.35 0.00 -0.15 -1.45 0.60 [27] -0.50 0.50 -1.00 0.00 0.00 1.40 0.00 0.50 0.00 -0.50 0.00 0.25 0.00 [40] 1.30 0.00 0.00 -1.80 0.00 0.00 0.00 0.85 0.00 0.00 1.55 0.25 0.25 [53] 0.00 0.00 0.00 0.80 0.75 -1.00 0.00 0.00 0.00 0.00 -0.25 0.00 0.00 [66] 0.00 -0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 [79] 0.00 0.00 0.00 0.00 0.00 0.00 -0.20 0.00 -0.80 0.80 1.05 0.00 0.00 [92] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.60 0.00 0.00 0.00 0.00 0.00 Table 8 The list of classification result of negative, positive and neutral sentiment [1] "Neutral" "Negative" "Positive" "Neutral" "Positive" "Negative" "Negative" "Neutral" [9] "Neutral" "Neutral" "Neutral" "Neutral" "Neutral" "Neutral" "Neutral" "Neutral" [17] "Neutral" "Positive" "Neutral" "Positive" "Neutral" "Positive" "Neutral" "Neutral" [25] "Neutral" "Neutral" "Neutral" "Neutral" "Positive" "Neutral" "Positive" "Positive" [33] "Neutral" "Neutral" "Neutral" "Neutral" "Neutral" "Neutral" "Positive" "Neutral" [41] "Neutral" "Positive" "Neutral" "Negative" "Neutral" "Neutral" "Neutral" "Negative" [49] "Neutral" "Negative" "Neutral" "Positive" "Neutral" "Neutral" "Positive" "Negative"…”

Section: Numericalizationmentioning

confidence: 99%

See 1 more Smart Citation

Twitter’s User Opinion About Master and Doctoral Degrees: A Model of Sentiment Comparison

Wiley

Lucas

2020

Indonesian J. Comput. Cybern. Syst.

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This paper examines the opinion of student candidate about their plan to study further to master degree (S2) and doctoral degree (S3). There is lack of approach in finding public opinion about the interest of student candidate in continuing study to higher level such as master degree or doctoral degree. Through this paper, the Twitter’s user opinions are extracted using certain data mining technique to find out three sentiment types (negative, neutral, and positive) by taking the most dominant type of emotions (i.e., anger, anticipation, love, fear, joy, sadness, surprise, trust). The dataset is divided into two groups of Twitter’s users. Both datasets represent group A those opinion is about continuing study further to master degree versus group B whose continuing to doctoral degree. The groups are then divided into three types of sentiment statements about master degree versus doctoral degree. The first group is their sentiment about continuing study further to master degree with the result: (a) 109 negative tweets, 1683 neutral tweets and 131 positive tweets. For the second group (e.g., student’s sentiments about continuing to doctoral degree), it has results: (a) 421 negative tweets, 7666 neutral tweets and 1805 positive tweets. The data are tested to give accuracy value of 85%. The result of this sentiment analysis is useful as a reference for universities to understand the development of sentiments (opinion) from Twitter’s users and help the institutions to improve their reputation and quality

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Section: Methodsmentioning

confidence: 99%

Section: Numericalizationmentioning

confidence: 99%

Twitter’s User Opinion About Master and Doctoral Degrees: A Model of Sentiment Comparison

Wiley

Lucas

2020

Indonesian J. Comput. Cybern. Syst.

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“…Setelah melakukan pengumpulan data, tahap ini melakukan proses pemahaman dan identifikasi kualitas data, lalu menemukan pengetahuan awal untuk dapat membentuk hipotesis. Hipotesis awal peneliti berdasarkan data yang tersedia dan penelitian sebelumnya yang dilakukan fitur Backward Elimination dapat mengurangi atribut yang kurang berpengaruh sehingga meningkatkan performa akurasi dibandingkan dengan hanya penggunaan algoritme k-NN [16].…”

Section: Data Understandingunclassified

Optimasi Backward Elimination untuk Klasifikasi Kepuasan Pelanggan Menggunakan Algoritme k-nearest neighbor (k-NN) and Naive Bayes

Yunitasari

Hopipah

Mayasari

2021

TMJ

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Mempertahankan kepuasan pelanggan merupakan sebuah tantangan besar bagi perusahaan. Salah satu upaya yang dapat dilakukan adalah memberikan pelayanan terbaik terhadap pelanggan berdasarkan aspek yang paling berpengaruh. Pada penelitian ini dilakukan optimasi fitur Backward Elimination pada klasifikasi kepuasan pelanggan dengan algoritme k-NN dan Naïve Bayes. Penggunaan fitur Backward Elimination bertujuan meningkatkan akurasi dan mengurangi jumlah atribut yang kurang berpengaruh. Hasilnya, dapat diketahui bahwa pemodelan terbaik tanpa Backward Elimination adalah algoritme Naïve Bayes dengan akurasi 99.04% dan nilai AUC mencapai 1. Sedangkan penerapan Backward Elimination bekerja lebih optimal pada algoritme k-NN dengan peningkatan sebesar 33.74% menjadi 97.28% dengan AUC 0.996. Hal ini menunjukkan bahwa kinerja fitur Backward Elimination efektif dalam optimasi klasifikasi kepuasan pelanggan dan dapat mengurangi atribut yang kurang berpengaruh.

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“…Data mining adalah proses yang menggunakan teknik statistik, matematika, kecerdasan buatan, dan machine learning untuk mengekstraksi dan mengidentifikasi informasi yang bermanfaat dan pengetahuan yang terkait dari berbagai database besar. Salah satu teknik yang ada pada data mining adalah klasifikasi [4], [5], [6].…”

Section: Data Miningunclassified

Penentuan Kelas Menggunakan Algoritma K Medoids Untuk Clustering Siswa Tunagrahita

Sariangsah¹,

Wanayumini²,

Rosnelly³

2021

mib

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So far, the class placement of mentally retarded students is based on the age of entering the child when registering at SLB C Muzdalifah, the Intelligence Quotient (IQ) test has not been tried for mentally retarded students in classifying student classes. It is important to group mentally retarded children to make it easier for teachers to prepare programs and implement educational services. It is important for the school to understand that in mentally retarded children there are individual differences with very large variations. That is, being at almost the same age level (calendar age and mental age) and the same education level, in fact individual abilities differ from one another. Thus, of course, special strategies and programs are needed that are adapted to individual differences. This research was made to classify and analyze data mining for class clustering students with the K-Medoids algorithm to help group students who want to occupy classes according to their level of intellectual disability. From the grouping results obtained 3 clusters, which have the highest number of students is the moderate mental retardation class and the lowest cluster is mild mental retardation, the Muzdalifah special school can prepare classes based on grouping for teaching and learning activities.

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Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm

Cited by 36 publications

References 1 publication

Twitter’s User Opinion About Master and Doctoral Degrees: A Model of Sentiment Comparison

Twitter’s User Opinion About Master and Doctoral Degrees: A Model of Sentiment Comparison

Optimasi Backward Elimination untuk Klasifikasi Kepuasan Pelanggan Menggunakan Algoritme k-nearest neighbor (k-NN) and Naive Bayes

Penentuan Kelas Menggunakan Algoritma K Medoids Untuk Clustering Siswa Tunagrahita

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