Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Iriawan, Nur; Fithriasari, Kartika; Ulama, Brodjol Sutija Suprih; Suryaningtyas, Wahyuni; Susanto, Irwan; Pravitasari, Anindya Apriliyanti

doi:10.21609/jiki.v11i2.536

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…The k-prototypes algorithm is one of the clustering methods based on partitioning (Pham et al, 2011) (Iriawan et al, 2018). This algorithm is the result of the development of the k-means algorithm (Mau and Huynh, 2021) (Ahmad and Dey, 2011) to handle clustering on data with mixed numeric and categorical type attributes (Dinh et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Jurnal Varian Full Text

Soraya¹

2022

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The eyes are one of the vital organs owned by humans. One of the common eye diseases is cataracts. This disease is characterized by clouding of the lens of the eye and can interfere with vision. Worst case, sufferers can experience blindness. Cataract maturity can be divided into four categories, namely incipient, immature, mature, and hypermature. Cataracts can be removed through surgery when the cataract is in the mature or hypermature phase. Cataract examination is usually done using a slit lamp. The lack of hospitals that have this equipment can cause delays in the healing process for cataract sufferers. This study created an image processing algorithm for the maturity classification process of cataracts using the Convolutional Neural Network method with LeNet network architecture. The algorithm that has been built is capable of classifying the maturity of cataracts with an accuracy rate of 93.33%.

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Section: Methodsmentioning

confidence: 99%

Jurnal Varian Full Text

Soraya¹

2022

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“…Proses randomisasi mempunyai distribusi yang berasal dari berbagai variabel data yang dikelompokkan berdasarkan data histori [16], Metode Monte Carlo menggunakan generate probabilitas distribusi bilangan acak yang diolah, kemudian divalidasi dengan data fakta untuk memastikan kondisi simulasi relatif sama kondisi sebenarnya [17]. Monte Carlo merupakan salah satu metode numerik yang dideskripsikan sebagai metode simulasi statis [18] yang dapat diintegrasikan secara numerik oleh prosedur simulasi [19]. Proses probabilitas yang digunakan dalam simulasi Monte Carlo adalah proses distribusi probabilitas kumulatif, yaitu menjumlahkan distribusi probabilitas yang ditambahkan pada probabilitas kumulatif sebelumnya, kecuali untuk nilai distribusi probabilitas untuk kelas pertama, nilai untuk distribusi probabilitas komulatif untuk kelas pertama merupakan nilai dari probabilitas kelas itu sendiri [20].…”

Section: Pendahuluanunclassified

Optimalisasi Prediksi Biaya Komisi Penjualan Mobil Menggunakan Metode Monte Carlo

2020

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Sales are the main source of income for every company. Every company in marketing a product, should control the potential market for profit. Predicting the number of sales is important in analyzing sales progress. This study aims to assist companies in predicting car sales and car commission cost budgets based on sales data from the previous year.The data used in the study are car sales data for 2017 and 2018 in the Arengka Automall Pekanbaru Showroom (SAA Pekanbaru).Data processing in research uses the Monte Carlo method.The results of tests that have been carried out state that car sales by Marketing within 1 year resulted in an average accuracy rate of 94% and sales commission fee of Rp 411.000.000.From these results in accordance with calculations performed manually so that with a large accuracy value, the application of the simulation using this Monte Carlo Method feasible to be applied by companies in future decision making to plan the estimated budget for the cost of a car sales commission and as a means to assess Marketing performance at SAA Pekanbaru.

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“…So to achieve this, each weight its individual update value ∆ ij . This adaptive update value evolves during the learning process based on its local sight on the error function E as follows [5]:…”

Section: Resilient Backpropagation Neural Network (Resilient Bpnn)mentioning

confidence: 99%

“…Teshnizi and Ayatollahi [4], examined by comparing logistic regression with Naural Network to predict student academic failure by yielding each classification 77.5% for result of classification logistic regression method and 84.3% for classification of Neural Network method. The resulting Neural Network method is better than the logistic regression method [4], using the Bayesian Bernoulli mixture regression model for Bidikmisi scholarship classification [5], andanother method that uses a binary logistic regression method [6].…”

Section: Introductionmentioning

confidence: 99%

On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java

et al. 2018

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Neural Network and Binary Logistic Regression are modern and classical data mining analysis tools that can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, Indonesia. One form of Neural Network model available for various applications is the Resilient Backpropagation Neural Network (Resilient BPNN). This study aims to compare the performance of the Resilient BPNN method as a Deep Learning Neural Network and Binary Logistic Regression method in determining the classification of Bidikmisi scholarship acceptance in East Java Province. After preprocessing data and dividing them into two parts, i.e. sets of testing and training data, with 10-foldcross-validation procedure, the Resilient BPNN and Binary Logistic Regression methods are implemented. The result shows that Resilient BPNN with two hidden layers is the best platformnetwork model. The classificationG-mean resulted by these both methods is that Resilient BPNN with two hidden layers is more representative with better performance than Binary Logistic Regression. The Resilient BPNN is recommended to be used topredict acceptance of Bidikmisi applicants yearly.

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Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Cited by 7 publications

References 13 publications

Jurnal Varian Full Text

Jurnal Varian Full Text

Optimalisasi Prediksi Biaya Komisi Penjualan Mobil Menggunakan Metode Monte Carlo

On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java

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