Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group

Bhawika, Gita Widi; Purwantoro, Purwantoro; Daengs, GS Achmad; Sudrajat, Dadang; Rahman, Arrafiqur; Makmur, M.; Rohmah, Rina Ari; Wanto, Anjar

doi:10.1088/1742-6596/1255/1/012043

Cited by 25 publications

(10 citation statements)

References 6 publications

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“…Maka dari itu perlu dilakukan estimasi terhadap jumlah penduduk Kecamatan Gunung Malela agar pihak daerah dapat meningkatkan fasilitas penduduk pada segi pendidikan, kesehatan, perekonomian, pembangunan, dan rencana tersebut membutuhkan data jumlah penduduk. Banyak cabang ilmu Komputer dapat memecahkan masalah yang kompleks, diantaranya: Sistem Pendukung Keputusan [3]- [9], Jaringan Saraf Tiruan [10]- [17], maupun Data mining [18]- [27]. Data Mining merupakan proses mencari pola atau informasi menarik dalam data terpilih dengan menggunakan teknik atau metode tertentu.…”

Section: Pendahuluanunclassified

The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District

Sinaga

Sumarno

Sari

2022

JOMLAI: Journal of Machine Learning and Artificial Intelligence

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Population growth in an area is important for development and is a benchmark for an area to develop. The way to predict population growth is to use Data Mining. Data mining is able to analyze data into information. This study will discuss the amount of population growth in the District of Gunung Malela. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make an estimate/prediction by utilizing old data regarding population growth so that it can produce a pattern of relationships. This Multiple Linear Regression method aims to make the best predictions. The research data used is the population in the Gunung Malela sub-district in 2016-2020. Based on the research that has been done using the Multiple Linear Regression method, the results of the population growth are 40078 residents. This means that there is an additional population of 469 people in Gunung Malela District. The results of this study can be input to the Gunung Malela Sub-District Office to anticipate the rate of population growth and it can be concluded based on this study that the Multiple Linear Regression method can be used to estimate the population.

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Section: Pendahuluanunclassified

The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District

Sinaga

Sumarno

Sari

2022

JOMLAI: Journal of Machine Learning and Artificial Intelligence

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“…Sehingga proses penyeleksian dalam penentuan Penerima PIP tidak terjadi secara sistematis dan terkadang tidak tepat sasaran. Banyak cabang ilmu komputer yang dapat menyelesaikan permasalahan diantaranya: Sistem Pendukung Keputusan [1]- [7], Jaringan Saraf Tiruan [8]- [15], maupun Data mining [16]- [25]. Dalam merekomendasikan penerima bantuan dana PIP tersebut, jadi memerlukan waktu yang cukup lama dan sering terjadi kesalahan.…”

Section: Pendahuluanunclassified

C4.5 Algorithm Classification for Determining Smart Indonesia Program Recipients at MIS Al-Khoirot

Ningse

Sumarno

Nasution

2022

JOMLAI: Journal of Machine Learning and Artificial Intelligence

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The purpose of the research is to assist the school in selecting student data as recipients of the PIP (Smart Indonesia Program) to be more objective and practical and to assist in increasing the accuracy of the targeting of the recipients of the PIP funds. In this study using Data Mining techniques using the C4.5 algorithm. The source of the research data used was obtained from observations and interviews with the MIS Al-Khoirot Tambun Nabolon Pematang Siantar school. The research variables used were parents' occupations, parents' income, KKS (Prosperous Family Card) holders, SKTM holders (Poor Certificate). In this study, the alternative used as a sample is the data of MIS Al-Khoirot students. The results of this study found that the most dominant attribute was the SKTM holder with a gain of 0.833764907, besides that this study produced 8 (eight) rules with an accuracy rate of 98.00%. Based on this, it can be concluded that the C4.5 algorithm can be used for the classification of the Determination of Smart Indonesia Program Recipients at MIS Al-Khoirot

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“…The research stages were carried out to achieve the research objectives, which can be seen in Figure 1. [19][20][21][22][23].…”

Section: Research Stagesmentioning

confidence: 99%

Comparison Fletcher-Reeves and Polak-Ribiere ANN Algorithm for Forecasting Analysis

Hasibuan

Hendraputra²,

Daengs

et al. 2022

J. Phys.: Conf. Ser.

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Each method and algorithm ANN has different performances depending on the algorithm used and the parameters given. The purpose of this research is to obtain the best algorithm information from the two algorithms that will be compared based on the performance value or the smallest / lowest MSE value so that it can be used as a reference and information for solving forecasting problems. The ANN algorithms compared were Conjugate Gradient Fletcher-Reeves and Conjugate Gradient Polak-Ribiere. The conjugate gradient algorithm can solve unlimited optimization problems and is much more efficient than gradient descent-based algorithms because of its faster turnaround time and less iteration. The research data used for the forecasting analysis of the two algorithms are data on the number of rural poor people in Sumatra, Indonesia. 6-10-1, 6-15-1, and 6-20-1 architectural analysis. The results showed that the Polak-Ribiere Conjugate Gradient algorithm with the 6-10-1 architecture has the best performance results and the smallest / lowest MSE value compared to the Fletcher-Reeves algorithm and two other architectures. So it can be concluded that the 6-10-1 architectural architecture with the Conjugate Gradient Polak-Ribiere algorithm can be used to solve forecasting problems because the training time to achieve convergence is not too long, and the resulting performance is quite good.

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Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group

Cited by 25 publications

References 6 publications

The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District

The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District

C4.5 Algorithm Classification for Determining Smart Indonesia Program Recipients at MIS Al-Khoirot

Comparison Fletcher-Reeves and Polak-Ribiere ANN Algorithm for Forecasting Analysis

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