Muhammad Noor Hasan Siregar
Universitas Graha Nusantara, Padangsidimpuan, Sumatera Utara

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Neural Network Analysis With Backpropogation In Predicting Human Development Index (HDI) Component by Regency/City In North Sumatera Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1006.682 KB) | DOI: 10.30645/ijistech.v1i1.3

Abstract

Human Development Index (HDI) measures human development outcomes based on a number of basic components of quality of life. As a measure of the quality of life, HDI is built through a basic three-dimensional approach. Data obtained from the Central Bureau of Statistics 2015 for Human Development Index (HDI) by Regency / City in North Sumatera Province consisting of 32 alternatives and with 4 parameters ie life expectancy (year), expectation, school length (%), the average length of school (year) and per capita real expenditure (Rp). By using backpropagation obtained result of 6 testing of architecture pattern that is: 4-5-1, 4-10-1, 4-5-10-1, 4-10-5-1, 4-10-20-1 and 4- 15-20-1 obtained best architectural pattern is 4-10-20-1 with epoch 2126, error 0.0011757393, execution time 00:16 and accuracy 100%.
ANN: Model of Back-Propagation Architecture on the Logs Production by Type of Wood Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 1, No 2 (2018): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (545.23 KB) | DOI: 10.30645/ijistech.v1i2.12

Abstract

Indonesia is rich in forest products. The production forest is a forest area that can be utilized for the community, such as logs, rattan, and some forest plants that have high economic value. This research aims to make the best architectural model by using artificial neural network. The method used is backpropagation algorithm. With this model it will continue to predict the output of logs. Data are sourced from BPS-Statistics Indonesia. Based on the results of research results of logs production using backpropogation method, obtained the result of 3 model architecture (18-18-1, 18-25-1 and 18-18-25- 1) that model of architecture 18- 25-1 is the best model with 72% accuracy, MSE: 0.0221670942 and epochs: 660.
Neural Network Analysis With Backpropogation In Predicting Human Development Index (HDI) Component by Regency/City In North Sumatera Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i1.3

Abstract

Human Development Index (HDI) measures human development outcomes based on a number of basic components of quality of life. As a measure of the quality of life, HDI is built through a basic three-dimensional approach. Data obtained from the Central Bureau of Statistics 2015 for Human Development Index (HDI) by Regency / City in North Sumatera Province consisting of 32 alternatives and with 4 parameters ie life expectancy (year), expectation, school length (%), the average length of school (year) and per capita real expenditure (Rp). By using backpropagation obtained result of 6 testing of architecture pattern that is: 4-5-1, 4-10-1, 4-5-10-1, 4-10-5-1, 4-10-20-1 and 4- 15-20-1 obtained best architectural pattern is 4-10-20-1 with epoch 2126, error 0.0011757393, execution time 00:16 and accuracy 100%.
Model Combination of Activation Functions in Neural Network Algorithms (Case: Retail State Sukuk by Group) Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 2, No 2 (2019): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.35 KB) | DOI: 10.30645/ijistech.v2i2.23

Abstract

This study aims to maximize the activation function used in backpropogation networks in finding the best architectural model. The case study used is the sale of state retail sukuk based on professional groups. The combination of activation functions used for training and testing is tansig-tansig, tansig-purelin and tansig logsig. The architectural model used is the architectural model 6-2-1 and 6-5-1. The evaluation parameters used are epoch, MSE training, MSE testing and accuracy level of truth. Data processing is assisted by using Matlab software. The results showed that the tansig-logsig activation function had more stable results than tansig-tansig and tansig-purelin.
Model Combination of Activation Functions in Neural Network Algorithms (Case: Retail State Sukuk by Group) Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 2, No 2 (2019): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v2i2.23

Abstract

This study aims to maximize the activation function used in backpropogation networks in finding the best architectural model. The case study used is the sale of state retail sukuk based on professional groups. The combination of activation functions used for training and testing is tansig-tansig, tansig-purelin and tansig logsig. The architectural model used is the architectural model 6-2-1 and 6-5-1. The evaluation parameters used are epoch, MSE training, MSE testing and accuracy level of truth. Data processing is assisted by using Matlab software. The results showed that the tansig-logsig activation function had more stable results than tansig-tansig and tansig-purelin.
ANN: Model of Back-Propagation Architecture on the Logs Production by Type of Wood Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 1, No 2 (2018): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i2.12

Abstract

Indonesia is rich in forest products. The production forest is a forest area that can be utilized for the community, such as logs, rattan, and some forest plants that have high economic value. This research aims to make the best architectural model by using artificial neural network. The method used is backpropagation algorithm. With this model it will continue to predict the output of logs. Data are sourced from BPS-Statistics Indonesia. Based on the results of research results of logs production using backpropogation method, obtained the result of 3 model architecture (18-18-1, 18-25-1 and 18-18-25- 1) that model of architecture 18- 25-1 is the best model with 72% accuracy, MSE: 0.0221670942 and epochs: 660.
The application of the Analytic Hierarchy Process method to the selection of dominant factors for adolescents who are prone to insecurity Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 4, No 2 (2021): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v4i2.107

Abstract

Teens are often stressed, worried, and overly insecure as a result of their high expectations. The feelings that occur during adolescent development can create and increase feelings of insecurity in their lives, which has a negative impact. Adolescents with an excessive sense of insecurity can suffer from mental disruption, which can lead to serious mortality. Of course, these factors can have a negative impact on adolescents' mental health. Adolescents' minds and psyches can be disrupted by mental illness. The goal of this study is to identify the dominant factor among a number of factors that can lead to insecurity when using the Decision Support System (DSS) technique. Analytical Hierarchy Process is the DSS method used (AHP). The data used in this study was gathered through observations and interviews with adolescents using a random questionnaire. Six factors were derived from observations and interviews: social environmental factors (A1), family environmental factors (A2), social media factors (A3), insecure factors (A4), trauma factors (A5), and education and work factors (A6). The results of the AHP method show that the main factors for adolescents who are easily insecure are social environmental factors (first), social media factors (second), and family environmental factors (third) (third).
The application of the Analytic Hierarchy Process method to the selection of dominant factors for adolescents who are prone to insecurity Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar
IJISTECH (International Journal of Information System and Technology) Vol 4, No 2 (2021): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v4i2.107

Abstract

Teens are often stressed, worried, and overly insecure as a result of their high expectations. The feelings that occur during adolescent development can create and increase feelings of insecurity in their lives, which has a negative impact. Adolescents with an excessive sense of insecurity can suffer from mental disruption, which can lead to serious mortality. Of course, these factors can have a negative impact on adolescents' mental health. Adolescents' minds and psyches can be disrupted by mental illness. The goal of this study is to identify the dominant factor among a number of factors that can lead to insecurity when using the Decision Support System (DSS) technique. Analytical Hierarchy Process is the DSS method used (AHP). The data used in this study was gathered through observations and interviews with adolescents using a random questionnaire. Six factors were derived from observations and interviews: social environmental factors (A1), family environmental factors (A2), social media factors (A3), insecure factors (A4), trauma factors (A5), and education and work factors (A6). The results of the AHP method show that the main factors for adolescents who are easily insecure are social environmental factors (first), social media factors (second), and family environmental factors (third) (third).