Prediksi  Pencapaian Target Peserta Keluarga Berencana Pasca  Persalinan  menggunakan Algoritma Backpropagation

Putri, Stefani Hardiyanti; Yuhandri, Y; Nurcahyo, Gunadi Widi

doi:10.37034/jsisfotek.v3i3.62

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…Proses algoritma backpropagation bertujuan untuk menghasilkan nilai akurasi dari sebuah peramalan [8]. Algoritma backpropagation dilatih dengan menggunakan metode pembelajaran terkontrol dan dikembangkan oleh aturan precoptron yang memiliki banyak lapisan dengan menghitung galat dari semua unit pengolahan dan mengubah bobot pada semua jaringan yang terhubung pada lapisan tersembunyi [9].…”

Section: Pendahuluanunclassified

Analisis Penerapan Jaringan Saraf Tiruan Backpropagation dalam Memprediksi Penjualan Produk Es Kristal

Anjani,

Prakasiwi,

Windarto

2023

jieee

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Ice Crystal Inti’s factory is the only producer of the ice crystals in Pematangsiantar. Untill now, the factory uses a simple system for recording sales, which creates difficulties in predicting sales. Preeiction calculation manually has a fairly high level of risk and hampers the sales performance process. For this reason, the factory need a system that can calculate sales predicting for ice crystal products and reduce that risk of lost. This study aims to make predictions using Artificial Intelligence with the Backpropagation algirithm. The data used is sourced from the Ice Crystal Inti’s Factory in Pematangsiantar for the 2020-2021 period. The process is done by dividing the training data and testing data to obtain the best architectural model. The training architecture model used to predict sales of ice crystal products is : 11-2-1; 11-25-1; 11-50-1; 11-50-75-1; dan 11-100-1. From a series of trials, the best pattern of the backpropagation architecture is 11-2-1 with a Means Square Error of 0.0009997950, an epoch of 414557, and an accuracy of 75% which will then be used to make predictions.

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Section: Pendahuluanunclassified

Analisis Penerapan Jaringan Saraf Tiruan Backpropagation dalam Memprediksi Penjualan Produk Es Kristal

Anjani,

Prakasiwi,

Windarto

2023

jieee

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“…Pembelajaran menggunakan algoritma backpropagation melalui proses insialisasi bobot awal, tahap aktivasi, weight training (perubahan bobot) dan tahap iterasi. Inisialisasi maksudnya memberikan nilai awal terhadap nilai-nilai yang diperlukan oleh neural network seperti weight, threshold [25]. Nilai-nilai yang diberikan pada tahap inisialisasi digunakan pada tahap aktivasi dengan melakukan perhitungan aktual output pada hidden layer dan menghitung aktual output pada output layer.…”

Section: Melakukan Prediksiunclassified

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Khairati

Putra²

2022

jsisfotek

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Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.

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“…Family planning is the right of every individual in society to plan when and how the individual has a family (Astri Nurdiana, 2020). The implementation of the family planning program aims to minimize the rate of maternal mortality during childbirth by anticipating an unplanned pregnancy process (Sitorus & Siahaan, 2018) so as to create a quality family (Putri, Yuhandri, & Nurcahyo, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Waterfall model in development of family planning participants information system

Fajar

Dar

Rohani

2022

SinkrOn

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The purpose of the family planning program is to improve the welfare of mothers and children, which is the basis for the realization of a prosperous society by controlling birth control as well as controlling population growth. The Department of Population Control and Family Planning in Labuhanbatu Regency has the task of carrying out family planning programs in the Labuhanbatu Regency area whose implementation is assisted by Family Planning Extension Officers in 9 existing sub-districts. District officers record and report family planning data in each sub-district every month. Recording and reporting of family planning data is still done manually. This manual system resulted in the process of submitting reports being delayed due to distance problems between sub-districts and districts. The delay in submitting this report has an impact on the performance of the institution itself. This study aims to overcome these problems with the solution of making a web-based family planning participant data collection information system. The system development method used is the Waterfall model. From the results of the implementation and testing of the system, it is found that the Codeigniter framework can be implemented in a family planning participant data collection information system quickly and easily. Tests using the Blackbox testing method also show valid results. From the results of this study, it can be concluded that to apply the waterfall method in designing and developing a KB participant data information system in Labuhanbatu Regency, it must go through five stages, namely, analyzing system requirements, designing systems using UML, implementing systems with a codeigniter framework, conducting trials with blackbox testing methods, and perform system maintenance. With this system, it is hoped that it can become a recommendation and reference for the Labuhanbatu Regency Population Control and Family Planning Service to implement it so that the problem of delays in reporting family planning data can be handled properly.

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Prediksi Pencapaian Target Peserta Keluarga Berencana Pasca Persalinan menggunakan Algoritma Backpropagation

Cited by 3 publications

References 1 publication

Analisis Penerapan Jaringan Saraf Tiruan Backpropagation dalam Memprediksi Penjualan Produk Es Kristal

Analisis Penerapan Jaringan Saraf Tiruan Backpropagation dalam Memprediksi Penjualan Produk Es Kristal

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Application of Waterfall model in development of family planning participants information system

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