Sistem Informasi Optimalisasi Perencanaan Permintaan Obat Dengan Metode Cluster Time Series Dan Neural Network Di Apotek K24 Pucang Surabaya

Pradita, Dias Ardha (2016) Sistem Informasi Optimalisasi Perencanaan Permintaan Obat Dengan Metode Cluster Time Series Dan Neural Network Di Apotek K24 Pucang Surabaya. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

[img]
Preview
Text
1314105065-Undergraduate_Thesis.pdf - Accepted Version

Download (1MB) | Preview
[img]
Preview
Text
1314105065-Paper.pdf - Accepted Version

Download (330kB) | Preview
[img]
Preview
Text
1314105065-Presentation.pdf - Presentation

Download (2MB) | Preview

Abstract

Obat merupakan komponen penting di bidang kesehatan. Salah satu sarana kesehatan adalah apotek. Persediaan obat di apotek harus selalu terjaga. Optimalisasi permintaan obat dapat dibantu menggunakan metode peramalan. Namun jenis obat di apotek sangat banyak dan tidak mungkin untuk diramalkan satu persatu, sehingga diperlukan pengelompokan menggunakan cluster time series untuk selanjutnya dilakukan peramalan menggunakan neural network. Pengelompokan didasarkan pada visualisasi dendogram menggunakan jarak autocorrelation. Identifikasi neural network dilakukan pada tiap anggota cluster sebanyak 10 kali training dengan membandingkan antara 1 hingga 5 neuron menggunakan metode feedforward. Tiap cluster dilakukan peramalan dengan input, neuron dan bobot paling optimum yang dilihat dari nilai RMSE terkecil. Data yang digunakan adalah permintaan obat dari bulan Maret 2015 hingga April 2016. Jenis obat yang digunakan sebanyak 7888 jenis dan di reduksi menjadi 130 jenis. Pembuatan sistem informasi diperlukan agar kedepannya pihak apotek dapat melakukan peramalan sendiri. ==================================================================================================== Medicine is one of the most important components the health sector. One of the medical facilities in the society is a pharmacy. Supplies of medications in pharmacies must always be maintained to fulfill the needs of society. Optimization of medicine demand can be helped by using forecasting methods. But the type of medicine at the pharmacy it very much and impossible to predict one by one, so it requires grouping by using cluster time series. A grouping is based on visualization dendogram and using autocorrelation based distance. Identification of the neural network performed at every cluster member as many as 10 times the training by comparing between 1 to 5 neurons using feedforward method. Every cluster performed forecasting with input neurons and the most optimum weights were seen from the smallest RMSE value. The data used is the demand for medicine from March 2015 until April 2016. Kind of medicine used is 7888 types and reduced to 130 types. Information systems need to be made, so that in the future the pharmacy can do their own forecasting.

Item Type: Thesis (Undergraduate)
Additional Information: RSSt 519.55 Pra s 3100016065839
Uncontrolled Keywords: Obat, Cluster Time Series, Dendogram, Neural Network, Feedforward, Jarak Autocorrelation, Medicine, Cluster Time Series, Dendogram, Neural Network, Feedforward, Autocorrelation Based Distance
Subjects: H Social Sciences > HA Statistics > HA30.3 Time-series analysis
Divisions: Faculty of Mathematics, Computation, and Data Science > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User: - Davi Wah
Date Deposited: 25 Feb 2020 07:41
Last Modified: 25 Feb 2020 07:41
URI: http://repository.its.ac.id/id/eprint/75120

Actions (login required)

View Item View Item