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Kusrini, Kusrini
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Computer Science & IT

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Peramalan Jumlah Penjualan Menggunakan Jaringan Syaraf Tiruan Backpropagation Pada Perusahaan Air Minum Dalam Kemasan Hasan, Nur Fitrianingsih; Kusrini, Kusrini; Fatta, Hanif Al
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 2 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i2.1607

Abstract

The inhibition of the production and distribution of bottled water has become a serious problem in the survival of the community and the company, so there is a need for a solution to this problem both short-term and long-term solutions. One of the things that can be done by the company or management is that the right amount of production and distribution is by forecasting sales. Sales forecasting is the process of predicting which products will be sold in the future made based on data that has ever happened. This paper aims to determine the level of accuracy of the use of Backpropagation ANN in estimating the sales of bottled water.The ANN architecture used is 12-10-1 with the MSE value of 0,00043743 and the MAPE value of 6.88%. Forecasting sales results of Robong Holo 600ml brand using Backpropagation ANN for 2019 is 2271 pcs in January, 2019 pcs in February, 1358 pcs in March, 917 pcs in April, 462 pcs in May, 324 pcs in June, 739 pcs in July, 370 pcs in August, 367 pcs in September, 1073 pcs in October, 765 pcs in November and 1388 pcs in December. Keywords— AMDK,Backpropagation,Jaringan Syaraf Tiruan,Penjualan,Peramalan
Analisis Cluster Data Interkomparasi Anak Timbangan dengan Algoritma Self Organizing Maps Solikin, Arif Fajar; Kusrini, Kusrini; Wibowo, Ferry Wahyu
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i2.3698

Abstract

Intercomparison was conducted to determine the ability and the performance of the laboratory. Intercomparison results are usually expressed in the range of En ratio values (En ?|1|) which express the equivalence of one laboratory with other laboratories. If the laboratory is declared unequal, then it needs to identify the source of the problem by itself. To make it easier, it can be done by Clustering which is one of the data mining techniques. Clustering is done by applying a self organizing map algorithm on the KNIME (Konstanz Information Miner) analytic tools. Several experiments were carried out with different layer size and data normalization status from one experiment to another experiment. The results were analyzed through pseudo F statistical test and icdrate test. The largest pseudo F statistic value was obtained from the 8th experiment (setting the layer size 2x2 without data normalization) with a pseudo F statistic value of 167.53 for 1kg artifacts and a Pseudo F statistic value of 104.86 for 200 g artifacts where the optimum number of clusters are 4. The smallest icdrate value was obtained from the 5th experiment (setting the 2x3 layer size without data normalization) with an icdrate value of 0.0713 for 1kg artifacts and icdrate value of 0.2889 for 200g artifacts with the best number of clusters being 6. From 12 laboratories can be grouped into 6 groups where each group has the same identification. There are groups 1, 3 and 6 have 1 member, while groups 2, 4 and 5 have 3 members.
Co-Authors Ferry Wahyu Wibowo Hanif Al Fatta Hasan, Nur Fitrianingsih Solikin, Arif Fajar