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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Dewi Eka Putri
Universitas Putra Indonesia YPTK, Padang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Prediksi Tingkat Penjualan Sepeda Motor dengan Metode Rough Set Eka Praja Wiyata Mandala; Dewi Eka Putri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3057

Abstract

The high level of motorcycle sales makes the showroom have difficulty in procuring variants of motorcycles to be sold. The many variants of motorcycles in one manufacturer, make different sales of each motorcycle variant, there are variants with high sales and some with low sales. So it is necessary to predict the level of motorcycle sales. This study uses data from one of the Honda motorcycle showrooms, namely the Hayati showroom, Pasaman branch. The data used is a recapitulation of motorcycle sales data in the second quarter of 2020. This study uses 24 data samples as a decision system. From the test results obtained 13 equivalence classes, then a reduction process is carried out to obtain 7 reducts and a rules generation process is carried out, then 41 rules are obtained with Motorcycle Prices as the dominant attribute in influencing the Sales Level attribute decisions with an incidence of 42% and min. support = 5.
Peramalan Produksi Serundeng Kentang dengan Fuzzy Tsukamoto Eka Praja Wiyata Mandala; Dewi Eka Putri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 3 (2020): Juli 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i3.2238

Abstract

Production is a very important process for a business that produces products. In a culinary business, for example, production is a determining factor so that the culinary products produced can be distributed to stores for sale. The culinary business is mostly done by small and medium businesses. One of them is UKM Yandi which a produces potato flakes as its culinary products. UKM Yandi distributes a potato flakes to 10 minimarkets in Padang every week. The problem that occurs is often the amount of production is greater than the demand of the minimarket while the remaining stock in the minimarket is still large, so there is often a buildup of products on the minimarket because it is not sold. This research provides a solution for UKM Yandi to forecast the production of potato flakes using the Fuzzy Tsukamoto approach, using input variables, the remaining stock at the minimarket in the previous week and the request submitted by the minimarket for the following week, while the output variable is the amount of production carried out by UKM Yandi. This research makes a web-based application that can be used by UKM Yandi owners to forecast the production of potato flakes, so they can be accessed from anywhere. The results of this research are able to help and facilitate UKM Yandi in forecasting the production of potato flakes because it produces a number with a certain number of packets to be produced
Penerapan Data Mining untuk Klasifikasi Hasil Panen Jamur Tiram Menggunakan Algoritma K-Nearest Neighbor Eka Praja Wiyata Mandala; Dewi Eka Putri; Randy Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5252

Abstract

Oyster mushroom is a type of mushroom that can be consumed by humans. Lots of food products are made from processed oyster mushrooms. This makes mushroom farmers intensively cultivate oyster mushrooms because they see good economic prospects. However, not all mushroom cultivation processes can be successful so it will have an impact on the yield of the oyster mushrooms. So it is necessary to classify so that it is easier for mushroom farmers to determine the amount of yield from the oyster mushroom. The classification was carried out because of the difficulty of mushroom farmers in determining the amount of harvest by looking at the width of the mushroom caps, the number of mushroom caps to the mushroom harvest time. This study proposes a data mining technique to classify oyster mushroom yields using the K-Nearest Neighbors algorithm so that it can help mushroom farmers in determining the yield of oyster mushrooms being cultivated. This study used a dataset of 42 mushrooms as training data and 1 mushroom data to determine the classification of the crops. From the results of testing on 1 mushroom with a cap width of 8 cm, the number of caps is 14 pieces and the harvest time is 49 days, the results of classification results obtained from this mushroom are Less with a Mean absolute error of 0.1419, Root mean squared error of 0.2111, Relative absolute error of 36.2177% and Root relative squared error of 48.002%. The results of this research can help mushroom farmers in classifying oyster mushroom yields.
Co-Authors Eka Praja Wiyata Mandala Randy Permana