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All Journal JIK Jurnal Ilmu Komputer Jurnal Sarjana Teknik Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Sebatik JURTEKSI Progresif: Jurnal Ilmiah Komputer Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Komtekinfo Jurnal Teknologi
Eka Praja Wiyata Mandala
Universitas Putra Indonesia YPTK Padang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : jurnal media informatika budidarma

 1 
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 Penjualan Pada Toko Retail Menggunakan Algoritma Backpropagation Neural Network Musli Yanto; Eka Praja Wiyata Mandala; Dewi Eka Putri; Yuhandri Yuhandri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 3 (2018): Juli 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i3.811

Abstract

Retail is one or more activities that add value to the product to the consumer either for family needs or for personal use. Retail can sell products depending on current market needs. The goods we enjoy today are not apart from retail services, retail helps producers / distributors and consumers so that every need will be fulfilled. In this problem the author tries to do retail store research in the city of Padang. This research aims to help retail stores to forecast procurement of goods. Artificial Neural Network Backpropagation can make the forecasting process for procurement of goods for the next period of time on each item on the retail and will ultimately be useful for retail store managers. The forecasting process begins with determining the variables that will be required in the network pattern, then the pattern of established network will be continued on the network training process by using backpropagation algorithm. After doing the network training process the researchers will do a comparison with some pattern of network that has been formed. The last process undertaken in this research is the process of determining the best network pattern of the average value of errors obtained from each training network pattern. In the final result of the forecasting process, the results of the calculation have a total error of = 3.57%. Judging from the forecasting process that will be done not only used to predict the procurement of goods but also can predict sales figures in retail stores. In principle, this research can help to determine the procurement of goods in the sales process that will minimize the losses that occur in every sales activity.
Implementasi Algoritma FP-Growth Untuk Menemukan Pola Frekuensi Pembelian Lauk Pada Rumah Makan Takana Juo Dewi Eka Putri; Eka Praja Wiyata Mandala
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i1.2643

Abstract

All dishes originating from West Sumatra are more popularly known as Padang cuisine. Padang cuisine is one of the most preferred dishes by Indonesians and foreign tourists. Restaurants that serve Padang cuisine, spread all over Indonesia. One of them is the Takana Juo Restaurant located in Singkawang, West Kalimantan. This restaurant serves a variety of side dishes with a distinctive taste from West Sumatra. The problem that occurs in this restaurant is that this restaurant often runs out of several types of side dishes, while there are still many other side dishes, so that many side dishes are often left out every day. So, a solution is needed to see the buying patterns of customers from restaurants in buying side dishes, so that the restaurant can arrange the side dishes that will be sold every day. The data mining approach is considered suitable for finding these customer buying patterns. This study uses data on side dishes sales in one day as a sample data of 12 transactions. Examples of the resulting buying patterns are 12 transactions that buy grilled chicken, 6 transactions buy beef rendang. The algorithm used to help restaurants find customer buying patterns is the FP-Growth Algorithm, so it can help restaurants regulate the number of side dishes to be sold each day. The result of this research is the decision in the procurement of side dishes at the restaurant which can be seen from the frequency pattern of side dishes purchases made by customers.
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 Afdhal, Muhammad Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Emil Naf'an Faradila Mustika Gunadi Widi Nurcahyo Kurnia, M Reza Tri Laksamana, Ridho Mardhiah, Sitty Muhammad Reza Putra Muhammad Ridwan Muhammad, Abulwafa Musli Yanto Naf'an, Emil Prawiro, Radius Putra, Dede Wira Trise Putri, Dewi Eka Rafli, M Nerdi Randy Permana Rhayatun Aviqah Rianti, Eva Ridho Laksamana Rini Sovia Rini Sovia Sarjon Defit Subrianto Chandra Sumijan Sumijan Yuhandri Yuhandri, Yuhandri Zulfikri, Rahmat