Articles
<![CDATA[Prediksi Jumlah Pemberian Kredit kepada Nasabah di Bank Perkreditan Rakyat dengan Algoritma C 4.5 ]]>
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Dewi Eka Putri]]>
<![CDATA[ Jurnal KomtekInfo Vol. 5 No. 1 (2018): Komtekinfo ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/komtekinfo.v5i1.7
<![CDATA[Bank Perkreditan Rakyat adalah lembaga keuangan bank yang hanya menerima simpanan dalam bentuk deposito berjangka, tabungan, dan/atau bentuk lain yang dipersamakan dengan itu dan menyalurkan dananya sebagai usaha BPR. Permasalahan yang dialami oleh BPR adalah pemberian kredit yang tidak tepat sasaran dan waktu tunggu keputusan nasabah yang lama. Maka dalam penelitian ini, kami mengusulkan suatu cara untuk memprediksi pinjaman yang dapat membantu BPR dalam mengambil keputusan yang akurat dan tepat sasaran. Metode yang digunakan untuk membuat prediksi tersebut adalah Algoritma C 4.5. Algoritma C 4.5 merupakan algoritma yang paling banyak digunakan untuk membuat prediksi dalam Data Mining. Jadi yang akan menjadi keputusan akhir dari prediksi pemberian kredit ini adalah keputusan besar atau kecilnya jumlah kredit yang akan diberikan kepada nasabah.]]>
<![CDATA[Hybrid Data Mining berdasarkan Klasterisasi Produk untuk Klasifikasi Penjualan ]]>
<![CDATA[Dewi Eka Putri]]>;
<![CDATA[Eka Praja Wiyata Mandala]]>
<![CDATA[ Jurnal KomtekInfo Vol. 9 No. 2 (2022): Komtekinfo ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/komtekinfo.v9i2.279
<![CDATA[Sales at minimarkets have had ups and downs since the Covid-19 pandemic. In this study, the problem was found, namely the number of products in minimarkets that were not sold or vice versa, there was a demand for products, but they were not available in minimarkets. Data mining can be a solution to solving this problem. This study proposes a hybrid data mining method by combining the K-Means and K-Nearest Neighbors algorithms. The combination of this method works in two stages, namely clustering the products sold which are then continued by classifying the sales of these products. The data used in this study is data for toiletries and washing products as many as 20 products. From the results of research conducted, there are 14 products that have many enthusiasts from 20 products. Of the 14 products used as training data, a sales classification was carried out for 1 new product with stock criteria 200, sold 100, and price Rp. 10,000. From the test results, the new product classification is High Sales with accuracy in the classification reaching 85.7143%. The use of a hybrid method between the K-Means and K-Nearest Neighbors algorithms has a significant influence in determining the classification results. For further research, it is recommended to have a prediction process from the classification results that have been found, so that they have a greater influence on the use of this hybrid data mining method.]]>
<![CDATA[Pengenalan Aplikasi Analisis Data untuk Pengelompokkan Pemasaran Jamur Tiram ]]>
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Dewi Eka Putri]]>;
<![CDATA[Randy Permana]]>
<![CDATA[ Majalah Ilmiah UPI YPTK Vol. 29 (2022) No. 2 ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/jmi.v29i2.128
<![CDATA[Jamur tiram merupakan jenis jamur yang dapat dikonsumsi baik jamur segar maupun jamur yang sudah diolah menjadi berbagai makanan. Budidaya jamur tiram menjadi salah satu usaha yang dilakukan oleh para petani jamur karena proses budidaya yang mudah dilakukan, media tanam jamur tiram yang mudah didapatkan hingga pengolahan jamur tiram yang dapat dijadikan berbagai macam makanan. Salah satu kelompok budidaya jamur tiram berada di Kampung Jamur, kecamatan Pauh Kota Padang. Di Kampung Jamur, para petani jamur melakukan budidaya jamur tiram mulai membuat media tanam, menghasilkan jamur tiram segar hingga produk makanan olahan dari jamur tiram. Beberapa produk yang dihasilkan adalah jamur segar, rendang jamur, es krim jamur, agar-agar jamur dan kerupuk jamur. Pemasaran produk ini dilakukan melalui forum jual beli di media sosial, menitipkan produk di minimarket sekitar dan pembelian secara langsung ke Kampung Jamur. Permasalahan yang diperoleh adalah kecilnya area pemasaran dan sedikitnya media pemasaran yang digunakan untuk memasarkan produk tersebut. Kegiatan ini memberikan pengetahuan kepada para petani jamur untuk mengembangkan area dan media pemasaran menggunakan marketplace. Tujuan dari kegiatan ini adalah mengenalkan sebuah aplikasi untuk melakukan analisis data yang dapat digunakan untuk melakukan klasterisasi pemasaran produk olahan jamur tiram. Aplikasi yang dikenalkan adalah aplikasi Weka versi 3.8.3. Hasil dari kegiatan ini adalah tingginya keingintahuan petani jamur tentang cara pemasaran produk dengan media promosi lainnya dan adanya minat dari beberapa petani jamur untuk menggunakan aplikasi Weka 3.8.3 dalam mengelompokkan pemasaran produk olahan jamur tiram.]]>
<![CDATA[Protokol L2TP dan IPsec Sebagai Keamanan Jaringan Pada Dinas Kominfotik Sumatera Barat ]]>
<![CDATA[Ridho Laksamana]]>;
<![CDATA[Emil Naf'an]]>;
<![CDATA[Eka Praja Wiyata Mandala]]>
<![CDATA[ Jurnal Sarjana Teknik Informatika Vol 10, No 3 (2022): Oktober ]]>
Publisher : <![CDATA[Teknik Informatika, Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/jstie.v10i3.25178
<![CDATA[Beberapa masalah muncul pada beberapa perusahaan-perusahaan baik itu dalam skala besar maupun skala kecil. Adapun dari berbagai macam masalah yang terjadi, salah satunya adalah masalah keamanan jaringan komputer. Pada Kantor Dinas Komunikasi, Informatika dan Statistik Sumatera Barat terdapat masalah keamanan jaringan, salah satunya masalah yang sering terjadi yaitu serangan dari berbagai macam malware. Salah satu solusi untuk mengatasi masalah tersebut ialah dengan menggunakan teknologi keamanan jaringan VPN (Virtual Private Network) dengan metode L2TP (Layer 2 Tunneling Protocol) dan metode IPSec (Internet Protocol Security) yang akan digunakan sebagai alternatif keamanan jaringan untuk meningkatkan keamanan pertukaran data perusahaan. Penelitian ini membuat jaringan private dengan menggunakan IP publik yang dikonfigurasikan pada mikrotik dan konfigurasi dibuat untuk meminimalkan biaya dan waktu implementasi. Protokol L2TP (Layer 2 Tunneling Protocol) dan IPSec (IP Security) mampu mengatasi serangan DDoS attack sehingga server tidak mudah down saat terindikasi serangan.]]>
<![CDATA[Penerapan Data Mining untuk Klasifikasi Hasil Panen Jamur Tiram Menggunakan Algoritma K-Nearest Neighbor ]]>
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Dewi Eka Putri]]>;
<![CDATA[Randy Permana]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023 ]]>
Publisher : <![CDATA[Universitas Budi Darma ]]>
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DOI: 10.30865/mib.v7i1.5252
<![CDATA[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.]]>
<![CDATA[Pemanfaatan Teknologi dalam Pengelompokkan Produk pada Minimarket ]]>
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Dewi Eka Putri]]>
<![CDATA[ Jurnal Teknologi Vol. 11 No. 2 (2021): Jurnal Teknologi ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/jitekin.v11i1.52
<![CDATA[The retail industry is currently growing rapidly, especially in Indonesia. One form of the retail industry is modern retail which includes supermarkets, minimarkets and others. This study focuses on the grouping of products sold at minimarkets. This research is caused by seeing the phenomenon of the large number of transactions that occur in one day, the result is the number of products sold. This makes it difficult for minimarket managers to determine the next product procurement. Therefore, This study is conducted to group the products sold so that the products that need to be procured are seen next. This study propose a software to perform the grouping using the K-means algorithm. For the data sample, this study obtained sales transaction data for 3 months from the Sastra Mart minimarket. In this study, manual calculations were carried out on 10 samples of beverage data taken randomly from sales transactions which would be divided into 3 clusters. The results of manual calculations, there are 3 drink data entered into the “Sangat Laris” cluster, 2 drink data entered the “Laris” cluster and 5 drink data entered the “Kurang Laris” cluster. The software produced from the research gives the same results as manual calculations in classifying products. This study has also carried out software testing to test all its functionalities, from the test results, everything runs normally and as expected.]]>
<![CDATA[Data mining technique for grouping products using clustering based on association ]]>
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Dewi Eka Putri]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v31.i2.pp835-844
<![CDATA[There is high competition between these minimarkets so many products sold in each minimarket are not sold until they expire. The aim of this study is to help retail managers cluster products in minimarkets. The data obtained will be processed using the hybrid data mining approach by combining two methods in data mining. In the first section, association uses the FP-Growth algorithm, and in the second section, clustering uses the K-means algorithm. From the experimental results, it can be seen that the proposed approach can minimize the number of products to be grouped. After the association process is carried out, from 29 products in 12 transactions, 6 products can be obtained that has a frequency above the minimum support and minimum confidence. After the clustering process, 6 products are grouped into 2 clusters, so that 1 product is included in the most interested product cluster and 5 products are included in the interested product cluster. We minimize data processing so that retail managers can process data directly from sales transaction data on the cashier's computer and can quickly get the results of product grouping.]]>
<![CDATA[Augmented Reality dengan Model Generate Target dalam Visualisasi Objek Digital pada Media Pembelajaran ]]>
<![CDATA[Randy Permana]]>;
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Dewi Eka Putri]]>;
<![CDATA[Musli Yanto]]>
<![CDATA[ Majalah Ilmiah UPI YPTK Vol. 30 (2023) No. 1 ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/jmi.v30i1.143
<![CDATA[Augmented Reality (AR) is a digital technology that allows the creation of a combination between the real world and digital content projections to produce additional valuable information for users. This technology has begun to implement in various fields of human life like industry, health, military, entertainment, and education. AR technology will be applied to the education sector in this community service activity. This activity aims to introduce AR technology and a development model based on the Model Generate Target (MGT) to service partners, namely SMA INS Kayu Tanam. The Generate Target model is an AR application development concept by adopting the concept of Digital Twins, where digital content projections will be made similar to real objects. The projected digital content will serve as a descriptive object from the real world, so users can interact more flexibly with objects from the real world. The activity was carried out by providing an introduction to AR technology, installing AR design software, and practicing making simple AR applications using the MGT concept for partner service to the community. In this activity, the designed AR will use spherical objects in the real world and the resulting digital projection is a virus. Projection of digital content onto spherical objects will provide better information and learning experiences in viral object recognition because 3D objects will appear and be attached to real objects. The expected results of this community service activity are introducing AR technology to partners engaged in education and providing a new solution in implementing new digital content-based teaching media that can be applied to several subjects such as biology, chemistry, and glasses to community service partners.]]>
<![CDATA[Decision Support System for Loan Eligibility using the Simple Additive Weighting (SAW) Method ]]>
<![CDATA[M Reza Tri Kurnia]]>;
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Radius Prawiro]]>
<![CDATA[ Journal of Computer Scine and Information Technology Volume 9 Issue 4 (2023): JCSITech ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/jcsitech.v9i4.84
<![CDATA[The Solok City Bakti Husada Employees Cooperative is a non-bank financial institution in the form of a cooperative that serves the needs of its members in lending services with collateral in the form of member savings. The aim of this research is to make it easier for the Bakti Husada Cooperative to determine the suitability of loans to borrowers and to create a Decision Making System using the Simple Additive Weighting (SAW) method which is able to assist the Bakti Husada Cooperative in determining the suitability of loans to borrowers. The data collection method was carried out by direct observation and interviews with employees from the Bakti Husada Cooperative, Solok City. The results obtained are effective in making decisions about loan eligibility at the Bakti Husada Cooperative in Solok City in accordance with the criteria that have been determined in the selection. This result is proven. By using this system, members who apply for a loan get a score that is in accordance with the criteria and weights that have been set and from there it can be seen if there are criteria that are not in accordance with the standard requirements of the member's application for a loan so that members can improve the loan requirements so that Loan applications are accepted by the cooperative. Gusnelawati obtained the calculation results using the SAW method to determine the feasibility of providing loans to cooperatives with a value of 0.9625 and was the 1st Best in making this decision.]]>
<![CDATA[Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail ]]>
<![CDATA[Rhayatun Aviqah]]>;
<![CDATA[Muhammad, Abulwafa]]>;
<![CDATA[Mandala, Eka Praja Wiyata]]>
<![CDATA[ Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
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DOI: 10.37859/coscitech.v4i3.5487
<![CDATA[Data transaksi penjualan merupakan sebuah hal yang dapat dimanfaatkan kembali untuk pengambilan keputusan bisnis. Namun pada kasus ini data transaksi tersebut tidak dimanfaatkan kembali untuk keperluan bisnis, dan hanya dijadikan sebagai arsip laporan penjualan. Algoritma FP-Growth yakni tingkatan dari algoritma asosiasi apriori yang menggunakan alternatif frekuensi itemset berdasarkan pada angka yang paling sering muncul pada tiap transaksi(frequent itemset) dalam sebuah kelompok data. Karakteristik dari algoritma FP-Growth yaitu struktur pada data yang dipakai berupa tree dengan nama FP-Tree. Dengan penggunaan FP-Tree, algoritma FP-Growth dapat mengekstrak frequent Itemset dari FP-Tree. Metode FP-Growth terbagi menjadi 3 tahapan-tahapan utama yaitu tahap pembangkitan conditional pattern base, tahap pembangkitan conditional FP-Tree, dan tahap pencarian frequent itemset. Dengan penerapan metode FP-Growth pada penelitian ini bisa digunakan untuk melihat pola penjualan produk. Hasil yang didapatkan berupa 5 interesting rules dengan memasukkan nilai min support 10% dan min confidance 50 % yaitu jika membeli popok maka membeli baju, jika membeli dot baby maka membeli baju, jika membeli topi maka membeli baju, jika membeli celana akan membeli baju, dan jika membeli singlet akan membeli baju. Diharapkan penelitian ini dapat membantu pemilik Retail Aura Moms Baby & Kids dalam memanfaatkan hasil transaksi penjualan sehingga hasil dapat dimanfaatkan dengan tepat. Hal ini dibuktikan dengan dibuatnya sistem E-Business yang dapat mengelola transaksi penjualan untuk menentukan pola pembelian produk pada Retail Aura Moms Baby & Kids.]]>
