Jurnal Informatika Ekonomi Bisnis
The Jurnal Informatika Ekonomi Bisnis (INFEB) is an interdisciplinary journal. It publishes scientific papers describing original research work or novel product/process development. The objectives are to promote an exchange of information and knowledge in research work, and new inventions/developments on the use of Informatics in Economics and Business. This journal is useful to researchers, engineers, scientists, teachers, managers, and students who are interested in keeping a track of original research and development work being carried out in the broad area of informatics in economics and business through a scholarly publication.
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<![CDATA[Data Mining Menggunakan Algoritma K-Means Clustering dalam Analisis Tingkat Potongan Harga Terhadap Harga Jual Sepeda Motor Honda ]]>
<![CDATA[Rafki Mauliadi]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.156
<![CDATA[Knowledge Discovery in Database (KDD) has a structured analysis process to obtain the latest information. Data mining plays a role in extracting hidden information with one method, namely clustering. The purpose of this study was to determine the appropriate level of discount for each type of Honda motorcycle. The data processed in this study were sourced from the Marketing Main Dealer for Honda Motorcycles, West Sumatra. Furthermore, this data is processed by the Data Mining technique using the K-Means Clustering Algorithm. The processing stage is to determine the number of clusters and centroids, then calculate the distance between the centroid point and each object in the data. Predefined objects are grouped to determine cluster members based on distance. The calculation is continued until the resulting centroid value remains and the cluster members do not move to another cluster. The results of testing this algorithm are 3 clusters with 42 test data, in cluster 1 there are 34 types of vehicles that get discounted prices, then cluster 2 of 7 types of vehicles can get discounts and cluster 3 of 1 type of vehicles can not get discounts. The analysis of the test results has been able to determine the level of discount on the selling price of Honda motorcycles. By grouping customer interest data, it can be recommended to provide discounted sales prices in order to help marketing management increase sales of Honda motorcycles.]]>
<![CDATA[Prediksi Kunjungan Wisata Kota Payakumbuh Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation ]]>
<![CDATA[Nurul Aulya]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.157
<![CDATA[Tourism is a whole related elements which consist of tourists, tourist destinations, travel, industry and so on which are tourism activities and abundant natural wealth. The tourism sector is a very important service-based sector. Tourism is the fastest growing, vibrant and strong economic sector development, it also contributes to Gross Domestic Product (GDP), job creation, social and economic development. Artificial Neural Networks are computer programs that can imitate thought processes and knowledge to solve a specific problem. One of which is applied by the Artificial Neural Network to predict tourist visits. By using the Backpropagation method, it will be known the prediction of the number of tourist visits. The Backpropagation method is very useful for Artificial Neural Networks predicting the number of tourist visits the following year. The data processed in this study were 12 data sourced from the tourism section of the Payakumbuh City Youth and Sports Tourism Office. Furthermore, the data is processed using Matlab software. The stages of backpropagation are initialization, activation, training and iteration. The calculation of the network pattern used and the accuracy level of the expected error is continued. The result of testing this method is that it can predict tourist visits. So the level of accuracy is 95%. The prediction process has been carried out to predict tourist visits to the city of Payakumbuh. With the level of accuracy obtained is met, it can be used to help the Payakumbuh City Tourism Office increase the number of tourist visits in the future and further improve tourism management.]]>
<![CDATA[Identifikasi Pola Penjualan Barang dalam Menjaga Stabilitas Stok Menggunakan Algoritma Fp- Growth ]]>
<![CDATA[Nurhaida]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.158
<![CDATA[To take advantage of a very large collection of databases, a method or technique is needed that can convert a myriad of data into useful information, one of the data that can be processed is sales data at the Kamang Mart Mini Market. Kamang mart mini market is a mini market that will meet the needs of the community. As an effort to support efficient services, an orderly and thorough work procedure is needed so that it will produce fast, accurate and timely information according to the needs of consumers or the community. To facilitate the mini market in managing data, a system is needed that can produce a decision to find out which products are most in demand and needed by consumers and predict the level of stock availability. So that a lot of data can be used optimally so that later the goods needed by consumers can be fulfilled properly by the mini market and can increase sales at the Kamang Mart minimarket and can also reduce the accumulation of goods that are less desirable by consumers. The transaction data that will be processed in this study are as many as 20 transaction data. The transaction data will be examined using one of the Data Mining techniques by association rule using the Fp-Growth algorithm with a minimum support value of 30% and a confidence value of 70%. So that the pattern of product purchases is obtained which is used as information to predict the level of stock availability of goods. This research is very appropriate to be used by supermarkets in order to convey information more quickly and accurately so that sales levels are increased and well controlled.]]>
<![CDATA[Data Mining menggunakan Metode Rough Set dalam Memprediksi Tingkat Penjualan Peralatan Komputer ]]>
<![CDATA[Imam Zuhdi]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.159
<![CDATA[Data mining is a job in the form of collecting and using data to get a rule, pattern, or relationship in large data. The output of this data mining can be used to facilitate future decision-making. The purpose of this study is to predict the level of sales of computer equipment to make it easier for sellers to meet consumer needs. The data processed in this study include several factors which will later be included in the Roughtset method. The method used is a Rough set. Factors include the name of goods, warranty, price, and level of sales. These factors will later be grouped in the Equivalence Class, where the same attribute values will be grouped. Then proceed to the next stage, namely the Discernibility Matrix which contains a collection of condition attributes that have different condition values. After that, proceed to the Discernibility Matrix Modulo D stage where the columns in the matrix are filled with a collection of condition attributes that have different conditions and different decision values. The next stage is Reduction, which is how to get the condition attributes used to get output in the form of knowledge. The last stage is knowledge which is the result of the reduction obtained. Then the results of the rough set application will be entered into the Rosetta application. The results obtained using the rough set method on 10 samples of computer equipment sales data, obtained 17 new rules or knowledge that can be used as guidelines in decision making to identify the level of computer sales.]]>
<![CDATA[Prediksi Peserta Didik Baru untuk Mengoptimalkan Promosi Menggunakan Algoritma Monte Carlo ]]>
<![CDATA[Muhammad Najib]]>;
<![CDATA[Faisal Roza]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.161
<![CDATA[Telkom Elementary school of Padang is the digital-based school that utilizes advanced technology to elevate the quality of teaching, service, and evaluation. Digitalization is supposed to be provided with digital tools in which those are the most important things for the development of the school. This element is significantly beneficial for assisting the process of the school promotion in terms of students admission. To go further, the use of technology in this school has been incredibly beneficial for improving the promotion process of students admission. In the beginning of 2019, Telkom elementary school of Padang has been utilizing technology for obtaining the data of new students such as the information of registrants’ identity and payment process. Currently, Telkom Elementary school of Padang needs more evaluation towards its previous data that has been derived by digital tools in order to optimize the promotion process. Therefore, optimizing the promotion in students’ admission process becomes the main objective of this study. In order to achieve the goal, the data that used in this study is derived from school year off 2020-2021 and 2022-2023. The data consists of registration number, registration date, students name, and the name of the previous school that has been attended. Furthermore, Monte Carlo has been selected as the method used in this study. Based on the Monte Carlo test, there are 124 registrants predicted in the school year of 2021-2022 with the accuracy rate of 84%, 115 registrants for the school year of 2022-2023 with 81% of accuracy level, and 129 registrants predicted for the upcoming school year of 2023-2024. Thus, this research is able to be a reference for optimizing the promotion process in students admission of Telkom Elementary School of Padang.]]>
<![CDATA[Analisis Sentimen terhadap Opini Feminisme Menggunakan Metode Naive Bayes ]]>
<![CDATA[Widya Wahyuni]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.162
<![CDATA[The perspective of the development of feminism centered on women around the world who wants to be free from pressure, oppression and inequality from men, continues to this day. Various public opinions about feminism are now contained in various social media. Long debates about criticism and support for feminism in equalizing women's position both in terms of intellect, and the role of women in making decisions. This research was conducted with the aim of looking at public sentiment based on opinions circulating on social media. Hashtags or hash tags related to feminism from social media are the main data that will be used to analyze public opinion sentiment about feminism and 600 data are obtained about feminism. The data obtained were separated into positive, negative and neutral opinions for analysis using Naïve Bayes (NB). The results of using the Naïve Bayes method obtained a recall value of 84%, precision 94% and Fi-Score of 86% with an accuracy of 88%. Through this research, the results of classification using the nave Bayes method in analyzing sentiment against feminist opinions have good performance.]]>
<![CDATA[Klasterisasi Menggunakan Metode Algoritma K-Means dalam Meningkatkan Penjualan Tupperware ]]>
<![CDATA[Iriene Putri Mulyadi]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.164
<![CDATA[Persaingan dalam dunia bisnis sangatlah ketat,pelaku dunia bisnis memiliki tantangan untuk mengatur strategi penjualan.Toko Asrafi Raya merupakan suatu toko yang bergerak di bidang penjualan tuppeware yang berada di daerah Kabupaten Pasaman Barat. Banyaknya data produk tuppeware dan stok barang yang harus dianalisis, maka pemilik toko harus bekerja keras dalam menentukan barang yang akan dibeli berikutnya dilihat dari stok yang ada.Sehingga kesulitan yang dialamipemilik Toko Asrafi Raya adalah kurangnya stok produk yang laku karena penjualan tinggi, dan menumpuknya produk yang tidak laku karena penjualannya rendah. Penelitian ini bertujuan agar memudahkan Toko Asrafi Raya dalam meningkatkan penjualan tuppeware dengan mengelompokkan produk yang sangat laris, laris dan tidak laris. Data yang digunakan dalam penelitian ini adalah data laporan penjualan terhadap produk tuppeware pada bulan februari sampai juni 2021 yang ada di Toko Asrafi Raya, dengan menggunakan metode algoritma K-Means clustering. Hasil dari penelitian ini mendapatkan 3 cluster yaitu cluster 1(C1)Sangat Laris,Cluster 2 (C2) Laris,Cluster 3 (C3) Tidak Laris. Hasil Penelitian ini digunakan untuk membantu pemilik toko Asrafi Raya dalam menentukan strategi penjualan pada Toko Asrafi Jaya.]]>
<![CDATA[Simulasi Sistem Pelayanan Rawat Jalan Pasien menggunakan Simulasi Kejadian Diskrit (Des) ]]>
<![CDATA[Della Zilfitri Zubir]]>;
<![CDATA[Felka Andini]]>;
<![CDATA[Muhammad Ridho]]>;
<![CDATA[Yosep Filki]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.165
<![CDATA[The hospital is one of the health service centers that play an important role in society. Health Centers must provide the best service for their patients. In practice, good or excellent service is not easy to realize because of various factors that exist in the field which are probalistic in nature. To maintain service quality, it is necessary to carry out evaluation, analysis, and improvement activities on the existing system. Evaluation, analysis, and improvement activities on the system can be carried out using system simulations with the aim of producing better services. This research was conducted in an outpatient service system using discrete event simulation with the help of promodel software. The purpose of this study is to apply a discrete event simulation model in outpatient services so that there is an improvement in outpatient services and the efficiency of existing human resources. The simulation results show that the simulation model used is valid and can be used to evaluate the outpatient service system.]]>
<![CDATA[Algoritma K-Means Clustering dalam Memprediksi Penerima Bantuan Langsung Tunai (BLT) Dana Desa ]]>
<![CDATA[Yosep Filki]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.166
<![CDATA[Taluk Village, Lintau Buo Subdistrict, Tanah Datar Regency is one of the villages that carries out the distribution of the Village Fund Direct Cash Assistance (BLT-DD) program. This direct cash assistance is one of the government programs whose funds are sourced from village funds whose distribution is to the underprivileged or poor in order to overcome economic recovery for people affected by the pandemic. However, in the evaluation of its implementation in 2021 and 2022, many problems were found in its distribution, especially in determining this assistance to the recipient community. The problems that arise are caused by the occurrence in data processing that uses a direct determination system or mechanism in deliberation by the village government to determine the priority community as recipients of the many who propose as applicants to the nagari government to get this assistance. Besides that, there are also problems such as errors in recipient criteria and often this program is not targeted at the recipients. The K-Means Clustering method is very precise in implementing this BLT-DD beneficiary predictor, because this method is one of the methods used in data grouping as a reference in decision makers for clustering large amounts of data, and in the end it will cluster recipients based on 3 clusters, namely worthy, considered and unworthy. The purpose of this study was to predict the right target recipients of BLT-DD. The data processed is the data proposed by the recipients of the BLT-DD Taluk Village in 2022. Based on the results of data processing using PHP MYSQL Software, from a sample of 25 data, 11 data are produced which are included in cluster 1 with the status of the beneficiary being said to be feasible, 5 data that are classified as eligible. including cluster 2 with considered recipient status and as many as 9 data belonging to cluster 3 with unfit status. From the test results obtained an accuracy rate of 83.33 % so that it can be recommended to assist the government of the village guardian in making policies.]]>
<![CDATA[Akurasi dalam Analisis Kompetensi Calon Tenaga Keperawatan Menggunakan Algoritma Apriori ]]>
<![CDATA[Habiburrahman]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (2022) ]]>
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DOI: 10.37034/infeb.v4i4.167
<![CDATA[Setiap lulusan S1 keperawatan (NERS) harus dinyatakan kompeten pada Uji Kompetensi Ners Indonesia (UKNI) yang diadakan Kemdikbud untuk mendapatkan Surat Tanda Registrasi (STR) sebagai syarat bekerja. Tingkat kelulusan mahasiswa keperawatan dalam mengikuti UKNI sangat rendah jika dibandingkan dengan beberapa negara lain. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang perlu dioptimalkan calon tenaga keperawatan dalam persiapan menghadapi UKNI dengan teknologi data mining. Data yang diolah dalam penelitian ini bersumber dari Appskep Indonesia, suatu startup yang bergerak di bidang pendidikan kesehatan, diantaranya try out dan bimbingan belajar UKNI. Appskep memiliki data kelulusan UKNI lebih dari 2000 pesertanya yang tersebar di seluruh Indonesia pada tahun 2021 dan 2022. Data tersebut dianalisis menggunakan Algoritma Apriori untuk menemukan rule-rule asosiasi yang terkait dengan kelulusan calon tenaga perawat dalam UKNI. Hasil dari pengolahan data peserta UKNI ini adalah ditemukannya beberapa set rule asosiasi yang mempengaruhi kompeten atau tidaknya calon tenaga perawat dalam UKNI. Rule asosiasi yang paling dominan adalah jika penguasaan materi keperawatan jiwa lebih dari 60 persen maka seorang perawat akan lulus pada UKNI, dengan support 55% dan confidence 100%.]]>
