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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591 Documents
<![CDATA[Pemodelan dan Simulasi Monte Carlo dalam Identifikasi Kebutuhan Bahan Bakar Minyak (BBM) ]]>
<![CDATA[Agustini, Sherly]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 3 (September 2022) ]]>
Publisher : <![CDATA[SAFE-Network ]]>
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DOI: 10.37034/infeb.v4i3.149
<![CDATA[Abstrak Saat ini PT Ismadi Salam Batam dalam menjalankan penjualan BBM belum menggunakan simulasi perhitungan kebutuhan persediaan BBM di SPBU untuk pengambilan keputusan pimpinan, hal ini menyebabkan sering terjadi kekosongan stok di masing-masing SPBU. karena itu penulis tertarik membuat sebuah system pemodelan simulasi perhitungan persediaan BBM menggunakan metode Monte Carlo dan Pola LCM. Tujuan dari penelitian ini nantinya untuk mensimulasikan perhitungan penjualan BBM dengan cara membandingkan penjualan BBM pada bulan-bulan sebelumnya untuk memprediksikan ketersediaan BBM dibulan berikutnya agar pihak manajemen tau berapa banyak stok BBM yang harus disediakan di masing-masing SPBU. Metode pengumpulan data yang digunakan pada penelitian ini adalah wawancara, observasi, dan studi pustaka. Metode pengembangan sistem yang digunakan adalah waterfall dengan pemodelan UML (Unified Model Language) yang terdiri dari use case diagram, activity diagram, sequence diagram dan class diagram. Sistem dirancang menggunakan Bahasa Pemrograman JAVA. Kata kunci: BBM, Identifikasi, Simulasi, Optimalisasi, Monte Carlo, PT Ismadi Salam]]>
<![CDATA[End-User Computing Satisfaction dalam Menganalisis Tingkat Kepuasan Pengguna Sistem Informasi Akademik ]]>
<![CDATA[Irwanzar]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 3 (September 2022) ]]>
Publisher : <![CDATA[SAFE-Network ]]>
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DOI: 10.37034/infeb.v4i3.150
<![CDATA[Universitas Terbuka (UT) merupakan Perguruan Tinggi Negeri dengan Sistem Belajar Jarak Jauh yang berdiri sejak tahun 1984, saat ini jumlah mahasiswa aktif lebih dari 340.000 gabungan dari seluruh Unit Program Belajar Jarak Jauh (UPBJJ) Se-Indonesia, diantaranya lebih dari 8.000 Mahasiswa aktif pada UPBJJ UT Pekanbaru. Sistem informasi akademik (SIA) Universitas Terbuka dengan alamat https://sia.ut.ac.id merupakan salah satu aplikasi yang diperuntukan buat mahasiswa untuk memenuhi kebutuhan layanan akademik dan untuk calon Mahasiswa baru yang ingin mendaftar ke UT. Pada semester 2021/22.2 atau masa registrasi 2022.1 (Des 2021 - Sekarang) SIA UT melakukan pembaharuan platform semula versi 4G bertranformasi ke 5G. Sejak masa pandemi Mahasiswa UT lebih aktif dalam menggunakan SIA UT dan Calon mahasiswa UT wajib mendaftar secara online tanpa harus datang ke kantor UPBJJ UT, dimana biasanya Mahasiswa banyak datang ke kantor UPBJJ UT untuk melakukkan registrasi matakuliah, lihat nilai serta layanan akademik lain dibantu oleh staf UT. Tujuan Penelitian ini dilakukkan untuk mengukur tingkat kepuasan pengguna pada SIA UT. Metode End User Computing Satisfation (EUCS) merupakan metode yang digunakan pada penelitian ini, untuk mengukur tingkat kepuasan pengguna pada atribut diantaranya Content, Accurary, Format, Ease of Use, dan Timeliness., berdasarkan data analisis didapatkan dari penilaian atribut Content 53%, Accurary 50%, Format 50%, Ease of Use 50%, dan Timeliness 51%. Hasil dari data analisis bisa disimpulkan bahwa persentase tertinggi pengguna SIA UT menyatakan puas pada atribut content dan ada beberapa atribut yang lain masih dalam kategori Bagus]]>
<![CDATA[Pengendalian Persediaan Darah untuk Pasien dengan Hemoglobin Rendah Menggunakan Metode Backpropagation ]]>
<![CDATA[Prasiwiningrum, Elyandri]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 3 (September 2022) ]]>
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DOI: 10.37034/infeb.v4i3.153
<![CDATA[The Blood Transfusion Unit (UTD) of the Rokan Hulu Regional General Hospital (RSUD) has an important role to fulfill the demand for blood from patients. Patients who need blood donation are patients with low hemoglobin levels. The problem faced by the UTD-RS is that they have not been able to meet the needs of each patient's blood request optimally. The reason is because it is not able to predict the need for blood that will come. To see the pattern of blood demand and then determine the appropriate inventory control method to assist the planning process for the fulfillment of blood supply at UTD in the next period. Materials (data) and Methods: The data processed in this study were patient data and blood demand data from January 2020 to January 2021. The data were sourced from the Laboratory Installation and UTD at the Rokan Hulu Hospital. The data is divided into training data and testing data. Then the blood demand data is processed by normalizing it first and then the prediction process is carried out using the Backpropagation method. Then analyzed and tested with the help of Matlab software. This study uses the best network architecture pattern produced is 5-5-1 with an accuracy rate of 68% and a Mean Squared Error value of 0.198. The backpropagation method used is able to help UTD Rokan Hulu Hospital to find out the blood needs that must be met so that the blood supply can be controlled. So that every blood request from patients with low hemoglobin can be met quickly.]]>
<![CDATA[Data Mining Dengan Metode Naïves Bayes Classifer dalam Memprediksi Tingkat Kepuasan Pelayanan Dokumen Kependudukan ]]>
<![CDATA[Wirma, Susi]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 3 (September 2022) ]]>
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DOI: 10.37034/infeb.v4i3.155
<![CDATA[Kepuasan masyarakat terhadap layanan dokumen kependudukan merupakan hal yang sangat penting dalam peningkatan kualitas layanan sesuai yang diinginkan oleh masyarakat pada Dinas Kependudukan dan Pencatatan Sipil. Tujuan dari Penelitian ini adalah untuk memprediksi tingkat kepuasan masyarakat terhadap kualitas layanan dan untuk mengetahui hasil dari akurasi yang telah didapatkan dengan menggunakan algoritma Naïve Bayes. Data yang diolah merupakan data layanan kependudukan Tahun 2022. Dari hasil data yang diolah, bisa digunakan untuk meningkatkan kualitas dalam pelayanan dokumen kependudukan dan juga bisa digunakan untuk mengevaluasi kinerja pelayanannya. Hal ini dilakukan untuk mengetahui sejauh mana nilai dan kualitas dari pelayanan yang diberikan oleh Dinas Kependudukan dan Pencatatan Sipil Kota Pariaman kepada Masyarakat. Metode Naïve Bayes yang digunakan dalam penelitian ini untuk mencoba memprediksi kepuasan masyarakat terhadap kualitas pelayanan pada Dinas Kependudukan dan Pencatatan Sipil. Hasil dari indeks kepuasan terhadap pelayanan dokumen kependudukan pada Dinas Kependudukan dan Pencatatan Sipil menggunakan metode Naïve Bayes mendapatkan hasil yang baik.]]>
<![CDATA[Data Mining Menggunakan Algoritma K-Means Clustering dalam Analisis Tingkat Potongan Harga Terhadap Harga Jual Sepeda Motor Honda ]]>
<![CDATA[Mauliadi, Rafki]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (December 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[Aulya, Nurul]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (December 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 (December 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[Zuhdi, Imam]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (December 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[Najib, Muhammad]]>;
<![CDATA[Roza, Faisal]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (December 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[Wahyuni, Widya]]>
<![CDATA[ Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (December 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.]]>
