p-Index From 2019 - 2024
14.376
P-Index
This Author published in this journals
All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal of Artificial Intelligence and Data Mining Rang Teknik Journal Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Systematics Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Applied Engineering and Technological Science (JAETS) JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak Login : Jurnal Teknologi Komputer Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science)
Claim Missing Document
Check
Articles

Found 6 Documents
Search
Journal : Jurnal Informatika Ekonomi Bisnis

Pemetaan Promosi dalam Penjaringan Calon Mahasiswa Menggunakan Algoritma Backpropagation Mhd Hary Kurniawan; Sarjon Defit
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 1 (2020)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.35 KB) | DOI: 10.37034/infeb.v2i1.17

Abstract

Promotion requires a large fee if it is not targeted when doing it. Backpropagation is an excellent method of dealing with the problem of recognizing complex patterns. Backprogation neural network each unit in the input layer is connected to each unit in the hidden layer. Student data from 2014 to 2018 is a comparison point. The results of testing of this method are calculations using a sample value of 5 years before using a comparative value of 2014 to 2018 totaling 602 data. This research uses 5-5-1 architecture, epoch 2000 and learning rate so that the data accuracy reaches 71% with an error value of 0.0099. The results of this study are 16 districts that become promotion recommendations. Ordering of forecasting the highest number of students to the smallest number of students, so it can be concluded that this method is very useful in mapping promotions.
Optimalisasi Penggunaan Lahan Perkebunan Kelapa Hibrida Menggunakan K-Means Clustering Henky Andema; Sarjon Defit; Yuhandri Yunus
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 2 (2020)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (560.81 KB) | DOI: 10.37034/infeb.v2i2.23

Abstract

Plantations are the main source of income for farmers in Indragiri Hilir Regency. This plantation is the plantation sector most widely cultivated by farmers is a coconut plantation. The best grouping of coconut cultivation areas is important in developing farmers' income. This study aims to help the Plantation Office in the process of making the best decision areas for planting coconut, especially hybrid coconut. The data used in this study is the data of hybrid coconut plantations in 2018. Data processing in this study uses the K-Means Clustering method with the number of 3 Clusters namely Cluster 0 (C0) Less Potential, Cluster 1 (C1) Enough Potential, Cluster 2 (C2) Very Potential for planting hybrid coconuts. The results of the clustering process with 2 iterations stated that for Cluster 0 there were 7 village data, for Cluster 1 there were 1 village data, and for Cluster 2 there were 2 village data.
Prediksi Tingkat Ketersediaan Stock Sembako Menggunakan Algoritma FP-Growth dalam Meningkatkan Penjualan Rahmad Aditiya; Sarjon Defit
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 3 (2020)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (397.076 KB) | DOI: 10.37034/infeb.v2i3.44

Abstract

Large data sets can be processed to become useful information, one of the data that can be processed is sales transaction data at UD. Smart Aliwansyah, which will become important information to increase sales. This study aims to find the pattern of product purchases to predict the level of availability of staple foods so as to increase sales. The data that is processed in this study uses the sales transaction data of goods obtained from the sales invoice of UD. Smart Aliwansyah, North Sumatra Tax Village. Based on these data, with the provision that a minimum of 2 types of goods in 1 transaction is examined using a data mining technique in association with the FP-Growth algorithm with a confidence value of 75% and a minimum support of 20%. The tools used by Rapidminer 9.4 are to obtain product purchasing patterns which are used as information to predict the level of stock availability. The result of the sales data processing process is the association rule. Association Rule is obtained in the form of a relationship between goods sold together with other goods in a transaction. From this pattern, it can be recommended to the shop owner as information for preparing basic food stocks to increase sales results. This research is very suitable to be applied to determine the patterns of consumer spending such as the relationship of each item purchased by consumers, so this research is appropriate for use by grocery stores.
Sistem Pendukung Keputusan Menggunakan Metode Simple Additive Weighting dalam Meningkatkan Pendapatan Jasa Fotografi Fanny Septiani Bufra; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 4 (2020)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (307.997 KB) | DOI: 10.37034/infeb.v2i4.53

Abstract

The photography business grew very rapidly and was very profitable. The intense competition made the photo studio suffer losses and even went out of business because it was unable to compete and made wrong decisions. Like during the Covid-19 Pandemic in 2020, several photo studios experienced a decline in revenue because there were no bookings for photo services or canceling agreed projects. The purpose of this study is to assist the owner of a photo studio or photographer in determining the best decision from an investment plan that has been planned based on predetermined criteria in order to increase photography service income. In this study using the Simple Additive Weighting method. The variables that are the main criteria in this decision-making system are Cost, Productivity, Priority Needs, and Availability. The alternative data used is the Photo studio Investment Plan data in July 2020. Based on the results of the calculations using the Simple Additive Weighting method, the results show that Alternative 1, namely Paid Promotion on Social Media, is recommended as the best decision with the highest preference value of the 12 sample data. tested is 0.93. Comparison of data from manual counting with the system created, namely the Website-based Decision Support System, resulted in the same calculation value. So that the accuracy value is 100% and is declared accurate. With this Decision Support System, it can produce objective decisions to assist owners in determining investment plans that can increase income from photography services. Bisnis fotografi tumbuh sangat pesat dan sangat menghasilkan. Ketatnya persaingan membuat studio foto mengalami kerugian bahkan sampai gulung tikar karena tidak mampu bersaing dan salah dalam mengambil keputusan. Seperti pada masa Pandemi Covid-19 ditahun 2020, beberapa studio foto mengalami penurunan pendapatan karena tidak adanya yang booking jasa foto ataupun membatalkan project yang telah disepakati. Tujuan dari penelitian ini adalah untuk membantu owner studio foto atau fotografer dalam menentukan keputusan terbaik dari rencana investasi yang sudah direncanakan berdasarkan kriteria yang telah ditentukan agar dapat meningkatkan pendapatan jasa fotografi. Penelitian ini menggunakan metode Simple Additive Weighting. Variabel yang menjadi kriteria utama pada Sistem Pengambilan Keputusan ini yaitu Biaya, Produktivitas, Prioritas Kebutuhan, dan Ketersediaan. Data alternatif yang digunakan yaitu data Rencana Investasi studio Foto pada bulan Juli 2020. Berdasarkan hasil dari perhitungan dengan menggunakan metode Simple Additive Weighting ini, didapatkan hasil bahwa Alternatif 1 yaitu Promosi Berbayar di Sosial Media direkomendasikan sebagai keputusan terbaik dengan nilai preferensi tertinggi dari 12 data sampel yang diuji yaitu 0.93. Dilakukan perbandingan data dari hitungan manual dengan sistem yang dibuat yaitu Sistem Pendukung Keputusan berbasis Website menghasilkan nilai perhitungan yang sama. Sehingga nilai keakurasiannya adalah 100% dan dinyatakan akurat. Dengan adanya Sistem Pendukung Keputusan ini dapat menghasilkan keputusan objektif untuk membantu owner dalam menentukan rencana investasi yang dapat meningkatkan pendapatan jasa fotografi.
Klasterisasi Bibit Terbaik Menggunakan Algoritma K-Means dalam Meningkatkan Penjualan Yuli Hartati; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 1 (2021)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (823.763 KB) | DOI: 10.37034/infeb.v3i1.56

Abstract

Tiara Bersaudara is a shop that sells seeds and agricultural needs. To maintain a stock of seeds that farmers are interested in, sellers must be able to analyze seed sales data. This process is difficult to do because UD has a lot of sales data. The existing problem can be solved by clustering seed sales data. Clustering is grouping data into several clusters based on the level of data similarity. The research objective was to group the best-selling seedlings in UD.Tiara Bersaudara in increasing sales. Seed sales data from January to April 2019 are data that will be processed in this study. The clustering method uses the K-Means algorithm by partitioning the data into clusters based on the closest centroid to the data. Then the test is done by comparing the calculation results with the Rapid Miner studio 9.7 software. Clustering is tested based on lots of data and many clusters. The data tested were 42 seedlings by obtaining 2 clusters, 4 data which were best-selling seeds as cluster one (C1), and 38 data which were unsold seeds as cluster two (C2). Best-selling seeds are the best seeds that can increase sales consisting of Bibit Jagung NK 212, Bibit Jagung NK 7328, bibit Jagung Pioneer 32, Bibit Jagung NK 617232. The results of this study can be used as benchmarks for decision support by UD.Tiara Berasaudara to set up a marketing strategy to increase sales.
Klasterisasi Dana Bantuan Pada Program Keluarga Harapan (PKH) Menggunakan Metode K-Means Abdul Azis Said; Sarjon Defit; Yuhandri Yunus
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 2 (2021)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.893 KB) | DOI: 10.37034/infeb.v3i2.66

Abstract

The Family of Hope Program (PKH) is a program that aims to reduce poverty and improve the quality of human resources. Optimizing the provision of assistance in accordance with the expectations of those in need. Data on the poor or integrated social welfare data is needed as a reference for grouping. This study aims to make it easier for the selection team to provide assistance in accordance with the predetermined criteria whether or not they deserve to receive the assistance. The data used in the study is data from 2019. The data processing in this study uses the K-Means Clustering method with 3 clusters, namely Cluster 1 (C1) Nearly Poor Households (RTHM), Cluster 2 (C2) Poor Households (RTM), Cluster 3 (C3) Very Poor Households (RTSM). The results of the clustering process with 2 iterations state that for Cluster 1 the amount of data is, for Cluster 2 the amount of data, and for Cluster 3 the amount of data. So this research is very helpful in relocating targeted assistance according to the family hope cluster.
Co-Authors Abdul Azis Said Adek Putri Adi Gunawan Adi Gunawan, Adi Agung Ramadhanu Agus Perdana Windarto Ahmad Zamsuri, Ahmad Am, Andri Nofiar Amran Sitohang Andri Nofiar Angga Putra Juledi Anggrawan, Anthony ardialis Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bisma Okmarizal Bosker Sinaga Daeng Saputra Perdana Daniel Theodorus Dayla May Cytry Dendi Ferdinal Deno Yulfa Ardian Dhena Marichy Putri Dinda Permata Sukma Dwi Utari Iswavigra Dwiki Aulia Fakhri Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma eriwandi Faisal Roza Fanny Septiani Bufra Fauzan Azim Fauzi Erwis Febri Aldi Febri Hadi Febrina, Yerri Kurnia Fitriani, Yetti Fristi Riandari Fristi Riandari Fuad El Khair Gunadi Nurcahyo Gunadi Widi Nurcahyo Habdi Habdi Halifia Hendri Handika, Yola Tri Haris Kurniawan Hasmaynelis Fitri Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Indah Savitri Hidayat Ira Nia Sanita Ismail Virgo Jefdy Kurniawan Jeri Wandana Juansen, Monsya Juledi, Angga Putra Khairul Azmi Kurniawan, Jefdy L. J. Muhammad Larissa Navia Rani Leoni Lidya M Syahputra M. Ibnu Pati Mardayatmi, Suci Mardison Mardison Mardison Meilinda Sari Meilinda Sari Melissa Triandini Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen MUHAMMAD TAJUDDIN Nadya Alinda Rahmi Nandel Syofneri Nanik Istianingsih Nopi Purnomo Nori Sahrun, Nori Novi Yanti Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhidayat Pati, Muhammad Ibnu Putra, Rahman Arief Putri, Adek R Rahmiyanti Rafiska, Rian Rahmad Aditiya Rahman Arief Putra Ramadhan, Mukhlis Ramdani Bayu Putra Rezki - Rian Kurniawan Rianti, Eva Rio Andika Malik Ritna Wahyuni Riyan Ikhbal Salam Rizki Mubarak Rusdianto Roestam S Sumijan Salam, Riyan Ikhbal Sandrawira Anggraini Sandy Mulyanda Setiawan, Adil Shahab Wahhab Kareem Sharon Shaza Alturky Sirait, Weri Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sri Dewi Sri Dewi Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Dwi Putra Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syahputra, M Syaljumairi, Raemon Virgo, Ismail Vivi Suryani Wahyuni, Ritna Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yuli Hartati Yunus, Yuhandri Yusma Elda Zulvitri, Z Zurni Mardian