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All Journal JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Daya Matematis Jurnal Manajemen Teori dan Terapan JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Equilibrium: Jurnal Ilmiah Ekonomi, Manajemen dan Akuntansi CARADDE: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Learning Education and Counseling EDUKATIF : JURNAL ILMU PENDIDIKAN Jurnal Jamu Indonesia Celebes Computer Science Journal Journal of Applied Science, Engineering, Technology, and Education Mattawang: Jurnal Pengabdian Masyarakat LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Visi Pengabdian Kepada Masyarakat ARRUS Journal of Social Sciences and Humanities Seminar Nasional Pengabdian Kepada Masyarakat Jurnal Ilmiah Ilmu Terapan Universitas Jambi Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Journal of Artificial Intelligence and Digital Business Seminar Nasional Hasil Penelitian LP2M UNM Maneggio Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications
Rizal Bakri
(1) Statistics Research Group, STIEM Bongaya, Jl. Mappaoudang No. 28, Makassar, 90223, Indonesia; (2) Department Of Digital Business, Universitas Negeri Makassar, Jl. Pendidikan No. 1, Makassar, 90222, Indonesia
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Journal : JURNAL SISTEM INFORMASI BISNIS

 1 
Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk Bakri, Rizal; Data, Umar; Astuti, Niken Probondani
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 9, No 2 (2019): Volume 9 Nomor 2 Tahun 2019
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1954.6 KB) | DOI: 10.21456/vol9iss2pp244-251

Abstract

Business analytics plays an important role in optimizing the management of product marketing strategies. One of the most popular analytical tools in business analytics is sales forecasting. Businesses need to conduct sales forecasting to optimize marketing management in the form of product availability predictions, predictions of capital adequacy, consumer interest, and product price governance. However, the problem that is often encountered in forecasting is the number of forecasting methods available so that it makes it difficult for business people to choose the best forecasting method. The aims of this research is to develop a forecasting software tha can be accessed online based on computational intelligence, which is a software that can make forececasting with various methods and then intelligently choose the best forecasting method. The software development method used in this study is the SDLC with waterfall model. The result of this research is the Auto sales forecasting software was developed using the R programming language by combining various package and can be accessed online through the page Http://bakrizal.com/AutoSalesForecasting. This software can be used to conduct forecast analysis with various methods such as Simple Moving Average, Robust Exponential Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. This software contains intelligence computing to choose the best forecasting method based on the smallest RMSE value. After testing the sales transaction data at the Futry Bakery & Cake Shop in Makassar, the results show that the Robust Exponantial Smoothing method is the best forecasting method with an RMSE value of 0.829  
Implementasi forecastHybrid Package menggunakan R Studio Cloud untuk Prediksi Pertumbuhan Dana Pihak Ketiga dan Pembiayaan Pada Bank Muamalat Indonesia Niken Probondani Astuti; Rizal Bakri
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 11, No 2 (2021): Volume 11 Nomor 2 Tahun 2021
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol11iss2pp97-104

Abstract

This study aims to find out how to forecast the growth performance of third party funds (TPF) and financing which is measured on a quarterly by applied the hybrid method with R Studio Cloud using ‘forecastHybrid’ package. This prediction is expected to provide information and data on the growth of third party fund and financing for Bank Muamalat which is experiencing problems of lack of capital and non-performing funds (NPF). Forecasting with Hybrid methods combines ARIMA auto forecasting methods, exponential smoothing forecasting methods, theta forecasting methods, neural network forecasting methods, seasonal and trend decomposition forecasting methods, and TBATS forecasting methods. The forecast results show that the Hybrid method is able to provide information as a decision-making material for Bank Muamalat
Co-Authors Abdukhamidov, Sarvar Ahmar, Ansari Saleh Amiriddinova, Muslima Andika Saputra ANDIKA SAPUTRA Andika Saputra Ardiansyah Halim Asfar Asmar Asmar, Asmar Astuti, Niken Probondani Bagus Sartono Data, Umar Farit M Afendi Farit Mochamad Afendi Fauzi, Muhammad Fitroh Halim, Ardiansyah Hamzah, Muh. Qardawi Hari Wijayanto Hari Wijayanto Hari Wijayanto Hartoto Hartoto Hartoto Hartoto Hasbiyadi, Hasbiyadi Hasyim Mochtar Hazan Azhari Zainuddin Irish Fiadyanti Indi Syafira Islamiah, Farida Laode Ahmad Sabil Mahmudova, Zarrina Makhmadieva, Charos Makhmudova, Aziza Muhammad Ilham Bakhtiar Muhammad Nusrang Nabila Putri Fiamanila Niken Probondani Astuti Niken Probondani Astuti Niken Probondani Astuti Niken Probondani Astuti Trisutomo Nurjakia Nurjakia Nurkhalifa, Nurkhalifa Nurlaely Razak Probondani Astuti, Niken Radja, Muh. Jusuf Ramlan Mahmud Razak, Nurlaely Rofeeva, Rukhshona Singh, Pawan Kumar Sobirov, Bobur SYAMSU ALAM Tafsir, Muhammad Toyirova, Shokhista Turdibekov, Khasan Umar Data Vafokulova, Mekhruza Wiyana, Anim Yuldosheva, Latofat Zilrahmi, Zilrahmi Zulkifli Rais