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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Ilmu Komputer dan Sistem Informasi
Nambi Sembilu
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Computer Science & IT Library & Information Science

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Developing a Web-Based Printing Transaction System Using The Prototype Method: A Case Study at Amanah Advertising Annisa Dita Putri Kartika; Anindo Saka Fitri; Nambi Sembilu
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4692

Abstract

Digital transformation is increasingly vital for micro, small, and medium enterprises (MSMEs), including those in the printing sector, to enhance their operational effectiveness. Prior to this study, Amanah Advertising managed its orders, transactions, and inventory manually, which frequently resulted in data recording errors and poor coordination. To address these challenges, this research developed a web-based transaction management information system using the Prototype method. The development process involved two iterative stages of user feedback to refine system functionality. Enhancements included the addition of key features, improvements to the user interface, and the implementation of discount management functionality. The system was developed using the CodeIgniter 4 framework to ensure better process integration and operational efficiency. Black-box testing validated that all system components functioned correctly. Furthermore, a User Acceptance Testing (UAT) evaluation using a Likert scale produced a feasibility score of 85.2%, categorized as “Very Good.” These findings indicate that the implemented system significantly improves operational efficiency, increases data accuracy, and successfully supports the digitalization efforts at Amanah Advertising.
Prediksi Harga Emas Menggunakan Gated Recurrent Unit Berdasarkan Inflasi, Nilai Tukar, dan Suku Bunga Moh. Wahyu Abrory; Rizka Hadiwiyanti; Nambi Sembilu
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 3 (2026): September 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i3.382

Abstract

Gold is a strategic asset widely used as a hedging instrument in Indonesia, with its price influenced by domestic macroeconomic factors including inflation, Bank Indonesia's benchmark interest rate, and the USD/IDR exchange rate. Classical statistical models such as ARIMA have limitations in capturing non-linear patterns and long-term dependencies in financial time series data. This study compares the performance of two deep learning algorithms, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in predicting the daily price of ANTAM LM gold based on the aforementioned macroeconomic variables. Data covering the period from January 2015 to September 2025 were obtained from PT Antam, Bank Indonesia, and Investing.com. The research process included data collection, preprocessing, exploratory data analysis, dataset splitting under three schemes (70:30, 80:20, and 90:10), model training with hyperparameter tuning, and evaluation using RMSE and MAPE metrics. Results demonstrated that GRU achieved the best performance under the 90:10 split scheme with an RMSE of IDR 35,417.91 and a MAPE of 1.47%, outperforming the best LSTM model which recorded an RMSE of IDR 96,808.45 and a MAPE of 4.58%. The best-performing GRU model was deployed into a Flask and React-based web application serving as a tool for investment decision-making.
Co-Authors Anindo Saka Fitri Annisa Dita Putri Kartika Moh. Wahyu Abrory Rizka Hadiwiyanti