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All Journal Jurnal Informatika Ekonomi Bisnis Jurnal Informatika Ekonomi Bisnis
Dendy K Pramudito
Informatics Engineering Department, Universitas Pelita Bangsa
Economics, Econometrics & Finance

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A Web-Based Material Quality Monitoring Information System for Manufacturing Quality Assurance Based-On Rapid Application Development Framework Khairunnisa Nasution; Dendy K Pramudito; Annisa Maulana Majid
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1447

Abstract

This research investigates the construction of a web-based Material Quality Monitoring Information System for PT MBR using the Rapid Application Development method. This study was undertaken because recording incoming materials, defect monitoring, and supplier assessment are still manual processes using Microsoft Excel, resulting in less efficient performance, time-dependent reporting requirements, and higher risk of data input errors and duplication. This research aims to create a system to perform material quality monitoring activities more efficiently, and that is integrated, rather than single task. Methods of data collection which are considered for our study include observation, interviews, and literature reviews. It encompasses requirements, design analysis, implementation, and testing. The development process is to design the system using the Unified Modelling Language and implemented with Laravel, Bootstrap, Chart.js framework and MySQL DB. The system produced has capabilities for handling incoming material data handling, documentation and managing defect data, material quality monitoring dashboard and defect percentage dashboard by suppliers for defect performance assessment. Further, the system can display material quality information in graphs and reports that can be accessed online in real time for ease of monitoring and decision-making of users. The work outcomes indicate that the system developed demonstrates the system integration in that all the material quality information can be integrated in one structured and easy-to-use web platform accessible more seamlessly to the users. Using the black box test results, all the system functions worked according to user requirement. And as such, it can be used by PT MBR for material quality monitoring and supplier evaluation.
A Web-Based Material Quality Monitoring Information System for Manufacturing Quality Assurance Based-On Rapid Application Development Framework Khairunnisa Nasution; Dendy K Pramudito; Annisa Maulana Majid
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1447

Abstract

This research investigates the construction of a web-based Material Quality Monitoring Information System for PT MBR using the Rapid Application Development method. This study was undertaken because recording incoming materials, defect monitoring, and supplier assessment are still manual processes using Microsoft Excel, resulting in less efficient performance, time-dependent reporting requirements, and higher risk of data input errors and duplication. This research aims to create a system to perform material quality monitoring activities more efficiently, and that is integrated, rather than single task. Methods of data collection which are considered for our study include observation, interviews, and literature reviews. It encompasses requirements, design analysis, implementation, and testing. The development process is to design the system using the Unified Modelling Language and implemented with Laravel, Bootstrap, Chart.js framework and MySQL DB. The system produced has capabilities for handling incoming material data handling, documentation and managing defect data, material quality monitoring dashboard and defect percentage dashboard by suppliers for defect performance assessment. Further, the system can display material quality information in graphs and reports that can be accessed online in real time for ease of monitoring and decision-making of users. The work outcomes indicate that the system developed demonstrates the system integration in that all the material quality information can be integrated in one structured and easy-to-use web platform accessible more seamlessly to the users. Using the black box test results, all the system functions worked according to user requirement. And as such, it can be used by PT MBR for material quality monitoring and supplier evaluation.
An IoT Technology as Smart Water Management System for Family Medicinal Plants Muhammad Akmal Ar Rasid; Dendy K Pramudito; Amali
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1455

Abstract

Watering of Family Medicinal Plants located in government institutions should not follow a regular interval. As they require adequate water to grow, over-watering can lead to wastage of water and can decrease efficiency in plant maintenance. The plants are typically watered by employees manually based on a schedule or by checking up on the plants. But, at times soil moisture can vary at any hour of the day and irrigation might not happen if a person is not available to check. There are chances of overwatering or underwatering the plants. In this paper we propose a system which uses Internet of Things to monitor the soil moisture level and make irrigation automated. NodeMCU ESP8,266 acts as the main controller of the system. A soil moisture sensor is used to sense the amount of water present in the medium where the plants are growing. A relay module is used to control the pump, a water pump is used to water the plants and finally an I2C LCD is used to display the real time information. We also use IoT Watering platform to display the monitoring data and control the system over internet. We performed field study, literature survey, requirement study, system design, implementation and testing of the prototype. The results showed that the system sensed the soil moisture levels and send the data to monitoring platform and display the data in real time. The water pump is used to water the plants automatically. If the moisture level falls below the threshold which is set in the system, the pump will switch on. If the moisture level is enough for the plants to survive, then the pump will switch off. This system allows us to have better control over watering of plants, measure the efficiency and make the process faster.
Web-Based Sales Forecasting Point-of-Sales System Using Simple Moving Average Method at Haka Farma Pharmacy Rifqi Herlambang; Dendy K Pramudito; Ernanto
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1466

Abstract

POS-POS system is a POS which manages sales transactions, inventory, and daily sales reports at Haka Farma Pharmacy, but it has no sales forecasting capacity. Therefore, many judgment and judgment making processes often need to go through previous procurement to determine the inventory stocks. In order to resolve this issue, this study was aimed at creating a web-based POS system in combination with a sales forecasting component in conjunction with a simple moving average module. Development of the system is conducted using waterfall, which involves requirement analysis, system design, implementation, testing, and deployment of the system. The techniques employed were observational, staff interviews through pharmacy members, and through in-depth literature review. The system developed in PHP using the Laravel framework, was implemented through the MySQL database managers for system. The software's forecast module also uses SMA method to calculate the average sales volume over the last three months in order to estimate demand in the future. Functions testing and testing of system: user authentication, product management, sales transaction processing, reporting, forecasting, and error analysis were done. Testing results were not inconsistent with the specification; all the system related features were functioning as expected. From paracetamol sales data, we found a 253-unit rate expected for December 2025. The MAPE data shows strong forecasting in the form of 3.16% MAPE (Mean Absolute Percentage Error) indicating its predictive power in this study. This confirms that the developed forecasting module can assist in inventory planning, reducing uncertainty for all decision makers, and optimize operational efficiency with evidence-based control of stock in Haka Farma Pharmacy.
Comparative Analysis of LSTM and GRU Models for Predicting BBCA and BBNI Stock Prices Muhammad Irfansyah; Dendy K Pramudito; Sanudin
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1467

Abstract

Stock price predictions always a challenging issue as the price data consists of variable, noisy, non-linear time dependence. This research aims as a testing ground to compare the performance of two deep learning algorithms, long short-term memory and gated recurrent units, in predicting the end price of PT Bank Central Asia Tbk, that is relatively stable compares to PT Bank Negara Indonesia Tbk which more volatile. Daily prices were obtained from Yahoo Finance over the previous period from January1, 2019 until Dec 1, 2025. Data cleaning, Min-Max normalization, 80:20 data split, and sequence-formation with a timestep of 60 were performed. To facilitate fair comparison and ensure that the performance differences are only due to architecture, both models were constructed with the same architecture (two layers of 50 units, 0.2 dropout, Adam optimizer, MSE loss function, and 50 epochs). It evaluated MSE, RMSE performance, MAE performance, R-squared with accuracy adjusted for a 5% tolerance range. The results suggest that GRU outperforms LSTM on both stocks. For the BBCA, GRU reached an RMSE of 101.12, MAE of 113.82, R-squared of 0.96, and tolerance accuracy of 98.52%, compared to BBNI which had an RMSE of 62.95, MAE of 71.24, R-squared of 0.92, and tolerance accuracy of 94.11%. This performance led us to realize that GRU is the numerical equivalent to this technique, and on both datasets, it was computationally faster. These results reveal that simpler GRU structure is more adaptive, especially with stocks of high volatility like BBNI.
An IoT Technology as Smart Water Management System for Family Medicinal Plants Muhammad Akmal Ar Rasid; Dendy K Pramudito; Amali
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1455

Abstract

Watering of Family Medicinal Plants located in government institutions should not follow a regular interval. As they require adequate water to grow, over-watering can lead to wastage of water and can decrease efficiency in plant maintenance. The plants are typically watered by employees manually based on a schedule or by checking up on the plants. But, at times soil moisture can vary at any hour of the day and irrigation might not happen if a person is not available to check. There are chances of overwatering or underwatering the plants. In this paper we propose a system which uses Internet of Things to monitor the soil moisture level and make irrigation automated. NodeMCU ESP8,266 acts as the main controller of the system. A soil moisture sensor is used to sense the amount of water present in the medium where the plants are growing. A relay module is used to control the pump, a water pump is used to water the plants and finally an I2C LCD is used to display the real time information. We also use IoT Watering platform to display the monitoring data and control the system over internet. We performed field study, literature survey, requirement study, system design, implementation and testing of the prototype. The results showed that the system sensed the soil moisture levels and send the data to monitoring platform and display the data in real time. The water pump is used to water the plants automatically. If the moisture level falls below the threshold which is set in the system, the pump will switch on. If the moisture level is enough for the plants to survive, then the pump will switch off. This system allows us to have better control over watering of plants, measure the efficiency and make the process faster.
Web-Based Sales Forecasting Point-of-Sales System Using Simple Moving Average Method at Haka Farma Pharmacy Rifqi Herlambang; Dendy K Pramudito; Ernanto
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1466

Abstract

POS-POS system is a POS which manages sales transactions, inventory, and daily sales reports at Haka Farma Pharmacy, but it has no sales forecasting capacity. Therefore, many judgment and judgment making processes often need to go through previous procurement to determine the inventory stocks. In order to resolve this issue, this study was aimed at creating a web-based POS system in combination with a sales forecasting component in conjunction with a simple moving average module. Development of the system is conducted using waterfall, which involves requirement analysis, system design, implementation, testing, and deployment of the system. The techniques employed were observational, staff interviews through pharmacy members, and through in-depth literature review. The system developed in PHP using the Laravel framework, was implemented through the MySQL database managers for system. The software's forecast module also uses SMA method to calculate the average sales volume over the last three months in order to estimate demand in the future. Functions testing and testing of system: user authentication, product management, sales transaction processing, reporting, forecasting, and error analysis were done. Testing results were not inconsistent with the specification; all the system related features were functioning as expected. From paracetamol sales data, we found a 253-unit rate expected for December 2025. The MAPE data shows strong forecasting in the form of 3.16% MAPE (Mean Absolute Percentage Error) indicating its predictive power in this study. This confirms that the developed forecasting module can assist in inventory planning, reducing uncertainty for all decision makers, and optimize operational efficiency with evidence-based control of stock in Haka Farma Pharmacy.
Comparative Analysis of LSTM and GRU Models for Predicting BBCA and BBNI Stock Prices Muhammad Irfansyah; Dendy K Pramudito; Sanudin
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1467

Abstract

Stock price predictions always a challenging issue as the price data consists of variable, noisy, non-linear time dependence. This research aims as a testing ground to compare the performance of two deep learning algorithms, long short-term memory and gated recurrent units, in predicting the end price of PT Bank Central Asia Tbk, that is relatively stable compares to PT Bank Negara Indonesia Tbk which more volatile. Daily prices were obtained from Yahoo Finance over the previous period from January1, 2019 until Dec 1, 2025. Data cleaning, Min-Max normalization, 80:20 data split, and sequence-formation with a timestep of 60 were performed. To facilitate fair comparison and ensure that the performance differences are only due to architecture, both models were constructed with the same architecture (two layers of 50 units, 0.2 dropout, Adam optimizer, MSE loss function, and 50 epochs). It evaluated MSE, RMSE performance, MAE performance, R-squared with accuracy adjusted for a 5% tolerance range. The results suggest that GRU outperforms LSTM on both stocks. For the BBCA, GRU reached an RMSE of 101.12, MAE of 113.82, R-squared of 0.96, and tolerance accuracy of 98.52%, compared to BBNI which had an RMSE of 62.95, MAE of 71.24, R-squared of 0.92, and tolerance accuracy of 94.11%. This performance led us to realize that GRU is the numerical equivalent to this technique, and on both datasets, it was computationally faster. These results reveal that simpler GRU structure is more adaptive, especially with stocks of high volatility like BBNI.
Co-Authors Amali Annisa Maulana Majid Ernanto Khairunnisa Nasution Muhammad Akmal Ar Rasid Muhammad Irfansyah Rifqi Herlambang Sanudin