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ROUTERS: Jurnal Sistem dan Teknologi Informasi
Published by Politeknik Negeri Lampung
ISSN : -     EISSN : 29621224     DOI : https://doi.org/10.25181
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science
ROUTERS: Jurnal Sistem dan Teknologi Informasi includes research in the field of Computer Science, Computer Networks and Engineering, Software Engineering and Information Systems, and Information Security. Editors invite research lecturers, reviewers, practitioners, industry, and observers to contribute to this journal. ROUTERS is a national scientific journal that is open to seeking innovation, creativity, and novelty. Either letters, research notes, articles, supplemental articles, or review articles. ROUTERS aims to achieve state-of-the-art theory and application in this field. ROUTERS provides a platform for scientists and academics across Indonesia to promote, share, and discuss new issues and the development of systems and information technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol. 3 No. 1, Februari 2025" : 6 Documents clear
PERANCANGAN SISTEM MANAGEMENT E-APPROVAL BERBASIS WEB DI PT. ARWANA CITRAMULIA TBK MENGGUNAKAN REACTJS Priyanto, Sandra; Arfian, Muhamad Hadi; Anwar, Nizirwan
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.3411

Abstract

The process of creating invoices has been carried out by PT. Arwana Citramulia Tbk is currently causing delays in billing invoices to customers because it requires a long process and takes quite a long time to create invoices. Delays in billing invoices to customers will cause problems in terms of payment and repayment of purchases of products that have been sold, so that the company's receivables report will increase. The need for a Web-Based E-Approval Management System is to simplify the process of approval activities by the relevant departments for the Approval process of product invoices and to apply the paperless concept to these invoices with digital signatures. The methods used to carry out the design are Unified Modeling Language, Extreme Programming and Black-Box Testing. The results obtained show that this system can simplify the approval process for product sales invoices by division heads and managers so that signing product sales invoices can be digital and it will be easier to send invoices to customers
Prediksi Kelulusan Siswa dengan Algoritma Pembelajaran Mesin: Aplikasi Regresi Linear dan Logistik pada Faktor-Faktor Pendidikan Fenni Aprilia; Anggraini, Rasti Aulia; Putri, Yunita Dwi
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.3897

Abstract

The main challenge in education lies in accurately predicting student graduation and understanding the factors influencing it. This study aims to provide a data-driven solution using machine learning algorithms, specifically linear regression to predict student exam scores and logistic regression to classify student graduation. The study contributes by developing predictive models that serve as tools to support strategic decision-making in educational institutions. This study utilized the Student Performance Factors dataset, comprising 6,607 samples with independent variables such as study hours, attendance, and parental involvement. Data analysis involved cleaning, transformation, and normalization before applying regression models. The findings showed that linear regression achieved a Mean Squared Error (MSE) of 3.256, indicating high accuracy in predicting exam scores. Logistic regression demonstrated an accuracy of 99.85% in classifying student graduation. These models complement each other by offering strategic insights to enhance educational quality.
PEMBUATAN SISTEM DETEKSI HARDCODE KREDENSIAL PADA REPOSITORY Nababan, Bill Jeferson; Haikal, Antoni; Maulidya, Sity Rahmy
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.3898

Abstract

Hardcoded Credential is the practice of embedding authentication information, such as usernames and passwords, directly into the source code of software or applications. This means that the credential information is not stored separately or managed securely, but rather integrated into the program code. This practice poses significant security risks, one of which is the difficulty of changing credentials, making modifications to the source code impractical and increasing security risks. This research proposes a web-based Hardcoded Credential Detection System that can detect Hardcoded Credentials in the Repository on Github, by implementing security tools in the form of Trufflehog to the website, the system can see the results of Hardcoded Credential detection after the detection process is complete. By using the Prototyping method which is one approach in software development by following a series of stages that are carried out sequentially and completed one by one before entering the next stage. The technologies used include ReactJs as a library for making Front-end, ExpressJs as a Framework for making Back-end with Javascript as a Programming Language, and MYSQL as a database. The results of this system can help in maintaining the security of Github repositories by providing the use of tools that can identify potential leaks of sensitive credentials. Thus, developers and security teams can take action to remove or secure those accidental credentials.
Dampak Berita Emas Palsu Terhadap Harga Saham PT Aneka Tambang TBK (ANTM): Analisis dan Prediksi) Ispaniyah, Ispaniyah; Tyas, Putri Cahyaning; Suseno, Akrim Teguh; Wulandari, Umi Meganinditya
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.3969

Abstract

Fake news or hoaxes, have become a major problem around the world in recent years. This phenomenon not only affects public opinion but can also affect various aspects of socio-economic life, including financial markets. Currently, global stock prices continue to rise and have reached their highest level since 2012-2013. One of the leading mining companies in Indonesia, PT Aneka Tambang Tbk (ANTM), is not entirely dependent on its share price. The impact of fake news on stock prices has become a topic of growing interest in the academic literature. Various previous studies have attempted to identify the relationship between the spread of fake news and stock price fluctuations. Using the RapidMiner application, an analysis of PT ANTM's stock price prediction was conducted using Neural Network (NN) and Linear Regression (LR) algorithms. To assess the accuracy of the prediction, the analysis is performed using the Root Mean Square Error (RMSE) results. The comparative analysis conducted shows that the Neural Network algorithm has a lower error rate of 14,806 +/- 0.000 compared to the Linear Regression algorithm which has a value of 22,379 +/- 0.000. This shows that the Neural Network algorithm has higher accuracy in predicting the share price of PT ANTM. A smaller RMSE value indicates a more accurate prediction. In addition, this study also identified that the time span of the data used (December 19, 2023 - June 19, 2024) can affect the prediction results. Based on the conclusions, the researcher suggests that using a dataset with a longer time span and applying other Deep Learning algorithms to improve prediction accuracy can be used for future research.
Peningkatan Performa Analisis Sentimen Ulasan Pelanggan terhadap Layanan Pengiriman Menggunakan Model Naïve Bayes yang Dioptimalkan dengan PSO Yuda Septiawan; Aglasia, Adimas; Muktiawan, Danang Ade
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.4001

Abstract

This research is motivated by the rapid growth of the delivery service industry and the importance of customer feedback in competition, especially for ID Express which has a mobile application. The main issue raised is how to analyse the sentiment of customer reviews on the ID Express app on the Google Play Store to improve service quality. User reviews, although rich in information, have not been optimally utilised, making it difficult for companies to understand user perceptions. In this study, we develop a new method to analyse the sentiment of ID Express app user reviews. This method integrates Naïve Bayes algorithm with Particle Swarm Optimisation (PSO)-based feature selection optimisation technique to produce more accurate analysis. The method used includes collecting user review data from the Google Play Store (2020-2023), preprocessing the data, implementing the Naïve Bayes algorithm, and applying PSO for feature selection. Model performance was tested with accuracy and F-measure metrics using 90:10 and 80:20 data sharing ratios. The results showed that the Naïve Bayes algorithm with PSO produced 52% accuracy at 90:10 ratio and 63% at 80:20 ratio, with F-measure values of 43% and 55% respectively. In conclusion, the use of PSO as feature selection improves the accuracy and F-measure of sentiment analysis using Naïve Bayes, especially at a data sharing ratio of 80:20.
Pengaruh Aksi Boikot Terhadap Harga Saham Unilever: Pendekatan Prediktif Dengan Neural Network Dan Linear Regression Yani, Ririn Yuli; Nidaa, Syafiqotun; Suseno, Akrim Teguh; Wulandari, Umi Meganinditya
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.4009

Abstract

PT Unilever Indonesia Tbk is a  multinational company that produces and markets various consumer goods in various countries to fulfill needs ranging from health, nutrition, daily care and so on. PT Unilever Indonesia Tbk is facing a crisis of calls for a boycott of products due to pro-Israel which has an impact on the Company’s reputation and performance. In the face of this situation, stock price prediction analysis is important to help investors in making decisions. To overcome this problem, this research applies Data Mining Techniques in predicting the share price of PT Unilever Tbk. The two algorithms used are Neural Network and Linear Regression, which are then tested using the Root Mean Squared Error (RMSE) evaluation method. Data processing is done using RapidMiner with historical data period from December 2023 to May 2024. Based on the analysis results, the Linear Regression algorithm produces an RMSE value of 22,745, showing a more accurate prediction compared to the Neural Network algorithm which has an RMSE value of 44,830. The test results show that predicting stock prices using Linear Regression has a lower error rate than the Neural Network. Thus, in this study, the Linear Regression algorithm is superior in predicting the stock price of PT Unilever Indonesia Tbk compared to the Neural Networj. The results of this study are also compared with previous research which shows thaht the accuracy of the stock price prediction model depends on the characteristics of the dataset and the method used. Some previous studies concluded that Neural Network is superior in capturing complex patterns in certain stocks, while Linear Regression is more suitable for data with linear relationships. Therefore, although Linear Regression is better in this study, model selection still needs to be tailored to the characteristics and objectives of the analysis.

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