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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 41 Documents
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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.
Customer Segmentation Based on Spending Patterns Using K-Means Clustering and PCA Dzakwan Akbar Perdana Wijaya; sasmita, Chesie fenta; Naufaldi Favian Archi
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 2, Juli 2025 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i2.3923

Abstract

Companies face challenges in understanding customer spending patterns, which can lead to ineffective marketing strategies. Traditional customer segmentation approaches often fail to accurately identify groups with different consumption behaviors. Therefore, this study proposes the implementation of the K-Means algorithm combined with Principal Component Analysis (PCA) to segment customers based on their spending patterns. This study uses a dataset containing customer spending information across various product categories, including wine, meat, fish, sweets, fruits, and gold. The Elbow method is applied to determine the optimal number of clusters, followed by K-Means clustering. The results are visualized using PCA to facilitate the interpretation of customer spending patterns. The findings indicate that the optimal number of clusters is six, with the Within-Cluster Sum of Squares (WCSS) decreasing from 50,000 for one cluster to 29,000 for six clusters. Cluster 3 exhibits the highest spending, particularly on meat at 566.91 and fish at 183.58, whereas Cluster 0 has the lowest spending, with its highest value being only 91.60 for wine. Silhouette Score evaluation shows that K-Means achieves a score of 0.4745, outperforming the Gaussian Mixture Model (GMM) with 0.0674
Evaluasi Usability Aplikasi Sibersih UP3 Jambi Menggunakan System Usability Scale (SUS) Refriansyah, Evan
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 2, Juli 2025 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i2.4167

Abstract

Digital transformation in public services encourages the development of internal information systems, such as the SIBERSIH application by PT. PLN (Persero) UP3 Jambi, which functions to record, report, and monitor the cleanliness of workspaces. The success of an application is not only determined by its features and technology, but also by its usability—how easy, efficient, and satisfying it is to use. Therefore, an initial usability evaluation of the SIBERSIH application was conducted using the System Usability Scale (SUS) method, which offers a simple and effective solution. This study contributes by providing an initial measurement of the usability quality of the SIBERSIH application based on direct experiences from early users. The method used in this study is the System Usability Scale (SUS), which consists of 10 questionnaire items with responses using a 5-point Likert scale. A total of 12 cleaning staff members were involved as early users. The data collected were analyzed using the standard SUS calculation formula. The evaluation results showed an average SUS score of 86.87. This score places the SIBERSIH application in the "acceptable" category within the acceptability range, receives a grade scale of B, and has an adjective rating of “excellent.” This means the application is considered very good and easy to use by non-technical users. This assessment indicates that the application meets the aspects of ease, convenience, and user satisfaction. In conclusion, the SIBERSIH application has a high usability rating and is well-received by early users. This evaluation provides a strong foundation for further development of the application to better support office cleanliness services.
Desain dan Rancang Bangun Sistem E-Learning Menggunakan Framework Laravel Berbasis WEB Jinan, Abwabul; Siregar, Manutur Pandapotan; Suryani, Dede Fika; Rolanda, Vicky; Muis, Abdul
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 2, Juli 2025 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i2.4182

Abstract

The design and development of a web-based E-Learning system using the Laravel framework aims to provide an effective and structured digital learning solution. This system is developed to address the limitations of face-to-face learning time in traditional classrooms and to leverage technological advancements in order to enhance educational quality. Utilizing Laravel as the primary development framework, the system is built with PHP, HTML, CSS, and JavaScript technologies, and MySQL as the database engine. The E-Learning platform features core functionalities such as instructional material management, class administration, structured user accounts (admin, teacher, and student roles), as well as support for material download and task submission. Testing results indicate that the system performs effectively and supports flexible and efficient teaching and learning processes. It is expected that this system will serve as a reliable and sustainable learning medium to support technology-based academic activities.
Penerapan Push Notification OneSignal Pada System Monitoring Suhu Menggunakan Kodular dan Firebase Berbasis ESP32 AR, Harlan
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 1, Februari 2026
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v5i1.4789

Abstract

Existing IoT-based temperature monitoring systems still have a weakness, where users must continuously open the application to monitor temperature conditions. Without an active alert feature, users risk delays in responding to critical conditions if the application is not running. This research aims to develop an active notification feature in an Android application using OneSignal push notifications. The goal is for the system to automatically provide early warnings to users without having to first open the application when temperature or humidity conditions exceed normal limits. The system was built using an ESP32 microcontroller, a DHT11 sensor, and the Firebase Realtime Database. The application development used the Kodular low-code platform integrated with the OneSignal API and Firebase Cloud Messaging (FCM) for notification distribution management. The results of this study show that the developed temperature monitoring application is capable of providing automatic notifications to users without having to run the application. This warning system will be activated if the temperature exceeds 35°C or humidity reaches more than 70%. Based on test data, the push notification feature proved very effective with an average delivery time of only 3.11 seconds, and remains reliable in providing early warnings even when the application is not running. So it can be concluded that the onesignal push notification feature on Kodular can be used to complement the IoT-based temperature monitoring system, as a notification to users if the temperature and humidity exceed the limit.
REDESIGN APLIKASI LAYANAN TUNGGU MENGGUNAKAN METODE LEAN UX Try Hafsani, Yuke; Anjeli, Saiba; Efendi, Yoyon
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 1, Februari 2026
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v5i1.4790

Abstract

The Tunggu Service Application is a digital service application owned by the Population and Civil Registration Service (Disdukcapil) of Pekanbaru City which not only provides population administration services, but also various supporting features that make it easier for the public to access services digitally. This application is designed to increase the ease of service access for the public without having to come directly to the Disdukcapil office. The existence of the Waiting Service application is expected to increase the effectiveness of the service, reduce queues in the office, and encourage the utilization of digital services in the government environment. However, based on the results of initial observation and initial testing to users, the appearance and usage flow of the Waiting Servic application is not fully in accordance with the user's needs. Some users stated that they had difficulty in understanding the flow of the service and assessing that the appearance of the application still needed to be improved. This condition has an impact on the user's comfort in using the application. Therefore, it is necessary to redesign the User Interface (UI) and User Experience (UX) so that the application can be used more easily and in accordance with the user's needs. The Lean UX method was chosen in this study because it focuses on user satisfaction through an iterative, collaborative, and user feedback-based design process. Based on the results of analysis, implementation, and evaluation, this research produces a final prototype which is a combination of prototype A and prototype B that has been validated in terms of appearance, functionality, as well as criticism and suggestions from users and technical parties. Prototype A was chosen for 3 features and prototype B was chosen for 3 other features. On the home page selected design B with a percentage of 57%, the service submission page selected design A with a percentage of 76%, and the schedule selection page selected design B with a percentage of 62%. In addition, the research results show that the proposed interface design has consistency in the use of colors, typography, icons, and layouts, as well as providing a user experience that is easier to understand in accessing features and information as needed. The final design was tested using the Usability Testing method to measure the level of ease of use and user acceptance of the proposed Layanan Tunggu application design.
Automasi Dokumentasi Aset Melalui Mekanisme Dynamic Report Generation dengan Transformasi Visual secara Real-Time Madya, Akbar
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 1, Februari 2026
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v5i1.4799

Abstract

Facilities and infrastructure management often have difficulty synchronizing data administration with visual documentation. This research develops an Android-based MySarPras application with SQLite architecture and integration of iText7 library and Apache POI. The novelty of this research lies in the visual transformation flow mechanism that optimizes memory management through downsampling techniques to produce PDF/Excel reports proportionally. The results of the Black Box test show that all CRUD functions run stably with a high level of interoperability through the Implicit Intent feature. The results of the performance test showed that the system was able to generate reports instantly with a duration of 1.8 seconds for 10 data to 9.3 seconds for 100 data assets with a file size of up to 30.3 MB. MySarPras has succeeded in digitizing the data collection process to be more accountable and efficient without internet dependence. The use of mobile technology offers a potential alternative means to efficiently address the bureaucratic needs of asset management in various organizations.
Analisis Performa Random Forest, Decision Tree, dan Naive Bayes untuk Deteksi Link Phishing Berbasis Fitur URL Silcilia; Salsabila, Nadira Parsha; Apriani, Tarisza
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 1, Februari 2026
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v5i1.4330

Abstract

Phishing attacks through fake links remain one of the most common cybersecurity threats and can lead to data breaches for computer network users. Manual detection is often ineffective because phishing methods continue to evolve, with link patterns that closely resemble legitimate domains. This experiment aims to analyze the performance of three classification algorithms—Random Forest, Decision Tree, and Naive Bayes—in detecting phishing links based on basic URL features. The experiment is expected to assist in the automatic recognition of phishing URLs based on link characteristics analyzed using machine learning methods. The process involves collecting a dataset containing both phishing and legitimate links, followed by feature extraction such as URL length, hostname length, number of specific symbols, detection of IP addresses in the domain, use of URL shortening services, and prefix-suffix patterns in the hostname. The dataset is divided into training and testing data with an 80:20 ratio. The models are trained using the three algorithms and tested to compare their accuracy, precision , recall, and F1-score. The testing results show that the Random Forest algorithm achieved the highest accuracy of 80.75%, with balanced precision and recall. Meanwhile, the Decision Tree achieved an accuracy of 77.73%, and Naive Bayes only reached 68.15%. These findings indicate that Random Forest is the most suitable for detecting phishing links based on simple URL feature analysis. Therefore, this model can be applied as an early detection system to minimize phishing attack risks in various environments.
Analisis Pengelompokan Lagu Terpopuler Spotify Menggunakan Algoritma K-Means Berdasarkan Popularitas Wibawa, Arko Fernanda; Valentiya, Juwita
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 1, Februari 2026
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v5i1.4713

Abstract

The rapid growth of music streaming platforms has created very large catalogs, making popularity patterns difficult to understand using a single indicator. Total streams reflect cumulative success, but they do not always represent current listening momentum. This situation motivates the need for song segmentation based on more informative popularity patterns to support decision-making for streaming platforms, artists, and labels. This study applied a data mining approach using K-Means clustering to group Spotify most-streamed songs based on streaming popularity indicators. The main contribution was a segmentation framework that combined total streams, daily streams, and a daily-to-total streams ratio to better capture current momentum. The method included data cleaning, missing value imputation, logarithmic transformation to reduce skewness, feature engineering of a ratio variable, feature standardization, K-Means training, cluster number selection using the elbow method and Silhouette Score, and evaluation using Inertia, Silhouette Score, the Calinski–Harabasz Index, and the Davies–Bouldin Index. The final model with k = 4 achieved an Inertia of 2673.011 and a Silhouette Score of 0.364835 and produced four interpretable segments. Cluster 0 represented super-trending songs with the highest daily-to-total ratio, cluster 1 represented legacy popular songs with low daily activity, cluster 2 represented mega hits with extremely high total streams and still strong daily activity, and cluster 3 represented consistently performing songs with stable daily streams. These segments provided practical insights for promotion prioritization, playlist curation, and trend interpretation.
Klasifikasi Kondisi Kesehatan Mental Mahasiswa Menggunakan Algoritma Logistic Regression Pratiwi, Dini; Roslina, Yulia
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 1, Februari 2026
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v5i1.4742

Abstract

Student mental health has become a critical issue in higher education, as it directly affects students’ well-being and academic performance. Academic, social, and psychological pressures faced by university students increase the risk of mental health disorders such as depression and anxiety. This study aims to classify students’ mental health conditions, particularly the risk of depression, using the Logistic Regression algorithm and to compare its performance with a baseline model and the K-Nearest Neighbors (KNN) algorithm. The dataset used in this study is the Student Mental Health dataset obtained from the Kaggle platform, consisting of 101 student records with demographic, academic, and psychological variables. The research process includes data preprocessing, splitting the dataset into training and testing sets with an 80:20 ratio, classification modeling, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that Logistic Regression achieves the best performance compared to the other models, with an accuracy of 0.85, precision of 1.00, recall of 0.57, and an F1-score of 0.73. The baseline model achieves an accuracy of 0.65 but fails to detect any depression cases, while KNN (k = 5) produces a lower accuracy of 0.55. Further analysis indicates that psychological factors such as Marital, Treatment, and Anxiety significantly contribute to the prediction of depression among students. Based on these findings, Logistic Regression is considered an effective and relevant approach for classifying depression risk among university students and has the potential to support early detection of mental health problems in higher education environments.

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