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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 7 Documents
 1 
Search results for , issue "Vol. 4 No. 1, Februari 2026" : 7 Documents clear
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.
Digitalisasi Sistem Absensi Real-Time Berbasis QR Code Dengan Validasi Lokasi Untuk Meningkatkan Akuntabilitas Kehadiran Personel Pamungkas, Bayu; Iswahyudi, Raden Teddy; Anwar, Nizirwan; Ariessanti, Hani Dewi
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.v5i1.4781

Abstract

Digital transformation has encouraged organisations to adopt information systems that are more efficient, accurate, and transparent, including in the management of personnel attendance. Conventional manual attendance systems still present significant limitations, such as high susceptibility to human error, data duplication, manipulation, and inefficiency in data processing and reporting. This study aims to design and develop a QR code–based attendance system as a digital solution to improve the reliability and accountability of attendance records. The software development process follows the Waterfall methodology, encompassing requirement analysis, system design, implementation, and testing stages. System modelling is conducted using unified modeling language (UML) diagrams and an entity relationship diagram (ERD) to clearly define system structure and operational workflows. The implemented system enables attendance recording through QR code scanning integrated with a centralised database in real-time, supported by QR validity checks, location validation based on location name or GPS radius, prevention of duplicate scans within a five-minute interval, and automatic determination of attendance status as PRESENT or LATE. System testing is performed using the Black Box Testing method to verify functional compliance from the user’s perspective without examining internal code structures. The testing results demonstrate that all core system functions operate in accordance with the specified requirements and business rules, responding appropriately to both valid and invalid input scenarios. Furthermore, each successful transaction consistently generates attendance records and activity logs, ensuring data integrity, traceability, and accountability. Overall, the proposed QR code–based attendance system effectively enhances efficiency, accuracy, and transparency in attendance management and is considered suitable for operational implementation and further development.

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