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Bekti Maryuni Susanto
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Jurnal Teknologi Informasi dan Terapan (J-TIT)
Published by Politeknik Negeri Jember
ISSN : 2354838X     EISSN : 25802291     DOI : https://doi.org/10.25047
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
This journal accepts articles in the fields of information technology and its applications, including machine learning, decision support systems, expert systems, data mining, embedded systems, computer networks and security, internet of things, artificial intelligence, ubiquitous computing, wireless sensor networks, and cloud computing. The journal is intended for academics and practitioners in the field of information technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 221 Documents
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Implementasi Load Balancing dengan Metode Policy Based Route dan Efek Failover untuk Optimalisasi Jaringan Internet Lalitya Nindita Sahenda; Prayugo Ardi Wibowo
Jurnal Teknologi Informasi dan Terapan Vol 11 No 1 (2024)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i1.378

Abstract

The rapidly developing internet has a positive impact on various sectors globally, including the impact on the world of education. In the field of formal education, the internet is not only used as an active learning media, but also as a media for conducting online-based exams, for example in conducting semester exams. SMK PPN 1 Tegalampel as a formal educational institution also uses the internet to support learning and teaching activities. Therefore, the internet is a very essential need in this school. Internet network management is very necessary in efforts to provide internet quality. One way to manage internet networks is to implement load balancing. Load balancing is applied to divide the network load between two different ISPs so that it is not overloaded. The load balancing method applied at SMK PPN 1 Tegalampel is policy Based Route (PBR). PBR divides the network load according to the policy or rules set by the network technician. Apart from loading balancing, office failover is also implemented in the school internet network. So if an ISP connection is lost, it will automatically be diverted to the backup line. With this failover effect, it guarantees that an internet connection will always be available even if the ISP is disconnected.
Optimizing Security with an Iot: A Data-Driven Visitor Identification Framework Choirul Huda; Lukman Hakim
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.421

Abstract

Security is an important factor that must be considered. The properties, such as computers, sensor devices, teaching aids, and other equipment, must be monitored in the best possible condition to be used whenever needed. If these items are stolen, it will interfere with students' learning abilities so that Learning Outcomes are not met. Currently, visitor identification systems are evolving, initiated by the implementation of IoT devices, face recognition, voice recognition, and so on. When these systems were executed, several obstacles were still found, such as the identification process being slow, requiring large amounts of training data, and the application interface only running on smartphone devices. Therefore, a breakthrough is needed to recognize visitors quickly, easily, and to boost protection. In this research, the author proposes an Identification Information System (IIS) for room visitors using a Data-Driven Modeling method based on the Internet of Things (IoT). This system is equipped with an IoT driver module to interact with the Raspberry Pi and a magnetic lock. It aims to allow a room administrator to control and lock doors online via a computer or smartphone. Based on the experiments that have been carried out, the proposed system is adequate to run optimally from some testing cases that have been designed.
PaletteStream: A Promotional, and Community Web-Based Platform for Visual Artists with Gamification Implementation Yunia Ikawati; Rosyid Ferdiansyah; Mohammad Robihul Mufid; Darmawan Aditama; Saniyatul Mawaddah
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.422

Abstract

In the digital era, social media has become a primary medium for visual artists to promote their work and engage with audiences. However, mainstream platforms such as Instagram and Facebook often fall short of addressing the specific needs of artists due to algorithms that are not tailored to the art domain. While specialized platforms like DeviantArt and ArtStation exist, most have yet to implement effective gamification features that could enhance user engagement and motivation. To address these challenges, PaletteStream is developed as a dedicated web-based platform for visual artists, focusing on collaboration, promotion, and community building. By integrating gamification elements using MVC Architecture, PaletteStream aims to facilitate artistic collaboration, improve the efficiency of art promotion, and foster an active and supportive artistic community. This project also contributes to technological innovation in the arts and advances in application development centered on user experience. The results reveal that the PaletteStream Platform for gamification systems properly awards points and badges based on established rules and provides considerable performance gains with an average API response time of less than 3 seconds.
Face Tracker Audio for Saronen Music Using Augmented Reality on Social Media Ahmad Walid Hujairi; Khoironi Khoironi; Much Chafid; Ahmad Khairul Umam; Ahmed David Anugerah
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.425

Abstract

Saronen music is a traditional Madurese music commonly played at cultural and traditional events. Saronen music typically combines traditional instruments such as gamelan, trumpet, kenong, korca, large drums, and small drums, producing a unique sound characteristic of Madura. This research aims to preserve saronen music through the development of an interactive filter on Instagram and Facebook using augmented reality. The face tracker feature allows users to interact with each instrument using head movements or facial expressions. The development method, MDLC encompasses concept, design, material collection, design, testing, and distribution. Functional research results indicate the filter can run well on Android and iOS operating systems. Testing on 31 social media users yielded positive feedback from the majority, stating that the filter has clear instructions, a low error rate, is easy to use, satisfying, and attractive. This innovation is expected to be a means of preserving saronen music for the younger generation through social media.
Develompent of Machine Learning Model to Predict Hotel Room Reservation Cancellations Eka Rahmawati; Galih Setiawan Nurohim; Candra Agustina; Denny Irawan; Zainal Muttaqin
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.431

Abstract

The frequent cancellations of hotel room reservations have become a pressing issue for the hospitality industry, especially in high-tourism areas such as Borobudur, Indonesia. This research develops a predictive machine learning (ML) model to identify cancellation probabilities to support proactive decision-making for hotel management. Using datasets from Borobudur-based hotels, key variables such as booking lead time, arrival month, and reservation outcomes were analyzed. Random Forest demonstrated the best performance, achieving an accuracy of 86.36% with a precision of 88.06%, recall of 93.65%, and F1-score of 90.77%. Logistic Regression demonstrated moderate effectiveness, while Bayesian Networks underperformed, highlighting the importance of robust algorithms for such tasks. The findings underscore the potential of ML models, particularly Random Forest, to reduce financial losses and enhance operational efficiency in the hospitality sector by anticipating cancellations and facilitating better resource allocation
A Stacking Approach to Enhance K-Nearest Neighbors Performance for Autism Screening Al Azies, Harun; Naufal, Muhammad
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.432

Abstract

The increasing prevalence of autism spectrum disorders necessitates improved early screening methods for children to ensure timely intervention and support. While existing screening techniques play a vital role, they often face challenges regarding accuracy, accessibility, and scalability. This research addresses these gaps by enhancing the K-Nearest Neighbors (K-NN) algorithm by implementing a stacking model that integrates multiple distance metrics—Manhattan and Minkowski—to improve predictive performance. Utilizing a public dataset, the study employed K-Fold Cross-Validation with K=5 to ensure a robust evaluation of the models. The results demonstrated that the stacking model achieved an average accuracy of 86.67%, significantly surpassing the traditional K-NN approaches, which reported accuracies of 82.67% for Manhattan and 81.33% for Minkowski. A user-friendly web interface was also developed to facilitate real-world application, allowing users to input data and receive immediate predictive outcomes regarding autism risk. These findings confirm the effectiveness of the stacking method in enhancing K-NN performance and highlight its potential for practical use in autism screening. Future research may explore alternative machine learning algorithms and additional features to refine the predictive capabilities and user experience further.
An Encryption Method of 8-Qubit States Using Unitary Matrix and Permutation Bekti Maryuni Susanto; Rizky Alfanio Atmoko; Erik Yohan Kartiko; Agung Teguh Setiyadi
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.433

Abstract

The paper explores the methods for encrypting and decrypting an 8-qubit states of quantum system using unitary and permutation matrix. Our approach utilizes a unitary matrix to create a new superpositions of an encrypted 8-qubits states. By applying a permutation matrix, we shuffle the state vectors, adding an additional layer of security. The encryption process will be performed on the encrypted state using the formula , where is the original state vector, is the unitary matrix, and is the permutation matrix. To ensure the total probability remains normalized, we showed that the resulting new 8-qubits state remains normalized. The decryption process is achieved by applying the following operations retrieving the original state. This paper also is showing that the original quantum state can be accurately recovered post-decryption. This highlights the robustness of our approach in maintaining the integrity of quantum information. Furthermore, we aim to create block for different 8-qubits state using a different key in each block from the initial unitary matrix and permutation . In order to implement these methods, we need to generate a new unitary matrix for each block. Either by random pick or using iteration. In fact, we showed how to create the new unitary matrix using iteration for each block. Here we showed that the new generated matrix is also a unitary matrix so that we can use iteration proses to create a new unitary matrix in each block for different 8-qubits state. Here we generate the unitary matrix from as key in block . This result in the encryption of each block for each 8-qubits state using the formula resulting in a more robust security. The encryption/decryption scheme we referenced can theoretically be implemented on modern quantum hardware but verifying operations involving hundreds of qubits would demand rigorous calibration and error correction
Educational Data Mining for Student Academic Performance Analysis Khoirunnisa' Afandi; M. Habibullah Arief; Martiana Kholila Fadhil
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.434

Abstract

Good student academic performance is the key to success in the quality of education at university. One of the factors that influence academic success by utilising information technology and data analytics. This research incorporates GPA scores and other external factors that can affect students' academic performance such as parents’ job and latest education, address, gender, extracurricular, etc. This research uses Machine Learning; Decision Tree, Random Forest, K-Nearest Neighbour, Support Vector Classifier, Naive Bayes, and Gaussian as methods to analyse and predict the academic performance of students of the Information Systems Study Program, Faculty of Computer Science at the University of Jember. The results showed that the Decision Tree algorithm has the highest accuracy value of 0.9264 followed by Random Forest and K-Nearest Neighbour. Meanwhile, the prediction results show that the Decision Tree, K-nearest neighbour, and Random Forest algorithms can predict the same results
Improving Online Exam Verification with Class-Weighted and Augmented CNN Models Ilham Fanani; Rianto Rianto
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.435

Abstract

The COVID-19 pandemic has shifted interactions to virtual platforms, significantly impacting education, particularly online exams. However, these online exams have vulnerabilities, including exam jockeys. This study proposes a face classification model using a Convolutional Neural Network (CNN) to verify online exam takers. The model uses preprocessing techniques, i.e. normalization, data augmentation, and class weighting, to balance data and enhance generalization utilizing TensorFlow. The results show an overall accuracy of 85%, with a precision of 86.34%, a recall of 84.24%, an F1-score of 85.28% for legal takers, and a precision of 83.65%, recall of 85.81%, and an F1-score of 84.71% for illegal takers. These results indicate the model's balanced performance between legal and illegal classes. By integrating CNN with tailored preprocessing and training strategies, this study addresses gaps in existing authentication methods, offering a robust approach to online exam verification. The proposed model shows a chance for practical applications. However, further optimization through larger datasets and advanced augmentation techniques is recommended to improve its accuracy and adaptability to diverse real-world contexts
Current Stabilisation of Lithium Polymer Electric Vehicle Battery Using Fuzzy Logic Control Arizal Mujibtamala Nanda Imron; Satryo Budi Utomo; Dimas Aldy Darmawan; Bambang Sri Kaloko
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.436

Abstract

Renewable energy in electric vehicles (EVs) is crucial and requires careful consideration. To determine the initial capacity of lithium polymer batteries used in electric vehicles development, the batteries must be tested under various load and discharge conditions. The issue is that an increase in the level of load typically results in a corresponding decrease in battery lifespan. To extend the operational lifespan of the battery, it is necessary to conduct a variety of loading tests. These procedures involve monitoring battery voltage, current, and temperature during discharge with a 5-watt lamp load. The results of the study demonstrate that fuzzy control is an effective method to minimize the increase in battery temperature by stabilizing the current used by the battery. The fuzzy control system effectively regulates the battery with a capacity of 3300 mAh and a voltage of 11.1 Volts, maintaining a stable current of 0.3 A from the 3rd minute until the battery reaches its maximum capacity at 63 minutes. Fuzzy control delays the battery's temperature rise by approximately 14 minutes compared to a system without it. Temperature rise significantly affects the discharge speed of lithium polymer batteries

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