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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 11 Documents
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Search results for , issue "Vol 11 No 2 (2024): December" : 11 Documents clear
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 Bekti Maryuni Susanto; Harun Al Azies; Muhammad Naufal
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
Design MicroServer Framework Library with Swoole for Real-time Application Development Muhammad Robihul Mufid; Yogi Pratama; Arna Fariza; Saniyatul Mawaddah; Yunia Ikawati; Darmawan Aditama; Muhlis Tahir
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.v11i2.437

Abstract

The PHP programming language is known as a synchronous programming language because the request execution model is carried out sequentially and is very easy to apply in creating systems with simple scenarios. With this model, there will be many challenges in developing real-time applications because sequential model execution can cause bottlenecks because it initializes threads on each request which causes more resource consumption, making it less suitable for handling I/O (Input/Output) operations on an Intense scale. This study aims to implement an asynchronous model in PHP by developing a Xel Async framework that can be used as a foundation for creating micro servers using the Swoole extension. This study will explain the framework developed starting from system modeling, component design, abstraction design that focuses on response time, throughput, and efficient and effective resource usage in handling heavy traffic. To see the performance of the framework developed, an analysis was carried out with other frameworks such as Express Js. And the results show that the Xel Async framework offers significant performance carried out on benchmark tests for 100, 250, 500, 1000 connections and is able to produce a better amount of latency. In addition, an analysis was also carried out for throughput on Xel Async and Express Js, which also produced better performance than Express Js
Comparative Analysis of Vectorization Methods for Academic Supervisor Recommendations Qotrunnada Nabila; Ardytha Luthfiarta; Mutiara Syabilla; Azizu Ahmad; Rozaki Riyanto
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.v11i2.438

Abstract

Selecting final project supervisors often poses challenges for students due to limited lecturer quotas and difficulties in finding suitable expertise matches. This study proposes using the Cosine Similarity method with vectorization approaches such as Bidirectional Encoder Representations from Transformers (BERT), FastText, Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec to enhance the accuracy of recommendation systems. Data sourced from Google Scholar underwent scraping, preprocessing, and vectorization to evaluate the most effective method for understanding context and recommending relevant supervisors. The analysis revealed that BERT and Word2Vec based approaches achieved superior performance, delivering a perfect hit ratio (1.00) and overcoming the limitations of TF-IDF and BoW in capturing technical language. This recommendation system is expected to streamline the supervisor selection process, minimize mismatches, and effectively support academic advisory processes across educational institutions
Sentiment Analysis of the Use of Makeup Products Using the Support Vector Machine Method Khairunnisa Khairunnisa; Sriani Sriani
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.v11i2.439

Abstract

Many beauty products have emerged from various brands by providing attractive offers for women who are their main targets. Product reviews can help consumers regarding the quality of using the product. However, the problem is, on the femaledaily.com website there is no distinction between negative, neutral, and positive reviews so that consumers must first read the review and it takes a lot of time and this problem really requires a classification process on the review into negative, neutral, and positive classes. This process cannot be done automatically, therefore sentiment analysis is needed. To find out the classification of positive, negative, and neutral sentiment on the product, the Support Vector Machine (SVM) method is used, the advantage of SVM in this case lies in its ability to handle high-dimensional datasets and still produce effective classification and SVM is also a good choice for sentiment analysis in the context of cosmetic product reviews. The classification results using the SVM method produce data into 3 classes, namely 510 positive reviews, 98 neutral, and 29 negative with an accuracy value of 77.97%, precision 78%, recall 100%, fi-score 88%
Classification of Chicken Meat Freshness Using Support Vector Machine and Hue Saturation Intensity Cintana Aisyah Rilia; Sriani Sriani
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.v11i2.440

Abstract

Chicken meat is a popular source of animal protein in Indonesia due to its high nutritional value, affordable price, and easy processing. The identification of chicken meat freshness is currently still done manually through visual or tactile inspection, but this method has limitations, especially if consumers are less skilled in distinguishing the quality of chicken meat freshness. Therefore, an automated system is needed to classify the freshness level of chicken meat based on images. This research aims to develop an image processing system in classifying the freshness level of chicken meat by utilizing the Support Vector Machine (SVM) method with Hue Saturation Intensity (HSI) based color feature extraction. This process is done by converting the RGB image into HSI, then extracting the Hue, Saturation, and Intensity values and classifying using a polynomial kernel. This study used 450 chicken meat images, with 360 training data and 90 test data. The developed system successfully achieved an accuracy of 65.56%. The test results show that the system is reliable in classifying the freshness level of chicken meat. This system has the potential to support the identification of meat freshness efficiently and objectively, while at the same time improving food safety.

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