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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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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.
IoT-Based Water Quality Monitoring System for Fish Ponds Using Fuzzy Inference Method Achmad Firman Choiri
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.v11i2.441

Abstract

This study uses the fuzzy inference method to develop an Internet of Things (IoT) system to monitor fish pond water quality. This system utilizes pH, Total Dissolved Solids (TDS), and temperature sensors to measure water quality parameters for fish health. Although many previous studies have discussed water quality monitoring, there are still limitations in applying IoT technology integrated with fuzzy inference methods for real-time data analysis. Many existing systems cannot provide information easily understood by fish farmers and are less accurate in measuring water quality parameters. Arduino Nano is the main microcontroller that processes sensor data, while the ESP8266 module is used for Wi-Fi connection for real-time monitoring through the thinger.io web-based application. Before testing, the sensors have been calibrated to ensure measurement accuracy. The test results on three water samples, namely tap water, tilapia pond water, and mujaer pond water, showed high accuracy and consistent results. The fuzzification results from the IoT device are close to the Simulink Fuzzy test results on each sample, with minor differences in tilapia pond water, likely caused by environmental factors such as aeration or sensor precision. This study aims to provide a system that is not only accurate but also presents data in a more understandable format so that it can help fish farmers make better pond management decisions. Thus, this study is expected to increase fish farming productivity through better and technology-based water quality management
K-Nearest Neighbors Optimization using Particle Swarm Optimization in Selection Digital Payments Ridwansyah Ridwansyah; Resti Lia Andharsaputri; Yudhistira Yudhistira; Irmawati Carolina; Suharjanti Suharjanti
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.442

Abstract

Fintech developments have increased the use of digital payment systems such as OVO and GoPay. However, selecting a payment method that suits user preferences is still a challenge. This research proposes a combination of K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) to improve the classification accuracy of digital payment systems. The dataset used comes from a survey of Fintech users with factors such as ease of application, data security, cashback and customer service. KNN is used as a classification method, while PSO is applied for feature selection to improve model efficiency. Evaluation is carried out using accuracy, precision, recall, and AUC. The research results show that accuracy increased from 94.00% to 95.47% after optimization with PSO. The most influential factors are customer service, user employment and cashback. However, the AUC value remains 0.500, which shows that the model still has limitations in optimally differentiating categories. Further research is recommended to explore other algorithms such as Random Forest and SVM, as well as developing a machine learning-based digital payment recommendation system
Clustering-based Machine Learning Approach For Predicting Tourism Trends From Social Media Behavior Candra Agustina; Eka Rahmawati
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.443

Abstract

Digital technology has significantly transformed tourist behavior, particularly in searching for, selecting, and sharing travel experiences. Social media has become a primary source of information, influencing travel decisions through real-time recommendations and user-generated content. However, the large volume of data generated by social media presents challenges in understanding and predicting tourist behavior. This study aims to analyze tourist behavior patterns using a clustering-based machine learning approach, specifically K-Means Clustering. The research examines engagement levels on platforms such as Instagram, TikTok, and TripAdvisor to categorize tourists into three key segments: Digital-Savvy Travelers, Passive Travelers, and Conservative Travelers. The results indicate that machine learning effectively analyzes large-scale tourism data, providing valuable insights for destination marketing, personalized recommendations, and service optimization. The findings highlight the potential of machine learning to identify emerging trends, improve customer segmentation, and enhance targeted promotional strategies. Understanding these patterns enables tourism businesses to create data-driven strategies aligned with modern travel behaviors. In a broader perspective, artificial intelligence can revolutionize tourism marketing, increase customer engagement, and improve the overall travel experience
Color Feature Selection Optimized with Bio- Inspired Algorithms in Classify Purity of Luwak Coffee Shinta Widyaningtyas; Muhammad Arwani
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.444

Abstract

Assessing the purity of Luwak Coffee is a complex challenge due to its unique production and limited availability, as visual inspection is unreliable. This study explores the use of image processing and feature selection to classify Luwak Coffee purity by analyzing 11 color features including RGB, HSV, HSL, and Lab color spaces. Two classification methods k-Nearest Neighbors (k-NN) and Random Forest (RF) were optimized using six Bio-Inspired Algorithms (Differential Evolution, Firefly Algorithm, Flower Pollination Algorithm, Harris Hawk Algorithm, Jaya Algorithm, and Particle Swarm Optimization) to identify the most important features for classifying the purity of Luwak Coffee. The results revealed that feature selection significantly improved accuracy, with the Jaya Algorithm paired with k-NN achieving the highest accuracy (0.918) using only three features (R_Mean, B_Mean, and H_Mean). For RF, the Flower Pollination Algorithm yielded the best performance (0.899) with three features. The study demonstrates a classification method coupled with Bio-Inspired Algorithms for classifying Luwak Coffee purity providing high accuracy as a non-destructive method. These findings contribute to the development of reliable tools for classifying purity of Luwak Coffee
Machine Learning-Based Book and Library Recommendation Application Using Content-Based Filtering Method Novit Saputri; Pulut Suryati
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.445

Abstract

This research focuses on the development of a Book Recommendation System application, which aims to simplify book searches based on reader preferences. Faced with the challenge of selecting books that match their interests, readers often find it difficult to navigate the vast array of information available. The availability of different genres and authors can make the selection process complicated and time-consuming. Therefore, the development of a Book Recommendation System application is a relevant and necessary solution. The author's research question is how to optimize the book search experience through accurate recommendation algorithms. This project will use a collaborative algorithm-based recommendation model to analyze reader behavior patterns and provide accurate book recommendations based on the similarity of reader preferences. The application that the author designed allows readers to easily find suitable books. The author is committed to overcoming readers' difficulties in dealing with the abundance of information, increasing the accessibility of books, and improving reader satisfaction. The success of the project is measured by the application's ability to provide satisfactory recommendations, find relevant works, and simplify the literature exploration process. With the development of the era of vast information, the development of a Book Recommendation System is a relevant solution to guarantee a more enjoyable and efficient reading experience for readers
Utilizing Data Mining Approach For Hypertension Diagnosis Classification Pudji Widodo; Heribertus Ary Setyadi; Hartati Dyah Wahyuningsih; Sundari Sundari
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.446

Abstract

Hypertension is one of the factors contributing to the highest death rates from non-communicable diseases in various countries. Every year, the number of hypertension sufferers increases significantly. It is estimated that in 2025, the number of hypertension sufferers will reach 1.5 billion individuals. Data mining aims to identify patterns that can help in decision making, classification, and prediction. One of the well-known algorithms or methods for classification is the Support Vector Machine (SVM). The SVM method aims to find the best hyperplane or decision boundary function that can separate two or more classes of data in the input space. This research purpose is to determine the classification results and accuracy of the diagnosis of hypertension using the SVM method. Eleven attributes used include age, smoking habits, physical activity, sugar consumption, salt consumption, fat consumption, alcohol consumption, lack of fruit and vegetable consumption, systolic and diastolic blood pressure. This research will utilize Jupyter Notebook tools and Python programming language as research tools. The SVM method was trained with various kernel attributes and hyperparameters to produce the best model. From the results it is known that the RBF kernel used with parameters ???? = 100 and ???? = 0.1 produces an accuracy of 97.5% which is the best model in classifying hypertension. From these results it can be concluded that the SVM method is able to produce a very good classification of hypertension diagnosis and can provide a diagnosis to detect hypertension early

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