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Journal of Intelligent Systems and Information Technology
Published by Apik Cahaya Ilmu
ISSN : -     EISSN : 30465001     DOI : https://doi.org/10.61971/jisit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal of Intelligent Systems and Information Technology (JISIT) focuses on providing scientific articles related to Intelligent Systems and Information Technology, which are developed by publishing articles, research reports and reviews. Journal of Intelligent Systems and Information Technology (JISIT) accepts scientific articles in the field of research: Artificial Intelligence, Data Mining, Text Mining, Web Mining, Machine Learning, Deep Learning, Natural Language Processing (NLP), Social Network Analysis, Expert system, Decision Support System, Computer Network Security related AI, Image processing, Computer Vision, Big Data, and related fields
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 11 Documents
 1   2 
Enhancing Project-Based Learning in STEM Education with Integrated Technology and Coding Kurniawan Arif Maspul
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.20

Abstract

This extensive study explores the advantages and significance of incorporating technology and coding into project-based learning to improve STEM education. This integration improves students' learning experiences by encouraging computational thinking, problem-solving ability, and creativity. Collaborative coding projects promote interdisciplinary learning and a thorough comprehension of multiple subjects, while online resources and self-paced learning platforms enable students to study coding outside of the classroom. Educators can design meaningful learning experiences that match technology use with learning objectives and stimulate social connections and cooperation by embracing theories such as constructivism, TPACK, and social constructivism. Inquiry-based learning, project-based learning, and self-regulated learning are all strategies that promote student engagement and learning in integrated technology and coding. Providing resources, professional development opportunities, and technical support systems promotes in efficient implementation. Educators may develop engaging project-based learning experiences that equip students with necessary skills for the future by embracing creative techniques and incorporating technology and coding.
Optimization of Information Technology Through Intelligent System Integration : Comprehensive Exploration Yuniana Cahyaningrum
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.25

Abstract

In the era of digital transformation, intelligent system integration is the main capital for optimizing the use of information technology. This article provides a comprehensive exploration to explore how intelligent system integration forms a strong foundation for changing the outlook of information technology. The main focus of this article is utilization in operational efficiency, smarter decision making, and product and service innovation. The basic concept of intelligent system integration is explained by providing an explanation of how artificial intelligence and information technology can interact synergistically. This article also discusses the role of artificial intelligence in improving the functionality of information systems and its positive impact on decision making. The benefits of intelligent system integration in increasing operational efficiency can be explained through the automation of tasks performed, increased productivity through faster data processing, and reduced human errors. This article also shows how intelligent system integration supports better decision making through in-depth and accurate data analysis. Additionally, this article discusses how intelligent system integration drives innovation in products and services. As input for future development, this article provides an in-depth overview of intelligent system integration trends that contribute to the optimization of information technology, by opening the door to an era of innovation and greater efficiency.
Web-Based Pharmacy Cashier Application Development Design Murni Marisma; Feri Wibowo; Lahan Adi Purwanto; Achmad Fauzan
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.26

Abstract

The cashier system at Arca Mandiri Pharmacy still uses manual transaction recording services; namely, the cashier uses recording transaction calculations in the sales transaction logbook and manually records drug stock in the drug stock book. This results in less effective and efficient recording of transactions and the stock of medicinal goods. Facilities and services to cashiers are expected to be provided more effectively and efficiently using a web-based cashier application so that it will improve service in transactions for customers. The system development method used is a waterfall model approach with four stages, namely analysis, design, code, and testing. The web-based cashier application implementation model allows the system to be used by cashiers and administrators with its advantages, namely automatically calculating the total amount of drug order prices in sales transactions, stock calculations, and routine transaction reports. The Cashier Application implemented at the website-based Arca Mandiri Pharmacy helps facilitate sales transactions, processing drug stock data, and printing reports that can be done quickly so that it is more efficient and effective in service.
Color Detector in an Image using Python and Computer Vision Library Dicky Ardianto; Arif Tri Widiyatmoko
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.27

Abstract

This research explores the implementation of a color detection system in images using the Python programming language and the Computer Vision library. The primary aim is to enhance the accuracy and efficiency of color detection, a critical component in the realm of automated systems and artificial intelligence. Leveraging Python's versatility and the specialized features of the Computer Vision library, the study conducts experiment to evaluate the proposed system's reliability across diverse image contexts and lighting conditions. The literature review encompasses fundamental color detection concepts, recent advancements in Computer Vision, and practical applications from prior relevant research. The anticipated outcome of this research is a substantial contribution to advancing our understanding of color detection within image processing, with potential implications for a more reliable and widely applicable system
Use Of Sosial Media Youtube For Collaborative Learning Indry Ayu Lestary
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.29

Abstract

The goals of this paper is analyze and evaluate of design instructional video content on the performance of students in collaborative learning methods. Design of video content used the research 3 models are video slides, props and explanations lecturer, tested to student. Using the concept of TAM, visible results from the ease and understanding of the students learning process. Results of the analysis is a video tutorial uses props learning can provide a different effect on the improvement of student learning outcomes than the use of video learning using slides and video that explain the material just same as usual.
Performance Optimization of Document Clustering for Harry Potter Series Comments using Cosine Similarity Firza Septian; Arief Zikry; Nina Dwi Putriani
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.30

Abstract

This research delves into the distinctive realm of comment clustering, focusing on the extensive discourse generated by the Harry Potter series. Leveraging a dataset from Kaggle, the study aims to optimize document clustering using cosine similarity within the K-Means algorithm. The research addresses the nuanced dynamics of sentiment and preferences within the Harry Potter fan community. A comprehensive methodology involves data collection, preprocessing, TF-IDF initialization, K-Means clustering with varying distance metrics, and result evaluation. The dataset of 491 respondents unveils diverse gender, geographical, and age distributions, adding complexity to the analysis. The K-Means clustering results highlight predominant positive sentiment, emphasizing the enduring popularity of the series. The study's originality lies in its focus on the Harry Potter cultural phenomenon, contributing to sentiment analysis and fan engagement discourse. The implications extend to researchers, practitioners, and enthusiasts seeking a deeper understanding of online discussions surrounding iconic media franchises.
Optimizing Startup Success Prediction Through SMOTE Oversampling and Classification Muhammad Najie; Ahmad Alif Sofian; Ribka Julyasih Sidabutar; Meida Cahyo Untoro
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.33

Abstract

Rapid technological advancements have led to a surge in the number of startups competing with innovative ideas. Predicting the chances of a startup's future success becomes crucial for entrepreneurs in making informed decisions and strategizing their growth. This study investigates the effectiveness of the Gradient Boosting classification algorithm in predicting startup success. To address potential class imbalance within the dataset, a pre-processing step utilizing Synthetic Minority Oversampling Technique (SMOTE) was employed. The dataset itself encompassed a wide range of variables related to startup attributes and performance metrics. The F1-score metric was utilized to evaluate the model's accuracy while minimizing false positive predictions that could potentially mislead investors. Gradient Boosting algorithm was employed to analyze the dataset, which was pre-processed using SMOTE to handle potential class imbalance. This technique helps to create synthetic data points for the minority class, resulting in a more balanced dataset for the classification model. The dataset itself encompassed a wide range of variables related to startup attributes and performance. The F1-score metric was utilized to evaluate the model's accuracy while minimizing false positive predictions that could potentially mislead investors. Gradient Boosting algorithm achieved an F1-score of 86% for predicting successful startups and 85% for predicting unsuccessful ones. The low false positive prediction rate of 7.9% on the test data further validates the model's reliability. The findings demonstrate the effectiveness of Gradient Boosting in predicting startup success with high accuracy and minimal false positives
Exploring the Performance of Whale Optimization Algorithm on Rosenbrock's Function Firza Septian
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.35

Abstract

Optimization of complex and nonlinear functions is essential across various domains, from engineering and finance to artificial intelligence and machine learning. Rosenbrock's function stands as a fundamental benchmark for evaluating optimization algorithms due to its highly nonlinear and multimodal nature. Among the multitude of optimization algorithms, the Whale Optimization Algorithm (WOA) has garnered attention for its inspiration from the social behavior of humpback whales. However, its performance on Rosenbrock's function remains relatively unexplored. This paper aims to investigate the effectiveness of the WOA specifically on Rosenbrock's function through rigorous experimentation and analysis. By evaluating convergence speed, solution accuracy, and robustness, this study sheds light on WOA's behavior when confronted with the challenges posed by Rosenbrock's function. Comparative analysis with other optimization algorithms further elucidates WOA's adaptability and scalability. The findings contribute valuable insights for selecting suitable optimization algorithms in real-world applications and advance understanding of optimization algorithms' behavior in challenging landscapes.
Addressing DNS Propagation Challenges with Repurposed STBs, ZeroTier Networking, and Indonesian ISP Integration Victor Benny Alexsius Pardosi; Sutariyani Sutariyani; Muhammad Ikhsanudin; Abdurrahman Naufal
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.46

Abstract

PT Transformasi Data Digital (HostData) operates as a web hosting and domain service provider in Indonesia, facing recurring challenges related to DNS propagation. Clients often encounter issues accessing their newly acquired domains or updated DNS records due to a lack of understanding of the propagation process. In response, HostData has developed a DNS Propagation checking system to streamline verification for clients and its support team. This system allows clients to monitor DNS propagation independently, acknowledging variations based on their Internet Service Provider (ISP). Leveraging six local ISPs—Telkomsel, Indosat, XL, Three, Smartfren, and Indihome—the system utilizes repurposed Set Top Boxes (STBs) as mini servers for real-time DNS value verification. These STBs connect via Zerotier to a Virtual Private Server (VPS) with a public IP address, serving as the central control unit. This innovative solution enables clients to confirm domain resolution and serves as an educational tool, offering insights into DNS propagation mechanics.
Comparison of Binary Logistic Regression and SVM to Classify Diabetes Sufferers Fibia Sentauri Cahyaningrum
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.76

Abstract

Diabetes is a chronic metabolic disorder characterized by high levels of glucose in the blood due to disruption of the insulin hormone which functions as a regulator of the balance of blood sugar levels. This disease continues to increase in prevalence in various countries, making it a global health problem. Diabetes has trigger factors that contribute to the incidence of the disease, such as age, gender, smoking habits, healthy eating patterns, high blood pressure, and others. Diagnosis of diabetes can be done by carrying out a fasting blood sugar test, a 2-hour postprandial (PP) blood sugar test, and a random blood sugar test. However, it is very possible for diagnoses made by health workers to have errors due to subjectivity and different experiences, so a fast and precise classification method is needed to classify patients undergoing diabetes examination based on variables related to diabetes. The classification method used in this research is binary logistic regression and Support Vector Machine (SVM). A similar study carried out classification of diabetes sufferers using the Naive Bayes and KNN methods by comparing the results with SVM, so in this study the binary logistic regression method and SVM will be used to determine the performance of the classification method. The data used is secondary data. Next, the data is divided into training and testing data. The analysis results show that the SVM method is slightly superior in classification accuracy of testing data, namely 97.75%. With this research, it is hoped that decisions on patients undergoing diabetes examination will be faster, more precise and effective, and classification methods with better performance can be applied

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