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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 6 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2024): July" : 6 Documents clear
Optimizing Startup Success Prediction Through SMOTE Oversampling and Classification Najie, Muhammad; Sofian, Ahmad Alif; Sidabutar, Ribka Julyasih; Untoro, Meida Cahyo
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 Septian, Firza
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 Pardosi, Victor Benny Alexsius; Sutariyani, Sutariyani; Ikhsanudin, Muhammad; Naufal, Abdurrahman
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
Analysis of the Role of Augmented Reality in Bringing Works of Art to Digital Spaces Yuniana Cahyaningrum; Cahya Surya Harsakya; Septianingrum, Salsabila Putri
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.v1i2.77

Abstract

Augmented Reality (AR) has become increasingly popular in the art world as a medium for bringing traditional works of art into the digital space. This research aims to analyze the role played by AR technology in the process of transforming works of art from the physical environment into the digital domain. This approach pays attention to the technical aspects, aesthetics and cultural impact of applying AR in digital art exhibitions. This research method involves literature analysis, case studies of various art exhibitions that use AR, and interviews with artists, curators, and exhibition visitors. Qualitative data was analyzed thematically to identify emerging patterns and trends in the use of AR in artistic contexts. The research results show that AR has great potential to enrich the visitor experience in digital art exhibitions by providing interactive and contextual layers that cannot be accessed through conventional mediums. Additionally, AR can also act as a tool to convey additional narratives, broaden the interpretation of works of art, and increase visitor participation in the creative process. The research also identified several technical and conceptual challenges associated with the use of AR in art, including issues of technological sustainability, accessibility, and artwork integrity. Nonetheless, overall, this research highlights the great potential of AR in bringing works of art into digital spaces and provides valuable insights for the future development of digital art exhibitions.
Website-based Collection Inventory Information System Design at Wayang Sendang Mas Banyumas Museum Prasuteja, Soni Gunawan; Wibowo, Adhi; Racma, Dhany Faizal; Widiastuti, Rosalina Yani; Suyudi, Suyudi
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.v1i2.68

Abstract

The Wayang Sendang Mas Banyumas Museum has 1011 collections that will continue to grow over time. With so many collections, there are obstacles in the inventory process, especially in the collection data reporting section. The current inventory process is still carried out semi-manually, using Microsoft Excel and Microsoft Word, resulting in less time efficiency in the process of making collection data reports. The purpose of this research is to prove that the existence of a Website-Based Collection Inventory Information System at the Wayang Sendang Mas Banyumas Museum can increase efficiency in the process of making museum collection data reports. This system was built with the help of the CodeIgniter Framework, the programming language used is PHP, and the system development method uses the prototype method, which is a technique for collecting specific information about the information needs needed by users quickly. The system that has been built is tested with several tests, including system testing using black-box testing and white-box testing methods; hypothesis testing using paired sample t-test; and testing the benefits using the ISO 25010 test model. The results of the benefits testing obtained a percentage of answers based on the criteria agree and strongly agree by 100% on the Performance Efficiency aspect. Based on the results of the benefits testing, it can be concluded that the system that has been built has succeeded in helping the museum in increasing efficiency in the process of making museum collection data reports.

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