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Journal of Data Science, Technology, and Artificial Intelligence
Published by CV. ADMITECH SOLUTIONS
ISSN : -     EISSN : 30891868     DOI : https://doi.org/10.63703/ditech
Core Subject : Science,
Computer Science & IT
The Journal of Data Science, Technology, and Artificial Intelligence is a semi-annual publication released in January and July. It covers a wide range of topics within the realms of data science, technology, and artificial intelligence. This interdisciplinary journal is a platform for scholars, researchers, and practitioners to share their insights, findings, and innovations in these rapidly evolving fields. The journal aims to foster discussion, collaboration, and advancement in the theory and application of data science, technology, and artificial intelligence through a combination of research articles, reviews, and case studies. It provides readers with valuable insights into emerging trends, methodologies, and practical applications that shape our understanding and utilization of data-driven technologies.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 7 Documents
 1 
Design and Build an Android-Based Mobile Application for Online Badminton Court Booking Priskila Parimanam; Harefa, Ade May Luky
Journal of Data Science, Technology, and Artificial Intelligence Vol. 1 No. 1 (2024): July 2024
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/ditech.v1i1.1

Abstract

In this digital era, the utilization of mobile technology is increasingly crucial to provide easier and faster services for users. In the design phase, a needs analysis and literature study were conducted regarding online booking applications and badminton court systems that already exist at the Sekip Badminton Center. The user interface design was created to allow users to easily book badminton courts according to their preferred time and location. This application incorporates essential features, including user authentication system, court availability calendar, time options, court type selection, and secure payment processing to enhance user convenience and experience. This research focuses on the development of an Android-based application. Quality testing processes were carried out to ensure the application functions well, is free from errors, and provides accurate results. The result of this research is a mobile application that assists users in quickly and efficiently booking badminton courts. The application improves accessibility and effectiveness in court booking, contributing positively to the development of the sports industry, particularly in the field of badminton courts.
The Implementation of Fuzzy Inference System for Predicting the Price of Oil Palm Fruit at PT. Tri Bahtera Srikandi Ayu Hantira; Nuraisana
Journal of Data Science, Technology, and Artificial Intelligence Vol. 1 No. 1 (2024): July 2024
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/ditech.v1i1.2

Abstract

Technology is widely implemented in various fields, especially in the field of oil palm fruit plantations. Oil palm plantations are one of the industrial sectors that directly benefit from the development of digital technology. Currently, many new innovations and technologies are emerging that can increase the productivity and welfare of oil palm farmers while maintaining the quality of the plants from time to time to maintain the price of oil palm fruit. PT. Tri Bahtera Srikandi is a palm oil mill that produces palm fruit into oil or CPO (Crude Palm Oil). Determining the price of oil palm fruit aims to provide fair prices to plantations and companies so that there are no losses from any party. However, the problem that arises is that when the price of palm oil fruit falls or increases periodically, the company will experience the risk of loss and uncertainty caused by fluctuations in the price of palm oil fruit. It is impossible for a company to be able to accommodate too much oil palm fruit for a long time so that if palm fruit is piled up, rot will occur in the palm oil fruit and this can affect production results and the amount of available oil or CPO (Crude Palm Oil). Therefore, it is important to predict the price of oil palm fruit in the future. Application of using the Tsukamoto fuzzy logic method in predicting the price of oil palm fruit at PT. Tri Bahtra Srikandi. It can be implemented in predicting the price of oil palm fruit accurately.
Decision Support System for The Acceptance of Medical Staff Using the Profile Matching Method at Mitra Sejati Hospital Finna Handayani; Tarigan, Nera Mayana
Journal of Data Science, Technology, and Artificial Intelligence Vol. 1 No. 1 (2024): July 2024
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/ditech.v1i1.3

Abstract

The Hospital is one of the healthcare referral centers that consistently provides comprehensive health services. One crucial factor influencing healthcare services is the professional, high-quality, and competent nursing staff. However, the assessment process for the acceptance of nursing medical staff at Mitra Sejati Hospital still relies on manual calculations. It is evident that many hospitals assess candidates based on their proximity to the nursing medical staff. Therefore, there is a significant need for a decision support system in the recruitment of medical staff. The method used in the recruitment of medical staff is the Profile Matching Method. The criteria used in this research are: 1) Highest Education. 2) Professional License (STR). 3) Basic Trauma Cardiac Life Support (BTCLS) certification. 4) Age. 5) Work Experience. This system is built by implementing the web-based Profile Matching Method with MySQL as its database. With the application of the Profile Matching Method in determining the acceptance of nursing medical staff, out of the 7 predetermined candidates, 4 candidates were accepted. They are Rusli Sako with a score of 4.82, Yusniar Lubis with a score of 4.52, DRS Mandir Sagala with a score of 3.98, and Suryadani with a score of 3.96.
Application of Linear Regression Method in Predicting Veil Sales (Case Study: Fauzan Kerudung Shop) Fitri Amalini; Harefa, Ade May Luky
Journal of Data Science, Technology, and Artificial Intelligence Vol. 1 No. 1 (2024): July 2024
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/ditech.v1i1.4

Abstract

The sales prediction system can support business transactions, especially those engaged in the trading sector, as an operational process that forecasts products to be sold in the future. One of the stores involved in the trading business is Toko Kerudung Fauzan. This kerchief store relies solely on estimates to determine the quantity of goods to be purchased from suppliers. This reliance on estimates has led to difficulties for the store owner in predicting future sales without performing calculations to maintain inventory levels in the store. Therefore, there is a significant need for a prediction system to determine kerchief sales in the future. The method employed for sales prediction is the Linear Regression Method. The number of data samples used consists of sales data collected from January to December 2022. Consequently, it can be stated that the decision to predict sales in 2023 shows a decline. 
Decision Support System for Premises Assessment of PT. Bank Perkreditan Rakyat NBP 33 with TOPSIS Method Sihaloho, Risma Veronika; Purba, Megaria
Journal of Data Science, Technology, and Artificial Intelligence Vol. 1 No. 1 (2024): July 2024
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/ditech.v1i1.5

Abstract

The premises assessment at PT. BPR NBP 33 still relies on a manual evaluation based solely on the highest number of votes, without considering premises criteria. The method employed is the TOPSIS method. This method has advantages such as a simple and easy-to-understand concept, efficient computation, and the ability to measure the relative performance of decision alternatives in a simple mathematical form. There are 5 (five) criteria used as measurement tools for premises assessment, including banking hall equipment, room comfort, toilets, parking facilities, and transaction facilities. Four (4) alternatives are considered. Based on the ranking results of the alternatives, the branch office with the best premises assessment is KCP. Batang Kuis, with a final score of 0.84440, securing the top ranking. This decision support system is developed using PHP programming language and MySQL database.
Feature Importance and Binary Classification using PyCaret Naswir, Ahmad Fadhil; Williem; Hasanul Fahmi
Journal of Data Science, Technology, and Artificial Intelligence Vol. 1 No. 1 (2024): July 2024
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/ditech.v1i1.16

Abstract

The rapid advancement of machine learning (ML) techniques has facilitated the development of robust models for various classification tasks. This study explores the application of PyCaret, an open-source, low-code machine learning library, to perform feature importance analysis and binary classification using the Titanic dataset from Kaggle. The dataset underwent preprocessing to convert categorical features into numerical values and to remove irrelevant columns. Multiple classification models were compared, with the Gradient Boosting Classifier achieving the highest performance, marked by an average accuracy of 81.52%. Detailed evaluation metrics, including precision, recall, F1 score, and AUC, further validated the model's effectiveness. Feature importance analysis identified gender (sex), fare, and age as the most significant predictors of survival, aligning with historical accounts. The results demonstrate PyCaret's capability to streamline the ML workflow, providing valuable insights and enabling rapid experimentation. This study highlights the potential of binary classification and feature importance analysis in handling large-scale datasets, where the identified important features can serve as a baseline for implementing advanced algorithms such as deep learning.
Improvement of Face Recognition Algorithm in Smart Home Security System Ghofir, Abdul; Rusdianto Roestam; Insidini Fawwaz
Journal of Data Science, Technology, and Artificial Intelligence Vol. 1 No. 1 (2024): July 2024
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/ditech.v1i1.21

Abstract

At present, human intervention is still needed in most security systems to control their functions. The implementation of machine learning plays an important role in smart home security systems for better control functions. The system will have the ability to learn user behaviour, which then represents it in the form of control of the system. One of the important capabilities possessed by a security system is to recognize the face of everyone who accesses a secured place. This paper introduces a face recognition algorithm which is enhanced through a filtration of its controlled Euclidean Distance. The Success Rate Formula is also added and applied for more convincing results. All required system functions are identified and registered as the first system development step. The type of sensor for each function is determined as input data for machine learning processing. Designing and coding the system is carried out on Arduino as its core physical control system before testing and evaluating the system.

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