Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Informasi dan Teknologi
Iswavigra, Dwi Utari Iswavigra
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementation of Fuzzy AHP in Prioritizing Hotel Selection for Various Activities Osvaldo Fernandy Wijaya; Iswavigra, Dwi Utari Iswavigra; Yunita Rahmasari
Jurnal Informasi dan Teknologi 2024, Vol. 6, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v6i2.561

Abstract

The hotel sector plays a pivotal role in tourism, serving as a crucial venue for businesspeople, tourists, and other guests, significantly boosting regional and national economies. Facing increasing competition due to the rise in attractive tourist destinations and business travel for specific agendas such as meetings and seminars, the hotel industry must optimize its selection process. This study aims to aid in hotel selection based on weighted criteria for various activities by implementing the Fuzzy Analytical Hierarchy Process (Fuzzy AHP). AHP, widely used for multi-criteria decision-making, and Fuzzy Logic, chosen for its effectiveness in handling uncertainty and ambiguity, form the methodological foundation. The study utilized criteria such as price, quality, security, and location, with alternatives including traveling, meetings, and seminars. Data from 30 anonymous respondents were processed using Microsoft Excel for Fuzzy AHP computations. Results indicated that for meetings, hotel selection prioritizes quality and security, while for seminars, price and location are more important. In traveling, the emphasis is evenly distributed but generally low across all criteria, with location being least significant. This research underscores the potential of Fuzzy AHP in improving decision-making accuracy in hotel selection based on varying activity preferences.
Determining Factors Affecting Mother's Decisions in Providing and Selecting Infant Formula Using Data Mining - Decision Support System Collaborative Iswavigra, Dwi Utari Iswavigra; Bella Gusniar; Miranda Ika Vania
Jurnal Informasi dan Teknologi 2024, Vol. 6, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v6i3.593

Abstract

Indonesia is one of the countries with a population of around two hundred million people, ranking fourth after the United States in the list of the most populous countries in the world. According to data from the Central Statistics Agency (BPS), the birth rate is projected to reach 4.45 million people in 2022, an increase of 0.22% from the previous year, which was 4.45 million people. According to the 2007 Indonesian Demographic and Health Survey (IDHS), the rate of exclusive breastfeeding in Indonesia was only 32%. Factors such as maternal health, infant health, and formula milk promotions influence mothers' decisions to use formula milk. The availability of numerous formula brands complicates the decision-making process, with each brand offering different nutritional claims. This study employs the K-Medoids clustering algorithm to analyze factors affecting mothers' choices in formula feeding and the TOPSIS method to determine the most suitable formula for infants aged 0-6 months. The research involves clustering data from a questionnaire distributed to 100 mothers in the Solo Raya region into five categories: maternal health, maternal employment, formula promotion, infant health, and breastfeeding education. Results indicate that maternal health is the most influential factor, followed by infant health, maternal employment, and formula promotion. Lack of breastfeeding education does not significantly influence formula choice. The TOPSIS method, applied to evaluate 10 formula brands against six nutritional criteria, identifies Lactogen 1 as the best formula for infants aged 0-6 months with a highest value of 2.777104826. This data-driven approach provides a clear, systematic method for selecting an appropriate formula based on specific nutritional needs.
Co-Authors Bella Gusniar Miranda Ika Vania Osvaldo Fernandy Wijaya Yunita Rahmasari