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All Journal Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT
Sawaviyya Anandianskha
STMIK YMI Tegal, Indonesia
Computer Science & IT Library & Information Science Mathematics

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Journal : Journal of Intelligent Decision Support System (IDSS)

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Decision Support System to assess customer satisfaction using Analytical Hierarchy Process Wresti Andriani; Gunawan Gunawan; Sawaviyya Anandianskha
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 4 (2023): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i4.163

Abstract

Transportation is an important aspect of mobility or global movement and activities. As public transportation that can be accessed online by the public, Gojek and Grab types of transportation provide transportation services and are growing rapidly. At the time of Covid 19 around 2020, online transportation was very important and much sought after. More and more online transportation companies are appearing, especially in Tegal City, so that there are more service offerings that consumers can use. User or consumer satisfaction measurements were carried out using Fuzzy Logic Method Analytical Hierarchy Process (AHP) on 200 consumers who used Gojek or Grab or other online transportation for 3 to 4 months in 2022 in Tegal City. The results obtained by customers or consumers were satisfied with Gojek transportation at 45%, with male consumers at 67%, and Grab at 37%, with male consumers at 65%, followed by other online transportation (X and Y). These results can be used as an option for consumers who expect the best service.
Application of sma method and ahp to predict the level of tidal flood vulnerability in Tegal City Bangkit Indarmawan Nugroho; Muhammad Farkhan; Sawaviyya Anandianskha; Gunawan Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.235

Abstract

This study examines the application of the Simple Moving Average (SMA) and Analytic Hierarchy Process (AHP) methods to predict tidal flood vulnerability in Tegal City. The objective is to develop a more accurate prediction method for tidal flood vulnerability. The methods used are a combination of SMA and AHP. The results indicate that this combination is effective in producing more accurate predictions compared to conventional methods. Villages such as Muarareja, Tegalsari, Mintaragen, and Panggung have been identified as highly vulnerable and require more intensive mitigation. The implications highlight the importance of a multi-method approach to understanding complex phenomena like flood vulnerability. For future research, it is recommended to integrate real-time weather data and consider socio-economic factors to enhance accuracy and relevance in disaster mitigation. The findings are expected to assist in better urban planning and resource allocation, as well as improve community resilience against tidal flood disasters.
Application of the nearest neigbour interpolation method and naives bayes classifier for the identification of bespectacled faces Aang Alim Murtopo; Sigit Januarto; Sawaviyya Anandianskha; Gunawan Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.242

Abstract

Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology
Co-Authors Aang Alim Murtopo Bangkit Indarmawan Nugroho Gunawan Isni Azmi Muhammad Farkhan Muhammad Sulthon Sarif Surorejo Sigit Januarto Wresti Andriani