cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282370070808
Journal Mail Official
mesran.skom.mkom@gmail.com
Editorial Address
Jl. Besar Namorambe, P. Mansion, MM No 14, Deli Serdang, Sumatera Utara Email: adajournals.ijids@gmail.com
Location
Kab. deli serdang,
Sumatera utara
INDONESIA
International Journal of Informatics and Data Science
Published by ADA Research Center
ISSN : -     EISSN : 30267315     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Data Science publishes manuscripts of Computer Science, but is not limited to the fields of: 1. Natural Language Processing Pattern Classification, 2. Speech recognition and synthesis, 3. Robotic Intelligence, 4. Big Data, 5. Informatics Techniques, 6. Image and Speech Signal Processing, 7. Data Mining 8. Decision Support System, 9. Experts System, and 10. Cryptography
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 15 Documents
 1   2 
Decision Support System for Selecting the Best Hotel Using the Multi-Objective Optimization Method on the Basis of Simple Ration Analysis (MOOSRA) Mesran; Nurwahid, Fahri; Sarumaha, Farel Notafelling; Lubis, Ridha Maya Faza
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.70

Abstract

Selecting the best hotel is an important decision-making process that requires careful consideration of various criteria. In this study, we use the Multi-Objective Optimization by Simple Ratio Analysis (MOOSRA) method to determine the most suitable hotel based on a set of predetermined criteria. The MOOSRA method is a multi-objective optimization technique that uses a simple ratio of the overall favorable and unfavorable criterion scores to avoid negative values and reduce the impact of large variations in criterion values. This study aims to develop a decision support system that helps customers choose the best hotel based on criteria. The MOOSRA method is applied to a decision matrix constructed from hotel attributes, and the overall performance score of each hotel is calculated using a simple ratio formula.
Film Popularity Analysis through Combined K-Means Clustering and Gradient Boosted Trees Agi Candra Bramantia; Desyanti; Jeperson Hutahaean; Erlin Windia Ambarsari
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.81

Abstract

The dynamic and competitive nature of the global film industry presents complex challenges in predicting film popularity, as success is shaped by the interplay of production investment, casting decisions, and audience preferences. This research addresses the limitations of previous studies that have focused primarily on direct relationships, such as budget versus box office returns, by introducing an integrated analytical framework that combines K-Means clustering and Gradient Boosted Trees (GBT) with explainable AI techniques. Utilizing the TMDB movie dataset and constructing features such as actor influence and studio power, the study segments films and predicts audience ratings while providing interpretable visualizations. The results reveal four distinct film clusters and demonstrate that actor influence and budget allocation are the most significant predictors of popularity. The proposed model achieves an R² score of 0.75 and a mean squared error of 0.35 in predicting audience ratings, while cluster analysis shows that Blockbuster films reach the highest average ratings (6.76), and Underperforming films the lowest (2.42). By integrating interpretable predictive modeling and interactive scenario tools, this research offers both theoretical advancement and practical value for industry stakeholders. However, the findings are limited by the available metadata and do not account for factors such as marketing or real-time audience trends, suggesting opportunities for future research to expand the analytical framework.
Decision Support System for Performance Assessment of the Best Salesperson with the Integration of Entropy and WASPAS Wang, Junhai; Setiawansyah; Isnain, Auliya Rahman
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.88

Abstract

The salesperson performance assessment is an important aspect of improving the effectiveness of a company's marketing strategy. However, this assessment process often faces the challenge of subjectivity, especially in determining the weights of the criteria used. To address this issue, this study implements a combination of the Entropy and WASPAS methods. The Entropy method is used to objectively determine the weights of the criteria based on data variation, while the WASPAS method is used to evaluate and rank alternatives. A case study was conducted on five salesperson personnel with the criteria used in selecting the best salesperson being sales target achievement, product mastery, communication skills, creativity, and work ethics. The results showed that Muhammad Iqbal (A3) ranked first with a score of 0.882, followed by Andi Saputra (A1) with a score of 0.796, Rizky Kurniawan (A5) with a score of 0.770, Budi Santoso (A2) with a score of 0.724, and Siti Rahmawati (A4) with a score of 0.655. The main contribution of this research is to present a more accurate and objective salesperson performance evaluation model through the integration of the Entropy–WASPAS method. This finding has practical implications for companies in selecting the best employees, identifying salesperson personnel with outstanding performance, and supporting strategic decision-making in human resource development in the marketing field.
Decision Support System for Determining Subsidized Food Receipt for Poor Families with SAW Method Elvia Hariska
International Journal of Informatics and Data Science Vol. 2 No. 1 (2024): December 2024
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i1.24

Abstract

In accordance with the regulations set by the Office of Kotasan Village, residents are entitled to receive subsidized food based on specific criteria. To determine who is selected to receive subsidized food and distribute it to low-income families will be conducted by the Kotasan Village Office. As a supporting tool to determine individuals eligible to receive subsidized food, a decision support system is required. Within the decision support system, there are several methods, one of which can be used is the Simple Additive Weighting (SAW) method. In this study, the author will address a case to find the best alternative from certain established criteria by comparing the alternatives using the Simple Additive Weighting (SAW) method. In this evaluation, the alternative Saidi ranks first with a Vi value of 0.910, followed by Zum in second place with a Vi of 0.820. In third position, Arju has a Vi value of 0.761. Meanwhile, the alternative with the lowest rank is Sri with a Vi of 0.425, followed by Supri with a Vi of 0.433 and Paiman with a Vi of 0.461.
Comparison of WSM and Weight Product Methods with WSM-Score and Vector Approaches Roziyani Setik; Asyahri Hadi Nasyuha; Sri Redjeki
International Journal of Informatics and Data Science Vol. 2 No. 1 (2024): December 2024
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i1.63

Abstract

Fertilizer is a material that is given to the soil or plants to meet the nutritional needs of the plant. Fertilization needs to be carried out rationally according to the needs of the plant. In the supply of fertilizers, farmers have difficulty in determining the best fertilizer for their plants, making it difficult to choose which fertilizers are good for their plants. In determining the best fertilizer, the decision support system (DSS) can be used as an alternative to help someone make decisions more effectively and efficiently by utilizing certain data and models. To solve the existing problems, it is necessary to conduct research in decision making using the Weighted Sum Model (WSM) and Weight Product (WP) Methods which can produce decisions based on the best fertilizer criteria that will be purchased by customers. The Weighted Sum Model (WSM) method is one of the simplest and easiest methods to understand its application, this method is also part of the MCDM (Multi-Criteria Decision Making) method in evaluating the value of each alternative. The Weight Product (WP) method is a method using multiplication to relate the attribute rating, where the rating of each attribute must be ranked with the attribute weight in question. From the results of the implementation of this system, it can be concluded that using the Weighted Sum Model and Weight Product method can help customers in the decision-making process for choosing the best fertilizer to use on their plants.

Page 2 of 2 | Total Record : 15

 1   2 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 2 No. 2 (2025): June 2025 Vol. 2 No. 1 (2024): December 2024 Vol. 1 No. 2 (2024): June 2024 Vol. 1 No. 1 (2023): December 2023