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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 19 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.
Decision Support System for the Selection of Outstanding Students Using the MAUT Method Ridha Maya Faza Lubis; Mesran Mesran; Nopri Prayoga; Sindi Apri Nesya; Agus Perdana Windarto; Rizkah Fadillah; Setiawansyah Setiawansyah
International Journal of Informatics and Data Science Vol. 3 No. 1 (2025): December 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v3i1.542

Abstract

The selection of outstanding students is one of the efforts of universities in giving awards to students who have the best academic and non-academic achievements. The problem that often occurs in the selection process of outstanding students is that assessments still focus on one main criterion, namely the Cumulative Achievement Index (GPA), so that the results obtained are less objective and do not reflect the overall ability of students. Therefore, this study aims to design and implement a Decision Support System (DSS) in the selection of outstanding students by considering various assessment criteria. The method used in this study is Multi Attribute Utility Theory (MAUT), which is one of the multicriteria decision-making methods. The criteria used in the study included GPA, foreign language skills, organizational activeness, number of scientific papers, and history of taking remedial exams. The MAUT method is used to calculate the utility value of each alternative based on the weight and value of each criterion, so that student rankings are obtained objectively. The results of the study show that the implementation of DSS using the MAUT method can help the selection process of outstanding students in a more systematic and transparent manner. Based on the calculation results, the best alternative was obtained in alternative A5 on behalf of "Loli" students with a final utility value (Ui) of 2.2196, so that it was designated as an outstanding student. The resulting system is expected to be a supporting solution in making decisions about the selection of outstanding students in the university environment.
Comparative Evaluation of Cloud Service Providers for Enterprise IT Migration using CRITIC-CoCoSo and Entropy-TOPSIS Asyahri Hadi Nasyuha; Ananda Hadi Elyas; Muhammad khoiruddin Harahap
International Journal of Informatics and Data Science Vol. 3 No. 1 (2025): December 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v3i1.569

Abstract

The growing number of public cloud computing service providers offering overlapping but non-identical combinations of availability, cost, security, scalability, technical support, and deployment speed has turned cloud provider selection into a genuine multi-criteria decision-making (MCDM) problem for organizations planning enterprise IT infrastructure migration. This study proposes a comparative decision support model that integrates Criteria Importance Through Intercriteria Correlation (CRITIC) for objective criteria weighting with the Combined Compromise Solution (CoCoSo) method for alternative ranking, and validates the resulting recommendation against an independent Entropy–TOPSIS pipeline. Five major cloud providers, AWS, Microsoft Azure, Google Cloud, Oracle Cloud, and Alibaba Cloud, were evaluated against six criteria: availability, monthly cost, security features, scalability, technical support, and deployment time. The CRITIC method identified security features (weight 0.203) as the most influential criterion, while CoCoSo ranked Google Cloud first with a score of 2.874, followed by AWS and Microsoft Azure. The independent Entropy–TOPSIS validation produced an identical ranking, with Google Cloud obtaining the highest closeness coefficient (0.864). A Spearman rank-order correlation of p = 1.000 between the two independent method pairs confirms full ranking consistency, indicating that the recommendation is robust to the choice of weighting and ranking technique within this illustrative case; because the underlying decision matrix is illustrative rather than independently audited vendor data, this consistency demonstrates the robustness of the method rather than a certified procurement recommendation. The proposed CRITIC–CoCoSo model, cross-validated with Entropy–TOPSIS, offers a transparent and reproducible framework for evidence-based cloud service provider selection.
Beyond Vendor Hype: An Objective IDOCRIW-MARCOS Model for Selecting AI-Based Workforce Mental Health Platforms Asyahri Hadi Nasyuha; Ananda Hadi Elyas; Muhammad Khoiruddin Harahap
International Journal of Informatics and Data Science Vol. 3 No. 2 (2026): June 2026
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v3i2.570

Abstract

The growing adoption of artificial intelligence (AI) in workforce mental health monitoring has produced a wide range of competing platforms that differ in prediction accuracy, response time, scalability, implementation cost, and user satisfaction, so that HR and occupational-health decision-makers currently choose among them largely on the basis of vendor marketing claims rather than a structured, evidence-based comparison, making platform selection a genuine multi-criteria decision-making (MCDM) problem. This study proposes a decision support model that integrates the Integrated Determination of Objective Criteria Weights (IDOCRIW) method with the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method to give organizations an objective, reproducible alternative to subjective AHP/TOPSIS-style vendor evaluations for AI-based workforce mental health platforms. IDOCRIW combines Entropy and Criterion Impact Loss (CILOS) to derive objective criteria weights directly from vendor-reported technical specifications, removing the need for expert pairwise comparisons, while MARCOS ranks alternatives based on their utility degree relative to an ideal and an anti-ideal solution. The proposed model was demonstrated using an illustrative case study of five AI platform alternatives evaluated against five criteria: prediction accuracy, response time, scalability, implementation cost, and user satisfaction. The IDOCRIW results indicate that scalability (weight 0.260) is the most influential criterion, followed by response time (0.220). The MARCOS ranking identifies SmartWell as the top-ranked alternative with a final utility function value of 0.981, ahead of MentalCare AI (0.973) and WorkSense AI (0.969); a follow-up TOPSIS comparison computed on the same weighted data confirms SmartWell's top position but reverses the third- and fourth-ranked alternatives, showing that the ranking method itself, and not only the criteria weights, materially affects the recommendation. A sensitivity analysis, including combined-criterion perturbation scenarios, confirms that the ranking of the top alternative remains stable under moderate weight changes. For HR practitioners, the model offers a transparent and reproducible basis for AI-vendor procurement decisions that reduces reliance on unverified accuracy claims in vendor marketing materials.
Comparative Predictive Performance of Support Vector Regression and Random Forest in Forecasting CPO Spot Prices Imam Saputra; Sobihatun Nur Abdul Salam
International Journal of Informatics and Data Science Vol. 3 No. 2 (2026): June 2026
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v3i2.615

Abstract

Accurate short-term forecasting of Crude Palm Oil (CPO) spot prices is critical for managing financial risk and stabilizing supply chains in volatile agricultural commodity markets. However, high non-linearity, temporal volatility, and sensitivity to market shocks present significant modeling challenges for traditional econometric tools. To address these issues, this study aims to develop an optimized machine learning framework that evaluates the comparative predictive performance of kernel-based margin regression against decision-tree ensembles for daily CPO spot price forecasting. Utilizing historical Malaysian CPO spot price data (2015–2024), the proposed solution combines multi-temporal feature engineering (autoregressive lags and simple moving averages) with a leak-free TimeSeriesSplit cross-validation and systematic grid-search hyperparameter tuning. Out-of-sample evaluation demonstrates that the tuned Support Vector Regression (SVR) model achieves superior predictive accuracy, registering an RMSE of 24.2191 USD, an MAE of 15.8937 USD, and a MAPE of 1.6940%. The SVR architecture significantly outperforms the optimized Random Forest (RF) Regressor (RMSE = 35.2395 USD, MAE = 23.8164 USD, MAPE = 2.5151%), yielding a 31.27% error reduction in RMSE. Feature importance analysis establishes that the 1-day lag (lag_1) contributes 66.75% of total split impurity reduction. Furthermore, SVR maintains homoscedastic stability during extreme late-2024 price surges exceeding 1113.00 USD, where Random Forest exhibits extrapolation truncation. This study contributes a validated, operationally sound decision-support blueprint for physical commodity risk management and clarifies algorithm selection under structural market shocks.

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