Prioritizing Educational Media for the Golden Age
A PROMETHEE-Based Analysis of Multiple Intelligences
DOI:
https://doi.org/10.55537/jistr.v4i02.1126Keywords:
PROMETHEE, Golden Age, Educational Media, Multiple IntelligencesAbstract
The golden age is a critical period in a child’s development, spanning from ages 0 to 8, during which learning ability, sensory functions, and emotional potential grow rapidly. At this stage, selecting appropriate educational media is crucial to optimally support the child’s multiple intelligences. In the golden age, the development of a child's memory during this period was excellent. Those at this age have the ability and enthusiasm to learn and the nature of high curiosity. This is one of the reasons for the need to optimize attention during that time. The selection of diverse educational media can determine the success factor in Conducting an analysis of the child's abilities. In this study, the research team will show how to make decisions in the selection of children's educational media by analyzing cognitive, sensory, and emotional potential using the Promethee method. This study aims to determine the most effective educational media for developing cognitive, sensory, emotional, and potential aspects of early childhood using the PROMETHEE method (Preference Ranking Organization Method for Enrichment Evaluation). PROMETHEE is a multi-criteria decision-making (MCDM) approach that helps prioritize alternatives based on predefined evaluation criteria. PROMETHEE in the last 5 years has been rarely used in the selection of educational media for early childhood in analyzing cognitive, sensory, and emotional potential. One of the methods that is often used in the selection of educational media is the ICT method. The research was conducted qualitatively in several regions of North Sumatra, involving parents of young children as key informants. The educational media analyzed included storybooks, puzzles, building blocks, and physical e-books. The response from early childhood to the sample of educational media provided is very diverse. This is based on the criteria tested on the sample. It was found that on average each sample given gave a good response to the child. Each sample given affects the testing criteria, be it cognitive, sensory, potential analysis, or children's emotions. The findings reveal that each media type influences child development differently, with physical e-books receiving the highest preference rankings. These results provide valuable insights for parents and educators in selecting educational tools that best support the optimal development of children's multiple intelligences.
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[1] G. D. Giebel et al., “Problems and Barriers Related to the Use of AI-Based Clinical Decision Support Systems: Interview Study,” J. Med. Internet Res., vol. 27, p. e63377, Feb. 2025, doi: 10.2196/63377.
[2] R. Wang, G. Nan, G. Kou, and M. Li, “Separation or integration: The game between retailers with online and offline channels,” Eur. J. Oper. Res., vol. 307, no. 3, pp. 1348–1359, Jun. 2023, doi: 10.1016/j.ejor.2022.09.037.
[3] Z. Yutian and W. Lu, “Application of Data Mining in Human Resource Management in Colleges and Universities,” in 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022, 2022. doi: 10.1109/ICMNWC56175.2022.10031950.
[4] S. Sandhya, A. Balasundaram, and A. Sivaraman, “Deep Learning and Computer Vision based Model for Detection of Diseased Mango Leaves,” Int. J. Recent Innov. Trends Comput. Commun., vol. 10, no. 6, 2022, doi: 10.17762/ijritcc.v10i6.5555.
[5] L. Mohan, J. Pant, P. Suyal, and A. Kumar, “Support Vector Machine Accuracy Improvement with Classification,” in Proceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020, 2020. doi: 10.1109/CICN49253.2020.9242572.
[6] M. Madanan, N. A. M. Zulkefli, and N. C. Velayudhan, “Designing a Hybrid Artificial Intelligent Clinical Decision Support System Using Artificial Neural Network and Artificial Bee Colony for Predicting Heart Failure Rate,” in 2021 International Conference on Computer Communication and Informatics, ICCCI 2021, 2021. doi: 10.1109/ICCCI50826.2021.9457007.
[7] R. Wu, B. Wang, and Z. Zhao, “Privacy-preserving medical diagnosis system with Gaussian kernel-based support vector machine,” Peer-to-Peer Netw. Appl., 2024, doi: 10.1007/s12083-024-01743-6.
[8] A. Ikhwan and N. Aslami, “Decision Support System Using Simple Multi-Attribute Rating Technique Method in Determining Eligibility of Assistance,” vol. 3, no. 4, pp. 604–609, 2022, doi: 10.47065/bits.v3i4.1370.
[9] R. Sa’diyah, “Urgensi Kecerdasan Emosional Bagi Anak Usia Dini,” Cakrawala Dini J. Pendidik. Anak Usia Dini, vol. 4, no. 1, pp. 1–19, 2018, doi: 10.17509/cd.v4i1.10375.
[10] H. Alhakami, “Enhancing IoT Security: Quantum-Level Resilience against Threats,” Comput. Mater. Contin., vol. 78, no. 1, pp. 329 – 356, 2024, doi: 10.32604/cmc.2023.043439.
[11] M. Kannan, M. Kumar, S. Saini, and V. Sharma, “AHP-WASPAS Approach for Choice of Non-Conventional Manufacturing Process,” in 2022 International Conference on 4th Industrial Revolution Based Technology and Practices, ICFIRTP 2022, 2022. doi: 10.1109/ICFIRTP56122.2022.10059423.
[12] I. W. Damaj, H. Al-Mubasher, and M. Saadeh, “An extended analytical framework for heterogeneous implementations of light cryptographic algorithms,” Futur. Gener. Comput. Syst., vol. 141, pp. 154 – 172, 2023, doi: 10.1016/j.future.2022.11.007.
[13] Masna Wati, R. H. K. Simbolon, J. A. Widians, and Novianti Puspitasari, “Sistem Pendukung Keputusan untuk Evaluasi Tingkat Kesejahteraan Masyarakat Menggunakan Metode PROMETHEE,” Digit. Zo. J. Teknol. Inf. dan Komun., vol. 12, no. 2, pp. 184–194, 2021, doi: 10.31849/digitalzone.v12i2.8115.
[14] G. P. R. Agusli, L. F. Gustomi, “Sistem Penunjang Keputusan Dalam Pemilihan Siswa Berprestasi Menggunakan Metode Promethee,” J. SISFOTEK Glob, vol. 9, no.
[15] J. Leng, M. Mo, Y. Zhou, Y. Ye, C. Gao, and X. Gao, “Recent advances in drone-view object detection,” J. Image Graph., vol. 28, no. 9, 2023, doi: 10.11834/jig.220836.
[16] B. Azam et al., “Aircraft detection in satellite imagery using deep learning-based object detectors,” Microprocess. Microsyst., vol. 94, 2022, doi: 10.1016/j.micpro.2022.104630.
[17] M. Jonsson, C. Gustavsson, J. Gulliksen, and S. Johansson, “How have public healthcare providers in Sweden conformed to the European Union’s Web Accessibility Directive regarding accessibility statements on their websites?,” Univers. Access Inf. Soc., 2023, doi: 10.1007/s10209-023-01063-1.
[18] K. E. Hemapriya and S. Saraswathi, “Laplacian Kernel Clustering-Based Improved Certificateless Signcryption for a Secure Marine Data Aggregation in Network of Wireless Sensors,” Int. J. Comput. Networks Appl., vol. 11, no. 2, pp. 177 – 190, 2024, doi: 10.22247/ijcna/2024/224445.
[19] J. Chen, Q. Luo, Y. Wei, X. Zhang, and X. Du, “The Sustained Oscillation Modeling and Its Quantitative Suppression Methodology for GaN Devices,” IEEE Trans. Power Electron., vol. 36, no. 7, 2021, doi: 10.1109/TPEL.2020.3043472.
[20] L. Hong, Y.-J. Zhai, J.-X. Wang, J. Zheng, and W. Hu, “Quantitative Assessment of Power Side-Channel Leakage Based on MMD; [基于最大均值差异的能量侧信道泄露量化评估],” Jisuanji Xuebao/Chinese J. Comput., vol. 47, no. 6, pp. 1355 – 1371, 2024, doi: 10.11897/SP.J.1016.2024.01355.
[21] L. Yang, “Research on quantitative evaluation method of teachers based on multiple linear regression,” in Proceedings - 2021 13th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2021, 2021. doi: 10.1109/ICMTMA52658.2021.00196.
[22] R. Romindo and S. Hardianti, “Penerapan Metode SMART ( Simple Multi-Attribute Rating Technique ) Dalam Sistem Pendukung Keputusan Pemberian Kredit Usaha Rakyat Pada Bank Sumut ( Studi Kasus : KCP Pasar Martubung ),” J. Comput. Networks, Archit. High Perform. Comput., vol. 1, no. 2, pp. 1–9, 2019, [Online]. Available: https://iocscience.org/ejournal/index.php/CNAPC/article/view/63/50
[23] D. Anggara, “Decision Support System SAW Method Exporter Foreign Trade Section,” J. Inf. Syst. Technol. Res., vol. 1, no. 1, pp. 23–31, Jan. 2022, doi: 10.55537/jistr.v1i1.91.
[24] S. Oei, “Group Decision Support System for Business Place Establishment using Fuzzy SAW Borda,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 5, pp. 964–969, Oct. 2020, doi: 10.29207/resti.v4i5.2459.
[25] S. Santoso, P. Harsani, E. P. Harahap, P. A. Sunarya, and Y. P. Ayu Sanjaya, “Enrichment Program using PROMETHEE for Decision Support Systems of Prospective Assistance Funds Disabilities,” in 2022 1st International Conference on Technology Innovation and Its Applications (ICTIIA), IEEE, Sep. 2022, pp. 1–5. doi: 10.1109/ICTIIA54654.2022.9935971.
[26] E. Guler and S. Y. Kandemir, “Evaluation of Wind Power Plant Potentials in the Marmara Region, Turkey via TOPSIS and PROMETHEE Methods,” in 7th Iran Wind Energy Conference (IWEC2021), IEEE, May 2021, pp. 1–4. doi: 10.1109/IWEC52400.2021.9467022.
[27] Q. Xie and M. Zhang, “Technical and Economic Evaluation of Pure Car Truck Carrier Based on CRITIC-G2-PROMETHEE,” in 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII), IEEE, Jun. 2024, pp. 652–657. doi: 10.1109/ICMIII62623.2024.00127.
[28] N. Usanase, B. Uzun, L. R. David, D. U. Ozsahin, and I. Ozsahin, “Application of Fuzzy PROMETHEE in the Analysis of Childbirth Techniques,” in 2024 17th International Conference on Development in eSystem Engineering (DeSE), IEEE, Nov. 2024, pp. 364–369. doi: 10.1109/DeSE63988.2024.10911929.
[29] E. ÇAKIR and E. DEMÍRCÍOĞLU, “Multi-Criteria Evaluation of Battery Electric Vehicles Via Circular Intuitionistic Fuzzy PROMETHEE,” in 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, May 2024, pp. 000151–000156. doi: 10.1109/SACI60582.2024.10619787.
[30] Z. V. Caloglu, M. Zontul, I. Yemen, and S. Bagriyanik, “Software Quality Measurement Modelling Using AHP and Promethee Methods,” in 2021 6th International Conference on Computer Science and Engineering (UBMK), IEEE, Sep. 2021, pp. 608–612. doi: 10.1109/UBMK52708.2021.9558959.
[31] H. Mahgfuri, R. D. Perdanakusuma, F. Basfianto, A. A. Sukmandhani, and I. P. Saputro, “A Recommendation System for Buying a used Car using the Promethee Method,” in 2022 International Conference on Sustainable Islamic Business and Finance (SIBF), IEEE, Oct. 2022, pp. 209–213. doi: 10.1109/SIBF56821.2022.9939732.
[32] L. Nur, A. Hafina, and N. Rusmana, “Kemampuan Kognitif Anak Usia Dini Dalam Pembelajaran Akuatik,” Sch. J. Pendidik. dan Kebud., vol. 10, no. 1, pp. 42–50, 2020, doi: 10.24246/j.js.2020.v10.i1.p42-50.
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Copyright (c) 2025 Rima Aprilia, Rina Filia Sari, Dedy Juliandri Panjaitan, Heba A. Fayed, Wilia Husna
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