Pengembangan Auto-AI Model Generatif Analisis Kompleksitas Waktu Algoritma Untuk Data Multi-Sensor IoT Pada Node-RED Menggunakan Extreme Learning Machine
DOI:
https://doi.org/10.25126/jtiik.2022976738Abstrak
Awal mulanya, algoritma hanya dipakai untuk solusi penyelesaian persamaan matematika sederhana, seperti aljabar, aritmatika, probabilitas, dan lainnya yang lebih banyak dikerjakan secara manual dan membutuhkan waktu dan upaya yang cukup tinggi seperti pada kasus penghitungan nilai kompleksitas waktu algoritma dengan model rumus T(n), baik untuk algoritma non-rekursif maupun rekursif. Namun dengan perkembangan teknologi komputer untuk AI, Machine Learning maupun Deep Learning, algoritma dengan basis AI tersebut, dalam penelitian ini dikembangkan untuk menemukan solusi general persamaan model T(n) secara otomatis dari desain algoritma sederhana atau kompleks. Langkah dalam penelitian digunakan pembuatan model generatif berbasis algoritma Extreme Learning Machine (ELM) berdasarkan pencatatan nilai waktu komputasi pada beberapa kali pengujian untuk mengotomasi penentuan model persamaan kompleksitas waktu algoritma secara general baik untuk pencarian best case, worst case maupun average case untuk non-rekursif, dan base case dan recurrent case untuk rekursif, maupun keduanya. Hasil komparasi nilai T(n) dari ELM, yang tercepat atau terkecil waktu komputasinya digunakan sebagai rekomendasi algoritma untuk pengolahan data multi-sensor pada Internet of Things (IoT) simulator maupun non-simulator menggunakan Node-RED dengan tambahan platform yaitu flespi dan Heroku, sebagai solusi general untuk semua jenis kasus dan analisis algoritmanya. Berdasarkan pengujian didapatkan selisih nilai antara data aktual dengan hasil prediksi dalam ukuran nilai rata-rata MAPE sebesar 11,90%, yang menunjukkan nilai kesalahan yang cukup kecil.
Abstract
Initially, algorithms were only used for solving simple mathematical equations such as algebra, arithmetic, probability, and others that were mostly carried out manually and required quite a lot of time and effort as in the case of calculating the value of the time complexity of the algorithm with the formula model of T(n), both for non-recursive and recursive algorithms. However, with the development of computer technology for AI, both Machine Learning and Deep Learning, the AI-based algorithms in this study were developed to identify general solutions to the T(n) model equation automatically from simple or complex algorithm designs. The steps in the study are utilized to create a generative model based on the Extreme Learning Machine (ELM) algorithm according to the recording of computational time values on several tests to automate the determination of time complexity equation model of the algorithm in general including the search of best cases, worst cases, and average cases for non-recursive, and base cases and recurrent cases for recursive, as well as algorithms that contain both. The results of the comparison of T(n) values from ELM revealed that the fastest or smallest computational time is used as the algorithm recommendations for multi-sensor data processing in the Internet of Things (IoT) simulators and non-simulators by utilizing Node-RED with additional platforms i.e., flespi and Heroku, as a general solution for the entire types of cases and analysis of their algorithms. Based on the tests that have been carried out, the difference in value between the actual data and the prediction results in the size of the MAPE average value of 11.90%, which shows a fairly small error value.
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CHOLISSODIN, I. & RIYANDANI, E., 2016. Review: A State-of-the-Art of Time Complexity (Non-Recursive and Recursive Fibonacci Algorithm). Journal of Information Technology and Computer Science, 1(1), pp.14–27. https://doi.org/10.25126/jitecs.2016112.
CHOLISSODIN, I., SUTRISNO, S., SOEBROTO, A.A., Hasanah, U. and Febiola, Y.I., 2019. AI, Machine Learning & Deep Learning. Filkom UB.
CROSSLEY, J.N. & HENRY, A.S., 1990. Thus spake al-Khwārizmī: A translation of the text of Cambridge University Library Ms. Ii.vi.5. Historia Mathematica, 17(2), pp.103–131. https://doi.org/10.1016/0315-0860(90)90048-I.
IWAMA, K. & TERUYAMA, J., 2020. Improved average complexity for comparison-based sorting. Theoretical Computer Science, 807, pp.201–219. https://doi.org/10.1016/j.tcs.2019.06.032.
LATHA, C.B.C. & JEEVA, S.C., 2019. Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, 16, p.100203. https://doi.org/10.1016/j.imu.2019.100203.
LEVITIN, A., 2012. Introduction to the Design and Analysis of Algorithms. 3rd ed. [online] Available at: <https://www.booksfree.org/introduction-to-the-design-and-analysis-of-algorithms-by-anany-levitin-3rd-edition-pdf/> [Accessed 7 August 2022].
LV, Z., WANG, N., MA, X., SUN, Y., MENG, Y. AND TIAN, Y., 2022. Evaluation Standards of Intelligent Technology based on Financial Alternative Data. Journal of Innovation & Knowledge, 7(4), p.100229. https://doi.org/10.1016/j.jik.2022.100229.
MCCONNELL, J.J., 2001. Analysis of algorithms: an active learning approach. Boston: Jones and Bartlett Publishers.
OLSHER, D.J., 2015. New Artificial Intelligence Tools for Deep Conflict Resolution and Humanitarian Response. Procedia Engineering, 107, pp.282–292. https://doi.org/10.1016/j.proeng.2015.06.083.
RUTANEN, K., 2018. Minimal characterization of O-notation in algorithm analysis. Theoretical Computer Science, 713, pp.31–41. https://doi.org/10.1016/j.tcs.2017.12.026.
WIĘCKOWSKI, J. & SHEKHOVTSOV, A., 2021. Algorithms Effectiveness comparison in solving Nonogram boards. Procedia Computer Science, 192, pp.1885–1893. https://doi.org/10.1016/j.procs.2021.08.194.
AGARWAL, R., SINGH, J., & GUPTA, V. 2022. Prediction of temperature elevation in rotary ultrasonic bone drilling using machine learning models: An in-vitro experimental study. Medical Engineering & Physics, 103869. https://doi.org/10.1016/j.medengphy.2022.103869
ALORAINI, F., JAVED, A., RANA, O., & BURNAP, P. 2022. Adversarial machine learning in IoT from an insider point of view. Journal of Information Security and Applications, 70, 103341. https://doi.org/10.1016/j.jisa.2022.103341
ARUMUGARAJA, M., PADMAPRIYA, B., & POORNACHANDRA, S. 2022. Design and development of foot worn piezoresistive sensor for knee pain analysis with supervised machine learning algorithms based on gait pattern. Measurement, 200, 111603. https://doi.org/10.1016/j.measurement.2022.111603
BANDEKAR, S. R., & VIJAYALAKSHMI, C. (2020). Design and Analysis of Machine Learning Algorithms for the reduction of crime rates in India. Procedia Computer Science, 172, 122–127. https://doi.org/10.1016/j.procs.2020.05.018
CASSOLI, B. B., ZIEGENBEIN, A., METTERNICH, J., ĐUKANOVIĆ, S., HACHENBERGER, J., & LAABS, M. 2021. Machine Learning use case in manufacturing – an evaluation of the model’s reliability from an IT security perspective. Procedia CIRP, 104, 1161–1166. https://doi.org/10.1016/j.procir.2021.11.195
JIN, S., LIU, N., & YU, Y. 2022. Time complexity analysis of quantum difference methods for linear high dimensional and multiscale partial differential equations. Journal of Computational Physics, 471, 111641. https://doi.org/10.1016/j.jcp.2022.111641
LINGITZ, L., GALLINA, V., ANSARI, F., GYULAI, D., PFEIFFER, A., SIHN, W., & MONOSTORI, L. 2018. Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer. Procedia CIRP, 72, 1051–1056. https://doi.org/10.1016/j.procir.2018.03.148
SEMERARO, F., GRIFFITHS, A., & CANGELOSI, A. 2023. Human–robot collaboration and machine learning: A systematic review of recent research. Robotics and Computer-Integrated Manufacturing, 79, 102432. https://doi.org/10.1016/j.rcim.2022.102432
ZHENG, T., & ZHAO, L.-P. 2022. Dynamic optimization analyses and algorithm design for the parallel heat exchange system in spacecraft. Applied Thermal Engineering, 212, 118519. https://doi.org/10.1016/j.applthermaleng.2022.118519
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