Supervised Learning from Data Mining on Process Data Loggers on Micro-Controllers


  • Adi Dwifana Saputra Saputra Universitas Pradita, Serpong, Banten
  • Djarot Hindarto Universitas Pradita, Serpong, Banten
  • Haryono Universitas Pradita, Serpong, Banten




Arduino, Data Logger, Data Science, Distance Sensor, Micro-Controller Device, Potentiometer


In processing data science, data is needed as input. Sometimes the data needed does not exist in public data, this is where the purpose of this research is made. The acquisition process is so important to process information into data. After that, the data is processed to make a decision. Micro-controller in controlling conditions, such as temperature, and humidity are very common devices, and a lot of research has been done. Sometimes discussing it only shows how to create a series and save it on online platforms, such as firebase,, and many others online platforms. So that the process of storing data on an external or online platform is an advantage for platform providers, where platform providers do not need to do business and get data for free. This is without realizing the researchers who have produced a micro-controller device. Many platforms for storing data range from hardware and software devices. Some devices are paid or open source. This research uses software tools that are open source. Because using open source-based tools it will be easy to develop and for further research purposes. The development of the following research by entering code into a micro-controller system or what is called an embedded system. Data is a very valuable asset. Because data is one of the most important components in processing in data science. And it is better to take care of the data logger. This research uses Arduino as a micro-controller and ultrasonic distance sensor and potentiometer

GS Cited Analysis


Download data is not yet available.



Ann, T. S. (2008). Espressif Micro Controller.

Gancliev, I., Taueva, A., Kutryanski, K., & Petrov, M. (2019). Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems. IFAC-PapersOnLine, 52(25), 299–304.

Hindarto, D. (2022). Perbandingan Kinerja Akurasi Klasifikasi K-NN, NB dan DT pada APK Android. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(1), 486–503.

Hindarto, D., & Handri Santoso. (2021). Android APK Identification using Non Neural Network and Neural Network Classifier. Journal of Computer Science and Informatics Engineering (J-Cosine), 5(2), 149–157.

Kholis, N., Yuliza, E., & Ekawita, R. (2022). UJI VARIASI PANJANG BLADE TURBIN ANGIN SUMBU HORIZONTAL. 7(2), 1–6.

Lawson, A. R., Giri, K., Thomson, A. L., Karunaratne, S. B., Smith, K. F., Jacobs, J. L., & Morse-McNabb, E. M. (2022). Multi-site calibration and validation of a wide-angle ultrasonic sensor and precise GPS to estimate pasture mass at the paddock scale. Computers and Electronics in Agriculture, 195(January), 106786.

Mabrouki, J., Azrour, M., Dhiba, D., Farhaoui, Y., & Hajjaji, S. El. (2021). IoT-based data logger for weather monitoring using arduino-based wireless sensor networks with remote graphical application and alerts. Big Data Mining and Analytics, 4(1), 25–32.

Mahendra Sanjaya, I. P. G., Indra Partha, C. G., & Khrisne, D. C. (2018). Rancang Bangun Sistem Data Logger Berbasis Visual Pada Solar Cell. Majalah Ilmiah Teknologi Elektro, 16(3), 114.

Mahzan, N. N., Omar, A. M., Rimon, L., Noor, S. Z. M., & Rosselan, M. Z. (2017). Design and development of an arduino based data logger for photovoltaic monitoring system. International Journal of Simulation: Systems, Science and Technology, 17(41), 15.1-15.5.

PUDIN, A., & MARDIYANTO, I. R. (2020). Desain dan Implementasi Data Logger untuk Pengukuran Daya Keluaran Panel Surya dan Iradiasi Matahari. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 8(2), 240.

Sarailidis, G., Wagener, T., & Pianosi, F. (2023). Integrating scientific knowledge into machine learning using interactive decision trees. Computers and Geosciences, 170(October 2022), 105248.

Song, X., Liu, X., Liu, F., & Wang, C. (2021). Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis. International Journal of Medical Informatics, 151, 104484.

Sze, E., Hindarto, D., & Wirayasa, I. K. A. (2022). Performance Comparison of Ultrasonic Sensor Accuracy in Measuring Distance. 7(4), 2556–2562.

van Eeden, W. A., Luo, C., van Hemert, A. M., Carlier, I. V. E., Penninx, B. W., Wardenaar, K. J., Hoos, H., & Giltay, E. J. (2021). Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression. Psychiatry Research, 299(October 2020), 113823.

Yandi, W. (2020). Prototipe Data Logging Monitoring System Untuk Konversi Energi Panel Surya Polycrystalline 100 Wp Berbasis Arduino Uno. Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering), 7(1), 55–60.

Yang, R., Liu, Y., Yu, Y., He, X., & Li, H. (2021). Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine. Energy Reports, 7, 5446–5454.


Crossmark Updates

How to Cite

Saputra, A. D. S., Hindarto, D. ., & Haryono, H. (2023). Supervised Learning from Data Mining on Process Data Loggers on Micro-Controllers . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 157-165.

Most read articles by the same author(s)

1 2 > >>