Garuda - Garba Rujukan Digital

Ahmad Ridwan

Universitas AMIKOM Yogyakarta

Author-ID : 10523224

Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Other

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Analysis of Accuracy and Computational Efficiency of Android-Based Palm Maturity Classification System Using K-Nearest Neighbor Method Ahmad Ridwan
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 3 (2026): JCEIT: Journal of Computer Engineering and Information Technology (July 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i3.63

Accurately determining the ripeness of oil palm Fresh Fruit Bunches (FFB) is crucial to maximizing the quality of Crude Palm Oil (CPO). Conventional methods rely on visual assessment or laboratory tests that are destructive, expensive, and inefficient at the field scale. This study proposes an android-based, non-destructive FFB ripeness classification system that uses color feature extraction and the K-Nearest Neighbors (K-NN) algorithm. A total of 65 FFB images directly from the tree are divided into training data (50 images) and test data (15 images) with three ripeness classes: raw, ripe, and overripe. Features are extracted through multilevel color thresholding segmentation, then calculated using RGB color averages, RGB normalization, and four Vegetation Indices (NDVI, SAVI, EVI, VARI). The test results show that the combination of Vegetation Indices with K-NN achieves the highest accuracy, 98% on the training data and 93.33% on the test data, with only one classification error. The system runs on-device with an average computation time of 3.3 seconds per image, demonstrating sufficient efficiency for real-time applications in plantations. This study concludes that the mobile approach based on K-NN and the Vegetation Index is worthy of adoption as a fast, accurate, and non-destructive harvest decision-support tool. However, further lighting optimization and dataset expansion are still needed for broader generalization. REFERENCES Alfatni, M. S. M., Mohamed Shariff, A. R., Ben Saaed, O. M., Albhbah, A. M., & Mustapha, A. (2020). Colour Feature Extraction Techniques for Real Time System of Oil Palm Fresh Fruit Bunch Maturity Grading. IOP Conference Series: Earth and Environmental Science, 540(1). https://doi.org/10.1088/1755-1315/540/1/012092 Bannari, A., Asalhi, H., & Teillet, P. M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. IEEE International Geoscience and Remote Sensing Symposium, 5, 3053–3055 vol.5. https://doi.org/10.1109/IGARSS.2002.1026867 Barrera, K., Rodellar, J., Alférez, S., & Merino, A. (2023). Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks. Computer Methods and Programs in Biomedicine, 240. https://doi.org/10.1016/j.cmpb.2023.107629 Boucetta, C., Hussenet, L., & Herbin, M. (2023). Improved Euclidean Distance in the K Nearest Neighbors Method. In U. R. Krieger, G. Eichler, C. Erfurth, & G. Fahrnberger (Eds.), the 23rd International Conference on Innovations for Community Services (pp. 315–324). Springer Nature Switzerland. Burge, M. J. (2022). Digital Image Processing: An Algorithmic Introduction. Springer Nature. Cherie, D., Herodian, S., Ahmad, U., Mandang, T., & Makky, M. (2015). Optical characteristics of oil palm fresh fruits bunch (FFB) under three spectrum regions influence for harvest decision. International Journal on Advanced Science, Engineering and Information Technology, 5(3), 255–263. https://doi.org/10.18517/ijaseit.5.3.534 Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/https://doi.org/10.1016/0034-4257(88)90106-X Khan, A. I., & Al-Habsi, S. (2020). Machine Learning in Computer Vision. Procedia Computer Science, 167(2019), 1444–1451. https://doi.org/10.1016/j.procs.2020.03.355 Makky, M. (2016). Trend in non-destructive quality inspections for oil palm fresh fruits bunch in Indonesia. International Food Research Journal, 23(1), 81–90. https://doi.org/10.4149/neo_2010_01_079 Makky, M., Soni, P., & Salokhe, V. M. (2014). Automatic non-destructive quality inspection system for oil palm fruits. International Agrophysics, 28(3), 319–329. https://doi.org/10.2478/intag-2014-0022 Naji, S., Jalab, H. A., & Kareem, S. A. (2019). A survey on skin detection in colored images. Artificial Intelligence Review, 52(2), 1041–1087. https://doi.org/10.1007/s10462-018-9664-9 Pertanian RI, K. (2023). Statistik Perkebunan Kelapa Sawit Indonesia 2022. In Direktorat Jenderal Perkebunan. https://ditjenbun.pertanian.go.id Purbolingga, Y., Ridwan, A., & Putri, D. M. (2025). A Machine Learning-Based Ambiguous Alphabet Recognition for Indonesian Sign Language System (SIBI). CogITo Smart Journal, 11(1), 1–14. https://doi.org/10.31154/cogito.v11i1.816.1-14 Resta, F. S. A., Setiawan, R., Rivai, M., Arif, R. El, Natawijaya, A., & Hadad, A. G. Al. (2026). Multimodal Radar-Vision for Oil Palm Fresh Fruit Bunch Ripeness Classification. IEEE Access, 14, 42975–42991. https://doi.org/10.1109/ACCESS.2026.3675310 Ridwan, A., Purbolingga, Y., & Hanisah, H. (2024). Utilizing Convolutional Neural Network for Learning Web-Based Braille Letter Classification System. Journal of Computer Networks, Architecture and High Performance Computing, 6(1). https://doi.org/10.47709/cnahpc.v6i1.3386 Saad, B., Ling, C. W., Jab, M. S., Lim, B. P., Mohamad Ali, A. S., Wai, W. T., & Saleh, M. I. (2007). Determination of free fatty acids in palm oil samples using non-aqueous flow injection titrimetric method. Food Chemistry, 102(4), 1407–1414. https://doi.org/https://doi.org/10.1016/j.foodchem.2006.05.051 Srivastava, S., & Sadistap, S. (2018). Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review. Journal of Food Measurement and Characterization, 12(4), 2758–2794. https://doi.org/10.1007/s11694-018-9893-2 Suharjito, Elwirehardja, G. N., & Prayoga, J. S. (2021). Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches. Computers and Electronics in Agriculture, 188, 106359. https://doi.org/https://doi.org/10.1016/j.compag.2021.106359 Wang, A. X., Chukova, S. S., & Nguyen, B. P. (2023). Ensemble k-nearest neighbors based on centroid displacement. Information Sciences, 629, 313–323. https://doi.org/https://doi.org/10.1016/j.ins.2023.02.004 Yan, K., Gao, S., Yan, G., Ma, X., Chen, X., Zhu, P., Li, J., Gao, S., Gastellu-Etchegorry, J. P., Myneni, R. B., & Wang, Q. (2025). A global systematic review of the remote sensing vegetation indices. International Journal of Applied Earth Observation and Geoinformation, 139(November 2024), 104560. https://doi.org/10.1016/j.jag.2025.104560

Early Detection System for Heartbeat Abnormalities in Autistic Children Using Support Vector Machine Ahmad Ridwan
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 1 (2025): JCEIT: Journal of Computer Engineering and Information Technology (Nov 2025)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i1.45

This research aims to develop an early detection system for heart rate anomalies in autistic children based on Heart Rate Variability (HRV) to prevent tantrum behavior that can endanger the child's physical and psychological health. Based on previous research, children with autism spectrum disorder (ASD) show a significant increase in heart rate (HR), especially when experiencing stress or anxiety, with some cases reaching above 120 bpm. At the same time, control groups such as children with language disorders do not show a similar pattern. This leads to the hypothesis that physiological monitoring using non-invasive technologies, such as Photoplethysmography (PPG), can detect changes in HR before a tantrum occurs. The purpose of this study is to design a wearable device based on a pulse sensor and NodeMCU that can integrate HR in real-time, extract HRV features in the frequency domain (VLF, LF, HF, and LF/HF ratio), and classify normal and anomalous conditions using the Support Vector Machine (SVM) algorithm. The system is designed to notify parents or caregivers via a Telegram bot when HR exceeds 114 bpm. The research methodology was experimental, conducted on two subjects: a 7-year-old boy and a girl on the autism spectrum during learning, quiet, and tantrum activities. Results showed that HRV parameters increased significantly during the tantrum condition and even during learning, indicating activation of the sympathetic nervous system. The SVM classifier achieved 98.9% accuracy in the tantrum condition, 82% in the learning condition, but only 61.1% in the transition from quiet to tantrum. Overall, the system proved effective at detecting hyperactivity but still requires further development regarding data volume, subject variation, and improvements in accuracy during the transition phase for widespread implementation. REFERENCES Aldabas, R. (2019). Effectiveness of social stories for children with autism: A comprehensive review. Technology and Disability, 31(1–2), 1–13. https://doi.org/10.3233/TAD-180218 Awanda Amelia Sadita, & Nurus Sa’adah. (2023). Temper Tantrum Behavior in Early Childhood as Communication with Parents. Journal of Insan Mulia Education, 1(2), 45–52. https://doi.org/10.59923/joinme.v1i2.7 Beauchamp-Châtel, A., Courchesne, V., Forgeot d’Arc, B., & Mottron, L. (2019). Are tantrums in autism distinct from those of other childhood conditions? A comparative prevalence and naturalistic study. Research in Autism Spectrum Disorders, 62(March), 66–74. https://doi.org/10.1016/j.rasd.2019.03.003 Chen, C., Li, C., Tsai, C. W., & Deng, X. (2019). Evaluation of Mental Stress and Heart Rate Variability Derived from Wrist-Based Photoplethysmography. Proceedings of 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2019, 65–68. https://doi.org/10.1109/ECBIOS.2019.8807835 Deichmann, F., & Ahnert, L. (2021). The terrible twos: How children cope with frustration and tantrums and the effect of maternal and paternal behaviors. Infancy, 26(3), 469–493. https://doi.org/10.1111/infa.12389 Farahdina, Irwanto, & Fithriyah, I. (2025). Risk factors for autism spectrum disorder diagnosed in Indonesia. Child`S Health, 20(5), 325–332. https://doi.org/10.22141/2224-0551.20.5.2025.1866 Fioriello, F., Maugeri, A., D’Alvia, L., Pittella, E., Piuzzi, E., Rizzuto, E., Del Prete, Z., Manti, F., & Sogos, C. (2020). A wearable heart rate measurement device for children with autism spectrum disorder. Scientific Reports, 10(1), 1–7. https://doi.org/10.1038/s41598-020-75768-1 Islmabouli, R., Brunner, M., Kumar, D., Sareban, M., & ... (2025). Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices. ArXiv Preprint ArXiv https://arxiv.org/abs/2508.19818%0Ahttps://arxiv.org/pdf/2508.19818 McCorry, L. K. (2007). Physiology of the Autonomic Nervous System. American Journal of Pharmaceutical Education, 71(4), 1–11. https://doi.org/10.1111/j.1399-6576.1964.tb00252.x Novani, N. P., Arief, L., & Anjasmara, R. (2019). Analisa Detak Jantung dengan Metode Heart Rate Variability (HRV) untuk Pengenalan Stres Mental Berbasis Photoplethysmograph (PPG). JITCE (Journal of Information Technology and Computer Engineering), 3(02), 90–95. https://doi.org/10.25077/jitce.3.02.90-95.2019 Novani, N. P., Arief, L., Anjasmara, R., & Prihatmanto, A. S. (2018). Heart Rate Variability Frequency Domain for Detection of Mental Stress Using Support Vector Machine. 2018 International Conference on Information Technology Systems and Innovation, ICITSI 2018 - Proceedings, 520–525. https://doi.org/10.1109/ICITSI.2018.8695938 Pinge, A., Bandyopadhyay, S., Ghosh, S., & Sen, S. (2022). A Comparative Study between ECG-based and PPG-based Heart Rate Monitors for Stress Detection. 2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, 84–89. https://doi.org/10.1109/COMSNETS53615.2022.9668342 Thapa, R., Pokorski, I., Ambarchi, Z., Thomas, E., Demayo, M., Boulton, K., Matthews, S., Patel, S., Sedeli, I., Hickie, I. B., & Guastella, A. J. (2021). Heart Rate Variability in Children With Autism Spectrum Disorder and Associations With Medication and Symptom Severity. Autism Research, 14(1), 75–85. https://doi.org/10.1002/aur.2437 Tsai, Y. Y., Chen, Y. J., Lin, Y. F., Hsiao, F. C., Hsu, C. H., & Liao, L. De. (2025). Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications. APL Bioengineering, 9(2). https://doi.org/10.1063/5.0256590 Weiler, D. T., Villajuan, S. O., Edkins, L., Cleary, S., & Saleem, J. J. (2017). Wearable Heart Rate Monitor Technology Accuracy in Research: A Comparative Study Between PPG and ECG Technology. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1292–1296. https://doi.org/10.1177/1541931213601804 Zhang, Y., Song, S., Vullings, R., Biswas, D., Simões-Capela, N., Van Helleputte, N., Van Hoof, C., & Groenendaal, W. (2019). Motion artifact reduction for wrist-worn photoplethysmograph sensors based on different wavelengths. Sensors (Switzerland), 19(3). https://doi.org/10.3390/s19030673

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