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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

Deep Learning-Based Multi-Tooth Segmentation on Panoramic Radiographs Using YOLOv8 Architecture Ahmad Ridwan; Pramawahyudi; Desy Purnama Sari; Hanisah; Yogendra Rao Musunuri
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Articles Research Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7710

This research introduces a multi-class tooth-level segmentation framework on panoramic radiographs using YOLOv8, trained on clinically annotated Indonesian dental data. A dataset of 302 annotated panoramic radiographs from patients at Universitas Andalas Dental Hospital was utilized, with each tooth precisely labeled according to international dental nomenclature. The model was trained using transfer learning with the YOLOv8 variant, optimized with the Adam algorithm, and evaluated using precision, recall, F1-score, and Intersection over Union (IoU). The results demonstrate that YOLOv8 is not only effective for lesion detection but also robust for fine-grained anatomical dental segmentation. The performance achieved 93.72% accuracy, 92.67% precision, 98.88% recall, and 95.58% F1-score, indicating high accuracy in tooth detection and boundary delineation. Qualitative analysis confirmed accurate segmentation across a wide range of anatomical variations, including crowding, impaction, and prosthetics. This research establishes YOLOv8 as a highly effective tool for dental image segmentation, offering significant potential to improve diagnostic efficiency, support odontological forensics, and enable automated patient record management. Future work will focus on integrating multi-class pathology detection and 3D reconstruction.

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

Pelatihan dan Pendampingan Literasi Etika Digital Berbasis Nilai Religius bagi Guru di SMP Muhammadiyah Al Mujahidin Junaidi Junaidi; Nurbayti Nurbayti; Zahrotus Sa'idah; Ahmad Ridwan
Jurnal Riset Pendidikan Multidisiplin dan Pengabdian Kepada Masyarakat Vol. 2 No. 1 (2026): Juni-Juli 2026
Publisher : SMA Negeri 1 Bangkinang Kota

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrppm.v2i1.261

In the era of digital disruption, teachers are required not only to have pedagogical competence but also to demonstrate social piety in their cyber behavior. However, phenomena show that the educational environment is still vulnerable to cyberbullying practices and digital ethical violations, both those that place educators as victims or unintentionally as perpetrators. Based on an initial survey at Muhammadiyah Al Mujahidin Middle School, 45% of teachers had been involved in toxic debates in WhatsApp groups, 60% did not understand the limits of the ITE Law, and 30% experienced mental anxiety due to the pressure of social media interactions. This condition indicates a disconnection between religious identity in the real world and digital identity. This service aims to transform teachers' social piety through digital ethical literacy based on religious values. The implementation method uses a Participatory Technology Development approach through four stages: (1) Socialization of "Digital Taqwa", (2) Ethics Literacy and Anti-Cyberbullying Workshop, (3) Implementation of Bully Alert, and (4) Clinical mentoring and evaluation. The results of the community service demonstrated a significant increase in participants' understanding of digital ethical literacy, from 35% to 82%, based on a post-test. Furthermore, the formation of internal "Digital Piety Ambassadors" and the drafting of a School Digital Ethics Standard Operating Procedure (SOP) are sustainable outcomes of the program. In conclusion, the integration of religious values into digital literacy education effectively reduces toxic cyber behavior and strengthens teachers' character as role models in the digital space.

Optimizing FTP Server Performance Using the Locality-Based Least Connection (LBLC) Algorithm in a Scheduling Algorithm Balancing System Ahmad Ridwan; Pramawahyudi Pramawahyudi; Enda Putri Atika; Budi Bayu Murti; Muzakki Ahmad
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1787

A simultaneous increase in internet user traffic often causes servers to become overloaded, leading to disruptions, particularly on File Transfer Protocol (FTP) servers. A load-balancing system using Linux Virtual Servers is a solution for distributing traffic evenly. This study aims to analyze the performance of ten scheduling algorithms in a load-balancing system for File Transfer Protocol server applications with an Internet Protocol tunnel topology. The research method involves implementing a server cluster using the Debian operating system with one load-balancing server and two real servers. This topology allows the real servers to be located on geographically separate networks. Performance testing was conducted on ten different scheduling algorithms using five simultaneous clients, measuring response time and throughput using network analysis software. The test results showed that the Least Connections Based on Locality algorithm provided the most optimal performance compared to the other algorithms. The algorithm recorded the lowest average response time of 0.6066 seconds and the highest average throughput of 43 kilobits per second. These results are significantly better than those from tests without a load-balancing system, which yielded a response time of 2.7332 seconds. It can be concluded that the Least Connections Based on Locality algorithm is the most effective when applied to File Transfer Protocol servers with an Internet Protocol tunnel topology to improve network service quality.

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