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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Masyarakat Madani Indonesia
Ashar, Rahmad
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Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Public Health Social Sciences

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Pemberdayaan Siswa SMK melalui Pelatihan IoT untuk Sistem Monitoring Tanaman dalam Mendukung Smart Farming Khaira, Ulfa; Saputra, Edi; Waladi, Akhiyar; Perdana, Yogi; Hanum, Nindy Raisa; Iftitah, Hasanatul; Ashar, Rahmad; Rozi, Syamsyida
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 1 (2026): Februari
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/9xvxt587

Abstract

Provinsi Jambi memiliki potensi besar di sektor pertanian, namun praktik konvensional masih menghadapi kendala terkait efisiensi, produktivitas, dan keberlanjutan. Smart farming berbasis Internet of Things (IoT) hadir sebagai solusi inovatif, namun implementasinya memerlukan sumber daya manusia yang kompeten. Kegiatan Pengabdian kepada Masyarakat (PPM) ini melibatkan sebanyak 20 orang siswa SMK, serta guru SMKN 1 Muaro Jambi, dengan tujuan meningkatkan kompetensi peserta dalam merakit, memprogram, dan memanfaatkan sistem monitoring tanaman berbasis IoT. Program dilaksanakan dalam empat sesi pelatihan yang meliputi pengenalan konsep smart farming dan IoT, pengenalan komponen, praktik perakitan sistem, serta dasar pemrograman mikrokontroler. Hasil evaluasi menunjukkan peningkatan signifikan pemahaman peserta, yang ditunjukkan oleh kenaikan nilai rata-rata post-test sebesar 26 poin (dari 37,1 menjadi 63,1), serta keberhasilan peserta dalam merakit prototipe sistem monitoring tanaman yang fungsional. Temuan ini menegaskan bahwa pelatihan berbasis praktik mampu menjembatani kesenjangan kompetensi siswa vokasi terhadap kebutuhan teknologi pertanian modern. Keberlanjutan program ini diharapkan dapat memperkuat peran sekolah vokasi dalam mendukung penerapan pertanian cerdas sekaligus membuka peluang karier dan kewirausahaan di bidang agroteknologi.
Parameter Efficient Models for Malaria Detection and Classification Using Small-Scale Imbalanced Blood Smear Images Waladi, Akhiyar; Iftitah, Hasanatul; Hanum, Nindy Raisa; Perdana, Yogi; Wahyuni, Fitra; Ashar, Rahmad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2558

Abstract

Malaria diagnostic automation faced critical challenges including severe class imbalance with ratios up to 54:1, limited datasets with 200 to 500 images, and computational inefficiency requiring separate model training for each detection-classification combination. This study developed a multi-model framework with shared classification architecture that trained classification models once on ground truth crops and reused them across all detectors. The framework systematically evaluated three YOLO Medium architectures for parasite detection and six CNN architectures for lifecycle and species classification across four complementary malaria datasets totaling 1,544 microscopy images. Detection achieved 70.84% to 96.27% mAP@50 with high recall of 71.05% to 93.12% minimizing missed parasites. Classification demonstrated dataset-dependent model selection with parameter-efficient EfficientNet models containing 5.3M to 9.2M parameters consistently outperforming ResNet variants with up to 44.5M parameters. EfficientNet-B1 achieved 91.51% accuracy on IML Lifecycle and 98.28% on MP-IDB Species, while EfficientNet-B0 achieved 86.45% on multi-patient MD-2019 dataset. ResNet50 achieved 96.13% on severely imbalanced MP-IDB Stages. Focal Loss optimization with alpha of 1.0 and gamma of 1.5 enabled robust minority class performance with F1-scores between 0.44 and 1.00 on ultra-minority classes demonstrating effective imbalance handling. The compact 46-89 MB models enabled practical deployment on resource-constrained hardware.
Co-Authors Akhiyar Waladi Edi Saputra Hasanatul Iftitah Nindy Raisa Hanum Perdana, Yogi Ulfa Khaira, Ulfa