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All Journal JMM (Jurnal Masyarakat Mandiri) Media Jurnal Informatika
Muhammad Zulfikri
Teknologi Informasi, Universitas Bumigora
Computer Science & IT Other

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Autism Classification Using MobileNetV3 Feature Extraction and K-Nearest Neighbor Algorithm Rahayun Amrullah Husaini; Gede Yogi Pratama; Kurniadin Abd. Latif; Muhammad Zulfikri; Kartarina Augustin
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5934

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behaviors. Early detection of ASD is crucial; however, conventional diagnostic methods rely heavily on clinical observation and expert assessment, which can be time-consuming and resource-intensive. Along with the rapid development of artificial intelligence, especially in computer vision and machine learning, automated image-based approaches have gained attention as alternative tools for ASD screening. This study proposes a hybrid classification approach that integrates MobileNetV3 as a feature extraction model with the K-Nearest Neighbor (KNN) algorithm for autism classification using facial image data. Unlike previous CNN–KNN approaches, this study specifically explores the use of MobileNetV3’s lightweight architecture to generate compact and discriminative facial features, which are then classified using KNN to evaluate its effectiveness in low-complexity and resource-efficient settings. This design highlights the novelty of combining an optimized lightweight CNN with a distance-based classifier for autism detection from facial images. The dataset used in this research was obtained from Kaggle and consists of 2,940 labeled facial images of children categorized into Autism and non-Autism classes. This study proposes a hybrid classification approach that combines MobileNetV3 as a lightweight feature extraction model with the K-Nearest Neighbor (KNN) algorithm for autism classification. Experimental evaluations were conducted over multiple independent runs to improve statistical reliability, and model performance was assessed using accuracy, precision, recall, and F1-score. The results indicate that the proposed hybrid model achieves satisfactory and consistent performance while maintaining computational efficiency. These findings suggest that integrating lightweight deep learning models with classical machine learning algorithms can provide an effective and resource-efficient approach for autism classification, with potential applicability as a supportive tool for early ASD screening rather than a definitive clinical diagnosis.
PEMBERDAYAAN KELOMPOK WANITA TANI MELALUI DIVERSIFIKASI PRODUK OLAHAN SINGKONG Abdul Muhid; Nyoman Yudiarini; Ida Ayu Made Dwi Susanti; Cokorda Javandira; Rifqi Hammad; Muhammad Zulfikri
JMM (Jurnal Masyarakat Mandiri) Vol 10, No 1 (2026): Februari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v10i1.36560

Abstract

Abstrak: Singkong merupakan komoditas pangan lokal yang melimpah di Lombok Utara, namun pemanfaatannya masih terbatas dan bernilai tambah rendah. Keterbatasan keterampilan dan inovasi pengolahan menyebabkan potensi singkong belum dimanfaatkan secara optimal Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kapasitas Kelompok Wanita Tani (KWT) ITB Asri di, Lombok Utara dalam mengembangkan diversifikasi produk olahan singkong sebagai upaya peningkatan kemandirian dan ekonomi lokal. Metode pelaksanaan menggunakan pendekatan Participatory Community Empowerment melalui tahap perencanaan, edukasi, praktik produksi, pendampingan, dan evaluasi. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan yang signifikan, dengan kenaikan pre-test dan post-test hingga 46% pada aspek produksi serta 26% pada aspek digital marketing. Beberapa produk unggulan seperti tepung mocaf, keripik singkong, dan brownies singkong berhasil dikembangkan dan diproduksi secara mandiri oleh KWT, didukung oleh pendampingan dalam pengemasan, branding, dan pemanfaatan pemasaran digital. Secara keseluruhan, kegiatan ini berhasil meningkatkan keterampilan, motivasi kewirausahaan, dan kapasitas ekonomi KWT, sehingga memberikan dampak positif terhadap pemanfaatan potensi lokal dan penguatan usaha berbasis pangan.Abstract: Cassava is a local food commodity abundant in North Lombok, but its utilization is still limited and has low added value. Limited skills and processing innovations mean that cassava's potential has not been optimally utilized. This community service activity aims to increase the capacity of the ITB Asri Women's Farmers Group (KWT) in Berangan Hamlet, Kayangan Village, North Lombok in developing diversified cassava processed products as an effort to increase independence and the local economy. The implementation method uses a participatory community empowerment approach through the stages of planning, education, production practice, mentoring, and evaluation. The results of the activity showed a significant increase in knowledge, with a pre-test and post-test increase of up to 46% in the production aspect and 26% in the digital marketing aspect. Several superior products such as mocaf flour, cassava chips, and cassava brownies were successfully developed and produced independently by the KWT, supported by mentoring in packaging, branding, and the use of digital marketing. Overall, this activity succeeded in increasing the skills, entrepreneurial motivation, and economic capacity of the KWT, thus having a positive impact on utilizing local potential and strengthening food-based businesses. 
Co-Authors Abdul Muhid Cokorda Javandira Gede Yogi Pratama Ida Ayu Made Dwi Susanti Kartarina Augustin Kurniadin Abd. Latif Nyoman Yudiarini Rahayun Amrullah Husaini Rifqi Hammad