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All Journal Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Syamsuryadi
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Pengenalan Kepribadian Melalui Tulisan Tangan Menggunakan Convulutional Neural Network Dengan LS Classifiers Guntara, Yusa Virginiawan; Syamsuryadi; Sukemi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 2 (2023): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v14i2.15193

Abstract

A person's handwriting is different and unique, even though it looks similar it is certainly not the same as someone else's writing. One's personality traits can be identified based on handwriting. One of the implementations is (handwriting recognition). To identify a person's personality, it can be classified by handwriting using the 'Graphology' field. The computational system to identify handwritten images can use the Convulution Neural Network method. Using the CNN method is expected to produce good accuracy with a low error rate. The CNN method is able to predict a person's personality through manuscripts as images. In addition, to increase the diversity of classifications, the Least Squared Classifiers method is needed. . LS Classifiers are designed to increase the variety of CNN methods in feature extraction and classification. The LS Classifier method is a classification method that estimates the w parameter vector and takes the best linear classifier based on the w parameter vector. Research has functions for users, including to find out someone's personality, especially extrovert and introvert personality. In this study CNN serves as Feature Extraction to classify Image and Ls Classifiers serves to increase diversity into 2 personality groups. The level of accuracy of the performance of the CNN & Ls Classifiers method in carrying out feature extraction and classification of handwritten images in determining personality has a good level of accuracy.
Model pemberdayaan kader posyandu dalam stimulasi perkembangan balita melalui box edukatif stimulus balita (Besti Balita) Fajar, Nur Alam; Flora, Rostika; Hasyim, Hamzah; Zulkarnain, Mohammad; Syamsuryadi; Rahmawaty, Annisa; Ananingsih, Esti Sri; Amalia, Risa Nur
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 8 No 3 (2025)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v8i3.22857

Abstract

Kader Posyandu merupakan elemen penting dalam mendukung stimulasi tumbuh kembang balita. Media Besti Balita merupakan alat edukatif inovatif yang dirancang sesuai standar kementerian kesehatan untuk membantu kader posyandu dalam memberikan stimulasi perkembangan sesuai tahapan usia anak. Kegiatan ini bertujuan untuk meningkatkan pengetahuan dan keterampilan kader posyandu dalam melakukan stimulasi perkembangan balita, serta memperkuat peran mereka sebagai garda terdepan dalam pencegahan stunting. Kegiatan ini dilaksanakan pada 24 Agustus 2024 di Puskesmas Mariana, Kecamatan Banyuasin 1, dan diikuti oleh 30 kader Posyandu dari enam desa lokus stunting. Tahapan kegiatan meliputi pretest, pemberian materi, demonstrasi, simulasi, dan posttest. Hasil kegiatan ini menunjukkan bahwa terjadi peningkatan pengetahuan kader secara signifikan (p-value=0,001). Sebelum intervensi dilakukan terdapat 66,7% kader memiliki pengetahuan kurang, akan tetapi pengetahuan seluruh peserta (100%) menjadi baik setelah diberikan intervensi. Media Besti Balita terbukti efektif dalam meningkatkan keterampilan kader dalam melakukan stimulasi perkembangan anak usia dini. Kegiatan ini diharapkan dapat menjadi model pemberdayaan kader Posyandu di daerah lain guna mendukung optimalisasi tumbuh kembang anak secara berkelanjutan.
Co-Authors Amalia, Risa Nur Ananingsih, Esti Sri Annisa Rahmawaty Guntara, Yusa Virginiawan Hamzah Hasyim Nur Alam Fajar Rostika Flora Sukemi Zulkarnain, Mohammad