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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Community Service (JCOS)
Darmawan, Marcellinus Aditya Vitro
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Public Health

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SOSIALISASI “ISI PIRINGKU” PADA SISWA KELAS 3 SDN 1 KEDUNGPELUK SIDOARJO Arum, Dewi Puspa; Wulandari, Nurul Nur Rohmawati; Darmawan, Marcellinus Aditya Vitro; Purwatitisari, Nursavita
Journal of Community Service (JCOS) Vol. 2 No. 4 (2024)
Publisher : EDUPEDIA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56855/jcos.v2i4.1148

Abstract

Sosialisasi "Isi Piringku" bertujuan untuk meningkatkan pemahaman gizi seimbang pada siswa kelas 3 SDN 1 Kedungpeluk, Sidoarjo. Kegiatan ini dilakukan melalui metode interaktif termasuk video edukatif dan presentasi langsung. Hasil pre-test dan post-test menunjukkan peningkatan signifikan dalam pemahaman siswa tentang pentingnya pola makan yang sehat dan seimbang. Peningkatan ini menunjukkan keberhasilan sosialisasi dalam memperkenalkan konsep gizi seimbang kepada anak-anak. Diharapkan, pengetahuan yang diperoleh dapat diterapkan dalam kehidupan sehari-hari dan disebarluaskan kepada keluarga dan teman-teman mereka.
OPTIMASI ALGORITMA K-NEAREST NEIGHBOR DENGAN ALGORITMA GENETIKA PADA DETEKSI PENYAKIT DIABETES MELLITUS Darmawan, Marcellinus Aditya Vitro; Haromainy, M. Muharrom Al; Junaidi, Achmad
JATISI Vol 12 No 2 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i2.11353

Abstract

This study discusses the optimization of the K-Nearest Neighbor (KNN) algorithm using Genetic Algorithm (GA) in detecting diabetes mellitus. The research includes stages of collecting datasets on diabetes mellitus symptoms, data preprocessing through normalization and dataset alignment, model implementation, and testing with various scenarios to achieve the highest accuracy. The data used consists of the Pima Indians Diabetes Database as dataset 1 and the Early Stage Diabetes Risk Prediction Dataset as dataset 2. The evaluation is conducted by comparing the accuracy results between KNN without optimization and KNN optimized using Genetic Algorithm. The study's results indicate that optimization is performed by finding the optimal combination of the k-value and the features used in classification. The Genetic Algorithm produces individuals with the best fitness based on the combination of k-values and features that yield the highest accuracy. Testing was conducted on two datasets with two different fold values. The best accuracy was obtained in the 10-fold test, where the accuracy for dataset 1 increased from 74.2% to 79.1% after optimization. Meanwhile, for dataset 2, the accuracy improved from 97.5% to 98.2% after optimization. There was an increase in accuracy for dataset 1, whereas for dataset 2, the improvement was not significant. The conclusion of this study is that optimizing the KNN algorithm using Genetic Algorithm has proven to enhance the accuracy of diabetes mellitus detection, especially in numerical datasets with more complex features.
Co-Authors Achmad Junaidi Arum, Dewi Puspa Muhammad Muharrom Al Haromainy Purwatitisari, Nursavita Wulandari, Nurul Nur Rohmawati