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All Journal Jurnal Informatika Global AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT
Amanda, Widia
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Computer Science & IT

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Jajanan Sehat Layak Konsumsi di Sekolah Dasar Nurul Hasaniah Rahmawati, Laila; Sianturi, Rosari Br; Simatupang, Naila Syahira; Zahiyyah, Nailah; Salsabila, Annisa; Sabrina, Dian; Mutia, Enzela; Salsabila, Hilwa; Fadilah, Siti; Amanda, Widia; Fauzani, Nabila; Barus, Ulian; Sultani, Dalmi Iskandar; Sutarini, Sutarini
Amaliah: Jurnal Pengabdian Kepada Masyarakat Vol 8 No 2 (2024): Amaliah Jurnal: Pengabdian kepada Masyarakat
Publisher : LPPI UMN AL WASHLIYAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32696/ajpkm.v8i2.3997

Abstract

Makanan jajanan yang sehat dan baik dikonsumsi terutama bagi anak sekolah dasar adalah makanan jajanan yang tidak hanya memenuhi standard gizi, tetapi juga memenuhi standard kesehatan. Dari hasil penelitian tim PKM yang dilaksanakan terhadap 25 responden pada tanggal 29 November 2024 di SD Nurul Hasaniah Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara, maka dapat diambil kesimpulan yaitu: Pengetahuan siswa tentang Pengetahuan siswa tentang jajanan sehat yang layak konsumsi sesudah diberikan edukasi sebagian besar berubah menjadi kategori baik. Ada pengaruh yang bermakna positif dari edukasi tentang jajanan sehat terhadap tingkat pengetahuan anak dalam memilih jajanan yang sehat dan tidak sehat.
Klasifikasi Pendapatan Menggunakan Algoritma Random Forest: Studi Kasus Dataset Adult Income Amanda, Widia; Voutama, Apriade
Jurnal Ilmiah Informatika Global Vol. 16 No. 2: August 2025
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v16i2.5407

Abstract

This research aims to classify a person's income based on demographic attributes using Random Forest algorithm, which is one of the popular ensemble learning methods in the field of machine learning. The dataset used is Adult Income from the UCI Machine Learning Repository, which consists of more than 32 thousand data with 15 attributes such as age, gender, education, education level, employment type, marital status and others. The research process includes data preprocessing, model pipeline creation, training, and performance evaluation. Preprocessing was done through the removal of irrelevant attributes, normalization of numerical data, and application of one-hot encoding on categorical data. The model was trained with default parameters and evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The evaluation results show that the model achieved an accuracy of 85.44%, with higher performance in classifying income classes ≤50K than >50K. The low recall value in the >50K class indicates that the model tends to be biased towards the majority class, which could be caused by data imbalance. Therefore, it is necessary to improve the model through hyperparameter tuning techniques, handling data imbalance, or exploring other algorithms such as Gradient Boosting. This research is expected to be the basis for developing accurate and applicable data-based prediction systems in the fields of economics, policy planning, and decision support systems that require analysis of individual income potential.
Co-Authors Apriade Voutama Barus, Ulian Fauzani, Nabila Laila Rahmawati Mutia, Enzela Sabrina, Dian SALSABILA, ANNISA Salsabila, Hilwa Sianturi, Rosari Br Simatupang, Naila Syahira siti fadilah Sultani, Dalmi Iskandar Sutarini, Sutarini Zahiyyah, Nailah