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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Journal of Applied Engineering and Technological Science (JAETS)
Diva Kurnianingtyas
Fakultas Ilmu Komputer Universitas Brawijaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec Yunico Ardian Pradana; Imam Cholissodin; Diva Kurnianingtyas
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 5 (2023): Mei 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pemindahan ibu kota negara dari DKI Jakarta ke Pulau Kalimantan telah menimbulkan perdebatan dan meningkatkan minat publik terhadap isu tersebut. Twitter menjadi media sosial yang populer untuk menyampaikan pendapat dan aspirasi masyarakat. Oleh karena itu, penelitian ini bertujuan untuk menganalisis sentimen masyarakat terkait pemindahan ibu kota menggunakan metode analisis sentimen. Dalam penelitian ini, metode Deep Learning, khususnya Long Short Term Memory (LSTM) dan word2vec yang digunakan untuk menganalisis sentimen tweet masyarakat. Dengan menerapkan metode LSTM dengan Word2Vec, diharapkan dapat diklasifikasikan apakah tweet masyarakat bersifat positif atau negatif terkait pemindahan ibu kota. Model LSTM yang dikembangkan dalam penelitian ini menghasilkan akurasi sebesar 95%, precision sebesar 93%, recall sebesar 93%, dan F1-measure sebesar 95%. Hasil tersebut menunjukkan bahwa metode ini efektif dalam menganalisis sentimen masyarakat terkait pemindahan ibu kota dan dapat memberikan pemahaman yang lebih baik mengenai pandangan publik terhadap perubahan tersebut.
Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children Satrio Agung Wicaksono; Satrio Hadi Wijoyo; Fatmawati Fatmawati; Tri Afirianto; Diva Kurnianingtyas; Mochammad Chandra Saputra
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6105

Abstract

Stunting in children remains a significant global health challenge, particularly in low- and middle-income countries. Addressing this issue requires an effective approach to predicting and preventing inadequate nutritional fulfillment. This study uses the Naïve Bayes approach to forecast nutritional needs for children's growth and development, providing practical information for stunting prevention efforts. The data used were sourced from 174 infant and toddler examinations at the Puskesmas Lawang, involving eight key attributes: gender, age, weight, height, head circumference, pre-screening, vision tests, and nutritional status. Key performance metrics were evaluated to validate the model's predictive capabilities, including accuracy, precision, recall, and F1-score. Six test scenarios were conducted using different percentages of training data (90%, 80%, 70%, 60%, 50%, and 40%) to evaluate the reliability of the Naïve Bayes method. Results indicated that the highest accuracy of 78.84% was achieved in the sixth test scenario. The third test scenario produced the highest precision at 97.5%, while the highest recall (100%) was observed in the first three scenarios. The highest F-measure of 90.3% occurred in the fourth scenario. These results suggest the algorithm's potential for early detection to decrease the number of stunting children. The study’s implications are twofold: practically, the model can be integrated into health monitoring systems to assist healthcare professionals and policymakers in designing more effective nutrition programs; theoretically, it highlights the adaptability of Naive Bayes for handling complex, multi-dimensional health data.
Co-Authors Fatmawati Fatmawati Imam Cholissodin Mochammad Chandra Saputra Satrio Agung Wicaksono Satrio Hadi Wijoyo Tri Afirianto Yunico Ardian Pradana