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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal PETISI (Pendidikan Teknologi Informasi)
Ratna Juwita, Ayu
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering

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Pelatihan Penggunaan Literasi Digital Untuk Menunjang Pendidikan Dan Umkm Khaerudin, Muhammad; Tukino; Ratna Juwita, Ayu; Rohana, Tatang; Winarni
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 1 (2024): Juni 2024
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i1.73

Abstract

The pandemic conditions have opened up opportunities for teenagers to do business. In conditions like this, it is hoped that teenagers can use their time positively and productively. Mental readiness and skills need to be prepared before someone enters the world of work. Meanwhile, applications in the field of information technology have a big impact in various areas of life, one of which is in the creative industry such as advertising, billboards, graphic design and digital image processing. One of them is supporting skills for the younger generation. The method of community service carried out by a team of lecturers at Bhayangkara University, Greater Jakarta this time is in the form of training in graphic design skills using computers and using CorelDraw and Photoshop software. This activity aims to increase the knowledge and skills of youth in improving the quality of their abilities in creating attractive graphic designs so that participants can compete to meet the demand for employment and also towards entrepreneurship. The training given to participants includes creating and completing product designs for advertising or printing needs. The results of graphic design skills training activities show that participants can design logos, business cards, invitations, leaflets, banners and other forms of advertising.
Penerapan Algoritma KNN dan Naive Bayes untuk Klasifikasi Stunting pada Balita di Desa Pasirjengkol Meriyana, Putri; Rizky Pratama, Adi; Nurlaelasari, Euis; Ratna Juwita, Ayu
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 6 No. 2 (2025): Jurnal PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v6i2.2020

Abstract

Stunting merupakan kondisi gangguan tumbuh anak balita yang terjadi karena kekurangan asupan gizi secara terus-menerus dalam waktu yang lama, sehingga menyebabkan anak mengalami gagal tumbuh secara fisik maupun perkembangan kemampuan berpikir. Penelitian ini menerapkan algoritma K-Nearest Neighbor (KNN) dan Naïve Bayes dalam klasifikasi status balita stunting di Desa Pasirjengkol berdasarkan data usia, jenis kelamin, dan tinggi badan. Dataset yang digunakan berjumlah 1.195 data yang dikumpulkan pada tahun 2023 dan 2024, namun setelah proses pembersihan data menjadi 1.192 data. Proses penelitian meliputi pengumpulan data, pra-pemrosesan penghapusan data duplikat, transformasi data mencakup pengkodean label , dan normalisasi dengan Min-Max Scaling , kemudian pemilihan fitur, pelatihan model, dan evaluasi menggunakan matriks konfusi . Hasil evaluasi menunjukkan algoritma KNN memberikan akurasi tinggi sebesar 97.90%, sedangkan Naïve Bayes sebesar 54.39%. Berdasarkan hasil tersebut, KNN menunjukkan kinerja yang lebih baik dalam mengklasifikasikan status stunting pada balita di Desa Pasirjengkol.
Air quality prediction using boosting-based machine learning models for sustainable environment Fauzi, Ahmad; Maharina, Maharina; Indra, Jamaludin; Ratna Juwita, Ayu; Hananto, Agustia; Nurlaelasari, Euis
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp515-523

Abstract

High levels of air pollution are extremely harmful to humans and the environment. They increase the risk of respiratory infections and lung cancer, especially among vulnerable populations. Therefore, developing effective pollution control measures is crucial for mitigating these negative impacts. We need to implement effective methods to predict and manage air quality for the sake of public health and a healthier environment. In recent years, machine learning (ML) methods have been increasingly utilized in air quality prediction due to their ability to analyze datasets and identify complex patterns. However, the reliability and accuracy of air quality prediction models remain a challenge. This study proposes a boosting-based ML model for predicting air quality. We implemented three stages in the proposed method. In the first stage, we conducted data preprocessing and analysis to eliminate noise, remove redundant data, and encode categorical features. In the second stage, we predicted air quality categories by leveraging 25 ML models, dividing them into three distinct categories. The results show that the extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and adaptive boosting (AdaBoost) models outperform the others in air quality prediction, achieving an accuracy of 99%. Finally, we compared these three models using 10-fold cross validation to ensure they generalize well in last stage.
Co-Authors Fauzi Ahmad Muda Hananto, Agustia Indra, Jamaludin Maharina, Maharina Meriyana, Putri Muhammad Khaerudin Nur ‘Azah Nurlaelasari, Euis rizky pratama, adi Rohana, Tatang Winarni