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All Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat Green Intelligent Systems and Applications Journal Information & Computer (JICOM)
Tandoballa, Lucky
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Penerapan Naive Bayes, Chi-Square dan SMOTE pada Opini Masyarakat Terhadap Fufufafa di YouTube Andreyas; Tandoballa, Lucky; Wijaya, Novan
Journal Information & Computer Vol. 3 No. 2 (2025): Journal Information & Computer
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jicomisc.v3i2.50304

Abstract

Penelitian ini bertujuan untuk menganalisis opini masyarakat terhadap komentar terkait akun kaskus fufufafa pada platform YouTube dengan menggunakan metode Naive Bayes, Chi-square, dan SMOTE. Dalam penelitian ini meliputi beberapa metode tahapan utama: mining dataset, pelabelan dataset, preprocessing, dan penerapan metode SMOTE untuk mengatasi ketidakseimbangan pada kelas. Penambangan data dilakukan dengan cara mengumpulkan data komentar pengguna dari video YouTube terkait pembahasan akun kaskus fufufafa. Kemudian dilakukan langkah pelabelan untuk mengklasifikasikan komentar menjadi sentimen positif, negatif, atau netral. Tahap preprocessing meliputi pembersihan data dari unsur-unsur yang tidak diperlukan seperti tanda baca, angka, dan karakter khusus. Untuk mengatasi masalah ketidakseimbangan pada kelas, Kami kemudian menerapkan Synthetic Minority Oversampling Technique (SMOTE) dimana jumlah komentar dengan sentimen tertentu lebih sedikit dibandingkan jumlah komentar yang lain. Hasil penelitian ini menunjukkan bahwa akurasi model Naive Bayes mencapai 60,5%, sedangkan penggunaan seleksi fitur chi-square dengan SMOTE meningkatkan akurasi menjadi 68,3%. Dalam hal ini menunjukkan bahwa penggunaan chi-square dengan SMOTE dapat meningkatkan akurasi prediksi sentimen sebesar 7,8%. Kesimpulan dari penelitian ini adalah model Naive Bayes dengan pemilihan fitur chi-square dengan SMOTE lebih efektif dalam memprediksi opini masyarakat dibandingkan model Naive Bayes tanpa pemilihan fitur tersebut.
PELATIHAN PEMBUATAN DESAIN GRAFIS MULTIMEDIA INTERAKTIF DI SMA NEGERI 13 PALEMBANG Tandoballa, Lucky; Wahyuni, Sri; Agustria, Kevin; Hartati, M. Kom, Ery
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): APTEKMAS Volume 7 Nomor 2 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Most importance of digital technology in the 4.0 era and 5.0 society, with a focus on the graphic design profession, which works with various visual media such as illustration, typography, photography, animation and video to create creative works such as brochures and advertisements. Presenting interesting and informative information through infographics is key to conveying messages to readers using apps like Canva. Canva, founded in 2012 by Melanie Perkins, is an online graphic design tool that allows users to easily create and edit designs. The use of Canva in the world of education, such as at SMA Negeri 13 Palembang, helps students understand how to use technology for various purposes, including learning, business, and creating personal biodata. Canva's main features include many available templates, although some of them cost money and require Internet support. This community service activity is effective in introducing and teaching the use of Canva to create presentations, posters, animations and personal biodata. The event ended with a group photo session, and the participants hoped that a similar program would be held again to deepen their understanding of Canva features.
Implementation of the You Look Only Once (YOLOv11) Algorithm to Detect the Ripeness of Golden Melons Tandoballa, Lucky; Hartati, Ery
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.934

Abstract

Melon is a horticultural commodity with high economic value, and characteristics such as sweetness, aroma, texture, and phytonutrient content significantly influenced consumer preference. Conventional methods for determining melon ripeness were time-consuming, required considerable expertise, and were often prone to subjective errors, ultimately slowing the production and distribution process. This study aimed to detect the ripeness level of golden melon fruit non-destructively using the YOLOv11 algorithm, focusing on external physical characteristics as the basis for classification. The objectives included applying transfer learning to categorize golden melon into ripe and unripe classes and evaluating model performance using precision, recall, mAP50, mAP50-95, and F1-score. The research methodology consisted of a literature review, dataset collection from previous studies, system design, implementation, and performance testing. The dataset was divided into 70% training, 20% validation, and 10% testing data, and the Adam optimizer was used during the training phase. Based on four experimental scenarios, scenario 3 produced the best and most consistent results, achieving a precision of 90.58%, a recall of 90.79%, an mAP50 of 97.31%, an mAP50-95 of 88.84%, and an F1-score of 92.97%. These findings demonstrated that scenario 3 offered optimal performance for detecting golden melon ripeness. Thus, the model was highly reliable overall.
Co-Authors Agustria, Kevin andreyas Hartati, Ery Hartati, M. Kom, Ery Novan Wijaya Sri Wahyuni