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All Journal Jurnal Teknik Informatika (JUTIF) Prosidia Widya Saintek
Verdiansyah, Egi
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PAPAYA TYPE CLASSIFICATION USING YOLOv8 Verdiansyah, Egi; Nurdiyansyah, Firman; Istiadi, Istiadi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2336

Abstract

Papaya (Carica papaya L) is a fruit that is easily found in subtropical and tropical regions, including Indonesia. With many varieties of papaya, the manual method used in distinguishing papaya types by humans depends on individual knowledge which can cause inaccuracies in the classification process. The manual classification process also takes a very long time if production is done on a large scale. Therefore, a technology for sorting automation is needed, especially in the industrial world. This research aims to classify papaya classes based on their type. The classification is divided into four classes, namely bangkok papaya, california papaya, hawai papaya, and red lady papaya. The classification process in this study uses the YOLOv8 model, where the total dataset is 1200 papaya images with a training data division of 88% (1050 images), 8% validation data (100 images), and 4% test data (50 images). The dataset is separated according to papaya fruit class. Data training was conducted with 300 epochs. The results show that bangkok papaya has a mAP value of 96%, california papaya 97%, hawai papaya 95%, and red lady papaya has 95% mAP. The average class has a precision value of 99.6%, and recall 100.0%. It can be concluded that the YOLOv8 classification model is able to achieve a high level of accuracy.
ANALISIS SENTIMEN ULASAN GAME MOBILE FIRST-PERSON SHOOTER DI GOOGLE PLAY STORE MENGGUNAKAN METODE PEMBOBOTAN TF-IDF Iriananda, Syahroni Wahyu; Putra, Rangga Pahlevi; Raihan, Anugrah Ahzul; Saputra, Deni Adi; Verdiansyah, Egi
Prosidia Widya Saintek Vol. 2 No. 2 (2023)
Publisher : Universitas Widyagama Malang

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Abstract

Paper ini membahas tentang analisis sentimen ulasan game mobile genre FPS menggunakan metode pembobotan TF-IDF. Dalam penelitian ini, penulis menggunakan 2180 ulasan yang telah divalidasi dan dibersihkan, di mana 1258 ulasan diklasifikasikan sebagai positif dan 922 ulasan sebagai negatif. Dengan menggunakan pembobotan TF-IDF dan pengujian model klasifikasi, penelitian ini mencapai tingkat akurasi sebesar 76%, dengan presisi 75%, recall 74%, dan F1-score 75%. Hasil ini menunjukkan bahwa metode pembobotan TF-IDF dapat menghasilkan analisis sentimen yang efektif dan otomatis untuk ulasan game mobile genre FPS, memberikan kontribusi penting dalam pengembangan metode analisis sentimen dalam konteks tersebut.
Co-Authors Firman Nurdiyansyah, Firman Istiadi Putra, Rangga Pahlevi Raihan, Anugrah Ahzul Saputra, Deni Adi Syahroni Wahyu Iriananda, Syahroni Wahyu