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Putra, Rangga Pahlevi
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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.
DETECTION OF LIKURAI DANCE MOVEMENT TYPES IN MALAKA REGENCY USING YOLOV8 BASED ON VIDEO Da Costa, Zania Abuk; Rahman, Aviv Yuniar; Putra, Rangga Pahlevi
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8815

Abstract

Indonesia is rich in traditional dances from every region, including the Likurai Dance, originating from East Nusa Tenggara, specifically in Malaka and Belu districts. This dance carries deep symbolic and historical meaning; however, it is currently threatened by lifestyle changes and globalization. Despite this, accurately and in real-time recognizing Likurai Dance movements remains challenging, particularly in detecting the specific dance movements. This research aims to test the effectiveness of detecting three types of Likurai Dance movements using documented digital video. The detection model is the YOLOv8 algorithm, known for detecting objects quickly and accurately. A YOLOv8-based platform is proposed to detect these dance movements precisely. In the testing, the YOLOv8 model demonstrated outstanding performance, achieving a very high mAP of 99.5% for the Wesei Wehali movement, 99.4% for the Be Tae Be Tae movement, and 99.1% for the Tebe Re movement. These results indicate that the model can detect dance movements with exceptional accuracy, precision, and recall rates above 98%. This research concludes that YOLOv8 has excellent potential in detecting traditional dance movements with high accuracy. These findings are significant for preserving and documenting the Likurai Dance and provide an educational means for younger generations to understand better and appreciate traditional cultural values.
DETEKSI CITRA SALIVA FERNING UNTUK MASA SUBUR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Luruk, Alfrida Demetria; Putra, Rangga Pahlevi; Priyandoko, Gigih
Prosidia Widya Saintek Vol. 5 No. 1 (2026)
Publisher : Universitas Widyagama Malang

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Abstract

Ovulasi merupakan fase penting dalam siklus menstruasi yang menandai masa subur seorang wanita. Identifikasi masa subur umumnya dilakukan melalui metode kalender, pengujian hormon, maupun pemeriksaan lendir serviks. Namun, metode tersebut memiliki keterbatasan dalam hal akurasi, biaya, serta kenyamanan pengguna. Penelitian ini mengusulkan pengembangan sistem deteksi masa subur berbasis citra saliva ferning menggunakan Convolutional Neural Network (CNN). Data citra saliva dikumpulkan dari 10 wanita berusia 18–35 tahun dengan total 90 sampel, terdiri dari tiga kategori: infertile, intermediate, dan fertile. Setiap citra diproses melalui tahapan cropping, grayscale, dan augmentasi untuk meningkatkan kualitas serta variasi data. Arsitektur CNN dibangun menggunakan tiga lapisan konvolusi dan max-pooling, serta dioptimasi dengan Adam optimizer (learning rate = 0.001). Proses pelatihan dilakukan dengan batch size 60 dan 20 epoch menggunakan Google Colaboratory. Hasil pengujian menunjukkan bahwa model CNN mampu mengklasifikasikan pola ferning saliva dengan akurasi yang tinggi, serta menghasilkan confusion matrix yang konsisten pada tiap kelas. Penelitian ini menunjukkan bahwa metode CNN memiliki potensi tinggi sebagai pendekatan non-invasif dan efisien untuk mendeteksi masa subur wanita berdasarkan citra saliva.
Co-Authors arrozi, lalu muhamad alawi Aviv Yuniar Rahman Chauliah Fatma Putri Da Costa, Zania Abuk Endah Puspitosarie Istiadi Luruk, Alfrida Demetria Ma'rifani Fitri Arisa Magai, Etinus Nugroho, Kuncahyo Setyo Raihan, Anugrah Ahzul Saputra, Deni Adi Syahroni Wahyu Iriananda, Syahroni Wahyu Verdiansyah, Egi