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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Binti Husna, Modesta
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Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Sentimen Terhadap Kualitas Aplikasi Bahan Ajar Digital Akademik Universitas Terbuka di Google Play Fatmasari, Rhini; Gata, Windu; Kusuma Wardhani, Nia; Prayogi, Kurnia; Binti Husna, Modesta
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 1 (2024): Maret 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i1.591

Abstract

Terbuka University is a leading institution that implements the optimization of digital transformation, especially in distance learning systems. To improve the quality of service to students and stakeholders, Terbuka University has developed the Terbuka University Digital Learning Materials application. This application offers several learning modules that can be accessed through the Google Play Store. This research aims to classify data using different labels related to reviews of the Terbuka University Digital Learning Materials application using the Long Short-Term Memory classification algorithm. Evaluation is conducted to find accuracy, f1-score, precision, and recall values. The research results show that classification with Long Short-Term Memory achieves an accuracy of 76.72% with the Vader label, and the accuracy with the TextBlob label reaches 74.21%. Confusion matrix evaluation shows precision results of 0.91 and recall of 0.78, with an f1-score of 0.84 for the Vader label. For the TextBlob label, the precision is 0.96, recall is 0.45, and the f1-score is 0.61. This research contributes positively to understanding the evaluation and classification of reviews of the Terbuka University Digital Learning application. Implementing the Long Short-Term Memory algorithm with the Vader label can be an effective choice to improve service and learning quality through the application.
IMPLEMENTASI YOU ONLY LOOK ONCE v8 DALAM DETEKSI MAKANAN WARUNG TEGAL UNTUK SISTEM PERHITUNGAN HARGA OTOMATIS Purnomo, Niko; Gata, Windu; Romadhona Kusuma, Muhammad; Marta Dinata, Riadi; Binti Husna, Modesta
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13465

Abstract

Warung Tegal (Warteg) merupakan usaha kuliner yang populer di Indonesia, tetapi sistem perhitungan harga makanannya masih manual, yang dapat menyebabkan kesalahan transaksi. Penelitian ini bertujuan mengembangkan sistem deteksi makanan otomatis menggunakan YOLO v8 untuk mengotomatisasi perhitungan harga.Dataset terdiri dari berbagai lauk warteg yang diproses dengan teknik augmentasi seperti pemotongan, rotasi, dan pencahayaan guna meningkatkan kinerja model. Hasil penelitian menunjukkan bahwa dalam pengujian terbaik dengan dataset 70:30 (20 epochs, batch size 16, learning rate 0.001), model YOLO v8 mencapai precision 0.602, recall 0.176, F1-score 0.32, box_loss 1.756, dan mAP@0.5 0.229. Tantangan utama meliputi keterbatasan dataset, kompleksitas latar belakang, dan kurangnya perbandingan dengan dataset publik. Meskipun dalam beberapa kondisi akurasi mencapai 75%- 100%, diperlukan dataset lebih besar dan perbandingan model lain untuk meningkatkan akurasi. Sistem ini berpotensi mendukung digitalisasi industri kuliner dan meningkatkan efisiensi transaksi di warteg.
Co-Authors Gata, Windu Marta Dinata, Riadi Nia Kusuma Wardhani, Nia Prayogi, Kurnia Purnomo, Niko Rhini Fatmasari, Rhini Romadhona Kusuma, Muhammad