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All Journal JUITA : Jurnal Informatika Journal of Computer Science and Information Technology
Indrawan Ady Saputro
STIMIK Amikom Surakarta
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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PENERAPAN METODE DESIGN THINKING DALAM PERANCANGAN UI/UX PADA STUDI KASUS : WARUNG MAKAN Muhammad Rizki F R; Indrawan Ady Saputro
Journal of Computer Science and Information Technology Vol. 2 No. 4 (2025): September
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v2i4.3058

Abstract

Perencanaan antarmuka pengguna (UI) dan pengalaman pengguna (UX) yang baik sangat penting untuk meningkatkan kepuasan pelanggan pada layanan digital. Tujuan dari penelitian ini ada pada perancangan ulang UI/UX aplikasi pemesanan makanan berbasis mobile dengan metode Design Thinking, yang terdiri dari lima tahap: Empathize, Define, Ideate, Prototype, dan Testing. Data dikumpulkan melalui kuesioner pada 10 pengguna warung makan. Hasil menunjukkan tingkat kepuasan pengguna rata-rata 6,5 ​​dari 10, serta pengujian black-box pada fitur login, pemesanan, checkout, hingga pelacakan pesanan dinyatakan valid. Hal ini membuktikan bahwa metode Design Thinking efektif menghasilkan desain UI/UX yang lebih sederhana, mudah dipakai, dan mampu meningkatkan kepuasan pengguna.  
Evaluasi Pengaruh Sentimen Berita terhadap Pergerakan Harga Minyak Mentah dengan Pendekatan Klasifikasi Ahmad Muhariya; Indrawan Ady Saputro; Dziky Ridhwanullah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28411

Abstract

Various factors, including market perception reflected in media information, Influence crude oil price fluctuations. This study aims to analyse the Influence of news sentiment on crude oil price movements using a deep learning–based sentiment analysis approach. The dataset consists of 108 news headlines and daily closing oil prices from January to May 2025. It is important to note that this dataset is relatively small for deep learning models like LSTM, GRU, and BiLSTM, which constitutes a major constraint for this study. The news text was processed with case folding, tokenisation, stopword removal, and lemmatisation (not stemming to preserve semantic integrity for BERT), then automatically labelled using the DistilBERT model. The BERT-based vector representations were used as input for three classification models: LSTM, GRU, and BiLSTM. The evaluation results showed that all three models achieved the same average validation accuracy of 85.27%. However, the GRU model is identified as the optimal performer, achieving the lowest validation loss (0.3324), indicating better generalisation performance than LSTM and BiLSTM. Further analysis reveals that news sentiment tends to align with oil price trends, particularly during significant market shifts.
Co-Authors Ahmad Muhariya Dziky Ridhwanullah Muhammad Rizki F R