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All Journal JSI: Jurnal Sistem Informasi (E-Journal) CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro IJIS - Indonesian Journal On Information System Jurnal Informatika Universitas Pamulang JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) IJEEIT : International Journal of Electrical Engineering and Information Technology Jurnal Ilmu Komputer dan Desain Komunikasi Visual Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Jurnal Teknologi Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) International Journal Engineering and Applied Technology (IJEAT) Jurnal Sains Informatika Terapan (JSIT)
Indra Yustiana
Universitas Nusa Putra
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Other

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Klasifikasi Arah Tren Harga Bitcoin Menggunakan Metode Ensemble Learning Random Forest Fadhlan Subhan Fadilah; Indra Yustiana; Nugraha
Jurnal Sains Informatika Terapan Vol. 5 No. 3 (2026): Jurnal Sains Informatika Terapan (Oktober, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i3.1248

Abstract

Bitcoin's extreme price volatility poses significant challenges for investors and traders in making informed investment decisions. Conventional technical analysis approaches are often subjective and unable to capture complex market dynamics accurately. This study aims to develop a Bitcoin price trend direction classification system as a more objective technical analysis tool using the Random Forest ensemble learning method. Historical Bitcoin price data from January 2017 to March 2026 with an H1 (1-hour) timeframe was obtained from MetaTrader 5 and Binance. Raw price data (Open, High, Low, Close, Volume) were processed through preprocessing stages and converted into 106 technical indicator features grouped into nine categories, including Trend Indicators, Momentum Oscillators, Volatility Features, and Rolling Statistics. The Random Forest Classifier model was built and optimized through hyperparameter tuning with time-series cross-validation, then evaluated using standard classification metrics as well as trading metrics under scenarios employing a trailing stop mechanism. The results show that the model achieved an accuracy of 58.65%, precision of 41.38%, recall of 99.50%, and F1-score of 0.5856. Although the low AUC-ROC value (0.5106) indicates the model can hardly distinguish between the two classes individually, the backtest generated significant trading performance with a net profit of 8,491 USD from an initial capital of 10,000 USD, a win rate of 45.08%, and a profit factor of 1.30. Feature importance analysis identified RSI_14_std_168 (3.77%), SMA_200 (3.03%), and ATR_pct_14_mean_168 (2.92%) as the three most influential technical indicators. This study concludes that although standard classification accuracy is limited, the Random Forest model combined with probability threshold optimization and risk management strategies can serve as a beneficial technical analysis tool. The system has been implemented as an interactive web dashboard providing real-time prediction features.
Implementasi Chatbot Rekomendasi Produk Skincare Berbasis Website Menggunakan Metode NLP TF-IDF Dan Cosine Similarity Firman Anugrah; Nugraha; Indra Yustiana
Jurnal Sains Informatika Terapan Vol. 5 No. 3 (2026): Jurnal Sains Informatika Terapan (Oktober, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i3.1256

Abstract

Perkembangan industri kosmetik memunculkan fenomena choice overload dan asimetri informasi, di mana konsumen awam sering menyampaikan keluhan menggunakan bahasa sehari-hari, sementara spesifikasi produk dijelaskan dengan terminologi medis. Hal ini menyulitkan konsumen dalam menemukan produk yang tepat. Penelitian ini bertujuan merancang agen percakapan cerdas (chatbot) berbasis website sebagai sistem rekomendasi skincare untuk menjembatani kesenjangan bahasa tersebut secara komputasional. Pengembangan sistem mengadopsi metode Software Development Life Cycle (SDLC) dengan model Waterfall. Basis data yang digunakan mencakup 280 data produk primer dari 11 merek terkemuka. Teks masukan pengguna diproses melalui tahapan Natural Language Processing (NLP). Teks tersebut kemudian dibobotkan menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF), dan tingkat kemiripannya dihitung menggunakan metrik Cosine Similarity. Hasil evaluasi menunjukkan sistem berhasil beroperasi dengan baik. Pengujian Black Box membuktikan seluruh fungsionalitas berjalan 100% tanpa galat (bug). Pengujian akurasi algoritma mencatatkan skor relevansi yang tinggi dalam merekomendasikan produk. Selain itu, hasil User Acceptance Testing (UAT) terhadap 33 responden memperoleh skor rata-rata 3,98 (kategori Baik), yang mengonfirmasi tingginya tingkat penerimaan pengguna. Sebagai kesimpulan, sistem rekomendasi ini terbukti efektif memecahkan masalah asimetri informasi dan memberikan solusi perawatan kulit yang terpersonalisasi.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina adang badrujaman Ahmad Handoko Syahputra Alun Sujjada Anggy Pradiftha Junfithrana Azzahra Putri Dini Agnia Hardianty Dudih Gustian Eliadi Zalukhu Eneng Nuraeni Fadhlan Subhan Fadilah Faturrachman, Mirza Firman Anugrah Gina Purnama Insany Gina Purnama Insany Ilham Maulana Yusuf Imam Sanjaya Irvan Sutarha IVANA LUCIA KHARISMA Ivana Lucia Khrisma Kamdan Kania Purnarahayu Lutvita Dwi Iklima Mohamad Salman Farizi Muhamad Satibi Mulya Nindia Maulawati Nugraha Nuraiman Obi Ramdhani Satish Kumar Damodar Seno Prasetyo Somantri Somantri Sri Rahmawati Yunita Ramanda