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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sistem Komputer Jurnal Teknologi Informasi dan Ilmu Komputer Infotech Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Komtika (Komputasi dan Informatika) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Share : Journal of Service Learning JURIKOM (Jurnal Riset Komputer) JURTEKSI Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Ilmiah Edunomika (JIE) INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics G-Tech : Jurnal Teknologi Terapan Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotech: Journal of Technology Information Jurnal Teknik Informatika (JUTIF) Journal Computer Science and Informatic Systems : J-Cosys Jurnal Dinamika Informatika (JDI) Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Journal of Comprehensive Science Duta Abdimas: Jurnal Pengabdian Masyarakat Biner : Jurnal Ilmiah Informatika dan Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Journal Research of Social Science, Economics, and Management Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Journal Of Artificial Intelligence And Software Engineering SmartComp CSRID Jurnal Komtika (Komputasi dan Informatika) Jurnal Teknik Informatika dan Teknologi Informasi Edcomtech: Jurnal Kajian Teknologi Pendidikan
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Journal : journal of artificial intelligence and software engineering

Modeling Of Centralized Exchange (CEX) Crypto Asset Platform Recommendation System Using Collaborative Filtering Utomo, Diva Reihan Ferdian; Nastiti, Faulinda Ely; Suryani, Fajar
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7179

Abstract

The rapid growth of crypto assets and the variety of Centralized Exchange (CEX) platforms make it difficult for traders to choose a platform that fits their preferences. This research aims to model a recommendation system for CEX platforms using Collaborative Filtering. User rating data for several CEX (Binance, Bybit, Bitget, Tokocrypto, Indodax) were collected via questionnaire. The K-Nearest Neighbors With Means (KNN With Means) method with cosine similarity is used to predict ratings based on the similarity of preferences between users. The model was trained and tested with a 75:25 train-test split. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used as evaluation metrics. Test results show low MAE and RMSE values (around below 1.0 on a 1–5 rating scale), indicating that the recommendations generated are quite accurate. It can be concluded that the Collaborative Filtering approach is effective in recommending CEX platforms according to user needs. This recommendation system is expected to assist traders – especially beginners – in choosing the right exchange more objectively.
Predicting Indonesian Inflation Rate Using Long Short-Term Memory (LSTM) Wijaya, Muhammad Krisna; Nastiti, Faulinda Ely; Farida, Anisatul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7178

Abstract

Inflation is a crucial economic indicator that requires an accurate prediction model. This research aims to develop a prediction system for the monthly inflation rate in Indonesia using the Long Short-Term Memory (LSTM) architecture. The method includes historical data acquisition from Bank Indonesia, preprocessing with Min-Max Scaler normalization, and training a univariate LSTM model. Evaluation results show excellent performance with an MAE of 0.2999, an RMSE of 0.3903, and an R² of 0.8796, indicating the model explains 88% of the data's variability. It is concluded that LSTM is effective for inflation forecasting in Indonesia and serves as a solid baseline for future research.
Co-Authors Aghni Rizqi Ni'mal 'Abdu Aghni Rizqi Nimal Abdu Agung Wicaksono Agustina Srirahayu Al Ayyubi, Raihan Abdurrahim Anisatul Farida Aprilisa Arum Sari Ardian Pamungkas Ardiyanto, Marta Arif Wicaksono Septyanto Arya Kusumadewa Aziiz, Dear Whizkid Benaya Chessa Sarmanela Bondan Wahyu Pamekas Dedy Hariyadi Dwi Hartanti Eko Purwanto Erfanti Fatkhiyah Ety Meikhati Fauziah Fanny Fazlurrahman Bima Fazlurrahman Fazlurrahman Fazlurrahman Hadi, Muhammad Heri Gunawan Husniati Mafatihus Solehah Ilyas, Daffa Imam Riadi Indraswari, Elsa Fachrisa Irawan, Ridwan Dwi Jayawarsa, A.A. Ketut Khotimah, Agil Husnul Kusumawati, Novi Manase Rezata Purba Margaretha Evi Yuliana Marta Ardiyanto Maulindar, Joni Mei Purweni Mira Erlinawati Moh. Muhtarom, Moh. Muftiyanto, Taufiq Nur Muhammad Alwan Nurdin Muhammad Frasha Candra Perdana Muhammad Ikhsanudin, Muhammad Musa, Shahrulniza Nindya Dwi Anggana Nur Muftiyanto, R. Taufiq Nurdin, Muhammad Alwan Nurmalitasari Nurmalitasari Nurohman, Nurohman Oktaviani, Intan Permatasari, Hanifah Pipin Widyaningsih Pradana, Rico Yoga Prasetyo, Yoga Andrian Prita Haryani Prita Haryani Purweni, Mei Ramadhan, Navin Ramasya Alief Islami Raihan Rifal Bayu Ardi Rina Arum Prastyanti Ringgo Ismoyo Buwono Riska Rosita Rudi Susanto Setyawan, Yanuar Anggit Singgih Purnomo Sopingi SRI SUMARLINDA Suryani, Fajar Tariq tino, Valen Toni Iksanudin Triana Triana Udhata Swardana, Avila Umar Choirul Hadi Uning Kristiana Utomo, Diva Reihan Ferdian Vihi Atina Virgian Galang Sasongko Vita Aryadi Vita Sofia Prihatini Wijaya, Muhammad Krisna Wijiyanto Wijiyanto, Wijiyanto Yafi, Eiad