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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) Edcomtech Jurnal Kajian Teknologi Pendidikan Jurnal Komtika (Komputasi dan Informatika) Share : Journal of Service Learning JURTEKSI Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Ilmiah Edunomika (JIE) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotech: Journal of Technology Information 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 Biner : Jurnal Ilmiah Informatika dan Komputer Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Journal Of Artificial Intelligence And Software Engineering SmartComp CSRID Jurnal Komtika (Komputasi dan Informatika)
Faulinda Ely Nastiti
Universitas Duta Bangsa Surakarta
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics Social Sciences

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Journal : Journal Of Artificial Intelligence And Software Engineering

 1 
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 Nimal Abdu Agustina Srirahayu Anisatul Farida Aprilisa Arum Sari Ardian Pamungkas Arif Wicaksono Septyanto Atina, Vihi Aziiz, Dear Whizkid Dedy Hariyadi Fazlurrahman Bima Fazlurrahman Fazlurrahman Fazlurrahman, Fazlurrahman Hartanti, Dwi Heri Gunawan Indraswari, Elsa Fachrisa Jayawarsa, A.A. Ketut Khotimah, Agil Husnul Kusumawati, Novi Maulindar, Joni Mei Purweni Mira Erlinawati Musa, Shahrulniza Nindya Dwi Anggana Nur Muftiyanto, R. Taufiq Nurdin, Muhammad Alwan Nurohman, Nurohman Oktaviani, Intan Pamekas, Bondan Wahyu Permatasari, Hanifah Pipin Widyaningsih Pradana, Rico Yoga Prasetyo, Yoga Andrian Purweni, Mei Rifal Bayu Ardi Rina Arum Prastyanti Setyawan, Yanuar Anggit Singgih Purnomo Sopingi Sri Sumarlinda Sumarlinda, Sri Suryani, Fajar Toni Iksanudin Triana Triana Uning Kristiana Utomo, Diva Reihan Ferdian Vita Aryadi Wijaya, Muhammad Krisna Wijiyanto Wijiyanto, Wijiyanto Yafi, Eiad