Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Alfis Arif
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Transportation Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pelatihan dan Implementasi Smart Sewing Machine (SSM) Guna Peningkatan Efisiensi Produksi dan Pengurangan Waste Kain pada Komunitas Penjahit Kriya Wanita Syahri, Riduan; Edowinsyah; Alfis Arif
LOSARI: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2025): Desember 2025
Publisher : LOSARI DIGITAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53860/losari.v7i2.536

Abstract

Textile waste is one of the largest contributors to solid waste, requiring innovative handling. Community partners, especially tailors and recycling activists, often face challenges in production efficiency, waste management, and digital market access. This community service aims to address these challenges through comprehensive training and technology utilization. The method employed involves training, mentoring, and hands-on practice focusing on three main aspects: 1) Textile Waste Utilization using creative upcycling techniques, 2) Simple Business Management to enhance business sustainability, and 3) Digital Marketing for recycled products. The main innovation in this activity is the introduction and practice of using SSM (Smart Sewing Machine), which can significantly increase the speed and precision of recycled product production. The results show a significant improvement in partners' technical skills in processing textile waste into economically valuable products, increased understanding of financial and inventory management, and expanded market access through digital platforms. This training successfully transformed the perception of textile waste into a sustainable business opportunity. It is hoped that this activity can serve as a model for both waste reduction and sustainable income generation for the community
Prediksi Cryptocurrency Berbasis LSTM Menggunakan Multi Modal Indikator Trading(Studi: Ethereum dan Solana) Arya; Yogi Isro' Mukti; Alfis Arif; -, Sigit Candra Setya
BETRIK Vol. 17 No. 01 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/83kqq228

Abstract

The dynamic development of the cryptocurrency market causes digital asset prices to experience high volatility, making it difficult for investors to accurately predict price movements. Therefore, an analytical method is needed to model price movement patterns in time series data. This study aims to develop a cryptocurrency price prediction model for Ethereum and Solana using the Long Short-Term Memory (LSTM) method with a multi-modal trading indicator approach. The dataset used consists of historical price data including open, high, low, close, trading volume, and technical indicators such as Exponential Moving Average (EMA), Relative Strength Index (RSI), and Bollinger Bands. The research process follows the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. The data were processed through normalization and time series windowing, with a training and testing data split of 80:20. The evaluation results using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) indicate that the model has good predictive performance. The Ethereum model produced an RMSE value of 129.08 and a MAPE of 3.26%, while the Solana model produced an RMSE of 8.30 and a MAPE of 3.63%. The developed model was also implemented in a Streamlit-based dashboard to visualize prediction results interactively, helping users monitor and analyze cryptocurrency price movements.
Co-Authors -, Sigit Candra Setya Arya Edowinsyah Syahri, Riduan Yogi Isro' Mukti