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All Journal ComEngApp : Computer Engineering and Applications Journal JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Varian ESTIMASI: Journal of Statistics and Its Application Jurnal Diferensial HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Enthusiastic : International Journal of Applied Statistics and Data Science MATHunesa: Jurnal Ilmiah Matematika Buletin Sistem Informasi dan Teknologi Islam Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Data Mining and Information Systems Kohesi: Jurnal Sains dan Teknologi Indonesian Journal on Data Science Parameter: Jurnal Matematika, Statistika dan Terapannya Emerging Statistics and Data Science Journal Teknologi : Jurnal Ilmiah Sistem Informasi
Shindi Shella May Wara
Universitas Pembangunan Nasional Veteran Jawa Timur
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Public Health Social Sciences Other

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Analisis Sentimen Ulasan Aplikasi Maxim Merchant dengan Support Vector Machine (SVM) dan Random Forest Rizkiyah, Selly; Rizqin, Indira Zein; Putri, Milla Akbarany Baktiar; Wara, Shindi Shella May; Hindrayani, Kartika Maulida
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.4765

Abstract

The development of digital technology, especially mobile devices, has led to an increase in application-based services. One important aspect in app development is to deeply understand user perception and satisfaction. This study aims to analyze user sentiment towards the Maxim Merchant application based on reviews obtained from the Google Play Store platform. A total of more than 2800 Indonesian-language reviews were collected using web scraping techniques. The review data was processed through pre-processing stages such as text cleaning, normalization, tokenization, removal of unimportant words, and stemming. Sentiments are categorized into positive and negative based on the review score, where scores of 1 to 3 are considered negative, and scores of 4 and 5 are considered positive. Word cloud visualization is used to show the dominant words of each sentiment category. The data is then converted into numerical form using TF-IDF and selected using the Chi-Square method. Classification was performed using Support Vector Machine and Random Forest algorithms. The evaluation results show that the Support Vector Machine algorithm performs better in classifying sentiment, especially in handling high-dimensional text data.
Sharpe Ratio-Based Dynamic Crypto Asset Allocation with Trend Filtering Using SMA Fauzan Adziima, Andri; Wara, Shindi Shella May; Nasrudin, Muhammad; Pratama, Alfan Rizaldy
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art1

Abstract

This paper proposes a dynamic cryptocurrency asset allocation strategy that combines Sharpe Ratio-based weighting with trend filtering using the Simple Moving Average (SMA) of Bitcoin (BTC). The model reallocates capital among a portfolio of seven major cryptocurrencies (BTC, ETH, BNB, SOL, TON, TRX, XRP) every three days, conditional on BTC trading above its respective SMA threshold (50-day, 100-day, or 200-day). When BTC trends below the SMA, the strategy shifts fully to USDT to minimize downside risk. Using historical data from January 1, 2024, to January 1, 2025, the study evaluates performance across three SMA configurations and benchmarks against a buy-and-hold baseline. Results show that the SMA-50 strategy achieved the highest cumulative return (+231.51%) and Sharpe Ratio (2.51), significantly outperforming both the longer SMA-based models and the baseline average return (+132.14%). Risk analysis indicates that shorter SMA windows allow more responsive exposure during market uptrends but increase short-term volatility. Overall, the findings support the use of hybrid strategies combining trend-following filters and risk-adjusted allocation for managing crypto portfolios in volatile environments.
Co-Authors AA Sudharmawan, AA Abda Abda Abdillah, Indah Rahma Adziima, Andri Fauzan Ahmad, Davin Anezta Alfan Rizaldy Pratama Alzam, Muhammad Arsyad Amellia Harmaimun Hidayah Amri Muhaimin Ananta, Aditya Putra Andri Faudzan Adziima Anugrah, Muhammad Cahya Raka Aviolla Terza Damaliana Damaliana, Aviolla Terza Diana Novitasari Dwi Mahardhika, Rivaldi Elmaliyasari, Shifa Endah Setyowati Fauzan Adziima, Andri Fernando, Moch. Firman Firqi Nashrullah, Ahmad Indira Kaffi, Laisal Karina Auralia Kartika Maulida Hindrayani Khairunisa, Adenda Kresna Wira Yudha, I Nyoman Kurniawan, Muhammad Erlangga Mahardhika, Rivaldi Dwi Marthalia Kusumarima Maulana, Mohammad Hikmal Maulida Hindrayani, Kartika Mega Rizkya Arfiana Muhaimin, Amri Muhammad Nasrudin Nabila Yudhitya Larasati Nashrullah, Ahmad Firqi Navsih, Muhammad Ghinan Nugroho, Muhammad Rafli Feandika Nur Indah Nirmalasari Pradipta, Alvino Hadiyan Pratiwi, Nanda Aulia Putri, Maretta Fairuz Luthfia Winoto Putri, Milla Akbarany Bakhtiar Putri, Milla Akbarany Baktiar rachmanto, Nugroho Fajar Ramadhani, Dafinah Rhomaningtias, Lina Rizkiyah, Selly Rizqin, Indira Zein Rusdiyanto, Nur Rahmat Saputra, Wahyu Syaifullah Jauharis Terza Damaliana, Aviolla Wahyu Syaifullah Jauharis Saputra Wiyono, Farhan Syah Putra Zahwa, Aniq Farichatus