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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika bit-Tech Jurnal PADMA: Pengabdian Dharma Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Abdi Jurnal Publikasi Journal of Research and Publication Innovation Indonesian Journal of Multidisciplinary on Social and Technology
Nasrul Hidayah
Universitas Pamulang
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : bit-tech

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The Application of Data Mining in Predicting Cryptocurrency Prices Using the Support Vector Machine (SVM) Method: Indonesia Ria Ester; Nasrul Hidayah; Dede Handayani
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2245

Abstract

Cryptocurrencies have rapidly emerged as one of the most exciting financial technology innovations in recent years. Among the various digital assets, XRP (Ripple) is one of the most popular, experiencing significant price fluctuations. This study aims to apply the Support Vector Machine (SVM) method in predicting the price of the XRP cryptocurrency, in the hope of providing a clearer picture of the investment prospects. The data used in this study includes the daily price movements of XRP from 2019 to 2023. In the research process, the date variable is selected as the input feature, and the closing price as the output to be predicted. Various kernel functions in SVM, including RBF, Polynomial, and Sigmoid, were tested to determine which one gave the best results. The results showed that the Polynomial kernel produced a Mean Absolute Percentage Error (MAPE) value of 45.40%, indicating better accuracy compared to other kernels. This study also explains the importance of choosing the right kernel function and overcoming the problem of underfitting that may occur due to the high volatility characteristics of cryptocurrencies. These findings not only enrich the understanding of machine learning techniques but also provide new insights for investors in data-based decision making. Recommendations for future research include the use of alternative prediction models and the integration of external information that can affect prices.
Integrating AHP and SAW for Verified Supplier Selection Decision Support in Small Retail Nasrul Hidayah; Angga Suryadi; Intan Kumalasari
bit-Tech Vol. 9 No. 1 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v9i1.4271

Abstract

Supplier selection is a critical procurement decision in small retail because supplier performance directly affects stock availability, delivery reliability, cost efficiency, and service continuity. This study develops and evaluates a web-based decision support system integrating the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) for supplier selection in a single small-retail case in Depok, Indonesia. AHP was used to derive weights for five criteria price, distance, product quality, service, and delivery performance while SAW normalized and aggregated the performance of ten supplier alternatives into final preference scores. The system was implemented using PHP and MySQL and assessed through independent computational cross-checking, black-box testing, white-box basis-path testing, and user acceptance testing. Delivery performance received the highest weight (0.5004), followed by distance (0.2527), price (0.1084), service (0.0796), and product quality (0.0589), with the consistency ratio remaining below 0.10. CV. Prima Sentosa ranked first with a preference value of 0.9118, narrowly ahead of PT. Rukun Mitra Sejati Cab. Depok at 0.9022. All 31 black-box test cases passed, the selected white-box modules achieved complete basis-path coverage, and system calculations matched independent calculations to four decimal places, demonstrating computational fidelity. User acceptance testing produced a mean rating of 97.00%, although lower scores for usability and interface-related aspects indicate that further refinement is needed. The study contributes an end-to-end, traceable AHP–SAW implementation that links preference elicitation, ranking, software verification, and formative user evaluation. The findings support the feasibility of the approach for the studied retail context, while broader generalizability and ranking robustness require further validation.
Co-Authors Angga Suryadi Dede Handayani Eko Sutono Erdi Sutriyatna Intan Kumalasari Maulana Muhamad Sulaiman Muhamad Arief Yulianto Rahmawati Rahmawati Rahmawati Rahmawati Raihan Asbi Hilal Ria Ester Romi Andrianto Supiyan, Dede Tahlia Jelita Putri