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All Journal Journal of Applied Data Sciences Solidaritas: Jurnal Pengabdian Journal of Digital Market and Digital Currency
Wahid, Arif Mu’amar
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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Exploring the Impact of Discount Strategies on Consumer Ratings: An Analytical Study of Amazon Product Reviews Berlilana, Berlilana; Wahid, Arif Mu’amar; Fortuna, Dewi; Saputra, Alfin Nur Aziz; Bagaskoro, Galih
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.163

Abstract

This research delves into the influence of discount strategies on consumer ratings within the e-commerce landscape, particularly on Amazon. A logistic regression model assessed how discount percentages and product categories affect consumer ratings. The study followed a rigorous methodology, beginning with comprehensive data collection across diverse product categories on Amazon. This was succeeded by a detailed exploratory data analysis (EDA), data preprocessing, and subsequent model building. The model was then subjected to an extensive evaluation process, encompassing accuracy, precision, recall, F1-score, and ROC-AUC metrics. The evaluation revealed that the model achieved an accuracy of 74.94%, a precision of 72.69%, and a recall of 74.94%. The F1 score was calculated at 69.26%, and the ROC-AUC score was notably 78.24%. These metrics underscore the model’s capability to accurately predict consumer ratings influenced by discount strategies. Key findings highlighted the significant predictive power of discount percentages and specific product categories, particularly 'Home Kitchen', suggesting a complex relationship between discounts, product types, and consumer responses. Theoretically, the study enriches the understanding of consumer behavior in e-commerce, highlighting the nuanced impact of discount strategies on consumer satisfaction, especially in online retail contexts. For e-commerce businesses and marketers, the findings underscore the importance of strategically employing discount strategies and tailoring marketing approaches to specific product categories. This study emphasizes managing customer expectations and maintaining product quality alongside discounts. This research provides valuable insights for optimizing e-commerce strategies and paves the way for future investigations. It opens up avenues for further exploration into factors like product quality, brand reputation, shipping times, and the potential of consumer segmentation and sentiment analysis in enhancing marketing effectiveness. The study marks a significant contribution to the field by linking discount strategies with consumer ratings, using advanced data analytics to inform e-commerce practices in the digital age.
Peningkatan Kapasitas Digital Berkelanjutan pada PAC GP Ansor Kroya Riyanto, Andi Dwi; Wahid, Arif Mu’amar; Pratiwi, Aniec Anafisah
Solidaritas: Jurnal Pengabdian Vol. 4 No. 2 (2024): Solidaritas: Jurnal Pengabdian
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat UIN Prof. K.H. Saifuddin Zuhri

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Abstract

Skill digital merupakan kompetensi dasar yang harus dimiliki oleh organisasi di era digital. GPAnsor Cabang Kroya mengalami permasalahan tidak semua organisasi ranting memiliki mediainformasi dan promosi. Pengelolaan media sosial yang dimiliki masih minimalis dan memerlukan pendampingan. Tujuan dari program ini adalah untuk meningkatkan kapasitas digital danmengatasi kesenjangan penggunaan media sosial di tingkat ranting. Program ini melibatkanserangkaian pelatihan praktis yang fokus pada pembuatan konten, interaksi dengan pengguna,dan strategi penggunaan platform digital untuk memaksimalkan jangkauan dan pengaruh sosial. Metodologi pelaksanaan meliputi diskusi awal, pelatihan interaktif, mentoring, monitoring,dan evaluasi. Output kegiataan adalah dnegan menekankan pada peningkatan jumlah akun aktif,kualitas konten, konsistensi publikasi, dan interaksi dengan pengikut. Evaluasi akhir menunjukkan peningkatan signifikan dalam keaktifan dan kualitas pengelolaan media sosial diantara peserta. Saran untuk perbaikan meliputi penerapan pelatihan virtual yang lebih luas dan pengembangan materi pelatihan yang lebih adaptif untuk mendukung keberlanjutan keterlibatan digitaldi masa depan.
Time Series Analysis of Bitcoin Prices Using ARIMA and LSTM for Trend Prediction Berlilana; Wahid, Arif Mu’amar
Journal of Digital Market and Digital Currency Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i1.1

Abstract

This study investigates the efficacy of ARIMA and LSTM models in predicting Bitcoin prices, emphasizing the importance of accurate price prediction for trading, risk management, and investment strategies in the volatile cryptocurrency market. The objectives are to analyze Bitcoin prices to identify underlying patterns and trends, compare the predictive performance of ARIMA and LSTM models, and provide insights into their practical applications for Bitcoin price prediction. A comprehensive dataset of Bitcoin prices from January 1, 2011, to December 31, 2023, sourced from CoinMarketCap, was used. Data preprocessing included handling missing values, removing duplicates, achieving stationarity through differencing, and normalizing data using MinMaxScaler. The ARIMA model's best-fitting parameters were identified using ACF and PACF plots, and it was trained with the statsmodels library. The LSTM model involved data preparation through windowing and train-test splitting, constructing a neural network with LSTM layers, and training using TensorFlow/Keras. Evaluation metrics included Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with comparisons based on accuracy and computational efficiency. The ARIMA model demonstrated impressive performance with an MAE of 2.308392356829177e-215 and an RMSE of 0.0, indicating a near-perfect fit to the training data. The LSTM model achieved an MAE of 0.00021804577826689423 and an RMSE of 0.00021916977109865863, showing robust performance in handling nonlinear and long-term dependencies. The ARIMA model excelled in computational efficiency with a training time of 2.548070192337036 seconds and a prediction time of 0.0009970664978027344 seconds, while the LSTM model required 378.69622468948364 seconds for training and 0.6859967708587646 seconds for prediction. The results highlight ARIMA's effectiveness in capturing linear trends and its suitability for short-term trading strategies, while LSTM is better for long-term investment strategies due to its ability to model complex patterns. Despite potential overfitting in ARIMA and high computational demands for LSTM, the study suggests exploring hybrid models, incorporating additional data sources, and developing advanced techniques to enhance predictive accuracy in future research.
Co-Authors Andi Dwi Riyanto Bagaskoro, Galih Berlilana Berlilana Berlilana Dewi Fortuna Pratiwi, Aniec Anafisah Saputra, Alfin Nur Aziz