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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Sistem Informasi dan Teknologi Jaringan
Rahma Syahri
Universitas Pembangunan Panca Budi
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Prediction of Digital Marketing Campaign Success Using Deep Neural Network Models Mayang Modelina Cynthia; Rahma Syahri; Muhammad Hafizh Al-Ghifari Rangkuti; Muhammad Akbar Firdaus; Rido Favorit Saronitehe Waruwu; Yasoziduhu Halawa; Tita Ritonga
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 6 No 2 (2025): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v6i2.33

Abstract

The rapid growth of digital advertising platforms has generated large volumes of complex and nonlinear campaign performance data, making accurate prediction of campaign success increasingly challenging. Traditional machine learning approaches often struggle to fully capture these nonlinear relationships. Therefore, this study proposes a Deep Learning approach using a Deep Neural Network (DNN) to predict the success of digital marketing campaigns based on key performance indicators such as impressions, clicks, CTR, CPC, CPM, engagement rate, and conversions.This research follows the CRISP-DM framework, including data understanding, preprocessing, model development, training, and evaluation. The dataset was obtained from digital advertising platform performance reports and processed through data cleaning, feature scaling, and train–test splitting. The proposed DNN model consists of multiple fully connected layers with ReLU activation functions and is optimized using the Adam optimizer. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC.The experimental results show that the proposed Deep Learning model achieves an accuracy of 87.6%, precision of 86.9%, recall of 85.8%, F1-score of 86.3%, and ROC-AUC of 0.91, indicating strong predictive performance. These findings demonstrate that Deep Learning effectively captures complex patterns in digital marketing data and provides reliable insights to support data-driven marketing decision-making.
ANALISIS PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING DAN DOUBLE MOVING AVERAGE PADA KASUS PERAMALAN PENJUALAN KOPI BUBUK DI LOPO MANDHELING COFFEE Andysah Putera Utama Siahaan; Siska Mayasari Rabe; Nathania Asyifa; Maha Valne Datin; Rahma Syahri; Rezkinah Rambe
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4199

Abstract

 Abstract: This study aims to compare the accuracy between time series forecasting methods, namely Double Exponential Smoothing (DES) and Double Moving Average (DMA), in forecasting ground coffee sales at Lopo Mandheling Coffee, an MSME in the beverage sector. The sales data used is monthly data from January 2024 to March 2025. The analysis was carried out by calculating the accuracy of each method using the MAPE, MAD, and MSE indicators. The results show that the DMA method provides more accurate forecasting results than the DES method for both types of coffee (Specialty and Premium). DMA has lower MAPE, MAD, and MSE values, so it is more recommended for use in sales forecasting in this MSME. This study provides a practical contribution for MSME actors in improving operational efficiency through a data-driven forecasting approach. Keyword: Sales forecasting; double exponential smoothing; double moving average;                 ground coffee; MSME Abstrak: Penelitian ini bertujuan sebagai perbandingan akurasi antara metode peramalan deret waktu, yaitu Dpuble Exponential Smoothing (DES) dan Double Moving Avarega (DMA), dalam meramalkan penjualan kopi bubuk di Lopo Mandheling Coffee, sebuah UMKM di sektor minuman. Data penjulan yang digunakan merupakan data bulanan dari Januari 2024 hingga Maret 2025. Analisis dilakukan dengan menghitung akurasi masing-masing metode menggunakan indikator MAPE, MAD, dan MSE. Hasil penelitian menunjukkan bahwa metode DMA memberikan hasil peramalan yang lebih akurat dibandingkan metode DES untuk kedua jenis kopi (Specialty dan Premium). DMA memiliki nilai MAPE, MAD, dan MSE yang lebih rendah, sehingga lebih direkomendasikan untuk digunakan dalam peramalan penjualan pada UMKM ini. Penelitian ini memberikan kontribusi praktis bagi pelaku UMKM dalam meningkatkan efisiensi operasional melalui pendekatan peramalan berbasis data. Kata kunci: Peralaman penjualan; double exponential smoothing; double moving average; kopi bubuk; UMKM
Co-Authors Andysah Putera Utama Siahaan Maha Valne Datin Mayang Modelina Cynthia Muhammad Akbar Firdaus Muhammad Hafizh Al-Ghifari Rangkuti Nathania Asyifa Rezkinah Rambe Rido Favorit Saronitehe Waruwu Siska Mayasari Rabe Tita Ritonga Yasoziduhu Halawa