Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA
Lili Tanti
Computer Science, Computer Science and Engineering, Potensi Utama University
Computer Science & IT

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

Found 2 Documents
Search

 1 
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm Erica Rian Safitri; Lili Tanti; Wanayumini Wanayumini
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44286

Abstract

This study aims to develop a predictive model for forecasting product sales using the Multilayer Perceptron (MLP) algorithm. The model's performance was evaluated using key metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The model achieved an MAE of 0.861, an MSE of 9.521, and an impressive R² score of 0.999, demonstrating its ability to accurately predict product sales with minimal error. Feature correlation analysis identified key variables related to the target prediction, which is the number of products ready for shipment, underscoring the importance of feature selection in enhancing model performance. Prediction results revealed variability among product sales, with products like Foodpak Matte 245 (Code 49) predicted to sell approximately 244.31 units, while others like Stiker Kertas (Code 90) showed lower sales forecasts. The findings suggest that strategic interventions may be necessary to boost sales for underperforming items and capitalize on the demand for popular products. Future improvements, such as optimizing the network architecture, experimenting with activation functions and optimization algorithms, and incorporating external factors such as market trends, could further enhance the model’s accuracy and predictive power. Overall, the MLP model demonstrates strong potential for product sales forecasting, providing valuable insights for business decision-making.
Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining Hartati Tammamah Lubis; Roslina Roslina; Lili Tanti
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44285

Abstract

The rapid growth of network data has increased the complexity of detecting anomalies, which are crucial for ensuring the security and integrity of information systems. This study investigates the use of the Isolation Forest algorithm for anomaly detection in network traffic, utilizing the Luflow Network Intrusion Detection dataset, which contains 590,086 records with 16 features related to network activities. The methodology encompasses data preprocessing (cleaning, normalization, and feature scaling), feature selection (bytes in, bytes out, entropy, and duration), model training, and performance evaluation. The results demonstrate that Isolation Forest can effectively identify anomalies based on feature patterns, isolating suspicious data points without the need for labeled datasets. However, performance metrics, such as accuracy (42.92%), precision (14.37%), recall (2.87%), and F1-score (4.79%), reveal challenges such as high false-positive rates and low sensitivity to true anomalies. These findings highlight the potential of the algorithm for dynamic, high-dimensional datasets but also indicate the need for further improvements through hyperparameter tuning, feature engineering, and alternative approaches. This study contributes to the development of adaptive anomaly detection frameworks for network security and suggests future integration into real-time systems for proactive threat mitigation. The study's findings are particularly relevant for enhancing network security in environments such as corporate and governmental networks, where real-time anomaly detection is crucial.
Co-Authors Erica Rian Safitri Hartati Tammamah Lubis Roslina Roslina Wanayumini