Article Per Year (5 Year)
p-Index From 2021 - 2026
1.8
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Creative Information Technology Journal JURTEKSI Jurnal Sistem Informasi Kaputama (JSIK) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Dharma Bhakti Ekuitas Jurnal ICT : Information and Communication Technologies Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka PUBLIDIMAS
Lili Tanti, Lili
Universitas Potensi Utama
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
DESIGN AND CONSTRUCTION OF SOIL MOISTURE DETECTION TOOL USING ANDROID BASED DECISION TREE ALGORITHM Aziz Ritonga, Mirwan; Tanti, Lili
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4194

Abstract

Abstract: Soil moisture is an important factor in determining the watering needs of plants for optimal growth. Therefore, accurate monitoring of soil moisture is necessary. This research aims to design and build a soil moisture detection tool based on the Decision Tree algorithm with the support of the YL-69 sensor for humidity measurement and the DHT11 sensor for temperature measurement to increase data accuracy. This system uses NodeMCU ESP8266 as a microcontroller and is integrated with an Android application as a user interface. Sensor interpretation data is analyzed using the Decision Tree algorithm to determine soil conditions (dry, damp or wet). The test results show an accuracy level of 95% from 300 data samples. Thus, this system is able to detect soil moisture effectively and can help increase the efficiency of crop management on a household and commercial agricultural scale. Keywords: agriculture, android, decision tree algorithm, sensors, soil moisture detection
Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining Lubis, Hartati Tammamah; Roslina, Roslina; Tanti, Lili
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.
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm Safitri, Erica Rian; Tanti, Lili; 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.
PELATIHAN PRESENSI ONLINE MENGGUNAKAN GOOGLE FORM DI YAYASAN PENDIDIKAN ISLAM AR-RIDHA Adhar, Deni; Safrizal, Safrizal; Tanti, Lili; Fahrozi, Wirhan
Jurnal Pengabdian Masyarakat Sabangka Vol 3 No 03 (2024): Jurnal Pengabdian Masyarakat Sabangka
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/sabangka.v3i03.1082

Abstract

The use of online attendance at several schools in the North Sumatra region can help efficiency in reducing physical materials (paper) and can be directly monitored/evaluated online. Currently, the school at the Ar-Ridha Foundation, one of the attendance methods implemented online, still uses WhatsApp to monitor online attendance. Due to many obstacles, sending attendance via WhatsApp is considered less effective. One of the barriers reported was the requirement for teachers to review each student's data submission individually. Using Google Forms to create virtual courses is one way teachers can apply online learning. Google Forms is a Google product. Google Forms is a free online program available to schools, non-profit organizations, and anyone with a Google Account. Using Google Forms will facilitate interaction between teachers and students. Google Forms makes it easy for students and teachers to stay in touch. Google Forms is a blended learning platform that Google developed for schools seeking to create, distribute, and assign paperless assignments
Co-Authors adhar, Deni Adhar, Deni Adhar Aziz Ritonga, Mirwan Bob Subhan Riza, Bob Subhan Budi Triandi, Budi Hartama, Dedy Iwan Fitrianto Rahmad Juli Iriani, Juli Lubis, Hartati Tammamah Pairin, Yusfrizal Bin Poningsih Poningsih, Poningsih Ridho, Irdian Riza, Bob Subahan Roslina Roslina, Roslina Safitri, Erica Rian Safrizal Safrizal Safrizal Salwani D, Zuki Salwani D Susianto Susianto Thanri, Yan Yang Wanayumini Windy, Micky Alviansyah Wirhan Fahrozi, Wirhan