Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Pengabdian Nasional (JPN) Indonesia
Nurmayanti, Laily
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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

Found 2 Documents
Search

 1 
Sistem Data Mining Penentuan Prioritas terhadap Penerima Bantuan Bencana Banjir dengan Metode Naive Bayes dan Klusterisasi K-Means (Studi Kasus: Wilayah Cengkareng 2025) Sarimole, Frencis Matheos; Nurmayanti, Laily
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jpni.v6i3.1609

Abstract

This research develops a ranking system for flood aid recipients in Jakarta, focusing on Cengkareng, by utilizing K-Means and Naïve Bayes algorithms. Data were obtained from Satu Data Jakarta (2025), comprising 158 records with attributes including region, sub-district, village, average water level, affected RWs, families, individuals, and flood events. The analytical workflow encompasses data cleaning and normalization, risk level clustering using K-Means (three categories: high, medium, low), and predictive classification with Naïve Bayes. Model evaluation at training-testing splits of 70:30, 80:20, and 90:10 reveals that the combined K-Means and Naïve Bayes approach achieves the highest accuracy of 98.18%, significantly outperforming conventional Naïve Bayes which reached only 43.47%. This improvement demonstrates the effectiveness of combining both algorithms for complex data classification. The developed system expedites the prioritization process, facilitates local teams in verifying recipient lists, and enhances the precision of aid distribution and evacuation. Field simulations with community members were conducted to assess the system’s practical implementation and ensure direct access to flood risk information. Future development will focus on integrating external variables such as real-time rainfall data and expanding field testing to other regions.
Prediksi Motif Batik dengan Menggunakan Metode Gabor Filter Convolution Neural Network Bili, Yudisman Ferdian; Tundo; Sutisna, Nandang; Putri, Atsilah Daini; Yuliantoro, Dita Tri; Nurmayanti, Laily
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 3 (2025): JULI-SEPTEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i3.3798

Abstract

This research aims to develop a batik motif classification system by utilizing Convolutional Neural Network (CNN) and Gabor Filter, in order to increase accuracy in texture feature extraction. The batik dataset used goes through a preprocessing stage, which includes normalization and data augmentation. During training, the model was tested with 10,000 iterations, using the Adam optimizer and the Categorical Cross-Entropy loss function, and evaluated via a confusion matrix. Test results show accuracy reaching 87%, with a precision and recall value of 90% each, and an F1-score of 89%. This method has proven effective for classifying batik motifs and has the potential to be applied in the fields of education, textile industry and cultural preservation.
Co-Authors Bili, Yudisman Ferdian Putri, Atsilah Daini Sarimole, Frencis Matheos Sutisna, Nandang Tundo Yuliantoro, Dita Tri