Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Statistika dan Komputasi (STATKOM)
Ningsih, Eva Lestari
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Social Sciences

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

Found 2 Documents
Search

 1 
Optimalisasi Peramalan Total Aset PT. BPD Kaltim Kaltara dengan Double Exponential Smoothing Brown Ningsih, Eva Lestari; Nurmayanti, Wiwit Pura; Widyaningrum, Erlyne Nadhilah; Pangruruk, Thesya Atarezcha
Jurnal Statistika dan Komputasi Vol. 3 No. 2 (2024): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v3i2.3525

Abstract

Background: Total assets can provide a comprehensive picture of the wealth owned by a company or institution, with total assets also helping to assess the scale of operations, stability, and the company’s ability to meet its financial responsibilities. Study on the total assets held by PT. BPD Kaltim Kaltara is interesting to do because it has an important role in advancing economic growth in the East Kalimantan and North Kalimantan regions. Digital transformation can influence how assets grow and how capital is structured. Objective: Predicting PT BPD Kaltim Kaltara’s total assets over the next three periods using the DES Brown method with the optimal constant. Methods: Double Exponential Smoothing Brown (DES Brown) with constants α = β = 0.3; 0.6; 0.7; 0.8. Results: The smallest MAPE value is obtained at the constant α = β = 0.3, indicating that the DES Brown method with this constant provides the most accurate forecasting results. Conclusion: The forecasting results for the next three periods show a stable upward trend, namely September at Rp48,389,055.93, October at Rp48,480,301.62, and November at Rp48,571,547.30. Thus, the DES Brown method has proven effective in forecasting the total assets of PT. BPD Kaltim Kaltara and can be used to support the company's financial decision making.
Clustering Regency in Kalimantan Island Based on People's Welfare Indicators Using Ward's Algorithm with Principal Component Analysis Optimization Ningsih, Eva Lestari; Mahmuda, Siti; Hayati, Memi Nor
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.5363

Abstract

Cluster analysis is used to group objects based on similar characteristics, so that objects in one cluster are more homogeneous than objects in other clusters. One method that is widely used in hierarchical clustering is Ward's algorithm. This method works by minimizing the sum of squared distances between objects in one cluster (within-cluster variance) to produce optimal clustering. However, one important assumption in using this method is that there is no high correlation between variables, or in other words, the data must be free from multicollinearity. Multicollinearity can cause distortion in distance calculation, resulting in less accurate clustering results. To overcome this problem, a Principal Component Analysis (PCA) approach is used to reduce the dimension and eliminate the correlation between variables by forming several mutually independent principal components. This research aims to cluster 56 districts/cities in Kalimantan Island based on 19 indicators of people's welfare in 2023, using Ward's algorithm optimized through PCA. Validation of clustering results is done using the Silhouette Coefficient value to assess the quality of clustering. This research method is a combination of Principal Component Analysis (PCA) and hierarchical clustering using Ward’s algorithm. PCA was applied to reduce 19 welfare-related indicators into four principal components that retained most of the essential information in the dataset. The clustering process based on these components resulted in two optimal clusters, as determined by a Silhouette Coefficient value of 0.651, which indicates a moderately strong cluster structure. The results of this research are that the first cluster consists of 47 districts/cities characterized by relatively low welfare levels, while the second cluster comprises 9 districts/cities with comparatively higher welfare conditions. These findings imply the existence of considerable disparities in welfare among regions on Kalimantan Island. The results can be used as a reference for policymakers in formulating more targeted and equitable development strategies
Co-Authors Erlyne Nadhilah Widyaningrum Mahmuda, Siti Memi Nor Hayati Nurmayanti, Wiwit Pura Pangruruk , Thesya Atarezcha Pangruruk