Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Technology and Computer (JOTECHCOM)
Harahap, Solianna
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
Analyzing the Impact of Information Systems Digitalization on Organizational Performance through the Technology Acceptance Model (TAM) Sitompul, Novian Paisal; Haqki, Bay; Harahap, Baginda; Harahap, Solianna; Panggabean, Erwin
Journal of Technology and Computer Vol. 3 No. 3 (2026): August 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The digitalization of information systems has become a key strategy for organizations seeking to improve operational efficiency, decision-making quality, and overall organizational performance. However, the successful implementation of digital technologies depends largely on users’ acceptance and willingness to adopt such systems. This study aims to analyze the impact of information systems digitalization on organizational performance using the Technology Acceptance Model (TAM). The research examines the relationships between perceived usefulness, perceived ease of use, user acceptance, and organizational performance. A quantitative research approach was employed by collecting data through structured questionnaires distributed to employees who actively use digital information systems within their organizations. The collected data were analyzed using Structural Equation Modeling (SEM) to evaluate the proposed research model and test the hypotheses. The findings indicate that perceived usefulness and perceived ease of use significantly influence user acceptance, which subsequently contributes to improved organizational performance. These results highlight the importance of designing user-friendly and beneficial digital information systems to maximize organizational outcomes. The study provides valuable insights for organizations in developing effective digital transformation strategies and enhancing sustainable organizational performance through technology adoption.
Application of the K-Means Clustering Algorithm to Categorize Pregnant Women's Knowledge and Attitudes Towards Smoking During Pregnancy Affandi, Egi; Harahap, Baginda; Harahap, Solianna
Journal of Technology and Computer Vol. 3 No. 3 (2026): August 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Smoking and secondhand smoke exposure during pregnancy remain a significant maternal and child health risk, yet conventional descriptive analysis tends to treat knowledge and attitude scores separately, making it difficult to identify combined risk profiles for targeted health education. This study applies K-Means Clustering, a data mining technique from computer science, to group pregnant women based on the combination of their knowledge and attitude scores toward smoking during pregnancy. Data were collected from 30 respondents through a questionnaire covering 10 knowledge items and 10 Likert-scale attitude items. The knowledge and attitude percentage scores were standardized (Z-score) and used as clustering features. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, both of which pointed to k = 3 as the most interpretable solution, yielding a final Silhouette Score of 0.687. The resulting clusters were labeled Good (mean knowledge 86.0%, mean attitude 86.5%), Moderate (65.0%, 66.0%), and Poor (43.0%, 41.2%), each containing 10 respondents. The Poor cluster was dominated by housewives with the highest average parity, indicating a priority target group for smoking-related health education programs. These findings demonstrate that K-Means Clustering can serve as a practical decision-support tool for prioritizing maternal health interventions based on combined knowledge-attitude profiles, complementing conventional descriptive statistics commonly used in public health research.
Co-Authors Affandi, Egi Baginda Harahap Erwin Panggabean Haqki, Bay Sitompul, Novian Paisal