Article Per Year (5 Year)
p-Index From 2021 - 2026
2.064
P-Index
This Author published in this journals
All Journal SMARTICS Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Informatika Global JSAI (Journal Scientific and Applied Informatics) Jurnal Abdimas Mandiri Jurnal Informasi dan Teknologi Reswara: Jurnal Pengabdian Kepada Masyarakat J-ENSITEC (Journal of Engineering and Sustainable Technology)
Evi Purnamasari
Universitas Indo Global Mandiri
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Faktor Evaluasi Usabilitas dalam Sistem e-learning dengan Panduan Tinjauan Sistematik PRISMA: Indonesia Indah Permatasari; Peny Meliaty Hutabarat; Evi Purnamasari
J-ENSITEC (Journal of Engineering and Sustainable Technology) Vol. 9 No. 02 (2023): June 2023
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jensitec.v9i02.3662

Abstract

The usability evaluation of e-learning system is an important stap to understanding the quality of the interaction between the user and the system in supporting the learning process. To understan the current approaches, a systematic review was condusted using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines. A systematic review was carried out on 6 (six) scientific databases within a certain publication period. After applying various exclusion criteria, there were fifteen documents for further analysis. An analysis of the full text of the selected articles was conducted to see the approach used in evaluating the usability of the e-learning system. The author found as many as 51 factors that became criteria in assessing the usability of e-learning systems. In general, the various existing approaches refer to the two basic approaches that have been offered by previous researchers. The latest approaches in assessing the usability of e-learning systems are still modifications and adjustments from the approaches that have been proposed previously. Modifications were made to adapt to the evaluation context and use of the studied e-learning system.
Prediksi Tingkat Keamanan Terhadap Pencurian Menggunakan Naive Bayes di Wilayah Sektor Kepolisian Merapi Barat Sutria Rahmi; Indah Permatasari; Evi Purnamasari
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13919

Abstract

Theft remains one of the most prevalent forms of criminal activity in the jurisdiction of the West Merapi Police Sector, significantly impacting public safety and community security. Historically, the handling and securing of this region has been reactive in nature, lacking a predictive system capable of estimating theft risks preventively. This study aims to develop a predictive model for regional security levels related to theft cases using a machine learning approach. The data utilized in this research comprises secondary data obtained from 459 theft case reports documented by the West Merapi Police Sector from 2021 to 2024. Ten relevant variables were selected as features, while three security level categories (Low, Medium, and High) served as target classes. Data preprocessing included data cleaning, variable transformation, and label encoding. The Naive Bayes algorithm was employed with a 70% training data and 30% testing data split. The results demonstrated that the Naive Bayes method achieved an accuracy of 76.09% in predicting regional security levels. The model exhibited optimal performance for the High security level class, while the Low class showed lower performance due to imbalanced data distribution. This research demonstrates that police case report data can be effectively utilized to support data-driven risk analysis and has the potential to serve as a decision-making tool for preventive measures by law enforcement agencies.
Co-Authors Agustina Heryati Doly Nofiansyah Dona Marcelina Dwi Asa Verano Endang Sri Lestari Endang Sri Lestari Hafiz Hidayatullah Husni Mubarat Indah Permatasari K. Ghazali K. Ghazali Leksi Saputra Lesi Hertati Muhammad Adli Dary Hanif Nazori Suhandi Nuraini Nuraini Peny Meliaty Hutabarat Saputra, Tedy Setiawan Sutria Rahmi Terttiaavini, Terttiaavini