Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA)
Farikhin
Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Engineering Materials Science & Nanotechnology Physics

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

Found 2 Documents
Search

 1 
Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints Keszya Wabang; Oky Dwi Nurhayati; Farikhin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4498

Abstract

Unsatisfactory public services encourage the public to submit complaints/ reports to public service providers to improve their services. However, each complaint/ report submitted varies. Therefore, the first step of the community complaint resolution process is to classify every incoming community complaint. The Ombudsman of The Republic of Indonesia annually receives a minimum of 10,000 complaints with an average of 300-500 reports per province per year, classifies complaints/ community reports to divide them into three classes, namely simple reports, medium reports, and heavy reports. The classification process is carried out using a weight assessment of each complaint/ report using 5 (five) attributes. It becomes a big job if done manually. This impacts the inefficiency of the performance time of complaint management officers. As an alternative solution, in this study, a machine learning method with the Naïve Bayes Classifier algorithm was applied to facilitate the process of automatically classifying complaints/ community reports to be more effective and efficient. The results showed that the classification of complaints/ community reports by applying the Naïve Bayes Classifier algorithm gives a high accuracy value of 92%. In addition, the average precision, recall, and f1-score values, respectively, are 91%, 93%, and 92%.
Acceptance and Success of Oss Rba (Online Single Submission Risk Based Approach) Information System Using the Utaut Ii and Delone & Mclean Models Prantiastio; Farikhin; Rinta Kridalukmana
Jurnal Penelitian Pendidikan IPA Vol. 9 No. 11 (2023): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i11.5958

Abstract

This research, conducted in South Sumatra Province, employs the Unified Theory of Acceptance and Use of Technology II (UTAUT II) and the DeLone & McLean Model to assess user satisfaction and system success in the OSS RBA implementation. This study utilized PLS-SEM software to model the research, employing a quantitative approach through Likert-scale questionnaires. The research focused on business actors in South Sumatra who registered their permits on the OSS RBA platform, where 41.129 businesses completed registration in 2022. Adhering to sampling criteria, the sample size was set at 250 samples to ensure credibility, balancing the number of parameters and indicators for latent variables, as 25 indicators were present. This research findings reveal that Performance Expectancy, Price Value, System Quality, and Service Quality significantly influence User Satisfaction, subsequently User Satisfaction significantly influence Net Benefit. Conversely, Effort Expectancy, Social Influence, and Information Quality do not significantly affect User Satisfaction. These insights provide a comprehensive understanding of the evolving business licensing landscape in South Sumatera Province
Co-Authors Keszya Wabang Oky Dwi Nurhayati Prantiastio Rinta Kridalukmana