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All Journal Jurnal Pekommas Journal of Information Technology and Computer Engineering
Nofriani, Nofriani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Harnessing Technology Acceptance Model (TAM) on Information System to Safeguard Accelerated Data Collecting and Processing Amid COVID-19 Pandemic Nofriani, Nofriani; Fatichuddin, Moh.
Jurnal Pekommas Vol 7 No 1 (2022): June 2022
Publisher : Sekolah Tinggi Multi Media “MMTC” Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jpkm.v7i1.4222

Abstract

COVID-19 has penetrated every aspect of human civilization. BPS-Statistics Indonesia, a government institute responsible for conducting statistical surveys across the country, also faces challenges in the aftermath. COVID-19 forces social distancing, preventing data collectors from meeting the respondents in person and collecting data offline. The data collecting that uses printed questionnaires and the centralized data processing in BPS Headquarters Office causes delays in the whole process. The issue needs to be resolved using a reliable system that can fasten the entire procedure without printed questionnaires, decreasing person-to-person contact and decentralizing the data processing to users' ends. This research proposes and evaluates SIDUBES, an information system BPS-Statistics of Bengkulu Province, to collect data on large and medium manufacturing surveys by harnessing the Technology Acceptance Model (TAM). The model evaluates users' perception of Perceived Usability (P.U.), Perceived Helpfulness (P.H.), Perceived Assurance (P.A.), Viewpoint of Using (V.U.), and Continuity Intention (CI) in measuring the acceptance level. The results show that SIDUBES have met most of the users' requirements; V.U. and P.H. have a positive effect on CI; P.U. has a positive impact on P.A., and; P.A. has a positive effect on P.H.
Machine Learning Application for Classification Prediction of Household’s Welfare Status Nofriani, Nofriani
JITCE (Journal of Information Technology and Computer Engineering) Vol. 4 No. 02 (2020)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.72-82.2020

Abstract

Various approaches have been attempted by the Government of Indonesia to eradicate poverty throughout the country, one of which is equitable distribution of social assistance for target households according to their classification of social welfare status. This research aims to re-evaluate the prior evaluation of five well-known machine learning techniques; Naïve Bayes, Random Forest, Support Vector Machines, K-Nearest Neighbor, and C4.5 Algorithm; on how well they predict the classifications of social welfare statuses. Afterwards, the best-performing one is implemented into an executable machine learning application that may predict the user’s social welfare status. Other objectives are to analyze the reliability of the chosen algorithm in predicting new data set, and generate a simple classification-prediction application. This research uses Python Programming Language, Scikit-Learn Library, Jupyter Notebook, and PyInstaller to perform all the methodology processes. The results shows that Random Forest Algorithm is the best machine learning technique for predicting household’s social welfare status with classification accuracy of 74.20% and the resulted application based on it could correctly predict 60.00% of user’s social welfare status out of 40 entries.
Co-Authors Fatichuddin, Moh.