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All Journal Science in Information Technology Letters Bulletin of Chemical Reaction Engineering & Catalysis
Sarina Sulaiman
Department of Biotechnology Engineering, Faculty of Engineering, International Islamic University of Malaysia, Kuala Lumpur
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT

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Factors Influencing open unemployment rates: a spatial regression analysis Purwaningsih, Tuti; Inderanata, Rochmad Novian; Pradana, Sendhyka Cakra; Snani, Aissa; Sulaiman, Sarina
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1202

Abstract

The present study employed spatial regression analysis as a methodological approach to get insights into the unemployment rates across Indonesian provinces in the year 2016. The official website of the Bureau of Labor Statistics (BPS) offers secondary data pertaining to several socio-economic indicators, including the Total Open Unemployment Rate, Economic Growth Rate, Human Development Index, Severity of Poverty Index, and School Participation Rates. The investigation employed the Geoda software package and encompassed Ordinary Least Squares (OLS) regression, Dependency/Correlation investigation, and Spatial Autoregressive Model. The data presented in the study revealed the existence of three distinct provincial groupings characterized by varying levels of unemployment rates. In the context of unemployment variance, the traditional regression model accounted for 30 percent of the observed variation. However, the spatial regression model used spatial dependencies to enhance accuracy in capturing the phenomenon. The aforementioned findings have the potential to assist policymakers in formulating strategies to address unemployment in regions characterized by distinct spatial attributes, hence offering a potential blueprint for other nations.
Web log augmented analytics and extraction for e-learning environment Mokhtar, Nur Azizah Mohammad; Sulaiman, Sarina; Pranolo, Andri
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1224

Abstract

E-Learning is a commonly used platform by most institutions, especially during the pandemic Covid-19. E-learning services include viewing, submitting, and uploading files, attempting quizzes, viewing forums, and downloading files. The data store in the servers grow on par with the increment of users in e-Learning@UTM every semester. As a result, the data have become extremely huge. These web log data can be used in augmented analytics to find meaningful insights. The web log data extracted are the log files of the history engagement of users and students’ grades. Data obtained are used in augmented analytics to study the pattern of the data and insights into meaningful information. This research focuses on classification of data through predictive analytics. Hence, predictive models are required. To prove a better outcome, building the model consists of three types of algorithms; Decision Tree, Artificial Neural Networks and Support Vector Machine which are used and compared. After extracting data from e-learning, the first step in building a predictive model is to do data collection, data pre-processing, and data transformation. These three classifiers use the pre-processed data and split the data into training and test sets afterwards. Each classifiers techniques are built and a confusion matrix is applied as a performance measurement to summarise the performance of a classification algorithm, respectively.
Enzymatic Transesterification Using Different Immobilized Lipases and its Biodiesel Effect on Gas Emission Mohamad Nor, Nur Fatin Syafiqah; Veny, Harumi; Hamzah, Fazlena; Muhd Rodhi, Miradatul Najwa; Kusumaningtyas, Ratna Dewi; Prasetiawan, Haniif; Hartanto, Dhoni; Sulaiman, Sarina; Sazali, Rozana Azrina
Bulletin of Chemical Reaction Engineering & Catalysis 2024: BCREC Volume 19 Issue 2 Year 2024 (August 2024)
Publisher : Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/bcrec.20143

Abstract

Biodiesel, a third-generation bio-fuels, offering several advantages over regular diesel fuel. Waste cooking oil (WCO) emerges as an ideal feedstock due to its availability and easy accessibility. In this work, biodiesel is utilized from two different types of immobilized lipases: Rhizomucor miehei lipase (RMIM) and Candida antarctica lipase B (CALB). The impact of the molar ratio of oil to methyl acetate (1:3-1:12) was evaluated for both lipases, and the resultant biodiesel was tested in diesel engine. The enzymatic transesterification was carried out in ultrasonic assistance and the results showed that the greatest yield of 81.20% at 45℃, using CALB as a biocatalyst, 1.8% (w/v) lipase and oil to methyl acetate molar ratio of 1:12 within 3 hours. Triacetin, by-product was determined their concentration for each molar ratio and analyzed using FTIR range of 500cm-1 to 4000cm-1, revealing a significant absorption peak at 1238.90cm-1. Biodiesel was blended with commercial diesel fuel in varying quantities of 7, 10, and 20% by volume (B20). The results were compared to Industrial Diesel Fuel 7% (B7) and Commercial Diesel Fuel 10% (B10). NOx and CO2 emission drops as the percentage of diesel/biodiesel blends increases, supporting WCO as a cost-effective biodiesel feedstock with low petrol pollution.
Co-Authors Andri Pranolo Dhoni Hartanto Hamzah, Fazlena Haniif Prasetiawan Inderanata, Rochmad Novian Mohamad Nor, Nur Fatin Syafiqah Mokhtar, Nur Azizah Mohammad Muhd Rodhi, Miradatul Najwa Pradana, Sendhyka Cakra Ratna Dewi Kusumaningtyas Sazali, Rozana Azrina Snani, Aissa Tuti Purwaningsih, Tuti Veny, Harumi