Indonesian Journal of Innovation and Applied Sciences (IJIAS)

Journal Information
EISSN : 2775-4162
Published by: Literacy Institute (10.47540)
Total articles ≅ 41

Latest articles in this journal

Ayesha Mohsin, Abeera Shehzad, Fatima Bilal, Fatima Imran, Sana Akhtar, Syeda Anna Fatima
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 80-87;

As everything in this world evolves or changes, so does our climate. Scientists have now proved that climate change is happening at a much faster rate than before. Pakistan is one of the most vulnerable continents to climate change impacts. Pakistan is considered as 7th most vulnerable country to climate change. Recently in Lahore, many major events occurred due to climate change like the occurrence of smog. The present study was conducted in 4 different tertiary institutions of Lahore, Pakistan. A descriptive survey design was specially employed for this study which used a stratified random sampling method for selecting the students. Moreover, a structured questionnaire titled climate change awareness was developed for collecting data from the students based on their level of awareness. According to this survey, 49.1% of the students know about the policies government is making regarding climate change. 62.5% of the respondents agreed that they have the necessary information to prepare for the impacts of climate change. The result of the findings showed a moderate level of awareness about climate change among the students. Awareness of climate change is an important ingredient for the successful implementation of climate change policy in the country. By improving the climate services and raising awareness about climate change and once it starts to grow it can be integrated into local, national, and sectoral development plans.
Muhammad Wasim Tasleem, Sehrish Rana Rajpoot, Muhammad Irfan, Muhammad Asad, Mehboob Ahmad, Farah Zafar
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 18-22;

The primary goal of this study was to investigate the prevalence of vitamin B12 insufficiency in various age groups. It is descriptive cross-sectional research in which 180 individuals were assessed for vitamin B12 from the district of Bahawalpur. Patients were placed into groups based on their ages. BMI and serum B12 levels were analyzed and a questionnaire was filled out by them. Data were statistically analyzed through T-test, ANOVA followed by post-hoc Dunnette T3 test, and frequency by Chi-square test. Results revealed that Vitamin B12 statically differed in groups of marital status, sun exposure/day/hour, diet groups, diabetic groups, and age groups. While BMI differed according to different weight groups, sun exposure/day/hour, diabetic groups. The frequency of all the groups statistically differed in all the groups. BMI had a negative correlation with the Vitamin B12 level. It was concluded that the male population was not at risk but the old population at the risk of low levels of vitamin B12.
Mithu Rana, Shahidul Islam, Ariful Islam
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 31-42;

The study has focused on the condition of children in the community, initiatives of tertiary sociology students for successful engagement, changes of the slum-dwelling children, and attainment of graduate and professional skills. A mixed-method of social survey, FGDs (Focus Group Discussion), and participant observation with close and open-ended questionnaires, face-to-face interviews, checklists, and Likert scale techniques have been used to collect primary data. The findings of the study show that students’ engagement helps to meet children’s educational, socialization, health care, and co-curricular needs and rights and some other needs of community people. It brings benefits for engaged students building stronger relations with graduate institutions, different organizations, and job providers, and attaining several graduate and professional skills, and abilities to become successful in personal and professional life.
Weilun Tang
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 43-49;

As the improvement of people’s living standards, more and more motor vehicles are emerging on the roads, which pose a great threat to the bicycle riders on the same routes. Among all the road accidents, more than half are caused by the inadequacy of bicycle signals. To guarantee the safety of these bicycle riders, it is important to show the real-time status of bicycles. Basically, the research method of this paper is design, simulation, and virtual verification. Based on STM32 microcontroller, the overall design scheme of an intelligent bicycle status indication apparatus was proposed, and the hardware and software design was specified. After scheme design, simulation was carried out and showed that with high-performance STM32 microcontroller, the apparatus can promptly show the status of bicycles, and the alarm will ring when other vehicles are in the dangerous range. Theoretical analysis which includes graphical analysis and calculation was successfully conducted, which proved that this design can effectively serve as a status indication apparatus and protect the bicycle riders from collisions.
Sana Mohsin Babbar, Tameer Hussain Langah
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 12-17;

Energy in any form is a vital source of producing electricity for daily utilization. Wind energy source as renewable energy is playing a pivotal role in generating power from electric gird owing to environmentally friendly feature. Due to the volatile and intermittent nature of wind energy, fluctuations and disparities occur in installing, monitoring, and planning in an energy management system. Therefore, forecasting and prediction are promising solutions to address mismanagement at the grid. Consequently, machine learning tools specifically neural networks have created a huge impact in forecasting wind power. In this study, the feed-forward neural network is adopted for predicting wind power. Additionally, for having precise and efficient results, different training models i.e. one-step sacent, resilient propagation, Bayesian regularization, scaled-conjugate gradient back propagations, and Levenberg-Marquardt are used to make the comparative analysis. From the simulations and results, it was concluded that Bayesian regularization training model is performing best and achieving high accuracy by obtaining 1.66 of RMSE and 6.06 of %MAPE. Eventually, it is concluded that neural networks can be a good choice to predict wind power for optimal solutions. Moreover, the proposed model can be applied to other renewable energy source predictions.
Sirajuddin, Hafied Cangara, Dadang A. Suriamihardja, Andi Alimuddin Unde
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 23-30;

The purpose of this study is to analyze the significant contribution of communication systems in the form of information sharing (X1), collaboration (X2), and coordination (X3) through the use of media (Y1) to disaster management in Kendari and North Konawe Regency, Southeast Sulawesi Province. This research method uses a survey method with a quantitative research approach. In this study, the variables in question are information sharing (X1), collaboration (X2), coordination (X3) through the media (Y1). Sampling using a proportional sampling technique of 60 samples. Data collection techniques through questionnaires to respondents for primary data, while observation and interviews as complementary methods for data collection. The calculations for reliability testing are carried out through SPSS 25 computer program. The result of the study shows that The communication system for natural disaster emergency response in the form of information sharing (X1), collaboration (X2), coordination (X3) through the use of media (Y1) has a significant and robust effect simultaneously on disaster management in reducing the risk of victims in Kendari, and North Konawe Regency, Southeast Sulawesi Province.
Adeleye Adeniyi Olarewaju, Abdullahi Ibrahim, Bate Garba Barde Barde, Amoo Florence Kemi, Asaju Catherine Iyabo, Yerima Mohammed Bello
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 1-11;

This research aimed to assess the mycological indoor air quality of the grains’ grinding mills situated in the Dutse ultra-modern market. A simple random sampling method was employed to select nine (9) shops where grains are milled. Settle plate method through the use of fifty-four (54) sterile sabouraud dextrose agar (SDA) plates was adopted for fungal isolation in the morning, afternoon and evening. Fungal isolates were subsequently identified using standardized methods. Results obtained indicate that depending on the sampling period and operation status of the grinding mills, all the sampling points examined were heavily contaminated with total mean fungal load in the morning (4084 CFU/m3), evening (3867 CFU/m3), and afternoon (3818 CFU/m3). However, the mean fungal load obtained in the morning from shop C (6426 CFU/m3) was significantly different from other shops (p< 0.05) while the mean fungal loads obtained across all the shops in the afternoon and evening were not significantly different from each other (p> 0.005). Mucor plambeaus (22.22%), Aspergillus flavus (16.67%), Aspergillus niger (20.37%), Fusarium spp. (22.22%) and Penicillium spp. (18.52%) were isolated across the grinding mills. Results obtained indicate that depending on the time of the day and operation status of the grinding mills, the studied indoor environment allowed fungal aerosols to build up which could serve as a potential reservoir of fungal infections. It is therefore recommended that safety measures should be adopted with a view to reducing fungal pollution at the grains’ grinding mills.
NasserA Habbat, Houda Anoun, Larbi Hassouni
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 60-67;

Twitter Sentiment Analysis is the task of detecting opinions and sentiments in tweets using different algorithms. In our research work, we conducted a study to analyze and compare different Algorithms of Machine Learning (MLAs) for the classification task, and hence we collected 37 875 Moroccan tweets, during the COVID-19 pandemic, from 01 March 2020 to 28 June 2020. The analysis was done using six classification algorithms (Naive Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest classifier) and considering Accuracy, Recall, Precision, and F-Score as evaluation parameters. Then we applied topic modeling over the three classified tweets categories (negative, positive, and neutral) using Latent Dirichlet Allocation (LDA) which is among the most effective approaches to extract discussed topics. As result, the logistic regression classifier gave the best predictions of sentiments with an accuracy of 68.80%.
Shiblee Nomani, Rasel, Imran Khan Reedoy
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 68-79;

This study attempts to examine the climate change in Bangladesh as a cause of industrialization. Over the last few decades, pollution of the environment has become a significant concern in the case of Bangladesh. Both qualitative and quantitative data were utilized to write this article. Primary and secondary data on the environment, national policy, and technology have been gathered. Research results show that rapid and unplanned industrialization has turned into the main cause of the endangered environment. The toxic waste materials of industries are dumped into water and ground, causing air pollution, water pollution, and soil pollution. As a result, the people of the riverbank are suffering a lot. Though industrial development is very much required for a country’s development, it is also undermining the environment which will destroy the natural balance and impose a long-term effect on climate in near future. In Bangladesh, industries are developed in an unplanned and centralized way without following any particular guidelines. The poor waste management system of industries are polluting rivers and toxic emission is polluting the air as well. Natural resources are used by the industries, causing an imbalance in nature. Forests are cut down massively, which increases the chance of various natural disasters. Industrialization has a long-term effect on climate change which also increases the average temperature of the earth known as global warming. Climate change also increases the chance of various natural disasters, unemployment, food scarcity, diseases, and extinction of wildlife.
Mohd Zaidi Abd Rozan
Indonesian Journal of Innovation and Applied Sciences (IJIAS), Volume 2, pp 50-59;

Impact Digital Entrepreneurship Apprentice Program ([email protected]) at Ministry of Higher Education Malaysia 2021 is a comprehensive nationwide six-month program. Forty-three teams consist of 43 Academic Supervisors, 129 institutes of Higher Learning students, and 43 Micro & Small Enterprises (MSE) owners conducted by Universiti Teknologi Malaysia. The program is aimed to develop capable students in maneuvering the digital business world. Students underwent an online Business and Digital Training, with apprenticeship and formal reportings. This article aims to present the impact of [email protected] activities by analyzing 43 case studies produced in the program. A pre-codification scheme that concentrates on the study goals was the method for data collection. Before the program, all the teams were informed of the required components to ensure uniformity of the report. The evidence of significant gain and impact on the MSEs businesses was drawn from the components. Other than the components, analytics hindsight, visual appeal, persuasion ability, perception on paid ads, posting timing, and synergies beyond the digital world activities were gathered, providing richer information and insights that increase business value. Such lessons are beneficial to all parties as all businesses are demanded to utilize digital platforms nowadays.
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