SAGA: Journal of Technology and Information System

Anticipation of Deep Discharge of Batteries in Solar Panels Based on the Particle Swarm Optimization (PSO)Algorithm

2024-07-02T10:00:11+00:00

Renewable energy is an alternative type of energy that can be used continuously without fear of environmental pollution. One of the things that can be used to get new renewable energy is using solar panels where later the electrical energy produced will be stored in batteries. A part from the advantages of a battery that can store electrical energy, but if it is used excessively and causes the battery to experience a deep discharge condition, steps are needed to over come it.This study uses the Particle Swarm Optimization method as an auxiliary method. And in this research it has been found that the auto switch system can charge battery 1 and battery 2 when the voltage level is below 11.88V or the DoD is already at 55% and stops charging when the voltage level is above 12.86Vfor battery 1and batteries 2.

Learner’s View on English Language Teaching by Hybrid System Technology in Supporting Maritime Sector

2024-07-02T10:54:05+00:00

Human Resource Development becomes the key to change the better condition. One of components which influence its development is by mastering the international language. Indonesia as the archipelago country must be strong on maritime sector followed by potential human resource development. One of the ways which could be done is by improving the communication skill by using English Language. English has been introduced for learners since they are in the kindergarten. They have learned English for more than 12 years after they graduate in level of Senior High School. The research question in this study was how was the learners' view on English Learning to become young generation having good communication skill? This research aimed to know the learner's view by reflecting their prior experiences in English learning that had been done. This study conducted a descriptive quantitative by using survey to get the data. The instrument of the research referred to the skill of communication components reflected to the learners' experiences in English learning process since the long period of time. There were 30 learners delivering their ideas on English learning to answer the research question. The result showed that the learners had the obstacles dominantly in linguistics, social and culture, even psychological factors. It could be concluded that becoming a good communicator by using English, it needs the exploration on the learner's view in order to omit his or her obstacle in learning or acquiring English Language. It's really hoped that this research could give the discourse for English learners and teachers so young generation could have good communication skill by using English to support the maritime sector as the nations in archipelago country.

Implementation of K-Nearest Neighbour (KNN) Algorithm and Random Forest Algorithm in Identifying Diabetes

2024-03-05T08:47:52+00:00

Diabetes, one of the noncommunicable diseases (NCDs), is currently a major health threat worldwide. So far, diabetes symptoms have only been diagnosed by people according to known physical characteristics without the support of factual evidence or other medical considerations. With the advancement of technology, it is possible to use algorithms to solve various kinds of problems. One of artificial intelligence (AI), machine learning, concentrates on creating systems that can learn from data. This research uses the K-Nearest Neighbor (KNN) and Random Forest algorithms that can be utilised as testing algorithms to identify diabetes. Classification is done based on training data that has been provided in the dataset. The purpose of this research is to determine the best classification in identifying diabetes with the K-Nearest Neighbor (KNN) algorithm and the Random Forest algorithm and is expected to provide more understanding of the implementation of machine learning models. comparing the two algorithms between the KNN algorithm and the Random Forest algorithm. By dividing the testing data and training data using a ratio of 20%: 80% randomised data 300 times. The results of the accuracy evaluation obtained from the Confusion Matrix show that the Random Forest Algorithm has the best accuracy value of 77%, Precision 89%, Recall 78% and F1-Score 83% with an estimator of 100 trees. While the KNN algorithm obtained accuracy of 73%, Precision 87%, Recall 73% and F1-Score 79% of the value of K = 7. Based on the comparison results of the two algorithms, it shows that the accuracy value obtained is greater than the Random Forest algorithm even though the value obtained is not much different.

Monte Carlo Simulation Application for Project Scheduling Improvements in The Shipping Industry

2024-07-11T09:29:24+00:00

This research was carried out at PT. X is a shipping yard that produces one of them, namely ship blocks. In the implementation of ship block building projects, there is uncertainty in the project's duration, so it needs to be completed by applying the Monte Carlo simulation method. Monte Carlo Simulation is a method used to model and analyze systems with risks and uncertainties. The basis of the Monte Carlo simulation is to conduct experiments on probabilistic elements through random sampling. This study aims to identify the probability of duration and percentage of possibility of the period in a project so that scheduling using Microsoft Project software is more measurable and optimal. Based on the Monte Carlo simulation results in ball software, the fastest duration for the H402 ship block project was obtained, namely 64 days, with a 0% probability of success. The longest time was 83 days with a chance of 100%, and the average duration for 73 days with a possibility of 65%, while the plan duration was 80 days with a probability of 94.7%. Then for the H501A block work, the fastest time produced is 74 days with a chance of 0%, the longest most extended is 82 days with a probability of 100%, and the average duration for 79 days with a possibility of 60-70% while the time of the plan is for 75 days with a probability of 9.8%.

Adaptable Architecture for the Digital Transformation of the Cameroonian University Ecosystem: A FORM/BCS Approach

2024-06-26T06:13:08+00:00

The university education ecosystem in most developing countries where demographics are evolving exponentially, faces the problem of the massification of education with its corollaries in terms of teacher shortages and deterioration in the quality of training, Cameroon is not left out. Faced with this situation, most stakeholders agree that the digital transformation of the ecosystem makes it possible to tackle this problem. Several solutions are proposed, ranging from the digitization of training materials downloadable by learners to the implementation of hybrid training combining the face-to-face model with distance training in direct streaming, including models “semi-face-to-face single-teacher multi-room synchronous” training courses, thus qualified because it would consist of exploiting ICT to allow the same teacher to be, through dedicated screens, simultaneously followed in several classrooms by learners with the possibility for the latter to communicate in real time with him thanks to judiciously positioned cameras. However, whatever the formula adopted, problems persist such as time which is not always sufficient, the shortage of teachers and insufficient infrastructure. Thus, this article, through the analysis of the proposed solutions and the use of the software product line engineering paradigm, proposes a formula and adaptable reference architecture for a computer system making it possible to deal with the problem of massification of teaching