Engineering, MAthematics and Computer Science Journal (EMACS)

Comparison HOR and AHP Methods in Risk Mitigation of Line Pipe Procurement

Muhammad Arwan Kholid, Christian Harito — Fri, 31 May 2024 00:00:00 +0000

OCTG (Oil Country Tubular Goods) is a type of pipe used for oil and gas exploration activities. To meet the demands for the fulfillment of Line Pipe material needs at PT Pertamina EP. The results of the analysis and identification of risk factors from 3 Subjet Matter Expert (SME) in Line Pipe material procurement activities. From 13 Process Activities, 16 Risk Events (Ei) and 35 Risk Agents (Aj) were obtained. In House of Risk (HOR) 1, the results of the calculation of the Aggregate Risk Potentials (ARPj) value of 35 Risk Agents (Aj), the highest Aggregate Risk Potentials (ARPj) with a value of 810. In House of Risk (HOR) 2, the results of the calculation of the Effectiveness to Difficulty ratio (ETDk) value of 4 Preventive Action (PAk), the highest Effectiveness to Difficulty ratio (ETDk) value with a value of 4860. In the Analytic Hierarchy Process (AHP), the results of the calculation of the Consistency Ratio value of 5 Criteria Weight the highest Criteria value with a percentage of 45.4% and the Consistency Ratio of 4 Alternatives the highest Alternative value with a percentage value of 44.06%. The best alternative solution in the selection of mitigation strategies for contract type selection is “TFC (Technical Framework Contract)” with the highest percentage and value. The TFC (Technical Framework Contract) contract type is in accordance with the PTK-007 Revision 5 Chapter IV Contract guidelines.

Prediction of Sudden Cardiac Death with Feature Selection Using Particle Swarm Optimization

David, Sani Muhamad Isa — Fri, 31 May 2024 00:00:00 +0000

The heart, a vital organ responsible for pumping oxygenated blood through blood vessels, is susceptible to disturbances in heart rate that can have adverse effects. According to data from the World Health Organization (WHO) since 2000, this disease has experienced the most significant increase in fatalities, rising from over 2 million to 8.9 million deaths. The prediction of Sudden Cardiac Death (SCD) continues to gain attention as a promising approach to saving millions of lives threatened by the occurrence of the disease. In this study, we propose the utilization of Particle Swarm Optimization (PSO) as a feature selection method to train the Support Vector Machine (SVM) and Logistic Regression. By employing the proposed algorithm, SCD can be predicted up to 30 minutes before the onset with an accuracy of 92.5%, by using PSO and SVM. Features are extracted from Heart Rate Variability (HRV) analysis and Discrete Wavelet Transform (DWT) obtained from ECG records of MIT-BIH normal sinus rhythm database & MIT-BIH Sudden Cardiac Death Holter database dataset. This paper also compares feature selection algorithm of PSO and Analysis of Variance (ANOVA) and found that PSO is better in accuracy, recall, and F1-score.

Overview of Text Based Personality Prediction Using Deep Learning

Kelvin, Yesun Utomo — Fri, 31 May 2024 00:00:00 +0000

Text-Based Personality Prediction (TBPP) has garnered increasing attention in recent years, particularly within the frameworks of the Myers-Briggs Type Indicator and the Big Five Personality Model. This study presents a comprehensive systematic review of TBPP methodologies, focusing specifically on research published since 2017. Leveraging Google Scholar, a meticulous selection process was employed to identify and analyze papers meeting relevance criteria. The selected studies were analyzed for research design, data collection methods, preprocessing techniques, and modeling approaches. Notably, the study identifies prevalent Natural Language Processing methods utilized in TBPP, such as Recurrent Neural Networks, Convolutional Neural Networks, Long Short-Term Memory networks, ensemble methods, and pre-trained models like BERT. Results indicate that combining knowledge graphs with Bi-LSTM models achieved the highest accuracy for Big Five traits at 71.5%, while a BERT-CNN-RNN ensemble reached 85% accuracy for MBTI. The synthesized findings offer valuable insights into the current landscape of TBPP, with the aim of informing both researchers and practitioners. Furthermore, the study provides recommendations for future research directions, emphasizing the importance of refining methodologies and addressing challenges to foster continued innovation in personality prediction within the TBPP domain.

Antiviral Medication Prediction Using A Deep Learning Model of Drug-Target Interaction for The Coronavirus SARS-COV

Renaldy Fredyan — Fri, 31 May 2024 00:00:00 +0000

Graph convolutional neural networks (GCNs) have shown promising performance in modeling graph data, particularly for small-scale molecules. Message-passing neural networks (MPNNs) are an important form of GCN variant. They excel at gathering and integrating particular information about molecules via several repetitions of message transmission. This capability has resulted in major advances in molecular modeling and property prediction. By combining the self-attention mechanism with MPNNs, there is potential to improve molecular representation while using Transformers' proven efficacy in other artificial intelligence disciplines. This research introduces a transformer-based message-passing neural network (T-MPNN) that is intended to improve the process of embedding molecular representations for property prediction. Our technique incorporates attention processes into MPNNs' message-passing and readout phases, resulting in molecular representations that are seamlessly integrated. The experimental results from three datasets show that T-MPNN outperforms or equals cutting-edge baseline models in tasks involving quantitative structure-property connections. By studying case studies of SARS-COV growth inhibitors, we demonstrate our model's ability to graphically depict attention at the atomic level. This enables us to pinpoint individual chemical atoms or functional groups linked with desirable biological properties. The model we propose improves the interpretability of classic MPNNs and is a useful tool for investigating the impact of self-attention on chemical substructures and functional groups in molecular representation learning. This leads to a better understanding of medication modes of action.

The Centralize Data by Utilizing CMIS to Assist Church Leaders in Making Strategic Plans (Case Study: XYZ Church)

Putri Sanggabuana Setiawan — Fri, 31 May 2024 00:00:00 +0000

Congregations are social entities that require a sense of community to function effectively. One of the most prominent locations for community engagement is the church. The diverse needs of congregants drive their attendance at church, including worship, social interaction, and spiritual growth. To fulfill these needs, churches offer a wide range of programs. To effectively address these needs, churches must have a clear understanding of the requirements of their congregants. A church management information system (CMIS) provides insight into the congregation's needs, enabling church leaders to comprehend their congregants' requirements and develop a strategic plan. The XYZ Church CMIS and data governance were developed based on a comprehensive literature review and input from IT professionals engaged in group discussions. This research's objective was to develop a CMIS to collect and manage data on the congregation and church. The insights from the CMIS have enabled church leaders to adjust event schedules. Prior to the implementation of the CMIS, the average attendance at events was below 20%. A review of the CMIS data over a 12-month period, in conjunction with the insights gained from group discussions with church leaders, revealed that event attendance had risen to above 70%. The church management information system has proven to be an effective tool for church leaders to create strategic plans for event scheduling, resulting in increased event attendance. In future research, it would be beneficial to consider not only enhancing event attendance but also increasing the number of congregants

Supply Chain Sustainability Measurement in Telecommunications Industry in Indonesia

Muhammad Asrol, Abdullah Nabil — Fri, 31 May 2024 00:00:00 +0000

In the fiercely competitive telecommunications industry of Indonesia, the significance of innovation and perpetual improvement cannot be underestimated. It is imperative for businesses to explore the potential of sustainable supply chain management, as it allows for the integration of environmental, economic, and social dimensions. By implementing a comprehensive and thorough approach, this study offers a theoretical framework for measuring sustainable supply chain performance. Through qualitative and quantitative methods, you'll be able to identify the attributes that greatly impact the success of your sustainable supply chain management. On this research Analytic Hierarchy Process (AHP) utilize to define Consistency Ratio (CR) and weight between dimension using SuperDecisions software. Then, Multidimensional Scaling (MDS) is utilized to measure performance of supply chain sustainability. At the same time, a stress value also measured to indicate the sustainability measurement of one specific dimension is sufficient in accordance with existing condition or not. Next, to better indicate which attributes are most influential on improving dimensional sustainability performance, it is essential to conduct a sensitivity analysis and Montecarlo analysis for each attribute. In the end, this research successfully determined which attributes that influence sustainable supply chain management and measure the value of supply chain sustainability in the telecommunications industry in Indonesia.

Enhancing Computer Science Education Through Electronic Team-Based Learning: A Hybrid Approach to Collaborative Learning

Brilly Andro Makalew, Bens Pardamean — Fri, 31 May 2024 00:00:00 +0000

This paper explores the implementation of electronic Team-Based Learning (e-TBL) as a hybrid educational solution, combining traditional TBL methodologies with electronic platforms to enhance collaborative learning experiences. The study aims to assess the effectiveness of e-TBL in improving knowledge acquisition, teamwork skills, and overall academic performance among computer science students. The research was conducted in five phases, including preparation, experimentation, and evaluation, with a focus on developing an e-learning application integrated with Moodle. The results indicated significant improvements in student motivation, engagement, and academic achievement. The study concludes with recommendations for integrating e-TBL in various educational settings, highlighting its adaptability and potential for enhancing learning outcomes in both traditional and online environments.

Study of Sanitation Infrastructure Operations Study of Sanitation Infrastructure Operations (Case Study: Wastewater Infrastructure in Ciamis Regency)

Suhenra Maulana — Fri, 31 May 2024 00:00:00 +0000

Sanitation infrastructure is intended to complement the needs of people who do not yet have access to wastewater infrasctucture include bathing, washing and toilet. Inadequate sanitation infrastructure in an area can cause problems of decreasing environmental quality in residential areas. Based on data from Ciamis Regency in Figures for 2022, it is stated that the population growth rate is 0.94%, this is one of the problems arising from the rapid population growth and various activities carried out, especially sanitation issues, which are related to domestic wastewater treatment including bathing, washing and toilet bathing facilities or Mandi, Cuci, Kakus (MCK). Ciamis Regency has a total of 63 MCKs that have been built in the 2010-2022 period, spread across 19 sub-districts and 62 villages with 63 MCK units. The results of the assessment of the functioning of the MCK infrastructure in Ciamis Regency show that 37 MCK units are in prime condition, 23 MCK units are damaged but can still function properly, 3 MCK units where users are hesitant to use the MCK. Efforts need to be made to repair damaged and/or non-functioning MCKs by increasing participation from the user community in the form of contributions to MCK managers. This is with the aim that people want to reuse and care for MCK as a form of sense of ownership and shared responsibility.

Computer Resource Utilization Analysis for Microsoft Excel and Python in Data Processing

Kelvin Kelvin, Wahidin Wahab, Meirista Wulandari — Fri, 31 May 2024 00:00:00 +0000

Data analysis is essential for gaining insights and making informed decisions. A crutial step in data analysis is data processing, which involves preparing and filtering raw data to ensure accuracy, consistency, and structure. While Microsoft Excel is commonly used for data processing, it is susceptible to human errors and has limitations in handling large datasets. Python provides an alternative by automating data processing through scripts executed by the interpreter. The superior software for data processing is obtained by comparing the computer resource utilization based on statistical theory approach, Wilcoxon signed-rank test. This test is appropriate because it does not require the assumption of a normal distribution, providing flexibility in comparing computer resource utilization between Microsoft Excel and Python. Microsoft Excel and Python proceed *.csv and *.xlsx files, then Task Manager recorded the data of computer resource utilization for each processing step. The Wilcoxon signed-rank test analyzes the data and evaluating two hypotheses. H0 (there is no any significant differences in computer resource utilization between Microsoft Excel and Python are calculated for each data processing) and H1 (there is significant differences in computer resource utilization between Microsoft Excel and Python are calculated for each data processing). The sum of ranks in Wilcoxon test are compared to the critical value from the Wilcoxon distribution table to determine the accepted hypothesis. Based on the Wilcoxon test results, hypothesis H1 is accepted, indicating a significant difference in computer resource utilization between Microsoft Excel and Python.

Combating Hoax and Misinformation in Indonesia Using Machine Learning What is Missing and Future Directions

Dwinanda Kinanti Suci Sekarhati — Fri, 31 May 2024 00:00:00 +0000

According to survey from several organizations in Indonesia to 10.000 respondents with age range from 13-70 years at 2022 and 2023, 56% respondents are mainly found hoax and misinformation on social media and online media platform with 45% respondents are hesitant with their ability to differentiate true information with hoax. Most of the hoax and false information researchers in Indonesia also still have some challenges such as on the dataset detection method. This research will use the systematic literature review using PICOC, inclusion-exclusion rules, and quality’s checklist. The results based on 20 papers are data crawler’s application usage, labelling, and text pre-processing are the major steps to improve the dataset with more than 10.000 data. There are also already some advance methodologies for hoax and misinformation detection in text form such as graph-based learning and special architecture design, yet there’s still a little number for the detection in media form. The recommendation includes the dataset improvement steps, literature, and methodologies in media form.

Editorial Page and Table of Content

Alexander Agung Santoso Gunawan — Fri, 31 May 2024 00:00:00 +0000