Journal La Multiapp最新文献

Program Design to Improve Punctual Graduates of Students in the Unsika Industrial Engineering Study Program by Using the 5C-4C Knowledge Conversion Method 利用 5C-4C 知识转换法提高云西卡工业工程学习课程学生准时毕业率的方案设计

Journal La Multiapp Pub Date : 2024-07-08 DOI: 10.37899/journallamultiapp.v5i3.1339

Wahyudin Wahyudin, Rachmi Yanita, Mega B. Herlambang, Dene Herwanto, Billy Nugraha

{"title":"Program Design to Improve Punctual Graduates of Students in the Unsika Industrial Engineering Study Program by Using the 5C-4C Knowledge Conversion Method","authors":"Wahyudin Wahyudin, Rachmi Yanita, Mega B. Herlambang, Dene Herwanto, Billy Nugraha","doi":"10.37899/journallamultiapp.v5i3.1339","DOIUrl":"https://doi.org/10.37899/journallamultiapp.v5i3.1339","url":null,"abstract":"The Faculty of Engineering, University of Singaperbangsa Karawang (Unsika) was established in 1995, one of which is the S1 Industrial Engineering (IT) Study Program, officially registered in 1996. Currently, the faculty is trying to achieve Superior Accreditation or A. The number of applicants for the Industrial Engineering Study Program will increase in 2023, reaching 891 prospective students through the SNBT route, but the capacity can only accommodate 56 people. However, it is in demand as the 4th out of 29 study programs at Unsika. The increase in the number of students is not in line with the on-time graduation rate. This study aims to identify the results of grouping students of the Industrial Engineering Study Program (S1) using the 5C-4C (Knowledge Conversion) process and then implement the relationship with learning activities for the 2017, 2018 and 2019 batches. Next, the data is transformed into information by using the 5C knowledge conversion method: contextualization, categorization, calculation, correction, and condensing. In order to build a program that increases the percentage of students who graduate on time, this information is transformed into knowledge utilizing the 4C knowledge conversion method: comparison, consequence, connections, and conversation. In this study, the population focus is on students of the Industrial Engineering Study Program from three batches, namely 2014, 2015, and 2016. The results of grouping Industrial Engineering students using the 5C process provide a deep understanding of the characteristics and factors that affect learning achievement. This provides a solid foundation for designing appropriate programs to increase the on-time graduation rate.","PeriodicalId":496778,"journal":{"name":"Journal La Multiapp","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Line Balancing Analysis Using Ranked Positional Weight and Region Approach Method in Nail Production 在钉子生产中使用位置权重排序法和区域法进行生产线平衡分析

Journal La Multiapp Pub Date : 2024-07-08 DOI: 10.37899/journallamultiapp.v5i3.1361

Rindra Samawa Aji, Endang Pudji Widjajati

{"title":"Line Balancing Analysis Using Ranked Positional Weight and Region Approach Method in Nail Production","authors":"Rindra Samawa Aji, Endang Pudji Widjajati","doi":"10.37899/journallamultiapp.v5i3.1361","DOIUrl":"https://doi.org/10.37899/journallamultiapp.v5i3.1361","url":null,"abstract":"At the moment industrial development, there is open competition on a national and international scale, the manufacturing and service industry sectors are developing very quickly. To create good, quality products and efficiency, the company must have a good track balance. As is the case in nail production. CV. XYZ is a company engaged in manufacturing. This company is located in the city of Sidoarjo, East Java. The aim of this research is to improve the balance of the work station trajectory of the nail production process at CV. XYZ. The method used in this research is the Region Approach and ranked positional weight. The best method chosen for this problem is the Region Approach method, because this method has the greatest line efficiency (68.17%), the smallest balance delay (31.83%), and the smallest smoothes index (100.63), so this method was chosen as the best method and can improve line balancing conditions in the company. The increase in line efficiency from 54.68% using the company method to 68.17% using the Region Approach method can occur due to a reduction in the number of work stations from 5 work stations as well as a decrease in the number of smoothing indexes from 168.20 to 100.63. which indicates that there is an increase in balance between work stations.","PeriodicalId":496778,"journal":{"name":"Journal La Multiapp","volume":"117 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stakeholder Identification and Collaboration Indicators in The Construction of Pedestrian Facilities in Jakarta 雅加达行人设施建设中的利益相关者识别与合作指标

Journal La Multiapp Pub Date : 2024-07-08 DOI: 10.37899/journallamultiapp.v5i3.1324

Kharisma Adikara, R. A. Machfudiyanto

引用次数: 0

Selection of Photocopy Business Branch Locations Using the Cut Off Point, AHP and Topsis Methods 使用切点法、AHP 法和 Topsis 法选择复印业务分部地点

Journal La Multiapp Pub Date : 2024-06-06 DOI: 10.37899/journallamultiapp.v5i2.1180

A. F. Nugraha, Fira Sabrina, Article Info, Penentuan Lokasi, Cabang Fotocopy

{"title":"Selection of Photocopy Business Branch Locations Using the Cut Off Point, AHP and Topsis Methods","authors":"A. F. Nugraha, Fira Sabrina, Article Info, Penentuan Lokasi, Cabang Fotocopy","doi":"10.37899/journallamultiapp.v5i2.1180","DOIUrl":"https://doi.org/10.37899/journallamultiapp.v5i2.1180","url":null,"abstract":"In order to develop and expand business expansion, the photocopy business owner plans to open a new business branch by renting a shophouse. The choice of location requires careful consideration of several factors because it will affect revenue and business strategy to get maximum profit and have an impact on business continuity. The purpose of this study is to analyze the decision to choose the location of the photocopy business branch with four alternative locations. Location selection was carried out using the Cut Off Point method to determine important criteria to consider, Analytical Hierarchy Process (AHP) was used to weight the selected criteria and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) as a complement in determining the location selection. The results with a Cut Off Point from five competent business experts, selected 8 criteria from 20 criteria such as rental prices, proximity to consumers, and market opportunities. Weighting criteria with AHP obtained three criteria with the highest weight, namely the price of renting a place, market opportunities, and cleanliness of the place. TOPSIS calculation results obtained preference values of 0.704 (Cipayung), 0.647 (Cakung), 0.477 (Cimanggis), and 0.297 (Ciputat). So, the shophouse in Cipayung was chosen as the best alternative.","PeriodicalId":496778,"journal":{"name":"Journal La Multiapp","volume":"22 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Mental Health of Elementary School (SD) Students using the Decision Tree Algorithm with K-Fold CV testing in Bone Bolango Regency, Gorontalo Province. 使用决策树算法和 K-Fold CV 测试预测戈伦塔洛省 Bone Bolango 地区小学生（SD）的心理健康。

Journal La Multiapp Pub Date : 2024-02-02 DOI: 10.37899/journallamultiapp.v5i1.853

Salahuddin Liputo, Frangky Tupamahu

引用次数: 0

Prediction of Mental Health of Elementary School (SD) Students using the Decision Tree Algorithm with K-Fold CV testing in Bone Bolango Regency, Gorontalo Province. 使用决策树算法和 K-Fold CV 测试预测戈伦塔洛省 Bone Bolango 地区小学生（SD）的心理健康。

Journal La Multiapp Pub Date : 2024-02-02 DOI: 10.37899/journallamultiapp.v5i1.853

Salahuddin Liputo, Frangky Tupamahu

引用次数: 0

Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques 利用预训练模型的迁移学习进行自定义对象检测，改进检测技术

Journal La Multiapp Pub Date : 2024-02-02 DOI: 10.37899/journallamultiapp.v5i1.843

Ashwaq Katham Mtasher, E. Al-wakel

{"title":"Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques","authors":"Ashwaq Katham Mtasher, E. Al-wakel","doi":"10.37899/journallamultiapp.v5i1.843","DOIUrl":"https://doi.org/10.37899/journallamultiapp.v5i1.843","url":null,"abstract":"Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.","PeriodicalId":496778,"journal":{"name":"Journal La Multiapp","volume":"22 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration 人工智能与无声的抗菌药耐药性大流行：全面探索

Journal La Multiapp Pub Date : 2024-02-02 DOI: 10.37899/journallamultiapp.v5i1.952

Mohammed F. Al Marjani Marjani, Rana K. Mohammed, Entithaar Mhwes Zghair, Yasmin Makki Mohialden

{"title":"Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration","authors":"Mohammed F. Al Marjani Marjani, Rana K. Mohammed, Entithaar Mhwes Zghair, Yasmin Makki Mohialden","doi":"10.37899/journallamultiapp.v5i1.952","DOIUrl":"https://doi.org/10.37899/journallamultiapp.v5i1.952","url":null,"abstract":"The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.","PeriodicalId":496778,"journal":{"name":"Journal La Multiapp","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration 人工智能与无声的抗菌药耐药性大流行：全面探索

Journal La Multiapp Pub Date : 2024-02-02 DOI: 10.37899/journallamultiapp.v5i1.952

Mohammed F. Al Marjani Marjani, Rana K. Mohammed, Entithaar Mhwes Zghair, Yasmin Makki Mohialden

{"title":"Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration","authors":"Mohammed F. Al Marjani Marjani, Rana K. Mohammed, Entithaar Mhwes Zghair, Yasmin Makki Mohialden","doi":"10.37899/journallamultiapp.v5i1.952","DOIUrl":"https://doi.org/10.37899/journallamultiapp.v5i1.952","url":null,"abstract":"The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.","PeriodicalId":496778,"journal":{"name":"Journal La Multiapp","volume":"7 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques 利用预训练模型的迁移学习进行自定义对象检测，改进检测技术

Journal La Multiapp Pub Date : 2024-02-02 DOI: 10.37899/journallamultiapp.v5i1.843

Ashwaq Katham Mtasher, E. Al-wakel

{"title":"Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques","authors":"Ashwaq Katham Mtasher, E. Al-wakel","doi":"10.37899/journallamultiapp.v5i1.843","DOIUrl":"https://doi.org/10.37899/journallamultiapp.v5i1.843","url":null,"abstract":"Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.","PeriodicalId":496778,"journal":{"name":"Journal La Multiapp","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0