Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.1982
Nur Aela Dewi, Nessia Alfadila Putri, L. Pamungkas
Pemanfaatan teknologi informasi memiliki peran penting dalam proses pembuatan, perubahan, penyimpanan, komunikasi, dan penyebaran informasi. Terutama dalam konteks bisnis perusahaan, terutama di bidang manajemen sistem informasi, teknologi informasi membawa manfaat yang signifikan dalam mengelola, mengorganisasi, merencanakan, dan mencapai tujuan sistem informasi. Pitcar Service merupakan sebuah entitas di sektor otomotif yang berbasis di Purwokerto, Jawa Tengah, menghadapi tantangan dalam optimalisasi kegiatan manajemen sistem informasi. Kurangnya integrasi sistem informasi mengakibatkan kendala dalam perencanaan, pemantauan, koordinasi, dan visibilitas. Untuk mengatasi hal ini, pendekatan Enterprise Architecture Planning (EAP) digunakan untuk merancang sistem informasi terintegrasi yang berbasis web dengan memanfaatkan perangkat lunak Odoo untuk manajemen proyek di Pitcar Service. Hasil dari penelitian ini dapat digunakan untuk merancang arsitektur data, arsitektur aplikasi dan teknologi, serta merencanakan implementasi sistem manajemen informasi terintegrasi selama 3 tahun ke depan. Implementasi EAP di perusahaan Pitcar Service diharapkan dapat memfasilitasi pengelolaan dan pengembangan arsitektur yang sesuai dengan kebutuhan bisnis, memberikan arahan yang jelas untuk pengembangan sistem dan teknologi, serta mengoptimalkan potensi perusahaan melalui pemanfaatan sumber daya yang efisien.
在创建、更改、存储、交流和传播信息的过程中，信息技术的利用发挥着重要作用。特别是在公司业务中，尤其是在信息系统管理领域，信息技术在管理、组织、规划和实现信息系统目标方面带来了巨大的好处。位于中爪哇 Purwokerto 的汽车行业实体 Pitcar Service 在优化信息系统管理活动方面面临挑战。缺乏信息系统集成导致在规划、监控、协调和可见性方面受到限制。为解决这一问题，Pitcar 服务公司采用企业架构规划（EAP）方法，利用 Odoo 软件设计了一个基于网络的集成信息系统，用于项目管理。这项研究的成果可用于设计数据架构、应用和技术架构，并规划未来 3 年综合信息管理系统的实施。预计在 Pitcar Service 公司实施 EAP 将有助于管理和开发符合业务需求的架构，为系统和技术开发提供明确的方向，并通过有效利用资源来优化公司的潜力。
{"title":"Enterprise Architecture Planning Pada Industri Otomotif Pitcar Service Menggunakan Odoo","authors":"Nur Aela Dewi, Nessia Alfadila Putri, L. Pamungkas","doi":"10.32736/sisfokom.v13i1.1982","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1982","url":null,"abstract":"Pemanfaatan teknologi informasi memiliki peran penting dalam proses pembuatan, perubahan, penyimpanan, komunikasi, dan penyebaran informasi. Terutama dalam konteks bisnis perusahaan, terutama di bidang manajemen sistem informasi, teknologi informasi membawa manfaat yang signifikan dalam mengelola, mengorganisasi, merencanakan, dan mencapai tujuan sistem informasi. Pitcar Service merupakan sebuah entitas di sektor otomotif yang berbasis di Purwokerto, Jawa Tengah, menghadapi tantangan dalam optimalisasi kegiatan manajemen sistem informasi. Kurangnya integrasi sistem informasi mengakibatkan kendala dalam perencanaan, pemantauan, koordinasi, dan visibilitas. Untuk mengatasi hal ini, pendekatan Enterprise Architecture Planning (EAP) digunakan untuk merancang sistem informasi terintegrasi yang berbasis web dengan memanfaatkan perangkat lunak Odoo untuk manajemen proyek di Pitcar Service. Hasil dari penelitian ini dapat digunakan untuk merancang arsitektur data, arsitektur aplikasi dan teknologi, serta merencanakan implementasi sistem manajemen informasi terintegrasi selama 3 tahun ke depan. Implementasi EAP di perusahaan Pitcar Service diharapkan dapat memfasilitasi pengelolaan dan pengembangan arsitektur yang sesuai dengan kebutuhan bisnis, memberikan arahan yang jelas untuk pengembangan sistem dan teknologi, serta mengoptimalkan potensi perusahaan melalui pemanfaatan sumber daya yang efisien.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"357 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894227","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.2008
Ahmad Fauzi, Kiki Ahmad Baihaqi, Anggun Pertiwi, Yudo Devianto, Saruni Dwiasnati
Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.
{"title":"Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8","authors":"Ahmad Fauzi, Kiki Ahmad Baihaqi, Anggun Pertiwi, Yudo Devianto, Saruni Dwiasnati","doi":"10.32736/sisfokom.v13i1.2008","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.2008","url":null,"abstract":"Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"430 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893876","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.2051
Suci Inayah, D. I. Sensuse, Sofian Lusa
E-commerce users in Indonesia continue to increase along with advances in digitalization. This causes a trend to occur where many offline shop entrepreneurs are responding to changes in consumer behavior by creating online shopping applications to maintain the existence of their business to be consistent with time progress. The purpose of this research is to find out what factors affect user acceptance of online retail applications used for online shopping at XYZ stores using the UTAUT2 acceptance model. In line with changes, case studies were conducted on grocery stores and retail stores that carried out digital innovation by creating online retail applications for their consumers. The research was conducted using a mixed method, data was collected through interviews with sources and using a questionnaire spread to 149 research sample consumers. The data processing technique uses PLS-SEM with SmartPLS tools. The research results show that 4 factors influence the use of online retail applications, including hedonic motivation, habit, behavioral intention, and application use. The results of this research can be used as material for management considerations to increase the excellence of the application so that user interest in online shopping using the application at XYZ store increases
{"title":"Analysis of Factors that Influence the Acceptance of Using Online Retail Applications: A Case Study of XYZ Wholesale and Retail Stores","authors":"Suci Inayah, D. I. Sensuse, Sofian Lusa","doi":"10.32736/sisfokom.v13i1.2051","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.2051","url":null,"abstract":"E-commerce users in Indonesia continue to increase along with advances in digitalization. This causes a trend to occur where many offline shop entrepreneurs are responding to changes in consumer behavior by creating online shopping applications to maintain the existence of their business to be consistent with time progress. The purpose of this research is to find out what factors affect user acceptance of online retail applications used for online shopping at XYZ stores using the UTAUT2 acceptance model. In line with changes, case studies were conducted on grocery stores and retail stores that carried out digital innovation by creating online retail applications for their consumers. The research was conducted using a mixed method, data was collected through interviews with sources and using a questionnaire spread to 149 research sample consumers. The data processing technique uses PLS-SEM with SmartPLS tools. The research results show that 4 factors influence the use of online retail applications, including hedonic motivation, habit, behavioral intention, and application use. The results of this research can be used as material for management considerations to increase the excellence of the application so that user interest in online shopping using the application at XYZ store increases","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"316 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894356","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.1987
Mustafa Kamal, Muhamad Muhamad, Yupit Sudianto, Muhammad Arkan Fauzan, Yuvens Anggito, Wahid Yasin, Hendrik Hermawan
In this era of cyber crimes, data security is an important aspect that needs special attention from an organization. This is reinforced by the ratification of Law Number 27 of 2022 on personal data security. The National Zakat Amil Institute (LAZNAS) Yayasan Dana Sosial al Falah (YDSF) as an institution with a legal entity and having data on more than 100,000 donors and partners, it also has an obligation to protect the personal data of donors and partners. The focus of this research is to evaluate and audit information technology at the LAZNAS YDSF, especially regarding the security aspect of information technology. Evaluations and audits were carried out using the ISO 27001 framework as a standardization of information technology security at the international level. In this study, information technology audits were conducted using quantitative methods. The assessment was carried out on seven main clauses that are priorities for the LAZNAS YDSF based on management priorities: compliance clauses, risk management, policies, assets, physical and environmental management, access control, and incident management. Data were collected using a questionnaire distributed to all the LAZNAS YDSF managers and employees. Fifty-five respondents, ranging from management to staff, were involved in filling out the questionnaire, ranging from management to staff. Based on the recapitulation of answers from respondents, it was found that the risk management and access control clauses had good results, with scores of 2,727 and 2,796. The compliance and incident management clauses have scores of 2.381 and 2.53, respectively; therefore, improvement efforts need to be made. By evaluating and auditing information technology that refers to the ISO 27001 standard, it is hoped that LAZNAS YDSF can protect and maintain the confidentiality, integrity, and availability of information, and manage and control information security risks.
在这个网络犯罪猖獗的时代，数据安全是一个需要组织特别关注的重要方面。关于个人数据安全的 2022 年第 27 号法律的批准加强了这一点。国家天课研究所（LAZNAS）Yayasan Dana Sosial al Falah（YDSF）作为一个具有法人资格的机构，拥有超过 10 万名捐赠者和合作伙伴的数据，因此也有义务保护捐赠者和合作伙伴的个人数据。 本研究的重点是评估和审计拉兹纳斯青年发展基金会的信息技术，尤其是信息技术的安全方面。评估和审计采用 ISO 27001 框架进行，该框架是信息技术安全的国际标准化。本研究采用定量方法进行信息技术审计。根据管理重点，对 LAZNAS YDSF 优先考虑的七个主要条款进行了评估：合规条款、风险管理、政策、资产、物理和环境管理、访问控制和事件管理。数据收集采用了向所有 LAZNAS YDSF 管理人员和员工发放调查问卷的方式。55 名受访者参与了问卷填写，其中既有管理人员，也有员工。根据对受访者答案的总结，发现风险管理和访问控制条款效果良好，得分分别为 2 727 分和 2 796 分。合规性和事件管理条款的得分分别为 2.381 分和 2.53 分，因此需要努力改进。通过对 ISO 27001 标准的信息技术进行评估和审核，希望 LAZNAS YDSF 能够保护和维护信息的机密性、完整性和可用性，管理和控制信息安全风险。
{"title":"Information Technology Security Audit at the YDSF National Zakat Institution Using the ISO 27001 Framework","authors":"Mustafa Kamal, Muhamad Muhamad, Yupit Sudianto, Muhammad Arkan Fauzan, Yuvens Anggito, Wahid Yasin, Hendrik Hermawan","doi":"10.32736/sisfokom.v13i1.1987","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1987","url":null,"abstract":"In this era of cyber crimes, data security is an important aspect that needs special attention from an organization. This is reinforced by the ratification of Law Number 27 of 2022 on personal data security. The National Zakat Amil Institute (LAZNAS) Yayasan Dana Sosial al Falah (YDSF) as an institution with a legal entity and having data on more than 100,000 donors and partners, it also has an obligation to protect the personal data of donors and partners. The focus of this research is to evaluate and audit information technology at the LAZNAS YDSF, especially regarding the security aspect of information technology. Evaluations and audits were carried out using the ISO 27001 framework as a standardization of information technology security at the international level. In this study, information technology audits were conducted using quantitative methods. The assessment was carried out on seven main clauses that are priorities for the LAZNAS YDSF based on management priorities: compliance clauses, risk management, policies, assets, physical and environmental management, access control, and incident management. Data were collected using a questionnaire distributed to all the LAZNAS YDSF managers and employees. Fifty-five respondents, ranging from management to staff, were involved in filling out the questionnaire, ranging from management to staff. Based on the recapitulation of answers from respondents, it was found that the risk management and access control clauses had good results, with scores of 2,727 and 2,796. The compliance and incident management clauses have scores of 2.381 and 2.53, respectively; therefore, improvement efforts need to be made. By evaluating and auditing information technology that refers to the ISO 27001 standard, it is hoped that LAZNAS YDSF can protect and maintain the confidentiality, integrity, and availability of information, and manage and control information security risks.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"167 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894248","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.1983
Lanjar Pamungkas, Nur Aela Dewi, Nessia Alfadila Putri
The fourth industrial revolution has brought significant changes in various sectors, and education has been greatly affected by technological advances. Automation, particularly in data processing, has simplified educational processes, particularly in managing student grade data. However, the increasing volume of data poses challenges in efficient processing. This research explores the application of K-Means clustering, a data mining technique, to cluster student grade data. This research uses the Elbow Method to determine the optimal number of clusters. The dataset, sourced from the Information Systems Study Program at the Telkom Institute of Technology Purwokerto, includes attributes such as Credits Taken, GPA, Number of Ds, Number of Es, and Credits Not Taken. The results identified three groups of students: "High Achievers," "Average Performance," and "Needs Improvement." Recommendations include academic challenges for high performers, better learning methods for average performers, and remedial programs for those who need improvement. This research demonstrates the efficacy of K-Means clustering in improving educational strategies and support systems based on student characteristics.
第四次工业革命给各行各业带来了重大变革，教育也受到技术进步的极大影响。自动化，尤其是数据处理方面的自动化，简化了教育流程，特别是在管理学生成绩数据方面。然而，日益增长的数据量给高效处理带来了挑战。本研究探索了数据挖掘技术 K-Means 聚类在学生成绩数据聚类中的应用。本研究采用肘法确定最佳聚类数量。数据集来自 Telkom 技术学院（Telkom Institute of Technology Purwokerto）的信息系统学习课程，包括已修学分、平均学分绩点（GPA）、D 数量、E 数量和未修学分等属性。结果确定了三类学生："成绩优异"、"表现一般 "和 "需要改进"。建议包括为成绩优秀的学生提供学业挑战，为成绩一般的学生提供更好的学习方法，为需要改进的学生提供补习课程。这项研究证明了 K-Means 聚类在根据学生特点改进教育策略和支持系统方面的功效。
{"title":"Classification of Student Grade Data Using the K-Means Clustering Method","authors":"Lanjar Pamungkas, Nur Aela Dewi, Nessia Alfadila Putri","doi":"10.32736/sisfokom.v13i1.1983","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1983","url":null,"abstract":"The fourth industrial revolution has brought significant changes in various sectors, and education has been greatly affected by technological advances. Automation, particularly in data processing, has simplified educational processes, particularly in managing student grade data. However, the increasing volume of data poses challenges in efficient processing. This research explores the application of K-Means clustering, a data mining technique, to cluster student grade data. This research uses the Elbow Method to determine the optimal number of clusters. The dataset, sourced from the Information Systems Study Program at the Telkom Institute of Technology Purwokerto, includes attributes such as Credits Taken, GPA, Number of Ds, Number of Es, and Credits Not Taken. The results identified three groups of students: \"High Achievers,\" \"Average Performance,\" and \"Needs Improvement.\" Recommendations include academic challenges for high performers, better learning methods for average performers, and remedial programs for those who need improvement. This research demonstrates the efficacy of K-Means clustering in improving educational strategies and support systems based on student characteristics.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"114 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894482","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.1972
E. M. Safitri, Indira Setia Amalia, Siti Mukaromah, A. Faroqi
E-learning is a form of innovation in technology used in educational field, including higher education. University of Pembangunan Nasional “Veteran” Jawa Timur is one of many universities that have implemented e-learning called ILMU to support the teaching-learning process. The application of ILMU as e-learning has yet to be utilised by lecturers, due to some challenges in implementation of ILMU regarding accessibility and features of ILMU. Meanwhile, successful implementation of a technology requires acceptance from its users. This research was acquited to define what acceptance factors that influence lecturers while accessing ILMU. This study is measured using UTAUT model. The research was carried on by quantitatively distributing questionnaires to 60 lecturers. Data were analyzed and processed using SEM-PLS technique and SMARTPLS 3.0 application. Factors that influence users to receive ILMU e-learning and significantly are effort expectancy, social influence, facilitating conditions, and behavioral intention. Meanwhile, performance expectancy does not influence users significantly to accept ILMU e-learning. These factors are key indicators to of the implementation and improvement of ILMU e-learning, thus it will develop a better implementation for the lecturers to use and accept it.
电子学习是一种用于教育领域（包括高等教育）的技术创新形式。爪哇国立师范大学（University of Pembangunan Nasional "Veteran" Jawa Timur）是众多实施电子学习（ILMU）以支持教学过程的大学之一。由于在实施ILMU的过程中遇到了一些关于ILMU的可访问性和功能方面的挑战，ILMU作为电子学习的应用还没有被讲师们所利用。同时，一项技术的成功实施需要得到用户的认可。本研究旨在确定影响讲师使用ILMU的接受因素。本研究采用UTAUT模型进行衡量。研究通过向 60 名讲师发放调查问卷的方式进行。数据使用 SEM-PLS 技术和 SMARTPLS 3.0 应用程序进行分析和处理。结果表明，影响用户接受 ILMU 在线学习的因素主要有努力期望、社会影响、便利条件和行为意向。同时，绩效期望对用户接受ILMU在线学习的影响不大。这些因素是实施和改进ILMU在线学习的关键指标，因此它将为讲师使用和接受ILMU在线学习提供更好的实施。
{"title":"Factors Influencing Acceptance of ILMU E-Learning Among Lecturers: An Empirical Study Based on UTAUT Model","authors":"E. M. Safitri, Indira Setia Amalia, Siti Mukaromah, A. Faroqi","doi":"10.32736/sisfokom.v13i1.1972","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1972","url":null,"abstract":"E-learning is a form of innovation in technology used in educational field, including higher education. University of Pembangunan Nasional “Veteran” Jawa Timur is one of many universities that have implemented e-learning called ILMU to support the teaching-learning process. The application of ILMU as e-learning has yet to be utilised by lecturers, due to some challenges in implementation of ILMU regarding accessibility and features of ILMU. Meanwhile, successful implementation of a technology requires acceptance from its users. This research was acquited to define what acceptance factors that influence lecturers while accessing ILMU. This study is measured using UTAUT model. The research was carried on by quantitatively distributing questionnaires to 60 lecturers. Data were analyzed and processed using SEM-PLS technique and SMARTPLS 3.0 application. Factors that influence users to receive ILMU e-learning and significantly are effort expectancy, social influence, facilitating conditions, and behavioral intention. Meanwhile, performance expectancy does not influence users significantly to accept ILMU e-learning. These factors are key indicators to of the implementation and improvement of ILMU e-learning, thus it will develop a better implementation for the lecturers to use and accept it. ","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"486 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893863","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.2018
Angga Bayu Santoso, Tri Widodo
Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.
{"title":"Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods","authors":"Angga Bayu Santoso, Tri Widodo","doi":"10.32736/sisfokom.v13i1.2018","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.2018","url":null,"abstract":"Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"29 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962122","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}
The classification method is part of data mining which is used to predict existing problems and also as predictions for the future. The form of dataset used in the classification method is supervised data. The random forest classification method is processed by forming several decision trees and then combining them to get better and more precise predictions. while a decision tree is the concept of changing a pile of data into a decision tree that presents the rules of a decision. From these two classification methods, researchers will compare the level of accuracy of predictions from both methods with the same dataset, namely the employee dataset in India, to predict the level of accuracy of employees who leave their jobs or still remain to work at their company. The number of records available is 4654 records. Of the existing data, 90% was used as training data and 10% was used as test data. From the results of testing this method, it was found that the accuracy level of the random forest method was 86.45%, while the decision tree method was 84.30% accuracy level. Then, by using the confusion matrix, you can see the magnitude of the distribution of experimental validity visually to calculate precision, recall and F1-Score. The random forest algorithm obtained precision of: 96.7%, sensitivity of: 84.7%, specificity of: 91.4%, and F1-Score of: 90.2%. Meanwhile, the decision tree algorithm obtained precision of: 95.7%, sensitivity of: 82.9%, specificity of: 88.4%, and F1-Score of: 88.8%.
{"title":"Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix","authors":"Ellya Helmud, Fitriyani Fitriyani, Parlia Romadiana","doi":"10.32736/sisfokom.v13i1.1985","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1985","url":null,"abstract":"The classification method is part of data mining which is used to predict existing problems and also as predictions for the future. The form of dataset used in the classification method is supervised data. The random forest classification method is processed by forming several decision trees and then combining them to get better and more precise predictions. while a decision tree is the concept of changing a pile of data into a decision tree that presents the rules of a decision. From these two classification methods, researchers will compare the level of accuracy of predictions from both methods with the same dataset, namely the employee dataset in India, to predict the level of accuracy of employees who leave their jobs or still remain to work at their company. The number of records available is 4654 records. Of the existing data, 90% was used as training data and 10% was used as test data. From the results of testing this method, it was found that the accuracy level of the random forest method was 86.45%, while the decision tree method was 84.30% accuracy level. Then, by using the confusion matrix, you can see the magnitude of the distribution of experimental validity visually to calculate precision, recall and F1-Score. The random forest algorithm obtained precision of: 96.7%, sensitivity of: 84.7%, specificity of: 91.4%, and F1-Score of: 90.2%. Meanwhile, the decision tree algorithm obtained precision of: 95.7%, sensitivity of: 82.9%, specificity of: 88.4%, and F1-Score of: 88.8%.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"119 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894481","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.2030
Kania Lovia Tiarazahra, Rita Ambarwati
Fuel oil or what is often referred to as BBM is one of the basic needs to drive all community activities. So the government appointed PT Pertamina as a single company which is a state-owned company to facilitate fuel needs for all levels of society. However, with increasing demand, the government formed a new policy to allow private companies to come in to meet all fuel demand. With this, PT Pertamina is no longer the only fuel supplier in Indonesia and must continue to develop mature strategies so that profits do not fade. One way is by examining sales data and predicting customer loyalty. The RFM method followed by the decision tree algorithm and k-means clustering is applied in this research, with the output being able to determine the level of customer loyalty, the level of salesman performance, as well as predicting the potential for customers to churn and its correlation with the salesman's skills. The data used as a reference for the research is sales transaction data obtained from PT Pertamina Patra Niaga Regional Jatimbalinus. And from the research, results showed that the majority of PT Pertamina Patra Niaga Regional Jatimbalinus customers are loyal customers. With a salesman, performance is divided into good performance and less good performance. This grouping is obtained based on the salesman's overall performance track record. As for customer churn predictions, it was found that there was 1 group of customers who were predicted to churn heavily, but this was not influenced by salesman performance, as evidenced by transaction track records in existing data
{"title":"Data-Driven Strategies for Fuel Distribution in Indonesia: A Case Study of PT Pertamina Patra Niaga","authors":"Kania Lovia Tiarazahra, Rita Ambarwati","doi":"10.32736/sisfokom.v13i1.2030","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.2030","url":null,"abstract":"Fuel oil or what is often referred to as BBM is one of the basic needs to drive all community activities. So the government appointed PT Pertamina as a single company which is a state-owned company to facilitate fuel needs for all levels of society. However, with increasing demand, the government formed a new policy to allow private companies to come in to meet all fuel demand. With this, PT Pertamina is no longer the only fuel supplier in Indonesia and must continue to develop mature strategies so that profits do not fade. One way is by examining sales data and predicting customer loyalty. The RFM method followed by the decision tree algorithm and k-means clustering is applied in this research, with the output being able to determine the level of customer loyalty, the level of salesman performance, as well as predicting the potential for customers to churn and its correlation with the salesman's skills. The data used as a reference for the research is sales transaction data obtained from PT Pertamina Patra Niaga Regional Jatimbalinus. And from the research, results showed that the majority of PT Pertamina Patra Niaga Regional Jatimbalinus customers are loyal customers. With a salesman, performance is divided into good performance and less good performance. This grouping is obtained based on the salesman's overall performance track record. As for customer churn predictions, it was found that there was 1 group of customers who were predicted to churn heavily, but this was not influenced by salesman performance, as evidenced by transaction track records in existing data","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"90 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894253","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}
Pub Date : 2024-02-15DOI: 10.32736/sisfokom.v13i1.2014
Diah - Purwandari
Online learning is a technology-based system, hence a process is required to ensure that students can embarace the technology, as the success or failure of a technology is determined by how well the user accepts it. Therefore, understanding the factors that drive the use of online learning is essential. This study aims to contribute to the literature on online learning in higher education during the COVID-19 epidemic by investigating the relationship between self-awareness and student acceptance of online learning. Several hypotheses were constructed using the TAM Model to investigate the relationship between the TAM construct and self-awareness as an antecedent. This study employed structural equation modeling (SEM-PLS) to investigate how 390 students in East Jakarta used online learning. The findings of this study revealed that self-awareness had a significant effect on perceived usefulness, perceived ease of use, and attitude, but it had no direct impact on the intention to continue using e-learning. Students' attitudes were considerably influenced by perceived usefulness and perceived ease of use. Perceived usefulness was the most influential factor on student attitudes, and attitude was a strong predictor of intention to continue utilizing online learning. The proposed model accurately predicted attitudes and intentions to continue to use e-learning.
在线学习是一个以技术为基础的系统，因此需要一个过程来确保学生能够接受该技术，因为一项技术的成败取决于用户对它的接受程度。因此，了解使用在线学习的驱动因素至关重要。本研究旨在通过调查自我意识与学生对在线学习的接受程度之间的关系，为 COVID-19 流行期间高等教育在线学习方面的文献做出贡献。本研究使用 TAM 模型构建了几个假设，以研究 TAM 构建与作为前因的自我意识之间的关系。本研究采用结构方程模型（SEM-PLS）来调查东雅加达的 390 名学生是如何使用在线学习的。研究结果表明，自我认知对感知有用性、感知易用性和态度有显著影响，但对继续使用在线学习的意愿没有直接影响。学生的态度在很大程度上受感知有用性和感知易用性的影响。感知有用性是对学生态度影响最大的因素，而态度则是继续使用在线学习意愿的有力预测因素。所提出的模型准确地预测了学生的态度和继续使用在线学习的意愿。
{"title":"Students' Intentions to Use E-Learning during the Covid-19 Pandemic: An Extended Technological Accaptance Model (TAM) Approach","authors":"Diah - Purwandari","doi":"10.32736/sisfokom.v13i1.2014","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.2014","url":null,"abstract":"Online learning is a technology-based system, hence a process is required to ensure that students can embarace the technology, as the success or failure of a technology is determined by how well the user accepts it. Therefore, understanding the factors that drive the use of online learning is essential. This study aims to contribute to the literature on online learning in higher education during the COVID-19 epidemic by investigating the relationship between self-awareness and student acceptance of online learning. Several hypotheses were constructed using the TAM Model to investigate the relationship between the TAM construct and self-awareness as an antecedent. This study employed structural equation modeling (SEM-PLS) to investigate how 390 students in East Jakarta used online learning. The findings of this study revealed that self-awareness had a significant effect on perceived usefulness, perceived ease of use, and attitude, but it had no direct impact on the intention to continue using e-learning. Students' attitudes were considerably influenced by perceived usefulness and perceived ease of use. Perceived usefulness was the most influential factor on student attitudes, and attitude was a strong predictor of intention to continue utilizing online learning. The proposed model accurately predicted attitudes and intentions to continue to use e-learning.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963158","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}
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