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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
Arjuna Subject : -
Articles 678 Documents
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Factors Influencing Acceptance of ILMU E-Learning Among Lecturers: An Empirical Study Based on UTAUT Model Safitri, Eristya Maya; Amalia, Indira Setia; Mukaromah, Siti; Faroqi, Asif
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1972

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. 
Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Abstract

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.
Analysis of Behavioral Use of Academic Information Systems with the Implementation of UTAUT 2 Integration at the Muhammadi-Palembang Institute of Health Science and Technology Donan, Hendri; Negara, Edi Surya Negara Surya; Sutabri, Tata; Firdaus, Firdaus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1978

Abstract

The utilization of Information Technology (IT) in higher education setting aims to enhance the quality of education, and this initiative is realized through the implementation of Information Technology at the Institute of Health Sciences and Technology Muhammadiyah Palembang (IKesT MP) in the form of an Academic Information System (SIMAKAD). SIMAKAD is a vital role as a tool to manage internal data and serves as an information hub for students. This research is conducted to evaluate the acceptance level of the UTAUT2 model and the impact of both the main and target variables within the UTAUT2 model. This research utilizes a quantitative method with 150 respondents, analyzed using SMART PLS 3.0 software." software. The research findings indicate that the acceptance level of the UTAUT2 model reaches 74%, signifying a high adoption rate. Variables like Perceived Value (p-Value: 0.019) and Habit (p-Value: 0.009) significantly influence Behavioral Intention, with a p-Value < 0.05, indicating that their hypotheses are accepted. On the other hand, variables such as Performance Expectancy (p-Value: 0.660), Effort Expectancy (p-Value: 0.417), Social Influence (p-Value: 0.652), and Facilitating Conditions (p-Value: 0.292) There is no substantial influence on Behavioral Intention as a result of using Information Technology (IT), indicating that their hypotheses have not been endorsed.. Additionally, the variable Hedonic Motivation (p-Value: 0.978) also does not can significantly impact one's inclination toward a  behavior Intention. However, variables Facilitating Conditions (p-Value: 0.000) and Behavioral Intention (p-Value: 0.000) have a positive impact on Use Behavior, indicating that their hypotheses are accepted. Conversely, the variable Habit (p-Value: 0.915) Does not exert a significant impact on Uss Behavior, resulting in the rejection of its hypothesis.
Enterprise Architecture Planning Pada Industri Otomotif Pitcar Service Menggunakan Odoo Dewi, Nur Aela; Putri, Nessia Alfadila; Pamungkas, Lanjar
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1982

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.
Classification of Student Grade Data Using the K-Means Clustering Method Pamungkas, Lanjar; Dewi, Nur Aela; Putri, Nessia Alfadila
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1983

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.
Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix helmud, ellya; Fitriyani, Fitriyani; Romadiana, Parlia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1985

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%.
Information Technology Security Audit at the YDSF National Zakat Institution Using the ISO 27001 Framework Kamal, Mustafa; Muhamad, Muhamad; Sudianto, Yupit; Fauzan, Muhammad Arkan; Anggito, Yuvens; Yasin, Wahid; Hermawan, Hendrik
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1987

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.
Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8 Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Pertiwi, Anggun; Devianto, Yudo; Dwiasnati, Saruni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2008

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.
Students' Intentions to Use E-Learning during the Covid-19 Pandemic: An Extended Technological Accaptance Model (TAM) Approach Purwandari, diah -
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2014

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.
Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods Santoso, Angga Bayu; Widodo, Tri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2018

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.

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