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Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Published by Universitas Prima Indonesia
ISSN : -     EISSN : 25802879     DOI : 10.34012
Core Subject : Science,
Decision Sciences, Operations Research & Management
This journal is about information systems and computer science.
Arjuna Subject : -
Articles 222 Documents
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Application of the Rapid Application Development Method to Analyze the MBKM Information System at Prima Indonesia University Wijaya, Teddy Chandra Wijaya; -, Vanness; -, Jefry; Tania, Andre; K.Nababan, Marlince Novita
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5463

Abstract

Independent Campus (MBKM) is an innovation carried out by the Ministry of Education, Culture and Research to produce graduates who are ready to face rapid technological advances and one of the campuses that has implemented it is Universitas Prima Indonesia (UNPRI), where students have difficulty with UNPRI MBKM activities. to take part in MBKM activities due to misinformation and lack of information. To improve the MBKM activity process, researchers will re-analyze the MBKM management information system, namely based on the RAD (Rapid Application Development) model. By implementing the Rapid Application Development (RAD) model in the MBKM program at Prima Indonesia University, it was able to analyze according to the administrator's needs, where the RAD model found that the analysis of business processes from student track records was incomplete, and for further research it was necessary to develop stakeholders who were willing to join with the campus.
Analysis of the Management Information System of MBKM at Prima Indonesia University Using the Waterfall Method Kaban, Sari Mutiara; Pincawan, Irma Kristina Br; Pangestu, Fadhil; Silitonga, Theofilus Kristian; K.Nababan, Marlince Novita
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Implementing MBKM activities in the field has challenges for students and the unavailability of MBKM information within the campus environment is an obstacle to participating in MBKM. This research is to analyze and build a web-based MBKM information system using the Laravel framework and an effective System Development Life Cycle (SDLC) model to support the implementation and monitoring of MBKM activities on campus. Researchers provide solutions to the obstacles faced by students in participating in MBKM activities, so that this program can run more smoothly and achieve the researchers' goals. The SDLC model is a model for developing website-based information systems. SDLC provides a systematic and structured approach to ensure that the software being developed meets requirements. The research results show that the SLDC model produces business processes that need to be developed, namely adding access rights for stakeholders to make it easier to assess study programs, and the need for involvement of system programs (Prodi) for assessing MBKM student activities. Researcher's suggestions for the security of using user accounts and access rights for Human Resources with experience in MBKM activities.
Classification Of Egg Quality Using The K-Nearest Neighbor Algorithm In Machine Learning Marantika, Windy; Gultom, Putri Romian; Agustine, William; Sinuhaji, Tama Ulina br; Aisyah, Siti; Amalia, Amalia; Radhi, Muhammad
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5471

Abstract

In addition to meat, fish, and milk, one of the staple foods consumed by the community is chicken eggs. Egg quality assessment is separated into two categories: exterior (egg shell) and interior (egg contents). However, the evaluation method used in this investigation is focused on evaluating the external quality of eggs. Pre-processing, feature extraction, classification, and evaluation are steps taken in the image processing method used to classify chicken eggs. Classification methods that can be used include the K-Means Clustering and K-Nearest Neighbor (KNN) methods and improved KNN. Based on the findings in the study, the KNN improvisation method can be used to classify chicken egg quality, with a test accuracy value of 91.67%.  
Data Mining Analysis In Minimizing Company Losses Using Fuzzy Time Series Method Saputra, Muhardi; Jones, Jones; Anderson, Wily; Ginting, Lindawati
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5474

Abstract

Losses are the most avoided by all business entities in this case the research obtained a research study at PT. Sumatera Sarana Sekar Sakti. The company suffered a big loss in the expenditure / spending section that was not managed properly. The existence of excess funds or shortages in each company's expenditure is a form of loss, not only in the form of material but even immaterial. Therefore, this research conducts an analysis by generating data predictions so that a value is obtained that will minimize company losses because it provides the right and efficient funds. The method used in prediction is Fuzzy Time Series. It is a new category of methods that have been widely used in various studies because they produce good predictive values. In this study, the Fuzzy Time Series method produces 0.82% error rate from data analysis of 1875 company expenditure transactions. Measurement of the prediction error rate using Mean Absolute Percentage Error which is often called MAPE. It is a measurement that is often used in various studies with data prediction categories.
Analysis of User Behavior of the Digital Korlantas Polri Application with Integrated UTAUT in the Community of East Java Province Poerwanto, Annisaa Putri Prameswari; Hawwin Mardhiana; Alifiansyah Arrizqy Hidayat
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5504

Abstract

Currently, more and more agencies are developing their services by utilizing technology in the form of mobile applications, including the Republic of Indonesia Police agency, especially the Traffic Corps with its mobile application-based service called Digital Korlantas Polri. This research aims to identify what factors influence users' interests, behavior and intentions towards the National Police Traffic Corps Digital application by testing 10 variable hypotheses built from the integration of the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), Theory models. of Planned Behavior (TPB) and Service Quality with the educational level factor proposed as a moderator. The results of this research show that Performance Expectancy and Effort Expectancy are the 2 main factors influencing Behavioral Intention. Other results show that factors such as Social Influence, Facilitating Conditions, and Perceived Risk have a negative influence where they can reduce the user's Behavioral Intention. Apart from that, the research results also show that Behavioral Intention and Word of Mouth can influence users to continue using this application and educational level factors have a negative influence on users' behavioral intention to continue using the application on an ongoing basis. Keywords: Unified Theory of Acceptance and Use of Technology, Theory of Planned Behavior, Technology Acceptance Model, Service
OPTIMIZATION OF NODE SIZE CONFIGURATION IN CNN-ELM MODEL FOR BRAIN TUMOR MRI IMAGE CLASSIFICATION Ahmad, Sulthan; Rahmat, Basuki; Tri Anggraeny, Fetty
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5581

Abstract

This study proposed a method to classify four types of brain tumors—Glioma, Meningioma, Pituitary, and Non-Tumor—using the Kaggle Brain Tumor MRI Dataset. The research involved stages of data collection, preprocessing, model design, model training, and evaluation. A hybrid Convolutional Neural Network - Extreme Learning Machine (CNN-ELM) algorithm was employed, demonstrating the importance of selecting the optimal number of hidden nodes for achieving high accuracy. The test results revealed that with 2000 hidden nodes, the CNN-ELM model achieved an overall accuracy of 98.86%, with F1-scores of 97% for Glioma, 98% for Meningioma, 100% for Non-Tumor, and 100% for Pituitary tumors. In comparison, the model with 1000 hidden nodes achieved an accuracy of 96.96%, while models with 3000 and 4000 hidden nodes achieved 98.10% and 96.58% accuracy, respectively. These findings highlight the critical role of hidden node selection in optimizing model performance. The CNN-ELM algorithm proves to be a viable alternative for classifying brain tumor MRI images, contributing to advancements in medical technology.
Grouping Diseases of Patients at RSU Mitra Medika Bandar Khalippa Medan Using the K-Medoids Clustering Method Putri, Ajeng Kiana; Nasution, Yusuf Ramadhan
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5583

Abstract

The aim of this research is to apply the methodK-Medoisin categorizing the illnesses of patients at the RSUBandar Khalippa Medika PartnersMedan. And to produce a system for grouping patient data based on Rapidminer and Google Colab on patient diseases.Based on the results of research on the application of the K-Medoids algorithm, it was found that the grouping of patient diseases at RSU Mitra Medika Bandar Khalippa used the RapidMiner application with a C0 (High) cluster of 3 diseases, a C1 (Medium) cluster of 6 diseases and a C2 (Low) cluster of 1 disease. Meanwhile, using the Google Colabs application with a C0 (High) cluster of 3 diseases, a C1 (Medium) cluster of 4 diseases and a C2 (Low) cluster of 3 diseases. The results of grouping patient disease data at RSU Mitra Medika Bandar Khalippa using RapidMiner, it was found that the disease with the highest grouping (C0)is a diseasePulmonary tuberculosis, Essential Hypertension and Diabetes Mellitus. Whereasgrouping patient disease datawith Google Colabs it was found that the disease with the highest grouping (C0)is a diseaseBronchus Or Lung, Trachea, Bronchus And Lung and Pleural Effusion. Keywords: Disease Grouping, RSU, MethodsK-Medois.
Utilization Of Discord Bots In Providing Manhwa Recommendations Using Content-Based Filtering Method Muhammad Farhan Maulana; Ani Dijah Rahajoe; Made Hanindia Prami Swari
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5600

Abstract

The high number of manhwa released today makes it difficult for readers to find manhwa that match their preferences, especially when trying to find manhwa that are similar to ones they have read before. The manual search process, either through recommendations from communities or online forums, often results in subjective and inconsistent suggestions. To address this issue, a Discord bot was developed that utilizes the Content-Based Filtering method as an automated solution to manhwa recommendation. This method uses the Cosine Similarity algorithm to measure the similarity between manhwa based on features such as title, genre, synopsis, and author. For comparison, the Euclidean Distance algorithm is used to evaluate the accuracy and performance of the recommendation. From the test results, the Cosine Similarity algorithm showed superior performance in providing recommendations based on the questionnaire results and showed a high level of user satisfaction with the developed Discord bot.
A Data Pre-processing Strategy Utilizing Adaptive Masking for the Classification of Pediatric Pneumonia Using VGG-16 Herawati, Yoshi Inne; Rahmat, Basuki; Hendra Maulana
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5604

Abstract

Pneumonia is still a leading cause of death in children, especially in areas with limited medical resources. This study aims to test several pre-processes to find the best set of pre-processes that can be applied to the children's chest X-ray dataset by applying adaptive masking, histogram equalization, CLAHE and Gaussian blur. Then, childhood pneumonia is classified using a CNN architecture, namely VGG-16. By applying these pre-processing methods, this study is divided into several scenarios. The highest accuracy was obtained from scenario 1, which used a combination of adaptive masking, histogram equalization and Gaussian blur, resulting in an accuracy of 94%. Scenario 2 uses histogram equalization and Gaussian blur with an accuracy of 92%. Then Scenario 3 uses a histogram equalization replacement for CLAHE with a combination of adaptive masking, CLAHE and Gaussian blur with 93% accuracy. Finally, scenario 4 uses a combination of CLAHE and Gaussian blur methods with 91% accuracy. In addition, this research also addresses the challenges posed by unbalanced data sets and the need for highly accurate detection tools.
Analysis Of Customer Satisfaction With Solaria Restaurants In Medan City Using K-Means Clustering Method Marpaung, Udur Mega; -, Anita; Gulo, Sapriliyani
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Customer satisfaction is the key to the success of a company in the modern business context. In strategic planning and business management, a focus on customer satisfaction has become essential to ensure that the services a company provides not only retain customers but also enable sustainable growth. In the case of Solaria Restaurant, it helps to find groups of customers with comparable satisfaction patterns, which allows businesses to optimize their marketing strategies and improve their service quality. Specifically, this study uses the K-Means Clustering method to evaluate customer satisfaction with Solaria Restaurant services. The research utilized an online questionnaire distributed to 250 surveyed people to assess factors such as service response and the physical appearance of the restaurant that affect customers' perceptions of the business. The results showed that customer satisfaction is generally considered very good, the results of clustering analysis of customer satisfaction at Solaria Restaurant resulted in the number of very good clusters is cluster I. This result increases our understanding of customer preferences. These results increase our understanding of customer preferences and build a basis for improvement strategies that focus more on improving the customer experience.

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