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Paradigma
Published by Universitas Bina Sarana Informatika
ISSN : 14105063     EISSN : 25793500     DOI : http://dx.doi.org/10.31294/paradigma
Core Subject : Science,
Computer Science & IT
The Paradigma Journal is intended as a medium for scientific studies of research, thought and analysis-critical issues on Computer Science, Information Systems, and Information Technology, both nationally and internationally. The scientific article refers to theoretical reviews and empirical studies of related sciences, which can be accounted for and disseminated nationally and internationally. Paradigma Journal accepts scientific articles research at Expert Systems, Information Systems, Web Programming, Mobile Programming, Games Programming, Data Mining, and Decision Support Systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 90 Documents
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Phyton-Based Machine Learning Algorithm To Predict Obesity Risk Factors In Adult Populations Lestari, Ade Fitria; Hardani, Sri; Rahmawati, Mari
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3242

Abstract

Obesity is a serious health problem because it can lead to a variety of diseases. Adults are prone to obesity due to several factors such as age, physical activity, weight, diet, gender, lifestyle and so on. Machine Learning as one of the methods for predicting and classifying factors of obesity especially in the adult population. In machine learning, there are various types of algorithms that can be used to classify data. In this study, using the K-Nearest Neighbor, Decision Tree and Naïve Bayes algorithms, 2111 datasets were used and processed using the Phyton programming language. The results were obtained from the comparison of the three algoritms with the highest accuracy of 93.6%, the Decision Trees with 79.6% and the Naïv Bayes with 60%.
Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO Widodo, Slamet; Brawijaya, Herlambang; Samudi, Samudi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3282

Abstract

This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.
Optimizing Heart Failure Detection: A Comparison between Naive Bayes and Particle Swarm Optimization Hamid, Abdul; Ridwansyah, Ridwansyah
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3284

Abstract

This research focuses on the importance of early detection of heart failure which is a serious global health problem. Given the variety of symptoms of heart failure, accurate early detection methods are needed with the aim of reducing the impact of this disease. This study uses the Naïve Bayes (NB) method which has been proven effective in classifying heart failure with significant variations in accuracy by integrating Particle Swarm Optimization (PSO) to improve the model. The evaluation model involves a confusion matrix including accuracy, precision, recall, and Area Under the Curve. The research results show that the integration of PSO in NB results in an increase in accuracy of 7.73%, an increase in precision of 6.42%, and an increase in recall of 1.93%. Although there was a small decrease in AUC. This research shows that the success of NB with PSO can help improve the performance of early detection of heart failure. This indicates the importance of this research in developing more accurate and effective detection methods for critical health conditions such as heart failure.
Comparison of Supervised Learning Classification Methods on Accreditation Data of Private Higher Education Institutions Noviyanto; Wahyudi, Mochamad; Sumanto, Sumanto
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3306

Abstract

This research aims to analyze and compare supervised learning classification methods using a case study of accreditation data for private higher education institutions within the LLDikti Region III contained in BAN-PT. In addition, this research also uses Weka machine learning software in its calculations. The initial step taken is to prepare the software used for supervised learning analysis, then pre-processing the data, namely labeling data that has a categorical data type, after that determining data for testing data. The next step is to test each classification method. The methods used for comparison are logistic regression, K-nearest neighbor, naive bayes, super vector machine, and random forest. Based on the calculation results, the Kappa Statistic and Root mean squared error values obtained are 1 and 0 for the logistic regression method, 0.979 and 0.0061 for the K-nearest neighbor method, 1 and 0.2222 for the super vector machine method, 0.969 and 0.0341 for the naive bayes method, 1 and 0 for the decision tree method, and 0.5776 and 0.1949 for the random forest method, respectively. The logistic regression and decision tree methods in this study get Kappa Statistic and Root mean squared error values of 1 and 0 respectively so that they are said to be good and acceptable, thus the two classification methods are the most appropriate methods and are considered to have the highest accuracy.
Sipkumhamai Application Success Analysis Using the Delone And Mclean Model Said, Fadillah; Octenta, Chintia; Octaviantara, Adi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.4608

Abstract

Evidence-based policy aims to increase the efficiency and effectiveness of policy settings and increase alternative opportunities. The Legal and Human Rights Policy Strategy Agency created the SIPKUMHAMAI application to support evidence-based legal and human rights policies, support legal and human rights research with better data, and provide information to the public about legal and human rights issues. It is very important to make efforts to provide comprehensive and systematic data and information on legal and human rights issues originating from real situations on the ground. In addition to overall legal and human rights issues, this data and information can be used to find out more about the causes of legal and human rights problems, identify deficiencies in law enforcement and human rights protection, and carry out analyzes and provide various recommendations to strengthen systems and mechanisms for enforcing law and human rights in Indonesia. To achieve this goal, a system evaluation must be carried out to determine which components need to be improved. This is necessary to determine whether the system used provides significant benefits for users and the organization. Using the Delone and McLean model, from the six relationships of Information System Success Model, it was obtained that only Hypothesis 7, Hypothesis 8, and Hypothesis 9 were significantly supported and accepted by the data. These findings provide several implications for eGovernment research and practice, especially regarding how to maximize applications. This paper concludes by discussing the limitations that the proposed hypotheses are not fully supported by the research results.
Mobile-based Application Development on Admission of New Students with Design Science Research Methodology Alkhalifi, Yuris; Ramadan, Rino; Atmaja, Rahdian Kusuma; Ispandi, Ispandi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.4765

Abstract

Increased use of mobile devices in recent years has led to a change in human behavior as users. Mobile devices today are being used for a wide range of sectors ranging from entertainment, and business to education. In the field of education, it can be used to interact between teachers and students, and lecturers with students, and can also be done for registration of New Student Admission. The presence of PMB registration through mobile devices can help prospective students apply wherever they are without having to come directly to the campus. It's not implemented by the Indonesian Siber University. (Cyber University). The Cyber University campus is currently implementing New Student Admission registration directly through the campus, so this process is still likely to take a long time. To solve the problem, this study will solve the problem of new student enrolment that is still being done manually to be digitized by building mobile-based applications. The method to be used is the Design Science Research Methodology (DSRM) known as the fast method because it includes the Agile software development model. The programming language used is the Dart-based Flutter framework. As a result of the research carried out, the mobile-based PMB application on the Cyber University was successfully constructed and in line with expectations. Candidate students can download the app on the Google PlayStore with the keyword Cyber PMB
Implementation MFEP Method in Developing Recommendation System for Program Keluarga Harapan (PKH) Recipients Nugroho, Wawan; Nurohim, Galih Setiawan; Setyadi, Heribertus Ary Setyadi; Perbawa, Doddy Satrya
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.4978

Abstract

Poverty occurs because of the imbalance between unlimited human needs and limited resources. This results in a lack of income to meet basic living needs. The Indoonesian government's efforts to alleviate poverty include providing assistance to the poor or underprivileged with assistance called Social Assistance, one of which is the Program Keluarga Harapan (PKH). Problems often occur in determining who is entitled to receive PKH assistance. The conventional selection process is considered inefficient because it requires a long process and the influence of the committee's subjectivity in the assessment, the criteria used in the survey are not in accordance with government regulations and the limited quota of total PKH recipients, so there are still people who do not receive PKH even though they meet the criteria. This research uses the Multi Factor Evaluation Process (MFEP) method. System testing uses the black box method and Boundary Value Analysis techniques which focus on finding system errors. To test the system's accuracy by comparing the MFEP process from the system results and facts based on PKH recipients in 2022 and producing an accuracy value of 91%.
Dempster Shafer Analysis in Mental and Emotional Health Monitoring Nasyuha, Asyahri Hadi; Sari, Dini Fakta; Azanuddin, Azanuddin; Khoiri, Muhammad Hafidz Ady
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.5044

Abstract

Monitoring and diagnosing mental and emotional health is a significant challenge in the healthcare field due to its complex and subjective nature. This research aims to develop an expert system using the Dempster-Shafer method in monitoring and diagnosing mental and emotional health conditions. The Dempster-Shafer method was chosen because of its ability to handle uncertainty and combine various evidence from different information sources. This analysis is designed to identify seven types of mental and emotional illness by considering twenty-four related symptoms. The results of the analysis show that this expert system can provide a more accurate and comprehensive assessment compared to conventional methods. It is hoped that the implementation of this expert system can be an effective tool for medical personnel in making diagnoses and determining appropriate treatment steps for patients with mental and emotional health conditions. This study also highlights the potential of the Dempster-Shafer method in other applications that require evidence-based analysis under uncertainty.
Selection of Marine Tourism Destinations in West Kalimantan Using the Analytical Hierarchy Process Method Rosyida, Susy; Hendi Minarto
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/xhq59h65

Abstract

Tourism in West Kalimantan offers various destinations rich in meaning and history. However, there are issues in managing tourism in this region, such as the lack of easily accessible information for tourists and the uneven distribution of visits to tourist destinations. Tourists often struggle to choose destinations that match their preferences due to the limitations of comprehensive and objective information systems. This results in some destinations becoming overcrowded while others are less visited. Technological advancements enable the development of Decision Support Systems (DSS) to address these issues and enhance decision-making in selecting tourist destinations. This research aims to develop a decision support system using the Analytical Hierarchy Process (AHP) method to simplify the decision-making process in choosing maritime tourist destinations in West Kalimantan. The primary goal of this research is to help tourists select maritime tourist destinations that best suit their preferences, improve their tourism experience, and support sustainable tourism development in West Kalimantan. This study employs the AHP method in the decision-making process. The research stages include problem identification, literature review, data collection through questionnaires, data analysis, and final weight calculation. Data were collected from 400 respondents in West Kalimantan and analyzed to determine the priority of tourist destinations based on criteria such as scenery, distance, accessibility, facilities, cleanliness, and cost. Based on AHP analysis, it was found that Temajuk Sambas is the most preferred tourist destination, followed by Temajuk Mempawah, Jawai Bahari, Samudera Indah, Pulau Lemukutan, and Tanjung Bajau. The developed decision support system provides clear guidance for tourists in selecting maritime tourist destinations in West Kalimantan according to their preferences.
Classification of Dog Emotion Using Transfer Learning on Convolutional Neural Network Algorithm Tribethran, Steven; Jacky Pratama Hasan, Nicolas; Rahman, Abdul
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.5295

Abstract

Recognizing your pet's emotions are very important to improve health, welfare and to detect certain diseases in the animal. The emotions in question are categorized into four categories, namely anger, happiness, calmness, and sadness. The model is designed by utilizing transfer learning techniques using the VGG16 architecture to perform image feature extraction for dog emotion classification based on the image of the animal's facial expression. The research produced an accuracy value of 96.72% on the training set and 88.05% on the validation set, as well as an average F1-Score value of 84.30% on the test set. This research shows the great potential of utilizing transfer learning in dog emotions classification and contributes to more advanced emotion recognition techniques to improve pet’s welfare.

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