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"Journal of Data Science
Published by SEAN INSTITUTE
ISSN : -     EISSN : 30252792     DOI : https://doi.org/10.58471
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT
The "Journal of Data Science" is a real journal that focuses on the field of data science. It covers a wide range of topics related to data analysis, machine learning, statistics, data mining, and related areas. The journal aims to publish high-quality research papers, reviews, and technical notes that contribute to the advancement of data science.The Journal of Data Science welcomes submissions from researchers, academics, and practitioners working in the field of data science. It provides a platform for sharing novel research findings, methodologies, algorithms, and applications in various domain
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 28 Documents
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Citizenship education: Foundations of Indonesian nationalism and democracy Dadang Mulyana
Journal Of Data Science Vol. 2 No. 01 (2024): Journal Of Data Science, March 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i01.4314

Abstract

Nationalism and democracy are two crucial aspects in building a strong and sovereign country. Citizenship education has the potential to become the main means of forming nationalist awareness and strengthening democratic values among Indonesian society. This research aims to examine the role of citizenship education in building the foundations of nationalism and democracy in Indonesia. This research uses a qualitative approach with descriptive methods. The research results show that the implementation of citizenship education has a significant impact in forming nationalist awareness and democratic participation among Indonesia's young generation. Respondents stated that through PKN learning, they better understand the values of Pancasila, their rights and obligations as citizens, as well as the importance of participation in political and social life. These results confirm that citizenship education plays an important role in forming strong national character and identity and improving the quality of participation in democracy, which is a crucial aspect in building a sovereign and just nation.
Application Of C4.5 Algorithm In Disease Classification Sipra Barutu
Journal Of Data Science Vol. 2 No. 02 (2024): Journal Of Data Science, September 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i02.5263

Abstract

In the modern era, information and communication technology (ICT) has a significant impact on the health sector, one of which is through the application of artificial intelligence (AI) for disease diagnosis. The C4.5 algorithm, one of the popular classification algorithms, shows great potential in helping doctors classify diseases more accurately and efficiently. Research shows that the C4.5 algorithm is able to achieve a high level of accuracy in classifying various types of diseases, such as diabetes mellitus, heart disease, and lung disease. Its advantages include ease of interpretation, resistance to data noise, and efficiency. However, its application also has several challenges, such as the availability of quality data, complex interpretation of results, and the potential for overfitting. Nevertheless, the C4.5 algorithm offers great potential to improve the quality of patient diagnosis and care. Further research is needed to overcome the challenges and improve the effectiveness of the C4.5 algorithm in disease classification, such as the development of anti-overfitting techniques, optimal attribute selection methods, and application to more types of diseases. With continued research and development, the C4.5 algorithm can become a valuable tool for doctors and other medical personnel in fighting disease.
Analysis Of The Use Of Information Technology In Rural Communities Astri Astri
Journal Of Data Science Vol. 2 No. 02 (2024): Journal Of Data Science, September 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i02.5264

Abstract

This study aims to analyze the use of Information Technology (IT) in rural communities. Amidst rapid technological developments, rural communities have also begun to adopt IT, although they still face various obstacles such as limited infrastructure and low digital literacy. Through literature studies and previous research, it was found that the use of IT has had a significant impact on rural communities, including social, economic changes, and access to health and education services. However, greater efforts are still needed to increase IT adoption in rural areas and overcome existing obstacles. Further research is also needed to better understand the factors that influence IT adoption and its long-term impact on rural development. Thus, this study is expected to provide valuable insights for policy makers, practitioners, and academics in efforts to improve digital inclusion and the welfare of rural communities.
Designing A Web-Based Information System At SMP Negeri 1 Kualuh Hilir Eva Rifka Sinaga
Journal Of Data Science Vol. 2 No. 02 (2024): Journal Of Data Science, September 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i02.5355

Abstract

The rapid development of information technology in the era of globalization has brought significant changes in various fields, including education. SMP Negeri 1 Kualuh Hilir, as an educational institution, requires a web-based information system to facilitate easy and flexible access to information for students, teachers, and parents. This study aims to design and implement a web-based information system with the integration of the Internet of Things (IoT) concept for smart trash management, using the MQTT protocol and the WeMos D1 (R2) microcontroller. The research methodology includes literature studies, observations, needs analysis, hardware and software design, implementation, and system testing and evaluation. The results of the study indicate that the developed system and tools function well, with positive assessments from trials and adequate evaluation results.
Decision Making Techniques For Selecting Female Dormitory Supervisors Using The Technique Method For Others Reference By Similarity To Ideal Solution Billy Siallagan; Vanessa Leisa sitanggang; Novita Limbong; Rony sidabariba
Journal Of Data Science Vol. 2 No. 02 (2024): Journal Of Data Science, September 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i02.5416

Abstract

The female student dormitory at Santo Thomas Catholic University Medan serves as a temporary residence for female students who come from outside Medan. Dormitory supervisors play an important role in the management and supervision of the dormitory, ensuring the safety and well-being of residents. However, the selection of new coaches faces obstacles, such as lack of qualifications and potential conflicts of interest. To address these issues, this research applies the Technique for Order Preference by Similarity to Ideal (TOPSIS) method as a decision-making tool. This method allows the comparison of alternatives to prospective coaches based on set criteria. The results show that the TOPSIS method is effective in determining suitable candidates for female dormitory coaches, thus supporting the establishment of a safe and supportive environment for female students. This research emphasises the importance of a transparent selection process and oversight mechanism to prevent conflicts and ensure the integrity of the candidate.
K-Means Clustering of Student Mid-Term and Final Exam Score Data Nella Ane Br Sitepu; Agnesia Rointan Sijabat; Cindy Rounali Limbong; Lenny Evalina Pasaribu; Einson O.B Nainggolan; Michael Manulang
Journal Of Data Science Vol. 2 No. 02 (2024): Journal Of Data Science, September 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i02.5417

Abstract

Clustering is a method in data mining that aims to group data based on similar characteristics. This research utilises the k-means clustering algorithm to group students based on their UTS and UAS scores, making it easier for lecturers to identify students' academic abilities. With the application of this method, it is expected to form groups of students who are intelligent, less intelligent, and moderate. In addition, this research also addresses the challenges in observing student grades which are often done manually, resulting in wasted time and effort. Through the k-means clustering approach, this research aims to improve the quality of education by providing insight for education managers in mapping student learning outcomes. The k-means method used includes determining the cluster centre point and calculating the distance between data, which is repeated until convergence is achieved. The results show that this method is effective in identifying student achievement patterns, providing a basis for decision-making to improve academic achievement in higher education.
Optimization of Kaayana Store Inventory through Transaction Pattern Analysis Using the Apriori Algorithm Suhardiansyah Suhardiansyah; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6398

Abstract

This study aims to optimize inventory management at Kaayana Store by analyzing sales transaction patterns using the Apriori algorithm. The transaction data collected shows that products with the codes ACC (accessories) and BJU (clothing) dominate purchases, accounting for 71.4% of total transactions. The analysis results identify a strong relationship between these products, which are frequently purchased together by consumers. Based on these findings, Kaayana Store needs to ensure the availability of ACC and BJU stocks to meet high demand, avoid stockouts, and improve operational efficiency. Proposed inventory management strategies, such as more precise product placement and bundling promotions, are expected to enhance customer satisfaction and support the sustainability of Kaayana Store's business
Analysis of Product Demand Prediction Using Decision Tree on Sales Data of Ceria Toys Store Anzas Ibezato Zalukhu; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6458

Abstract

Ceria Toys faces challenges in efficiently managing the inventory of electric bicycles, as product demand is influenced by factors such as market trends, seasons, and changing consumer preferences. To address this challenge, this research employs data mining techniques with the decision tree algorithm to predict product demand and assist in inventory management. The evaluation results of the predictive model show varying performance across product categories. The precision for the "Hot" category is 58.36%, while for the "Less Popular" category, it is 64.18%. The recall for the "Hot" category reaches 83.71%, but the recall for the "Less Popular" category is only 32.82%. Although the model performs better in predicting hot products, there is still room to improve the detection of less popular products. To enhance effectiveness, Ceria Toys can balance the dataset or adjust the model. With this information, the store can better prepare stock for hot products and optimize the management of less popular products. These steps are expected to maximize sales, reduce excess stock, and improve overall customer satisfaction.
Analysis of the Most Popular Study Programs at Haji University of North Sumatra Using the Decision Tree Algorithm Dewi Sartika; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6459

Abstract

This study aims to analyze the clustering of the most popular study programs at Universitas Haji Sumatera Utara using the Decision Tree algorithm. This algorithm successfully grouped the study programs based on the applicants' interests, considering gender as the primary variable. The analysis results show that the most popular study programs among women are the Bachelor of Midwifery and the Bachelor of Nursing programs, which each have a very high number of female applicants. On the other hand, programs such as the Regular Bachelor of Law and Management show a more balanced interest between women and men, with Management having almost equal gender proportions. This classification model performed very well in detecting female applicants, with a high recall (95.51%) and good precision (79.84%). However, the model struggles to identify male applicants, with low recall (18.40%) and suboptimal precision (54.76%). This indicates that the model is more sensitive to predicting female applicants. Therefore, it is recommended that Universitas Haji Sumatera Utara enhance more inclusive and balanced marketing strategies, as well as optimize both regular and non-regular registration pathways to attract a more even interest from both genders, in order to achieve gender equality across various study programs and improve the efficiency of student admissions
Prediction of Building Permit Approval in Medan City Using the Naïve Bayes Algorithm for Investment Prospects Nelviony Parhusip; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6461

Abstract

Property investment in Medan City has become increasingly important in line with economic growth and rapid infrastructure development. Over the past decade, property investment in Medan has shown significant growth, as evidenced by the expansion of residential areas, boarding houses, hotels, and integrated apartments such as Manhattan Square, Jati Junction, and Podomoro Deli Park. This study aims to predict and identify patterns of Building Permit Approval (PBG) in Medan City that are significantly related to investment prospects. The algorithm used in this study is Naïve Bayes, implemented using the Orange tool, which enables the prediction of building permit approvals in Medan City. The key findings of this study indicate the existence of significant building permit approval patterns and the identification of potential investment areas. The implications of this research are crucial for investors and developers in formulating more effective investment strategies

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