cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Paska Marto Hasugian
Contact Email
siskahhasugian@gmail.com
Phone
+6281264451404
Journal Mail Official
editorjournal@seaninstitute.or.id
Editorial Address
Komplek New Pratama ASri Blok C, No.2, Deliserdang, Sumatera Utara, Indonesia
Location
Unknown,
Unknown
INDONESIA
Jurnal Info Sains : Informatika dan Sains
Published by SEAN INSTITUTE
ISSN : 20893329     EISSN : 27977889     DOI : -
Core Subject : Science,
Computer Science & IT
urnal Info Sains : Informatika dan Sains (JIS) discusses science in the field of Informatics and Science, as a forum for expressing results both conceptually and technically related to informatics science. The main topics developed include: Cryptography Steganography Artificial Intelligence Artificial Neural Networks Decision Support System Fuzzy Logic Data Mining Data Science
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 420 Documents
 37   38   39   40   41 
Determining The Amount Of Tuition Fees For New Budidarma Students With Data Mining Using The K-Means Clustering Algorithm Sinar Sinurat
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

One of the variables that determines the quality of education in higher education is the amount of fixed tuition fees that must be paid by students to the academic community, which must be in sync with their parents' income. Although the quality of education can be measured from the consistency of supervision, compliance of teachers and students with the System Operating Procedure (SOP), and the availability of complete teaching and learning infrastructure. In private universities, to determine fixed tuition fees, one method that can be used is to find patterns or information on fixed tuition fees at other private universities in the same area (region) by drawing from a large database, which is data mining. It is very important for a private university to know the patterns of prospective students from the data in the database owned by a campus. This technique is the K-Means Clustering Algorithm. The results of the discussion will describe the amount of affordable tuition fees that will be provided with a list with variations based on the study program chosen by prospective students, where each department is distinguished by the completeness of administration and infrastructure in the study program.
Sentiment Analysis Of Indonesia National Team Naturalization Using Bidirectional Encoder Representations From Transformers Diva Ahmad Maulana; Chaerur Rozikin; Aries Suharso
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

In this era of rapid development of information technology, the number of internet users is increasing, supported by the popularity of social media as a medium for sharing information and interacting. The X social media platform is one of the media that is often used to convey public opinion. One of the hot issues discussed on X social media is the Indonesian National Team naturalization program. This program has triggered various public responses, both pro and con. This study aims to analyze public sentiment regarding the program using the Bidirectional Encoder Representation from Transformer (BERT) algorithm with the Knowledge Discovery in Database method. Data was collected using scraping techniques on the X social media platform which were then selected and labeled positive, negative, and neutral. Testing the BERT algorithm using the pre-trained indoBERT model was tested by dividing the training and testing data 80:20, and evaluated with a confusion matrix. With a dropout of 0.3, the evaluation results showed the highest accuracy value of 90%, precision 81%, recall 74%, and f1-score 77%. The results of this study are expected to be useful for evaluation materials and to support decision making by related parties.
Optimization of C4.5 Algorithm Performance Using Particle Swarm Optimization in Predicting Stunting Risk Ma'mur, Khaerul
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

Stunting is a serious global health problem, especially in developing countries. It is caused by chronic malnutrition in children, especially toddlers, which inhibits physical and cognitive growth. Stunting also has the potential to reduce quality of life and productivity in the future. Therefore, early detection of stunting risk is crucial so that appropriate interventions can be provided. Currently, data mining-based classification methods, such as the C4.5 algorithm, have been widely used to predict stunting risk. However, the performance of the C4.5 algorithm in terms of accuracy and efficiency is still lacking, especially in attribute selection and parameter settings. This research aims to improve the accuracy of the C4.5 algorithm in predicting stunting risk by implementing Particle Swarm Optimization (PSO) as an optimization technique. PSO is chosen because of its ability to find optimal solutions quickly and efficiently through the principles of particle social behavior. By using PSO, this research is expected to optimize the attribute selection process and parameter settings in the C4.5 algorithm, so as to produce a more accurate classification model in detecting stunting risk. The result of this research is a significant increase in prediction accuracy compared to the use of the C4.5 algorithm without optimization, so that the resulting model can be a more reliable tool for governments, health institutions, and other policy makers in designing interventions and strategies to overcome stunting.
Functional Testing of The Dana E-Wallet Transaction Features Using Black Box Testing Neng Eva Masliah; Rifa Vida Zahrani; Khairunnisa Dwi Wahyuningtyas; Tiara Putri Latifani Dianata; Muhamad Aditya Suhendar; Muhamad Nabil Arrafi; Subhanjaya Angga Atmaja
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

The rapid development of financial technology in Indonesia has driven the adoption of digital wallet applications such as DANA, which offers various digital transaction features. However, the high usage rate is not always accompanied by guaranteed functional reliability of the application. This study aims to test the functionality of transaction features in the DANA application through a quantitative approach using the black box testing method. The research design is descriptive quantitative, utilizing 20 test scenarios developed based on Equivalence Partitioning (EP) and Boundary Value Analysis (BVA) techniques, with sampled transaction features including login, PIN verification, money transfer, adding a bank account, and requesting money. The analysis technique involves calculating the number of valid and invalid scenarios based on the match between the actual output and the expected output. The results show that 14 out of 20 scenarios (70%) performed as expected, while the remaining six failed, with most failures found in the login and PIN verification features. These findings highlight weaknesses in the input validation system and insufficiently informative error notifications. This study makes an important contribution to the development of e-wallet applications, particularly in improving validation quality, authentication security, and the overall user experience.
Application of Linear Regression Method in Predicting Chicken Egg Sales Amran Sitohang; Neramayana Br Tarigan; Lira Virna; Allya Dwi Cantika
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

This study aims to predict chicken egg sales using a simple linear regression method based on historical sales data for 12 months, from April 2024 to March 2025. This study uses a quantitative approach with a longitudinal study design. The total sampling technique is used because all monthly sales data is analyzed without exception. Data was collected through sales report documentation from a single egg distributor business unit that operated consistently during the study period. Data analysis was carried out through a preprocessing process, calculation of variable values X (month), Y (sales), and calculation of XY and XX to obtain regression coefficients. The results of the calculation show that the linear regression equation obtained is Y=150.1515233− 6.24126X. This shows that there is an average decrease in sales of 6.24 egg boards every month. Furthermore, this regression model is used to predict chicken egg sales in the period April 2025 to March 2026. The predicted results show a continued downward trend, with sales projected to decline drastically to near zero by March 2026. The conclusion of this study is that there is a downward trend in chicken egg sales during the two years of observation. These results provide an early warning for business actors to evaluate business strategies and take anticipatory steps. This research also opens up opportunities for further study of external factors that may affect sales.
Analysis of the Monte Carlo Method in Simulation of Snake and Ladder Game Using R Programming Afif Yasri; Ramlan Marbun; Harefa, Ade May Luky; Muhammad Syahputra Novelan
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

This study applies the Monte Carlo method to simulate the classic board game "Snakes and Ladders" using the R programming language. The research aims to explore how randomness and probability influence the number of moves needed to complete the game and to provide a statistical overview of game outcomes. A simulation of 10,000 iterations was conducted, where each iteration represents one complete game play, starting from position 1 and ending exactly at position 100. The results show that players require an average of 51.41 moves to finish the game, with a minimum of 8 and a maximum of 394 moves. These results illustrate the highly variable nature of the game due to random dice rolls and the presence of snakes and ladders that can significantly alter a player's position. Visualization techniques such as histograms, density plots, boxplots, and line graphs were used to represent the distribution and variability of moves. The findings demonstrate the effectiveness of Monte Carlo simulations in analyzing stochastic systems, where outcomes are driven by random variables. This research contributes to the understanding of probabilistic modeling and can serve as a simple yet insightful example of applying computational methods to real-world scenarios.
Median Filter Optimization and Sharpening Techniques to Improve Digital Image Quality Riko Prananda Prayugo; Lailan Sofinah Harahap
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

The quality of digital images often degrade due to noise disturbances, especially impulsive noise such as salt and pepper. The aim of this study is to optimize the use of the median filter as a denoising technique and to combine it with a sharpening method to enhance the sharpness and clarity of image structures. The proposed approach involves applying an adaptive median filter reduce noise while preserving edge details, followed by a convolution kernel based sharpening technique to further emphasize visual features. The performance of the method is evaluated by compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) between the processed images and the original images. The experimental results demonstrated that this combined approach significantly improve both PSNR and SSIM values after filtering and sharpening, indicating that the synergy between median filtering and sharpening effectively restore the visual quality of digital images. These findings can serve as a foundation for the development of adaptive image preprocessing systems to handle impulsive noise.
Eco-Friendly Material Innovation in Sustainable Architectural Design Adhita Nugraha Mestika; Suranto, Suranto
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

The increasing urgency of environmental challenges has positioned sustainable architecture as a critical field within contemporary design practice. This article examines the latest innovations in environmentally friendly materials and their transformative impact on sustainable architectural design. Through a comprehensive qualitative analysis of recent developments, this research explores how bio-based materials, carbon-negative products, and circular economy principles are reshaping construction methodologies. The study reveals that emerging materials such as cross-laminated timber, mycelium-based composites, hempcrete, and carbon-capturing concrete offer significant environmental benefits while maintaining structural integrity and aesthetic appeal. These innovations demonstrate potential reductions in embodied carbon of up to 40% compared to conventional materials, while enabling buildings to achieve net-zero or carbon-negative status. The research also identifies key implementation challenges including cost considerations, regulatory frameworks, and the need for enhanced industry adoption. The findings suggest that the integration of these innovative materials, supported by advanced assessment methodologies and policy frameworks, represents a paradigmatic shift toward regenerative architectural practices that actively contribute to environmental restoration rather than merely minimizing harm
The Application of Problem-Based Learning Model to Improve Students’ Mathematical Problem-Solving Skills in Junior High School Andreas P Peranginangin
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

This qualitative study investigates the application of the Problem-Based Learning (PBL) model to enhance mathematical problem-solving skills among junior high school students. PBL is a student-centered approach that uses real-world, open-ended problems to drive learning, emphasizing collaboration, critical thinking, and self-directed study. Through observations, interviews, and document analysis in a junior high school setting, this research provides a comprehensive understanding of how PBL implementation impacts students' abilities to solve complex mathematical problems. Findings demonstrate that PBL effectively fosters improved problem-solving strategies, deeper conceptual understanding, and greater student engagement. This study supports integrating PBL as a viable pedagogical model for mathematics education at this level.
Binary Classification of Academic Outcomes Using Ensemble Learning and Neural Networks: A Case Study on OULAD Yulianto, Lili Dwi; Satriawan Desmana; Sutikman, Sutikman; Winarsih, Winarsih
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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

Abstract

The importance of academic classification in online learning platforms is increasingly recognized as it helps in assessing student performance, early detection of issues, and identifying factors that influence academic success. This study uses the Open University Learning Analytics Dataset (OULAD) to predict students' academic success in various classification areas, including Distinction vs Non-Distinction, Withdrawn vs Non-Withdrawn, Pass vs Non-Pass, and Pass vs Fail. The aim of this research is to compare machine learning and deep learning techniques, such as Random Forest, Gradient Boosting, AdaBoost, LightGBM, and Voting Classifier, with a deep learning model based on Dense Neural Networks (DNN) to produce the best possible predictions. Relevant features are also selected using feature selection and dimensionality reduction strategies, including autoencoders and Recursive Feature Elimination (RFE). The results show that LightGBM and Gradient Boosting perform best in several classifications, with an accuracy of 75.47% for Pass vs Fail. On the other hand, DNN requires further refinement but shows potential in handling more complex classifications. In addition to identifying students at risk of failing, this method provides a deeper understanding of the variables affecting academic success in online learning environments.

Page 39 of 42 | Total Record : 420

 37   38   39   40   41 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 16 No. 01 (2026): Info sains, 2026 Vol. 15 No. 02 (2025): Info sains, Desember 2025 Vol. 15 No. 01 (2025): Informatika dan Sains , 2025 Vol. 14 No. 04 (2024): Informatika dan Sains , 2024 Vol. 14 No. 03 (2024): Informatika dan Sains , Edition July - September 2024 Vol. 14 No. 02 (2024): Informatika dan Sains , Edition, June 2024 Vol. 14 No. 01 (2024): Informatika dan Sains , Edition March 2024 Vol. 13 No. 03 (2023): Informatika dan Sains , Edition December 2023 Vol. 13 No. 02 (2023): Jurnal Info Sains : Informatika dan Sains , Edition September  2023 Vol. 13 No. 01 (2023): Jurnal Info Sains : Informatika dan Sains , Maret 2023 Vol. 12 No. 02 (2022): Informatics and Science, September 2022 Vol. 12 No. 1 (2022): March, Informatics and Science Vol. 11 No. 2 (2021): September, Informatics and Science Vol. 11 No. 1 (2021): March, Informatics and Science Vol. 10 No. 2 (2020): September, Informatics and Science Vol. 10 No. 1 (2020): March, Informatics and Science More Issue