J-Icon : Jurnal Komputer dan Informatika

DECISION SUPPORT SYSTEM FOR APPLYING STUDENTS TO PIP SCHOLARSHIPS USING AHP AND TOPSIS METHODS

2025-03-29T06:03:03+00:00

Education is an effort to help individuals achieve their maximum potential. In accordance with Law Number 20 of 2003 concerning the National Education System, basic education is the earliest level in the national education system. State Elementary School (SD) 018 Loa Janan is an elementary school located in Tani Bahagia Hamlet, Batuah Village, Loa Janan District, Kutai Kartanegara Regency, East Kalimantan Province. To support the welfare of its students, the school enrolls its students in various scholarship programs, including the Smart Indonesia Program (PIP). However, the absence of clear criteria causes difficulties for schools in determining which students are eligible to apply for PIP scholarships. Therefore, a Decision Support System (SPK) will be implemented to assist schools in the selection process of students who will be proposed for PIP scholarships. This research aims to achieve this. Decisions will be taken using a combination of the Analytical Hierarchy Process (AHP) method to calculate criteria weights and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to rank alternatives. There are five criteria and 67 alternatives. Criteria weights will be calculated using the AHP method, while alternative ranking will be carried out using the TOPSIS method. The calculation results show that a student named Elbara Mukti received first place with a preference value of 0.8589. Students with the highest preference scores will be proposed by the school to receive a PIP scholarship

THE USE OF AI AND IOT IN INFECTIOUS DISEASE HEALTH MONITORING A SYSTEMATIC REVIEW

2025-04-03T10:26:26+00:00

This systematic review evaluates the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in infectious disease monitoring. The review analyses the recent development, implementation, and effectiveness of AI-IoT systems in surveillance, early detection, and outbreak prediction. An analysis of peer-reviewed literature from 2021 to 2024 reveals key trends, challenges, and opportunities in the application of these technologies in public health. AI plays an important role in analysing big data to detect patterns and predict the spread of diseases, while IoT provides the infrastructure for real-time data collection through interconnected devices. The results of this review show that the combination of AI and IoT can speed up diagnosis, improve public health response, and facilitate remote patient monitoring, especially in hard-to-reach areas. However, there are some key challenges that need to be addressed, such as data privacy, cybersecurity, and interoperability between systems. In addition, the successful implementation of these technologies requires multidisciplinary collaboration between the fields of technology, health, and policy. The review also highlights the potential benefits of AI and IoT integration in addressing complex public health issues, especially in the context of mitigating and controlling future outbreaks. The development of safer and more integrated technologies is necessary to maximise their positive impact. AI and IoT synergies offer great opportunities to improve global health systems, but their sustainable implementation requires more attention to relevant technical, ethical, and policy aspects

REVENUE SEGMENTATION FROM PAYMENT AGGREGATOR USING K-MEANS CLASSTERIZATION METHOD

2025-03-29T06:51:20+00:00

Customer habits can influence revenue from financial technology-based companies in choosing the type of payment partner. Customers are very selective in having vendors oriented towards convenience, promotions given, and benefits offered. This study describes the application of data mining for clustering, using the K-means method by classifying income in a payment aggregator. The research aims to identify patterns and similarities in revenue data to help decision-making and business analysis. The K-means algorithm is used to partition income data into groups based on their similarities. The research results show that testing uses various quantities: k = 2; DBI = 0.023, k = 3; DBI = 0.209, k = 4; DBI = 0.116 with a max run of ten. This study obtained the best results at a value of k = 4, with a clustering pattern in four payment type categories: copper, silver, gold, and platinum. The results showed that the payment aggregator categories with the highest income values were the CASH, MINIATM TRANSFER, and VA TRANSFER methods with 10.78% from revenue. From the pattern and information provided, the company needs to maintain features that support the payment aggregator with the highest revenue, while for the payment aggregator generating lower revenue, an evaluation is required to consider adding merchants in order to boost transaction frequency and amounts.

APPLICATION OF K-MEANS ALGORITHM IN KINDERGARTEN SCHOOL LOCATION CLUSTERING OF SCHOOL SELECTION STRATEGY BY PARENTS

2025-03-29T11:02:29+00:00

This research aims to improve the kindergarten school location clustering model to support parents' school selection strategies. The main issue raised is the need to understand parents' preferences more deeply in choosing the right school for their children. To achieve this goal, the K-Means algorithm was applied and analyzed to cluster parents' data based on characteristics such as occupation, education, and residential location. This research utilizes a quantitative method with an exploratory descriptive approach. The results showed that the K-Means algorithm successfully formed two clusters with different characteristics. Cluster_0 includes groups with more centralized or close locations, education levels that tend to be low, and types of jobs that are at the lower middle economic level, while cluster_1 groups with more dispersed or distant locations, higher education levels, and jobs that are at higher economic levels. The quality of the resulting clusterization is considered quite good, with a Davies-Bouldin Index (DBI) value of 0.151. The application of the K-Means algorithm is proven to be effective in identifying groups of parents with different preferences, so it can be a foundation for schools in developing more targeted and tailored service strategies. This research makes an important contribution to the application of clustering techniques to support marketing strategies and decision-making in the early childhood education sector.

USE OF UI/X ON WEBSITE RECOMMENDATION OF LAPTOP SPECIFICATIONS WITH K-MEANS ALGORITHM

2025-04-03T10:19:15+00:00

The process of choosing a laptop that suits their needs is often a challenge for consumers because of the variety of specifications and features offered. Many consumers find it difficult to make the right choice, especially because the information available is often not well structured. In addition, each individual's needs vary, ranging from use for daily productivity to special needs such as gaming or graphic design. Therefore, this study aims to develop a prototype design of a laptop recommendation system using the K-Means clustering algorithm, which is able to group laptop specification data into certain clusters based on the similarity of features. A total of 25 laptop specification data were used in this analysis, with the main parameters being RAM capacity and SSD capacity. The data was processed using the data mining method, and the K-Means algorithm was applied to perform grouping. The optimal number of clusters is determined using the elbow method to ensure accurate and relevant results. The results of the grouping show that laptops can be classified into specific groups that represent consumer needs, such as use for daily productivity or high-load work. The prototype design of this system was created using Figma to visualize an intuitive and easy-to-use user interface (UI). With this prototype design, it is hoped that it can be a reference in the development of a system that makes it easier for consumers to choose a laptop that suits their preferences and needs.

UTAUT Analysis of E-learning Users: A Case Study at ABC University

2025-04-12T02:44:27+00:00

The concept of e-learning has been adapted and used in Indonesia since the 1940s, and its development has
continued to progress to this day. The COVID-19 pandemic has led to more comprehensive use of elearning. Unfortunately, ABC University was not prepared for the drastic shift to fully online learning, and
its e-learning system is not yet integrated with the academic information system (SIAKAD). Other
challenges include system bugs and a lack of optimization in discussion forums. As a result, students and
lecturers are not able to use e-learning to its full potential. To identify the root causes of these issues, this
study aims to determine the influencing factors of e-learning usage at ABC University, with the findings
intended to provide recommendations for the university to address the challenges it faces. The study uses a
mixed-method approach, combining quantitative and qualitative methods to process and validate the data.
The UTAUT (Unified Theory of Acceptance and Use of Technology) approach is applied to identify the
factors that affect e-learning usage, with analysis conducted using PLS SEM (Partial Least Squares
Structural Equation Modeling). The study results show that among the five factors examined—Performance
Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and
Behavioral Intention (BI)—only Social Influence (SI) significantly impacts e-learning usage, while the
other four factors do not have a significant effect. The recommendations from this study include the need
for system integration between e-learning and SIAKAD to ensure optimal use, as well as conducting
maintenance every three months, facilitating meeting accounts, and adding audio-video features in
discussion forums.

WEBSITE DESIGN FOR NUTRITION STATUS CLASSIFICATION OF TODDLERS USING UI/X WITH K-MEDOIDS ALGORITHM

2025-04-12T11:33:20+00:00

Nutritional needs in Indonesia vary based on age, gender, physical activity, and an individual's health condition. According to Regulation of the Minister of Health of the Republic of Indonesia No. 28 of 2019, the Recommended Dietary Allowance (RDA) issued by the Ministry of Health provides guidelines on daily energy (calorie) requirements. For infants aged 0 to 12 months, the required intake is 550–725 kcal. The toddler phase (0–5 years old) is a golden period of growth, during which physical and brain development occurs rapidly. Malnutrition during this period can lead to growth disorders such as stunting, which has long-term effects on a child's health and intelligence. To determine a toddler's nutritional status, it is essential to classify their status based on weight and height ratio, commonly measured using Body Mass Index (BMI). BMI is used to determine whether a child's weight falls into the normal, underweight, or obese category. Therefore, regular monitoring is necessary to detect nutritional problems early, enabling proper intervention. This study aims to develop a website using the k-medoids algorithm to assess toddlers' nutritional status. The calculation process in this study, which involves 30 toddler data samples, determines the number of toddlers in each cluster: normal nutrition status, undernutrition, and obesity. The study also applies a Confusion Matrix to evaluate the clustering performance, including accuracy, precision, and recall. The evaluation results show that the k-medoids algorithm performs perfectly, achieving 100% accuracy for all clusters. This indicates that k-medoids successfully classifies the data into clusters without errors.