J-Icon : Jurnal Komputer dan Informatika

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

Muhammad Yusuf Halim — 2025-03-29

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

Martinus Correia Talo — 2025-03-29

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

Dimas Fahmi Suntoro — 2025-03-29

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

Nurkhasanah Fadhila Syifa — 2025-03-29

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.