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The problem faced by Kelurahan Kebagusan is that there is no structured system in determining the allocation of aid funds to the community, so the distribution process is often uneven and less targeted. So far, the determination of aid recipients is only based on subjective observations and manual assessments that have the potential to cause ineffectiveness in decision making. Therefore, a decision support system was designed to assist the kelurahan in prioritizing beneficiaries objectively and transparently. This system uses the Simple Additive Weighting (SAW) method, which allows the assessment process to be carried out based on the weights of several predetermined criteria, such as economic conditions, number of dependents, employment status, and home ownership. The result of this system development is a desktop-based application using the Java programming language, which is expected to help the kelurahan in managing data and performing calculations quickly and accurately, so that the allocation of aid funds can be more targeted and fair.   Keywords: Decision Support System, Simple Additive Weighting, Fund Allocation, Kebagusan Village.

Poverty is one of the main issues in Indonesia although many policies have been implemented by the government to overcome this problem. With this problem, a study was conducted which aims to identify factors that affect poverty in East Java in 2023 using the principal component analysis (PCA) method. PCA is a multivariate analysis technique used to extract information from correlated data, so as to summarize several variables into principal components. In this study, the variables used include the number of poor people, percentage of poor people, poverty severity index, open unemployment rate, labor force participation rate, and life expectancy from 38 districts/cities in East Java. It was found that the data characteristics had low variance with the exception of one variable, and met the assumptions of multivariate normal distribution, interrelationship between variables, data sufficiency, and correlation between variables suitable for PCA. Factor analysis with PCA produces two main components, namely community living conditions and labor conditions, which can represent the original variables in their influence on poverty in East Java. Suggestions from this study are expected to be a reference for policy makers in improving community welfare and labor conditions in East Java. Future research is expected to add related variables to obtain more detailed results.

The design of a village fund assistance information system using the C4.5 algorithm aims to optimize the selection process for prospective aid recipients at the Ogan Ilir Regency Social Service (DINSOS). Currently, the village fund assistance selection process often takes a long time and is prone to inaccuracy and unfairness due to the limitations of the manual system. The C4.5 algorithm was chosen to build an effective decision tree in classifying based on predetermined criteria, such as income, number of dependents, employment, housing status, and expenses. By utilizing the Gain or Gain Ratio value of each attribute, the C4.5 algorithm is able to produce a clear decision tree, which makes it easier for DINSOS to make decisions objectively and transparently. This information system is designed with an easy-to-use user interface and a structured database to facilitate the management of aid recipient data. The results of the implementation of this system show increased accuracy in determining prospective aid recipients and time efficiency in data processing, thus supporting efforts to evenly distribute village fund assistance in Ogan Ilir Regency in a targeted manner.

A network is a collection of interconnected devices that aim to share data and resources. With the development of science and various technological collaborations in this digital era, it brings many benefits and real changes to all aspects of life. Work that used to be done by humans has now been gradually replaced by sophisticated technology and adequate internet, resulting in the difficulty of getting a job and increasing unemployment rates. This literature review aims to analyze and classify the networks, collaborative technology and the internet of things impact on business. The development of technology and digitalization can never be avoided, but we can live side by side by using it wisely and utilizing it well.

Work accidents are one of the problems that often occur in companies/agencies where accidents happen to employees/workers and cause serious physical injuries. BPJS Employment is an insurance program that is trusted by agencies/companies by claiming Work Accident Insurance (JKK) which can help in easing the financial burden on families as well as initial efforts to handle cases of work accidents that occur. The main aim of this research is to assist companies in handling work accident cases that occur. The data used in this research includes work accident reports collected from the Bpjs Ketenagakerjaan Stabat office. The method used is the clustering method with the K-means algorithm, which was chosen because of its ability to group fairly large amounts of data with fast and efficient computing time. By using the clustering method that has been used to process work accident case data at Bpjs Ketenagakerjaan in Stabat, we can produce new information from the 672 data that have been tested. From 672 work accident case data at Bpjs Employment in Stabat, 3 clusters were obtained with the results of Cluster 1 having 2 work accident case data, Cluster 2 having 9 work accident case data and Cluster 3 having 9 work accident case data.

Unemployment is a persistent problem in the labor market, thus hampering economic development and national prosperity. Indonesia, including its capital Jakarta, continues to face significant levels of unemployment compared to neighboring countries. This research focuses on analyzing the structure of unemployment in Jakarta using K-Means Clustering to categorize unemployment data based on age groups (2020-2022) sourced from the Central Statistics Agency. Analysis carried out via RapidMiner revealed three clusters:-Cluster 0: Age 30-60 years and above, Cluster 1: Age 20-24 years, Cluster 2: Age 15-19 and 25-29 years. The findings show that the 20-24 year age group has the highest unemployment rate (399,167 people), while the 30-60 year and above age group shows the lowest unemployment rate (75,560 people). This clustering approach provides insight into the distribution of unemployment by different age demographics in Jakarta, highlighting areas where targeted interventions may be needed to effectively address this socio-economic challenge