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Subcontractor selection is a crucial factor in determining the success of a construction project. The selected subcontractor not only plays a role in expediting the completion of the work but also influences the overall quality, cost, and timeliness of the project. Mistakes in decision-making, such as selecting a less competent subcontractor, can result in delays in completion, increased project costs, and even decreased construction quality. Therefore, a systematic, measurable, and objective method is needed to support the subcontractor selection process. This study aims to implement the Analytical Hierarchy Process (AHP) method as an approach in a decision support system for subcontractor selection. AHP was chosen because it can decompose complex problems into simpler structures by determining criteria weights and comparing alternatives. The criteria used in this study include expertise, work experience, timeliness of work, equipment availability, and bid price. By assigning weights to each criterion, the selection process can be carried out more transparently and measurably. The case study was conducted at PT. Tatha Group, a company engaged in the construction services sector. In this study, AHP was used to prioritize alternative subcontractors to be selected to assist in project implementation. The research results show that the AHP method produces clear, structured results and supports more accurate decision-making. Thus, the application of AHP not only minimizes subjectivity in assessments but also provides accountable recommendations for selecting the best subcontractor for the project's needs.

A hotel is one of the places needed as lodging facilities. The selection of a hotel is indispensable to the goals and needs of the visitor. In addition, a strategic location and a comfortable atmosphere are also considerations. The lack of information about hotels is one of the problems for visitors when it comes to a city to determine the desired hotel. This study develops a Decision Support System (SPK) to choose the best hotel in Medan City using the Outranking RElation Sorting method on basis of Threshold Exploration (ORESTE). This system is designed to assist visitors in choosing a hotel that meets the desired criteria. The Outranking RElation Sorting method on basis of Threshold Exploration (ORESTE) is used to determine the weight of each attribute and perform alternative ranking of hotels. In this study, the criteria used include the type of hotel, location, facilities, service and price. This system is expected to help visitors in making more precise and objective decisions. The results of the study show that this SPK system can help visitors in choosing the best hotel that suits their needs.

The growth of the internet has influenced the tourism industry because the internet makes it easier for individuals to obtain reviews about places to visit and because the internet is a tool used by tourist site managers to assess the quality of their offerings. The increase in the number of tourists of almost two million in just three years in West Sumba is proof of this influence. Social media is a tool that people use to interact with each other online; some people have multiple accounts on platforms such as Instagram, WhatsApp, Facebook, Telegram, Twitter, and so on. Tourists can receive recommendations for tourist attractions based on price and type of trip desired through a tourist attraction recommendation system that uses the KNN algorithm. Three factors were used in this research: activity, type of tourism, and type of price. An accuracy of 63.16% is found in the test results using the KNN algorithm and the Rapid Miner application with a K value of 5. The analysis results show that the K-Nearest Neighbor (K-NN) approach can be used as a guideline for recommending tourist destinations to visitors in West Sumba.

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.