This study aims to determine the influence of financial literacy, use of investment platforms, and herding behaviour on investment decisions among Generation Z in Jabodetabek. The method used in this research is a quantitative approach through instrument testing, classical assumption testing, hypothesis testing, and multiple linear regression analysis using SPSS 26. The accessible population in this study consists of 255 students, with a research sample of 156 students selected using proportionate stratified random sampling. The data used in this research is primary data obtained through questionnaires distributed via Google Forms. The results of the study conclude that: (1) the financial literacy variable has a positive effect on investment decisions; (2) the use of investment platform variable has a positive effect on investment decisions; (3) the herding behaviour variable has a positive effect on investment decisions; (4) the variables financial literacy, use of investment platform, and herding behaviour have a simultaneous influence on investment decisions.

Accurate sales forecasting is essential for retail Micro, Small, and Medium Enterprises (MSMEs) to optimize operations and inventory planning in the digital economy. This study compares the forecasting accuracy between Artificial Intelligence (AI)-based methods (Random Forest, Decision Tree) and traditional techniques (Moving Average, Exponential Smoothing) using 3,600 transaction records from five retail MSMEs over three months. A quantitative experimental approach was employed to evaluate model performance under real-world conditions, including market fluctuations and seasonal anomalies. Evaluation metrics include Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and cross-validation techniques. The findings indicate that the Random Forest model achieves superior accuracy (MAPE = 8.5%) compared to traditional methods (MAPE = 15.2%). Explainable AI (XAI) using SHAP and LIME further enhances transparency and managerial trust. Although traditional methods offer faster computation and ease of interpretation, AI-based models show resilience against unpredictable sales patterns. This research recommends hybrid adoption strategies that balance predictive power with interpretability for MSMEs with limited technical capacity. The results contribute to the discourse on digital transformation and intelligent forecasting in the MSME sectors.

The rapid development of technology increases the need for effective and efficient information. Information that is not managed properly loses value, especially when large amounts of data are available, making conventional methods no longer adequate to analyze the potential of the data. Therefore, a system capable of analyzing, summarizing, and extracting data into useful information is required. The Department of Agriculture and Food Security, as an agency that handles food security, agriculture, animal husbandry, animal health, and fisheries, is responsible for supporting the increase in agricultural yields to meet the food needs of the population and encourage economic growth. To achieve this goal, the agency needs to utilize technology to process agricultural data quickly and accurately. The system built using the apriori method can analyze data efficiently and provide recommendations for increasing agricultural yields. Based on the test results, a support value of 9% and a confidence of 68% were obtained, with the rule If the crop is Cassava, then the production yield is 6000-8000 tons.

Land and Building Tax (PBB) is a type of area regulated by the government in determining the amount of tax for implementation and development as well as increasing the prosperity and well-being of the people. Based on taxpayer compliance data in Tanjung Keliling Plantation, the results of tests carried out using the Clustering algorithm can determine the variables of ownership area, hamlet name and payment level. Clusters 1,2,3 of 600 PBB taxpayer data, namely where cluster 1 has 166 data, can be grouped based on the Ownership Area of "500,001-600,000m2" with the Hamlet Name "Ujung Bangun" and the Payment Level "Quite Good". Cluster 2 consists of 196 data, which can be grouped based on ownership area "200,001-300,000m2" with the hamlet name "Karang Jati" and payment level "fairly good".  And Cluster 3 with a total of 238 data, can be grouped based on the Ownership Area "400,001-500,000m2" with the Hamlet Name "Mojosari" and the Payment Level "Quite Good".

Handling of dental disease problems requires that it be handled quickly and correctly, but not all teams of dental experts can carry out treatment quickly due to the lack of a team of dental experts who are in the workplace or hospital 24 hours a day.  Apart from that, the public also has very little knowledge of information about dental disease, so that to treat dental disease, people have to consult a dentist. To classify images of dental disease, feature extraction is needed. Feature extraction is taking characteristics of an object that can describe the image. One example of image feature extraction used is Red, Green, Blue (RGB). This feature extraction is often used to identify or classify an image. Dental image data that will be used in the classification process are tooth abrasion, anterior crosbite, cavities and gingivitis. K-Nears Neigbor is the simplest data mining algorithm.  The aim of this algorithm is to find the results of the closest distance classification for each object.  In determining the distance, the data is initially divided into two parts, namely training data and testing data. After receiving the training data and testing data, the distance from each testing data (Equilidence Distance) to the training data is calculated. The K-Nearest Neighbors method can be applied to classify dental disease based on images of types of dental disease using Matlab software. As a result of the image data training process, 40 image data were input, training results obtained were 100%.

Hyperthermia is a condition characterized by symptoms such as dehydration, muscle spasms, dizziness, weakness, nausea, vomiting, and fatigue, which can harm the patient's condition. The causes of hyperthermia can vary, ranging from lack of fluids to excessive physical activity. RSU Putri Bidadari has doctors who are experts in treating various diseases, including hyperthermia. However, several obstacles often occur in the direct consultation process, such as long queues, long distances, limited time, and costs. Therefore, a technology-based system is needed that is able to manage hyperthermia symptom data and help diagnose the disease early, so that patients can get information and early treatment quickly. This method is used to manage the symptoms selected by the patient to determine the possibility of the disease with a high level of confidence. Based on the analysis of the selected symptoms, this system is able to produce the most accurate diagnosis with the case of hyperthermia type Heat exhaustion, with a confidence level of 50.26%.

Epilepsy, or apoplexy, is a chronic disease characterized by recurrent seizures and impaired consciousness due to disorders of the central nervous system. In developing countries, including in RSU Putri Bidadari, epilepsy management is often hampered by high consultation costs, resulting in suboptimal quality of treatment and patient recovery. To overcome this challenge, a system is needed that can facilitate the diagnosis and treatment of epilepsy more efficiently. By using this method, RSU Putri Bidadari can improve the precision of epilepsy diagnosis and determine more appropriate treatment steps, despite limited resources. The Bayes method, as a statistical approach, offers a potential solution to improve the accuracy of diagnosis through data-based probability estimation of diseases and symptoms reported by patients such as frequent hunger, thirst, urination, weight loss, vaginal infections, easy fatigue, tingling legs, and blurred vision. The analysis results of the system show an estimated probability of 73% for patients suffering from generalized epilepsy. The Bayes method-based system is expected to help RSU Putri Bidadari in providing more effective treatment and improving the overall quality of life of epilepsy patients.

The Internet of Things (IoT)-based automatic bell system is designed to enhance the efficiency of bell operations in educational institutions by utilizing modern technology. This research aims to develop a system that can control the bell automatically according to the class schedule, while also enabling remote control via a mobile application using the Blynk platform. The system is built using an ESP8266 as the main microcontroller, a DFPlayer Mini module for audio playback, and an RTC DS1307 for time management. The results show that the system functions as expected, both in automatic mode based on the schedule and in manual mode through the mobile application. Testing and debugging demonstrated that integration with WiFi networks allows for flexible and effective bell control. For further development, it is recommended to add a power backup feature, web interface, and push notifications to improve system reliability and flexibility. This system provides an efficient and practical IoT solution for automating bell operations in educational environments.

Chikungunya is a disease caused by the Chikungunya virus (CHIKV) transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Some of the symptoms are sudden fever, chills, pain in the joints, muscle pain in the neck, shoulders and limbs, rash. Treatment of sufferers is aimed at complaints and symptoms that arise and the drugs given are generally only to relieve existing symptoms, such as giving Antipyretic drugs to relieve fever, Antiemetic drugs to relieve nausea / vomiting, or Analgetics to relieve joint pain only, from these problems many people also do not understand the handling of chikungunya disease, therefore it is necessary to have a system that can collect information to collect information and conduct counseling activities on the treatment of Chikungunya disease to the general public and especially to the elderly to provide knowledge about Chikungunya disease using the Certainty Factor method. The purpose of this research is to provide information and knowledge to the public about Chikungunya Disease Treatment and the Elderly know the medicine for Chikungunya Disease treatment. From the results of applying the certainty factor method to diagnose chikungunya disease, it can be obtained that the diagnosis result is acute chikungunya disease with a confidence level of 95.19%.

The process of accepting new students generates a lot of data in the form of profiles of students who register. From year to year there is an increase in the number of prospective new students who come from several areas in Binjai City, Langkat Regency and surrounding areas, so the location of the socialization of new student admissions promotions every year is increasing and wider. And from several schools that have been visited and are expected to provide new prospective students, in fact, it is not proportional to the final number of prospective students who register. In this study, applying the K-Means Clustering algorithm using 3 variables namely, region, school origin, major. In determining the location of new student admissions promotions, the promotion team first identifies what factors will influence the determination of promotional locations ranging from region, school origin and majors that are considered to be set as promotional locations. Based on the results of grouping new student admission data of STMIK Kaputama Binjai using the K-means Clustering method from 20 data that has been processed, 3 clusters and 3 iterations are produced where cluster 1 has 9 data, cluster 2 has 2 data and cluster 3 has 9 data.