Lubuk Karet Village has great potential in rubber production. However, the rubber sales process is often faced with various obstacles, such as difficulty in determining the right price and lack of transparency in transactions. This study is an analytical study using a qualitative approach. The aim is to analyze and design a prototype of a rubber sales application. This study uses questionnaires and interviews as the main data to answer research questions. The output of this study is the identification of problems and concerns of farmers related to how to calculate market prices, difficulties in recording transaction history, and minimal transparency of information regarding selling prices in the wider market. Then the second finding is regarding the prototype design of a prospective rubber sales application which will later be further developed by researchers. From the results of the analysis in this study, it is hoped that there will be further development of the application with farmer data entry features, rubber price calculations, and transaction tracking. It is hoped that this application can provide convenience for farmers in selling their crops, increase farmers' income, and provide transparent information regarding market prices. In addition, this application can also be a promotional media for local rubber products.

The web-based land transportation management system is a modern solution for managing transportation operations by utilizing web technology to facilitate monitoring, scheduling, and optimization of fleet usage. This study aims to analyze the strengths, weaknesses, opportunities, and threats in the implementation of a web-based land transportation management system using SWOT analysis. The results of this study are expected to serve as a guide for companies or transportation management agencies in designing and developing efficient and effective systems. Although there are several challenges, such as dependency on stable internet infrastructure and relatively high implementation costs, the potential benefits, such as increased operational efficiency, cost reduction, and improved customer service quality, make this system highly relevant and have strong prospects for application in the land transportation industry.

Fraud in digital transactions has become a serious issue threatening the security and integrity of the fintech and e-commerce sectors. To address this problem, machine learning technology has emerged as an effective solution for automatically detecting anomalies and fraudulent transactions. This study aims to analyze the application of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Ensemble Learning, in detecting fraud in digital transactions. The research adopts a quantitative approach with experimentation, testing the effectiveness of the three algorithms using a digital transaction dataset consisting of both fraudulent and non-fraudulent transactions. The results show that the Random Forest algorithm performs the best in terms of accuracy and recall, followed by Ensemble Learning, which enhances fraud detection performance by combining multiple prediction models. Meanwhile, SVM demonstrates satisfactory performance but is prone to overfitting issues when handling large and complex datasets. The study also finds that the problem of imbalanced data can affect model accuracy, and data balancing techniques such as oversampling are required to improve fraud detection performance. Overall, the findings suggest that machine learning, particularly Random Forest and Ensemble Learning algorithms, can be relied upon to improve fraud detection in digital transactions. However, challenges such as model interpretability and the need for periodic algorithm updates still need to be addressed to enhance the effectiveness of fraud prevention systems in countering the ever-evolving nature of fraud.

In  recent  years,  air  quality  has  become  an  increasingly  important  issue  in  various  cities  around  the  world.  Real-time  air  quality  monitoring  is  essential  for  identifying  pollution  problems  and  taking  appropriate  actions.  This  article  discusses  the  design  of  an  Internet  of  Things  (IoT)-based  air  quality  monitoring  system  using  the  prototype  method.  The  system  is  designed  to  monitor  air  quality  parameters  such  as  PM2.5,  PM10,  CO2,  and  temperature  in  real-time  and  present  the  data  to  users  through  a  web-based  application.

Rice pest control is one of the main challenges in the agricultural sector in Indonesia. Pests such as planthoppers, stem borers, and rats can cause significant losses to crop yields. Currently, many farmers have difficulty identifying pest types and how to control them quickly and effectively. Therefore, a technological solution is needed that can detect pests directly and provide recommendations for action. This study aims to design an Android-based application that is able to detect types of rice plant pests using visual images and provide recommendations for handling. This application is designed using digital image processing methods and is supported by a large pest database. This technology is expected to be an efficient and practical solution for farmers. The research method used in developing this application is a qualitative method, involving interviews with farmers, agronomists, and data collection related to pests and their damage patterns. This application utilizes AI-based pattern recognition technology to detect pests through photos taken directly by farmers. The results of the study showed that this application was able to detect several types of major pests with an accuracy of up to 85%. In addition, this application provides recommendations for handling steps based on guidelines from the Ministry of Agriculture. The trial showed that this application can help farmers identify pests faster than manual methods. The main contribution of this research is to create a technology-based solution to agricultural problems in Indonesia, especially in the rice sector. With this application, farmers can increase their yields through early identification and proper pest management. In future implementations, this application will continue to be developed to detect additional pests and expand its database. It is hoped that this application can be an important tool in supporting smart farming in Indonesia.

This study aims to analyze the implementation of Metaverse technology as a medium for socializing religious moderation at the Palembang Religious Education and Training Center. Using a qualitative approach, this research explores how Metaverse can facilitate the dissemination of religious moderation values and its potential in improving the understanding of balanced, tolerant, and inclusive religious practices among participants. Data was collected through interviews, observations, and document analysis. The research findings show that Metaverse offers a dynamic and immersive platform to deliver training and discussions on religious moderation, allowing participants to interact more deeply with the material presented. However, challenges such as limited access to technology and understanding of the virtual environment were also found. This research highlights the need for further adaptation and support to maximize the effectiveness of this technology in building religious moderation.

An Internet of Things (IoT)-based logistics monitoring system is an innovation designed to enhance efficiency, transparency, and speed in supply chain management. The implementation of IoT in logistics provides an ideal solution for real-time monitoring of goods, which was previously difficult with conventional methods. This system integrates IoT sensors such as GPS for location tracking, along with temperature and humidity sensors to monitor goods requiring special attention, such as food and pharmaceuticals. By automating processes through IoT technology, goods distribution becomes faster, reducing reliance on manual controls, speeding up decision-making, and minimising human error. This system enables companies to monitor the condition and location of goods in real-time, offering high visibility into the logistics status. With comprehensive monitoring, companies can proactively address issues and ensure goods remain in optimal condition throughout the shipping process. An IoT-based logistics monitoring system has the potential to enhance a company’s competitiveness through efficiency and accuracy in supply chain management. When effectively implemented, IoT technology can not only improve service quality and customer satisfaction but also strengthen a company’s position in an increasingly competitive market.

The Samsat Digital application is designed to improve efficiency and transparency in the management of motor vehicle taxes in South Sumatra. The main goal is to provide the best service to the public using information technology. The Samsat Digital application is an innovative solution that aims to make it easier for the public to access motor vehicle tax administration services. This application provides various features that allow users to make vehicle tax payments, check tax status, extend STNK, and search for information related to Samsat services online. Through mobile application-based technology, Samsat Digital seeks to reduce long queues at Samsat offices, increase time efficiency, and provide a more practical user experience. This system is equipped with secure authentication features, tax payment reminder notifications, and integration with various digital payment methods such as e-wallets, mobile banking, and credit cards.Keyword : Aplikasi Samsat Digital.

During this time, the development of technology has progressed rapidly.  In this sense, many companies have followed this technological development and introduced it into the collection of inventory data of assets, goods, and their financial reports. To obtain accurate and precise data, an inventory data collection system is required. Manual accounting calculations mean that Depot Kayu Kusen Laris III often records inventory incorrectly, which affects the resulting financial reports. Therefore, this study aims to develop an inventory information system that will support accurate data collection at Kayu Kusen Laris III Depot. The conclusion of this study is that the inventory data information system is designed using Access and SQL database.

Palm oil is one of the main commodities in Indonesia which has an important role in the agribusiness sector. An efficient palm oil harvesting process greatly determines the productivity and quality of the harvest. PT BKI SSB, as a company operating in the palm oil sector, continues to strive to improve operational efficiency and ensure optimal harvest quality. One way to achieve this is by implementing machine learning technology in the harvesting activity monitoring system. This article discusses the application of machine learning to monitor and analyze oil palm harvesting activities at PT BKI SSB. Through the use of this technology, it is hoped that it can increase accuracy, efficiency, and provide better data-based solutions in managing company resources.