Jurnal Sains, Teknologi & Komputer

Implementation of a decision support system in the selection of scientific articles using the weighted product method at the rumah jurnal larisma

Elfida Putri Handayani Nasution — 2026-01-09

The increasing number of scientific articles submitted to journals demands an objective, consistent, and efficient selection process. Manual selection processes involving multiple criteria have the potential to create subjectivity and slow down editorial decision-making. This study aims to implement a Decision Support System in the selection of scientific articles using the Weighted Product method at LARISMA Journal House. This study uses a quantitative approach with fifteen articles as alternative decisions assessed based on five criteria: scope suitability, research novelty, methodological clarity, writing structure, and reference quality. The weight of the criteria is determined based on the journal manager's priorities. The results show that the Weighted Product method is able to produce an objective and measurable article ranking, thus assisting editors in determining the priority of articles worthy of further processing. The implementation of this decision support system has been proven to improve the efficiency, objectivity, and consistency of editorial decision-making in the management of scientific journals.

Application of the weighted product (WP) method in determining construction project priorities at PT. Graha Karya Perkasa Medan

Siti Azzahra — 2026-01-09

PT. Graha Karya Perkasa is a company engaged in the construction and infrastructure sector, so it requires an appropriate decision-making process in determining which projects should be prioritized. This study aims to determine the priority order of three projects, namely the construction of the MBG Warehouse, the construction of the Flyover Bridge, and the construction of the Dam using the Weighted Product (WP) method. The assessment is carried out based on three main criteria, namely cost, duration, and quality. Research data was obtained through interviews with project heads and available planning documents. Based on the results of calculations using the Weighted Product method, the Flyover Bridge project is the project with the highest priority value, followed by the MBG Warehouse and the Dam. The Weighted Product method has been proven to be able to provide objective results in the process of determining project priorities.

Implementation of the weight product method as a decision support system in determining young coconut business strategies

Ailsa Rira Sinaga — 2026-01-10

Young coconut farming is a micro-enterprise with significant market potential in Indonesia. However, strategic decision-making is often subjective, resulting in suboptimal strategies. This study aims to implement the Weighted Product (WP) method as a decision support system for determining young coconut business strategies. The criteria used include business capital, raw material availability, business location, market potential, and business risk. The strategic alternatives analyzed included expanding branches, improving product quality, and digital marketing. The results showed that digital marketing had the highest preference score, and therefore, it is recommended to be implemented as the primary strategy in developing young coconut businesses.

Digital transformation in marketing 2025: the role of artificial intelligence in improving customer experience

Syawaluddin Syawaluddin — 2026-01-28

The development of digital technology in 2025 shows increasingly significant acceleration, particularly marked by the increasing use of Artificial Intelligence (AI) in modern marketing strategies. AI integration enables companies to create a more personalized, responsive, and adaptive customer experience (CX) through the application of predictive analytics, marketing automation, and real-time data processing. This article aims to analyze the strategic role of AI in improving customer experience in the context of digital marketing in 2025. The research method used is a systematic literature review of 35 scientific articles, industry reports, and reputable international publications relevant to the study topic. The results of the analysis show that AI contributes significantly to improving customer experience through four main dimensions: data-driven personalization, intelligent customer interactions, service and operational efficiency, and accurate and contextual product recommendation systems. These findings confirm that AI integration is no longer optional, but rather a strategic necessity for companies in strengthening digital transformation, increasing customer loyalty, and maintaining business competitiveness in the increasingly dynamic digital marketing era.

IoT-based automatic water circulation to control the pH level of water in hydroponic plants

Abdullah Munawwar — 2026-04-30

Water pH stability is a crucial factor in hydroponic systems because it directly affects the ability of plants to absorb nutrients. A common problem is that the process of monitoring and controlling pH is still done manually, making it less efficient and potentially causing imbalances in the plant's environmental conditions. This study aims to design and implement an Internet of Things (IoT)-based automatic water circulation system to control water pH levels in hydroponic plants. The research method is carried out through system design using an ESP32 microcontroller integrated with pH sensors, temperature sensors, and light sensors, as well as actuators in the form of a water pump and UV lamp. This system is capable of monitoring conditions in real time and performing automatic control connected to a website-based platform. The results of the study show that the system is able to maintain pH stability in the optimal range of 6.5–7.5 with a good level of accuracy and increases efficiency in hydroponic management. Thus, this system can be an effective and practical solution in supporting technology-based hydroponic plant cultivation.

Design and implementation of an IoT-based automatic package box security system with vibration sensors and GSM notifications using ESP32

Fathahul Ammar Hasibuan — 2026-04-30

The development of Internet of Things (IoT) technology has provided solutions to improve security systems, including securing package boxes from the risk of theft. This study aims to design and implement an IoT-based package box security system equipped with vibration detection features as an anti-theft system, automatic call notifications using a GSM module, and control and monitoring via a website. The research method used is the system design and development method which includes the hardware and software design stages, system implementation, and performance testing. The system was built using an ESP32 microcontroller integrated with a vibration sensor, buzzer, SIM800L GSM module, relay, solenoid door lock, and a website as a medium for remote monitoring and control. The results of the study show that the system is able to detect physical disturbances in the package box well through the vibration sensor. When vibrations exceed the threshold, the system activates an alarm and makes an automatic call to the user. The system is also able to display the status of the package box in real-time and provide open/close control via the website. Based on the test results, the system has an accuracy level above 90% with a fast response time.

Evaluation and improvement of security using penetration testing (pentest) methods of host-based intrusion detection system (hids) and intrusion detection system (ids) at the Sei Sekambing C II village office

Mara Sakti Siregar — 2026-04-30

The rapid development of information technology demands a strong and reliable network security system, especially in government agencies that manage important public data. The Sei Sekambing C II Village Office has utilized computer networks to support public service activities, but still faces various network security risks, such as illegal access, malware, and system vulnerabilities. This study aims to evaluate and improve network security using penetration testing methods, as well as the implementation of an Intrusion Detection System (IDS) and a Host-based Intrusion Detection System (HIDS). The results of the study revealed several security vulnerabilities, such as insecure File Transfer Protocol (FTP) services, excessive open ports, and successfully detected brute force attacks. After improvements in the form of patching, migrating FTP services to Secure File Transfer Protocol (SFTP), and updating IDS/HIDS signatures, the level of network security experienced a significant increase with a reduction in vulnerabilities of up to 66%. This method has proven effective in strengthening network security infrastructure in government environments.

Design and development of a Web3-based student graduation badge system for FIKTI UMSU using non-transferable NFTs and smart contracts on the Ethereum testnet

Zaid Ahlun Ulumuddin — 2026-04-30

Conventional management and verification of academic credentials still face limitations regarding document processing and verification workflows. This research aims to design and develop a Web3-based student graduation badge system utilizing non-transferable Non-Fungible Tokens (NFTs)—specifically Soulbound Tokens (SBTs)—via smart contracts on the Ethereum Sepolia Testnet. The system was developed using the Waterfall model, encompassing requirements analysis, system design, implementation, and testing. Smart contracts were developed using Solidity and Hardhat, incorporating modifications to the ERC-721 standard to restrict token transfers, while the web-based interface was built using React.js and integrated with the blockchain via Ethers.js. Badge metadata is stored using the InterPlanetary File System (IPFS). Functional testing results demonstrate that all nine test scenarios were successfully executed, yielding the expected outcomes. The system supports mass badge issuance via `mintBatch`, prevents token transfers, enables validity revocation and restoration through `softRevokeBatch` and `hardRevokeBatch`, and facilitates credential verification via a public webpage. These results indicate that SBTs serve as a viable alternative for managing digital graduation badges by providing a blockchain-based mechanism for credential issuance, management, and verification.

Design and development of an IoT-based smart waste bin with automatic incineration and remote monitoring

Fahri Syahnanda — 2026-04-30

Household waste management challenges encourage the development of technologies that support more integrated waste handling. This study aims to design and develop an Internet of Things (IoT)-based smart waste bin prototype with an automatic incineration mechanism and remote monitoring. The system was developed using the Prototype Model by integrating a NodeMCU ESP8266, an HC-SR04 ultrasonic sensor for waste-level detection, a limit switch as a fail-safe mechanism, and a servo motor and flame gun for automatic incineration. System status notifications were delivered to users through a Telegram Bot. The results showed that the prototype successfully performed waste-level detection, timer-based automatic incineration, fail-safe operation, and status notification according to the defined test scenarios. In the combustion test using 500 g of mixed paper and cardboard, 22 g of residual mass remained, corresponding to a mass reduction of 95.6%. The ignition mechanism succeeded in 10 out of 10 trials, while Telegram notifications were received with a delay of approximately 2–4 seconds. These results demonstrate that waste detection, automatic incineration, operational safety, and remote monitoring functions can be integrated into a single IoT-based prototype.

Apple Inc. (AAPL) stock price forecasting using a stacked long short-term memory model with Yahoo Finance data

Prayoga Sungkowo — 2026-04-30

Stock price prediction is a complex time-series problem because price movements are dynamic and exhibit temporal patterns. This study aims to implement and evaluate a Long Short-Term Memory (LSTM) model for forecasting the closing price of Apple Inc. (AAPL) stock using historical data obtained from Yahoo Finance for the 2022–2024 period. The data were preprocessed using Min-Max Scaling, transformed into sequences with a 60-day time step, and chronologically divided into 80% training data and 20% testing data. The model employed two LSTM layers with a dropout rate of 0.2, the Adam optimizer, and Mean Squared Error as the loss function. The evaluation results yielded a Root Mean Squared Error (RMSE) of 3.31 and a Mean Absolute Error (MAE) of 2.68. The visualization indicates that the predicted values generally follow the actual price trend, although deviations occur during several periods of sharper price changes. These findings indicate that the LSTM model can learn temporal patterns in AAPL closing prices and generate forecasts that approximate the actual values.