Publication Search

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

This research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.

This study aims to design and develop a web-based New Student Admission System (PPDB) Website at MIS Chairul Bariyyah Medan Krio to overcome the limitations of manual registration systems. The method used is Agile, as it supports iterative, flexible, and adaptive system development according to user needs. Data collection techniques include observation, interviews, literature study, and documentation. The system is developed using the Python programming language with the Django framework and MySQL database. The results show that the developed system improves the efficiency of the registration process, minimizes data recording errors, and facilitates real-time management of applicant data. In addition, the Website also serves as an information and promotional medium for the school that can be accessed anytime. Based on Blackbox testing and User Acceptance Test (UAT), the system is proven to run well and is easy to use. Therefore, this web-based PPDB system is effective in improving the quality of Administrative services at MIS Chairul Bariyyah.

This study analyzes the performance of the Dual Modulus RSA algorithm in securing text data using Python. The rapid growth of digital technology has increased the risk of data security threats, making efficient and secure encryption essential. Dual Modulus RSA is a modification of the classic RSA algorithm that uses two different moduli in the encryption and decryption process, thus increasing security levels because attackers must factorize two moduli simultaneously. This research uses an experimental quantitative approach by measuring the execution time of encryption and decryption processes with variations in plaintext length (5, 10, and 15 characters). Implementation was carried out using Python 3 with the time.perf_counter() function for microsecond-precision measurement. The results show that the Dual Modulus RSA algorithm successfully encrypts and decrypts all test plaintexts correctly. Encryption time ranged from 0.0212 ms to 0.0823 ms, while decryption time ranged from 0.0422 ms to 0.0955 ms. There is a positive linear relationship between plaintext length and processing time. Decryption is consistently slower than encryption due to the larger private key exponent (d1=2753, d2=3533) compared to the public exponent (e=17). The main factors affecting performance are exponent size, dual modulus overhead, CPU caching effects, and Python interpretation overhead. This study recommends using Dual Modulus RSA with hybrid encryption for practical implementation to balance security and performance.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

The gap between academic curriculum content and modern industrial needs is often an obstacle for fresh graduates in the Information Technology field, particularly in the rapidly evolving Artificial Intelligence (AI) sector. This study aims to identify the relationship patterns among technical competencies (hard skills) most demanded by the global industry. The method employed is Association Rule Mining with the Apriori algorithm to discover association rules between skills, and Network Graph Analysis to visualize the topological map of these competencies. The research dataset covers 15,000 AI job vacancies from the 2024-2025 period, analyzed in depth using Support, Confidence, and Lift Ratio evaluation parameters to validate the strength of relationships between items. The results show that Python is the central competency with the highest frequency of occurrence. Strong association rules were found indicating that proficiency in TensorFlow has a high probability of requiring Python proficiency. The Network Graph visualization reveals three main competency clusters: Data Engineering Ecosystem, Deep Learning, and Infrastructure. These findings offer a strategic foundation for aligning curricula with the job market. Focusing on strengthening the identified competency clusters is expected to directly enhance the relevance and work readiness of graduates.