Publication Search

The development of digital technology has encouraged the government to utilize social media as an effective means of public communication. The Ministry of Agriculture is one institution that actively uses Instagram to convey information, build a positive image, and interact with the public. This study aims to determine the Public Relations strategy in managing the Instagram social media account @kementerianpertanian. This study uses the Public Relations strategy theory from Cutlip and Center, which consists of four stages: problem identification, planning and programming, communication and action, and evaluation. The research method used is a descriptive qualitative approach, with data collection techniques through in-depth interviews and documentation of the Ministry of Agriculture's Public Relations team. The results show that the Ministry of Agriculture's Public Relations has implemented a planned communication strategy, starting from planning informative and educational content, utilizing Instagram features such as reels, stories, and live to increase interaction, to routine evaluation based on engagement data (likes, shares, comments, and saves). This strategy also adapts to social media trends and audience needs. Public Relations actively responds to user questions and comments to maintain two-way communication. However, challenges such as negative comments, limited resources, and changes in the Instagram algorithm are obstacles that still need to be overcome. Overall, the implemented strategy has successfully supported the improvement of the image and transparency of public information within the Ministry of Agriculture.

Dynamic programming (DP) is a foundational technique for solving multistage optimization problems; however, its classical formulation often incurs a cubic time complexity, rendering it impractical for large-scale real-world applications. This study proposes an accelerated dynamic programming framework that leverages quadrilateral inequality and convex monotonicity theory to significantly reduce computational overhead. By formally establishing the structural conditions under which DP decision boundaries exhibit monotone behavior, the proposed method eliminates redundant state transitions and maintains a sequence of admissible decisions through an efficient stack-based mechanism. The resulting algorithm achieves a worst-case complexity of O(n2) and can be further optimized to O(n) when convex monotonicity holds, representing a substantial improvement over traditional O (n3) techniques. Experimental validation using the classical stone-merging problem demonstrates that the accelerated method executes more than 60 times faster than the standard approach for large inputs, while maintaining identical optimal results. These findings confirm that embedding geometric regularities into DP formulations not only enhances performance but also broadens the applicability of dynamic programming to computation-intensive optimization tasks. This research contributes a mathematically grounded, scalability-oriented solution for accelerating dynamic programming, with implications for logistics, production planning, data segmentation, and other large-scale decision systems.

MP3 audio files are often used in a variety of fields, but they are prone to security risks such as eavesdropping and illegal access. This study proposes a super encryption method by combining the algorithms of Rivest Shamir Adleman (RSA) and ElGamal to improve the protection of audio data. RSA was chosen for its efficiency and ease of implementation, while ElGamal offers a high level of security through discrete logarithmic complexity. The combination of the two is expected to address the weaknesses of each algorithm and strengthen file security. The system is developed using the Python programming language with the Visual Studio Code environment. The encryption process is done in layers: MP3 files are first encrypted with RSA, and then the results are encrypted again with ElGamal. The decryption is done in reverse order. Tests are conducted with various MP3 file sizes to measure the effectiveness and performance of the system. The results show that this method is able to secure audio files so that they cannot be accessed without the private keys of both algorithms. Although processing times are increased compared to single encryption, the level of security obtained is much higher. This approach can be an effective solution for protecting digital audio data, particularly MP3 format, and could potentially be applied to a wide range of other data types that require a high level of security.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

Currently, the use of data mining technology has become essential in enhancing business management efficiency, including in the trending coffee shop industry. Data mining allows business owners to analyze sales information in depth, enabling more accurate decision-making regarding inventory management, promotions, and sales strategies. This study aims to implement the Apriori algorithm to analyze sales data at Menrabic Coffee Shop. The Apriori algorithm is used to discover association patterns or relationships between products frequently purchased together by customers, which can assist management in providing inventory that aligns with customer preferences. The research method illustrates the detailed implementation process of the Apriori algorithm, starting from sales data collection, data cleaning, programming, and analysis of the results. The implementation uses web programming languages such as HTML, CSS, MySQL, and JavaScript, while back-end logic is programmed with PHP. The results of applying this algorithm reveal the most popular sales patterns among customers, providing valuable insights for management to improve operational performance and customer satisfaction. Therefore, this study demonstrates that applying data mining with the Apriori algorithm can be an effective tool for understanding consumer behavior and supporting data-driven decision-making at Menrabic Coffee Shop. By utilizing these insights, management can optimize inventory, enhance sales strategies, and ultimately increase overall business efficiency.

Micro, Small, and Medium Enterprises (MSMEs) are strategic sectors in the Indonesian economy, but often face challenges in efficient and data-driven production management. This article highlights the urgency of the Real Work Lecture (KKN) program as a form of student service in assisting MSMEs, especially in optimizing the production of snacks typical of Tebing Syahbandar. This research aims to optimize the production output of the Untir-untir Titik Factory with an Integer Linear Programming (ILP) approach using the Branch and Bound algorithm. Primary data is obtained through interviews and production documentation, including product type, raw material needs, operational costs, selling prices, and profit margins. The initial analysis was carried out using the simplex method using POM QM software to obtain a linear solution, which was then refined with the Branch and Bound algorithm so that the results were in the form of integers. The results of the study showed that the optimal solution was achieved by producing 25 bales of kolong-kounder and not producing other types of snacks, resulting in a profit of Rp1,650,000 per day. These findings show that the ILP approach with Branch and Bound is able to significantly increase the efficiency and profitability of MSMEs. In addition, this method can be used as a basis for quantitative-based production decision-making. This research also emphasizes the strategic role of KKN in technology transfer and real solution-based assistance for MSME actors in the region, thereby supporting the sustainable strengthening of the local economy.

This study aims to optimize the cake production profit at Cake by Cece using the Branch and Bound algorithm. The data used include raw material requirements per batch, daily raw material availability, and selling prices for three types of cakes: Cookies, Brownies, and Cinnamon Roll. The optimization model is formulated as an Integer Linear Programming problem with the objective of maximizing total daily profit. The model is solved using the simplex method followed by the Branch and Bound algorithm to obtain valid integer solutions. The results indicate that the optimal production combination is 2 batches of Cookies, 2 batches of Brownies, and 3 batches of Cinnamon Roll, yielding a maximum profit of IDR 233,000 per day. This solution satisfies all raw material constraints and is feasible for daily operational implementation. This study provides quantitative recommendations to support production decision-making in culinary sector MSMEs.

This research aims to develop a transaction data security system on the web-based Pancadaya Waste Bank application by applying a hybrid cryptographic algorithm that combines Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA). The problem faced in the previous system is the weak recording and security of customer transaction data, because the process is still carried out manually so that it is prone to recording errors, loss of important information, and potential misuse of data by unauthorized parties. To answer these problems, this study uses the Rapid Application Development (RAD) method which allows the application development process to be carried out quickly, flexibly, structured, and according to user needs. The research method used was a qualitative approach with interview techniques with the management of the Pancadaya Waste Bank and the Environment Office, as well as an in-depth literature study on the application of hybrid cryptographic algorithms in modern information systems. The system is built using the PHP programming language, MySQL database, and OpenSSL library as the main support for the data encryption and decryption process. The implementation of the algorithm is carried out by encrypting transaction data using AES for efficiency and speed, then the AES key is secured through RSA to ensure a higher level of security while preventing illegal access. The test results showed that the system was able to encrypt and decrypt transaction data in real-time, as well as display transaction results in the form of digital notes on deposit and balance withdrawal activities. In addition, performance tests using GTmetrix showed that the application has excellent speed, stability, and processing efficiency, making it feasible to be widely implemented in Pancadaya Waste Bank operations.

This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.

The study presents a comprehensive framework for optimizing customer retention budget by integrating clustering, classification, and mathematical optimization techniques. The study begins with the IBM Telco dataset, which is prepared through data cleansing, encoding, and scaling.  In the preliminary phase, customer segmentation is performed using K-Means clustering, with k = 3 and k = 4 identified as optimal based on the elbow method and Silhouette score. The configurations produced three (Premium, Standard, Low) and four (Premium, Standard Plus, Standard, Low) customer segments based on purchase preferences, which served as input features for churn prediction. In the second phase, the dataset was divided into training and test sets in an 80:20 ratio, followed by data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Multiple classification algorithms were evaluated, including Naive Bayes (NB), Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) using F1-score as the performance metric. CatBoost and LightGBM, with k values of 3 and 4, respectively, were the highest-performing classification models, with only minimal differences in performance.    Ultimately, customer segmentation established customer prioritization, whereas churn prediction assessed customer churn likelihood. Four distinct configurations were assessed utilizing mixed-integer linear programming (MILP) to optimise retention budget allocation within uniform budget constraints, discount amounts, and churn thresholds. In both the k=3 and k=4 scenarios, CatBoost surpassed LightGBM, with CatBoost at K=3 effectively discounting 66% of at-risk consumers across all three segments, hence improving the intervention's efficacy and budget allocation, making it the ideal choice for maximizing customer retention. The results demonstrate the importance of segmentation in enhancing retention budgeting and budget optimization, particularly concerning parameter sensitivity.

The rapid advancement of information technology demands higher education graduates to possess strong digital competencies, particularly algorithmic literacy. This form of literacy involves understanding data structures, programming logic, and computational thinking in problem-solving. This community service program aimed to strengthen algorithmic literacy among students as a foundational skill for building a professional career in the field of information technology. Conducted at the Information Technology Study Program of Universitas ‘Aisyiyah Yogyakarta, the program included need assessment, instructional material development, interactive training sessions, case-based group work, and evaluation through student presentations. The results showed significant improvement in students’ understanding of algorithms and their ability to design efficient and systematic solutions. By applying a problem-based and collaborative learning approach, students also developed essential 21st-century skills such as communication and teamwork. The case presentation-based evaluation provided comprehensive insights into their critical and logical thinking abilities. This program successfully contributed to preparing students not only as technology users but also as future innovators in the digital era.

Efficient resource allocation is a major challenge in various sectors, especially when faced with limitations in quantity, time, and cost. This study aims to examine the application of linear programming as an optimization method in solving assignment problems, where a number of resources must be optimally allocated to a number of specific tasks. Through a literature study approach, this study examines various relevant previous study results, especially in the Indonesian context. The results of the study indicate that linear programming is able to improve operational efficiency, reduce costs, and produce a more balanced task distribution. However, this model has limitations in dealing with non-linear conditions and data uncertainty, so integration with other methods such as fuzzy logic or genetic algorithms is needed. This study is expected to broaden the understanding of the benefits of linear programming and encourage its wider application in quantitative-based decision making.

This study uses a qualitative method with a literature review approach to examine the effectiveness of the two-phase method in solving linear programming problems. The two-phase method is one of the techniques in linear programming used when the objective function does not have a feasible initial solution directly. Through the exploration and analysis of various scientific literature sources from 2019 onwards, this study finds that the two-phase method can systematically provide optimal solutions, especially in complex problems with multiple constraints. Previous research has shown that this method is effective in accelerating the identification of basic feasible solutions and minimizing unnecessary iterations compared to the standard simplex method. Additionally, the two-phase method offers better numerical stability and high reliability in both industrial and educational applications. However, its effectiveness largely depends on the understanding of the algorithm and the ability to design an appropriate mathematical model. The results of this study are expected to contribute theoretically to the development of efficient solution techniques for linear optimization problems.

This research employs a Systematic Literature Review (SLR) approach to analyze the application of the simplex method as a tool for solving real-world optimization problems. Mathematics education plays a crucial role in developing critical thinking and achieving accurate results. Linear programming, with its highly effective simplex method, is a branch of mathematics proven to solve various optimization challenges such as cost minimization, profit maximization, distribution, and scheduling. In practice, the simplex method is an alternative algorithm that systematically searches for the optimal solution to problems by moving from one feasible solution to another until the best result is achieved. This study identifies, examines, evaluates, and interprets relevant studies on the simplex method's application in real-world contexts. Our review of 20 journals reveals that the simplex method is highly effective in solving a wide range of optimization problems, from large-scale industries to Micro, Small, and Medium Enterprises (MSMEs). Examples of its application include optimizing fish feed costs at PT. Indojaya Agrinusa, maximizing profits for Seblak MSMEs, determining optimal sales quantities for areca nuts, and optimizing the production of tofu and pottery crafts, among others. The simplex method's ability to consider various limiting factors has proven to be extremely helpful.

This study aims to examine the use of Macaulay2 software in improving students' computational thinking skills in mathematics learning, especially in abstract algebra material. Macaulay2 as an open-source Computer Algebra Sistem (CAS) provides a symbolic and algorithmic environment that encourages logical and systematic exploration of mathematical concepts. Literature reviews show that the use of Macaulay2 can strengthen conceptual understanding, train computational thinking, and introduce mathematical programming to students. However, technical challenges, limited Indonesian language teaching materials, and limited educational infrastructure are obstacles that need to be overcome through collaboration between educational stakeholders.

The PG45 DC motor is a drive system used on sediment detection boat. To achieve the desired stability and speed, it is necessary to apply a control system to the sediment detection boat drive system. Control systems need to be tuned to ensure that they function properly and are responsive to changes. In order to complement the previous research, further research was carried out focusing on determining the PID control parameters on the angular speed of the PG45 DC Motor using Simulink. The PG45 DC motor works based on the Arduino programming algorithm that has been designed so that it can rotate at a predetermined speed. This research modeled the sediment detection ship system on Simulink with a similarity rate of 94.09%. The results of this study indicate that the tuning method used, namely trial and error, produces good control on the sediment detection ship system model that has been assembled in Simulink with the value of Kp = 100; Ki = 5; Kd = 15 obtained the value of rise time = 0.2474 seconds and settling time = 0.4104 seconds and overshoot = 0.2175%%.

Mathematical logic is a branch of science that studies the principles of logical reasoning and how to judge the truth of a statement. This article discusses the role of mathematical logic in everyday life, especially in education, technology, and finance. Mathematical logic has been proven to help improve critical, analytical, and systematic thinking skills, making it easier for individuals to solve various problems more effectively and efficiently. In the world of education, mathematical logic supports the development of rational, argumentative, and problem-solving skills, and is an important foundation in the science and mathematics curriculum. Teachers can leverage this logic to encourage students in building consistent reasoning and logical argumentation. In the field of technology, mathematical logic is the foundation in the development of modern computing systems, such as algorithms, programming, artificial intelligence, and data structures. Without mathematical logic, digital systems such as search engines, software, and applications cannot function optimally. In finance, mathematical logic plays a role in budget strategy, investment decision-making, statistical analysis, and rational risk management. With a good understanding of logic, individuals and institutions can make more wise and measurable financial decisions. Although his contribution is large, there are still many people who do not understand the basic concepts of mathematical logic. This can be overcome through more inclusive education, contextual approaches, and interactive learning based on real problems, both in schools and non-formal environments. This study emphasizes the importance of mathematical logic as a foundation in supporting a more rational, efficient, and structured life in today's modern era. Increasing mathematical logic literacy can be a national strategy to create a society that thinks critically, is competitive, and ready to face global challenges.

Micro, Small, and Medium Enterprises (MSMEs) have a significant role in the economy, including MSMEs in the culinary sector such as Putri Ice Cream Business. Decision making related to optimal resource allocation is crucial to increase the efficiency and profitability of MSMEs. This study aims to apply the linear programming method with the primal simplex algorithm to minimize production costs in the case study of Putri Ice Cream MSME. The objective function is to minimize the total production cost, as well as the limitations related to the availability of main raw materials and machine production capacity. Data on main raw materials and machine production capacity were obtained from the Putri Ice Cream MSME which was the case study. The results of the study show that the application of the primal simplex method produces an optimal solution that provides recommendations for product combinations for each type of Putri Ice Cream with minimal production costs. Sensitivity analysis was also conducted to test the stability of the optimal solution to changes in capital. This study provides a practical contribution to Putri Ice Cream MSME in optimizing their resource allocation and increasing cost efficiency through a quantitative approach.    

Computational thinking skills are one of the important competencies in the world of education in the 21st century, especially in the fields of science, technology, engineering, and mathematics (STEM). GNU Octave as an open source software similar to MATLAB offers a numeric programming environment that can be used to train and develop students' computational thinking skills. Researchers use a literature study method that aims to see the extent to which GNU Octave can be used in learning to improve students' computational thinking skills. The literature sources used come from national and international journal articles, conference proceedings, and other trusted sources over the past 10 years. The results of the review show that GNU Octave is effective as a learning tool for numeric programming, problem solving, and mathematical modeling, and has a positive contribution to honing students' algorithmic and analytical thinking skills.