Publication Search

This study designs and implements an up-down counter system based on an AT89C2051 microcontroller programmed in assembly using the MC-51 application. The system modifies an existing digital clock board by mapping the display selector pins, seven-segment segment pins, pushbuttons, and buzzer to the microcontroller ports. The research method consists of literature review, hardware identification, algorithm design, assembly programming, program downloading, and functional testing using a 5 V DC supply. The implementation uses a four-digit common-cathode seven-segment display and a multiplexing routine to show the counter value in real time. The functional test shows that the system can display the initial value, increase the value through the up button, and decrease the value through the down button. The display is readable during operation, and the program can run on the target circuit after being downloaded to the AT89C2051. This study confirms that assembly programming on MC-51 can be applied to implement a simple counter system on a reused digital clock circuit. The main limitations are the absence of explicit button debouncing, overflow and underflow protection, quantitative response-time measurement, and non-volatile data retention.

Python programming has become a fundamental competence in the digital era, yet students often struggle to transform algorithmic logic into functional code. This gap between conceptual understanding and practical implementation skills requires a thorough investigation into learning challenges within the Industrial Informatics Engineering Technology (TRIN) program at Politeknik Manufaktur Bandung. Grounded in Bloom's Revised Taxonomy and Cognitive Load Theory, this descriptive quantitative study utilized a Likert-scale questionnaire and an objective comprehension test administered to 87 third-year students. Data were analyzed using descriptive statistics to map performance across three aspects: conceptual understanding, syntactic comprehension, and implementation ability. Results indicate the conceptual aspect achieved the highest average of 4.15, followed by syntax at 3.56 and implementation at 3.54, with objective test accuracy rates of 76.09%, 65.52%, and 67.36%, respectively. Major obstacles identified include difficulties with looping, debugging, and comparison operators. Therefore, enhanced structured practice and Project-Based Learning approaches are recommended to strengthen students' implementation competencies.

The increase in activity and security threats on Ubuntu server causes the volume of system logs to become very large and difficult to analyze manually. This condition potentially leads administrators to experience delays in detecting abnormal activities, such as repeated login attempts and web access patterns related to online gambling promotions. Therefore, this research aims to develop a machine learning-based Early Warning System capable of automatically detecting anomalous activities. The system is developed using the Python programming language and runs on an Ubuntu server by utilizing authentication logs and web access logs as the main data sources. The anomaly detection model is trained using normal activity data collected directly from the Ubuntu server logs to learn standard system behavior patterns. During the operational phase, the system reads server logs in real-time, extracts activity features, and analyzes them using the Isolation Forest algorithm. Activities detected as anomalies trigger alert notifications via Telegram to the administrator without performing automatic blocking. The results show that the system is able to provide early warnings for suspicious activities, thereby helping to improve server security more effectively.

The distribution of social assistance in Indonesia faces challenges such as inaccurate recipient data, overlapping programs, and limitations of traditional data management systems that lead to inaccurate targeting of aid. This study proposes a social assistance distribution optimization system using the Greedy algorithm that assesses recipient priorities based on economic conditions, number of family members, location, and urgency of needs with certain weights to produce objective rankings. This system is implemented in a JavaScript-based web application without external frameworks, making it lightweight and easily accessible. Simulations with 20 prospective recipients and a quota of 10 slots and validation with a dataset of 10,000 entries show that the Greedy algorithm produces identical results to Dynamic Programming but is much faster (669 times faster). In terms of complexity, this algorithm has O(n log n) time and O(n) space, and meets the requirements of the Greedy Choice Property and Optimal Substructure, making it a practical and efficient solution for managing large-scale social assistance distribution in Indonesia.

Optimization problems in computer science often arise when a system must select the best combination from several alternatives under limited resources such as capacity, time, or cost. One commonly used optimization model is the Knapsack Problem, which involves selecting a number of items with specific weights and values to obtain the maximum profit without exceeding the available capacity. This study aims to analyze and compare the performance of the Greedy algorithm and Dynamic Programming in solving the 0–1 Knapsack Problem. The research employs a quantitative experimental approach by implementing both algorithms in a computer program and testing them on several datasets with different sizes. The evaluation parameters include the maximum value obtained and the algorithm execution time. The results show that the Greedy algorithm has faster execution time and more efficient memory usage, but it does not always produce an optimal solution. In contrast, the Dynamic Programming algorithm consistently produces an optimal solution, although it requires greater computational time. Therefore, the choice of algorithm should be adjusted to system requirements, whether prioritizing computational efficiency or optimal solution quality.

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.

This study investigates the integration of computational thinking (CT) principles with adaptive curricula to enhance problem-solving skills in undergraduate programming education. Traditional programming curricula often emphasize syntax and basic concepts, neglecting critical problem-solving strategies. The adaptive curriculum framework used in this study combines CT skills such as decomposition, pattern recognition, abstraction, and algorithmic thinking with personalized learning experiences. A mixed-method approach, combining qualitative and quantitative research, was employed to assess the effectiveness of this integrated approach. The results show significant improvements in students' problem-solving abilities, conceptual understanding, and engagement compared to a control group following a traditional curriculum. Students in the experimental group, which received the adaptive curriculum, demonstrated better performance in applying algorithms and debugging code. Additionally, students expressed higher levels of engagement and motivation, suggesting that the personalized learning environment fostered greater academic involvement. The study highlights the importance of integrating CT principles with adaptive learning frameworks to create a more inclusive and effective learning environment that accommodates diverse learning needs. The findings suggest that adaptive curricula can bridge gaps in traditional education by providing personalized support and ensuring that students progress at their own pace. This approach is especially beneficial for programming education, where both conceptual understanding and practical problem-solving skills are critical for success. Future research should explore the long-term impact of adaptive learning frameworks and investigate how these technologies can be integrated with traditional teaching methods to maximize their effectiveness.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

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.

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.

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.

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 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 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.