Articles
Implementing Conditional Random Fields on English Text Grammar Analysis
Ahmad, Fadhil;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.81
This study explores the implementaion of the Conditional Random Fields (CRF) algorithm in the grammatical analysis of English texts, specifically in the task of Part of Speech (POS) tagging. CRF is a statistical model effective in classifying words into grammatical categories such as nouns, verbs, adjectives, and others. The research methodology includes a literature review and experimental implementation using labeled datasets, integrated into a web-based application. The implementation results demonstrate that the CRF model provides accurate tagging results and can be utilized for sentence structure analysis in English texts. The application is developed using the Python programming language, supported by the NLTK and sklearn-crfsuite libraries, and uses the Flask framework for the user interface. This research is expected to contribute to the development of technology-based tools for English language learning.
Implementation of the Backtracking Algorithm for Optimizing Work Shift Scheduling
Ainna Khansa;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.82
This research aims to implement the backtracking algorithm for optimizing shift scheduling at PLTU SSP. The study is motivated by the complexity of manual shift scheduling, which is prone to human error and struggles to accommodate various constraints such as employee availability, preferences, and operational needs. The backtracking algorithm was selected due to its ability to search systematically for optimal solutions that satisfy all constraints, based on Depth First Search (DFS). The research methodology includes requirements analysis, system design, algorithm implementation, testing, and results evaluation. The application of the backtracking algorithm produced schedules that accurately meet constraints and consider employee preferences. The results indicate that the backtracking algorithm can generate effective and efficient schedules. The implementation of the backtracking algorithm is expected to improve the quality of shift work management, positively impacting productivity, employee welfare, and the smooth operation of PLTU SSP.
Implementation of the Greedy Algorithm for Optimal Police Patrol Route Search in the Jurisdiction of Semendawai Suku III Police Sector
Nandra Sari, Vingky;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.83
Police patrols represent a strategic effort to maintain public security and order. However, determining an optimal patrol route remains a challenge, particularly in ensuring time and distance efficiency. This study aims to identify the optimal police patrol route in the jurisdiction of the Semendawai Suku III Police Sector using the Greedy algorithm. This method was selected for its ability to rapidly generate solutions by choosing the most favorable option at each step. The data utilized in this research include ten villages identified as high-risk areas based on the number of criminal reports recorded in 2024, as well as inter-village distances collected through regional mapping. The application of the algorithm resulted in a total patrol distance of 121.2 kilometers, following the sequence: Police Sector (A) → Sriwangi (B) → Kerujon (C) → Karang Endah (D) → Margorejo (E) → Taman Agung (F) → Taraman (H) → Kota Tanah (I) → Melati Jaya (J) → Nirwana (K) → Karang Marga (G) → returning to the Police Sector (A). This study contributes to data driven patrol strategy management, enhancing both the efficiency and effectiveness of police operations in maintaining regional security stability.
Implementation of the Greedy Algorithm in Phrase Pattern Matching for a Text Recognition System
Amelia, Risky;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.85
The Greedy pattern-matching algorithm is a phrase pattern-matching method that works by selecting the optimal solution at each step without backtracking. This approach is applied in text recognition systems for keyword search, natural language processing, and automatic text filters. This research analyzes the performance of the algorithm through computational experiments and literature review by evaluating the efficiency of execution time, number of character comparisons, and matching success rate. The results show that the algorithm offers high speed in pattern matching, especially on large datasets, as it is able to optimally shift the search index. However, its accuracy decreases when handling complex patterns or phrases that have many similarities. By combining this algorithm with heuristics or data preprocessing techniques, its drawbacks can be minimized, thus remaining an effective solution in text recognition systems that require fast and real-time processing.
Optimizing Sprint Planning in Agile Methodology Using Greedy Algorithm
Dwi Aprian Widodo;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.86
Sprint planning is a pivotal process in Agile-based software development, where project success heavily depends on the team's ability to select and deliver the most valuable tasks within limited time and resources. A core challenge in this process is determining the optimal set of tasks that can be completed in a sprint, considering the constraints imposed by story point capacity. This decision-making problem closely resembles the classic Knapsack Problem in combinatorial optimization. This paper investigates the implementation of the Greedy algorithm as a heuristic approach to solve this problem by selecting tasks based on their value-to-story-point ratio. The Greedy strategy simplifies task selection by making locally optimal decisions at each step, thereby enabling efficient prioritization of high-value tasks without exceeding the sprint limit. A comparative experiment using real-world data was conducted to evaluate the effectiveness of the Greedy method against manual selection. The results demonstrate that the Greedy algorithm not only utilizes story point capacity more efficiently but also maximizes the total value of tasks included within the sprint. In some scenarios, it even achieved higher priority scores while consuming fewer story points. These findings affirm the practicality of Greedy-based optimization in Agile environments, particularly for rapid and scalable sprint planning. Future work may explore hybrid models or more advanced algorithms such as Dynamic Programming for enhanced optimization outcomes.
Implementation of YOLO Algorithm in Adolescent Suicide Ideation Monitoring System Based on Real-Time Data Analysis
Yunike;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 1 (2025): December 2024 - February 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i1.87
This study aims to develop and implement a suicide ideation monitoring system in adolescents based on the YOLO (You Only Look Once) algorithm with real-time data analysis. The YOLO algorithm is used to detect facial expressions that reflect negative emotions, such as sadness and anxiety, which can be early indicators of suicidal ideation. The research methods used are qualitative and quantitative approaches, including the collection of facial image data, model training using the TensorFlow and OpenCV frameworks, and testing the system's performance in detecting facial expressions in real time. The system test is carried out by comparing the results of YOLO detection against reference data to measure the accuracy and speed of detection. The results of the study show that the developed system is able to detect facial expressions with an accuracy rate of 92% and an average detection speed of 30 milliseconds per frame. In addition, the system can be integrated with communication platforms to provide warning notifications to related parties as a form of early intervention. Thus, this study proves that the YOLO algorithm is effective in developing a suicide ideation monitoring system based on real-time data analysis so that it can be a preventive solution in supporting adolescent mental health.
Implementation of the Backtracking Algorithm for Bandwidth Management in the Network of SMA Negeri 1 Belitang
Hafizena, M. Fachri;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.88
Internet has become an essential necessity in the field of education, particularly in supporting the teaching and learning processes in schools. Effective bandwidth management ensures efficient and equitable network usage for all users. This study implements the Backtracking algorithm as a solution for bandwidth management at SMA Negeri 1 Belitang. The algorithm is utilized to optimize bandwidth allocation based on usage priority and the dynamic number of users. The results indicate that this method can enhance bandwidth utilization efficiency, reduce delays, and improve the quality of service (QoS). Therefore, the Backtracking algorithm can serve as an alternative solution for achieving optimal network management in schools.
Flood Fill and Scanline Fill Algorithm Optimization to Improve Design and Animation Application Performance
Sholahuddin, Fakhri;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.89
Flood and Scanline Fill algorithms are two primary methods in the color-filling process in design and animation applications. However, limitations in computational efficiency often cause long rendering times, especially for high-resolution images and complex areas. This study aims to optimize both algorithms by implementing parallel processing using multi-threading technology and GPU-based processing. This implementation is expected to improve color filling performance compared to conventional methods significantly. Testing was carried out by comparing the execution time of the algorithm before and after optimization in various usage scenarios. The results showed that the parallel processing technique accelerated the color-filling process by up to 60% under certain conditions. Thus, this approach improves the efficiency of design and animation applications, especially in real-time rendering.
Utilization of Fibonacci Algorithm to Determine Product Bundling Discounts in Culinary Business
Aziman, M. Fauzan;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i2.91
Micro, small, and medium-scale culinary businesses (MSMEs) often face challenges in determining product bundling discount strategies due to limitations in data analysis. This study examines the effectiveness of implementing the Fibonacci algorithm in determining food product bundling discounts to increase sales and profitability. The method used is a quantitative experiment with an algorithm simulation approach, where discounts are determined based on the Fibonacci number sequence (1%, 2%, 3%, 5%, 8%, etc.). Data were obtained from the Lumpia Beef Lumer culinary MSME in Bekasi Regency and analyzed using Python, while the results were visualized in a soft Excel document. A comparison graph of the number of sales and income before and after the application of discounts was compiled in Excel based on daily transaction data. The results show an increase in sales volume of 60% and gross income of 60% after this strategy was implemented. The tiered discount strategy based on Fibonacci has proven attractive to customers, encouraging bundling purchases without significantly reducing profit margins. This approach offers a systematic and adaptive data-driven solution and can be used by other MSMEs to develop more effective and sustainable marketing strategies.
Implementation of Backtracking Algorithm in Determining Operation Schedule
Warianti, Sri;
Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 1 (2025): December 2024 - February 2025
Publisher : Yayasan Rumah Ilmu Professor
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DOI: 10.56988/chiprof.v4i1.117
Scheduling surgical operations in hospitals is a complex process requiring efficient allocation of limited resources such as specialist doctors, operating rooms, and time slots. This study implements the backtracking algorithm to create an optimal surgery schedule from Monday to Thursday, involving 8 specialist doctors (3 general surgeons and 5 obstetricians-gynecologists), and 1 orthopedic surgeon who is available only on Wednesdays and Thursdays. With 4 operating rooms and 5 sessions per day, the backtracking algorithm explores all possible scheduling combinations to avoid conflicts in time and location. The implementation results show that the algorithm successfully generates a conflict-free schedule while also assigning on-call doctors for emergency (cito) cases in surgery, obstetrics-gynecology, and orthopedics. Visualization in Excel tables and daily graphical layouts aids in verification and intuitive interpretation. This approach proves effective in handling complex and dynamic hospital scheduling scenarios.