Publication Search

As software technology becomes more complex, software maintenance costs become more expensive. In connection with this, the development of software engineering makes the software system has many Composition choices that can be adjusted to the needs of the user. Error fixing involves analyzing Error Summary and modifying code. If bug-fixing steps are made as efficiently and effectively as possible then maintenance costs can be minimal. The purpose of this research is to establish a tool of machine learning for identifying Composition Error Summary and to find out the types of special Composition choices that can be used to save costs, time, and effort. In this study, the T-test was applied to appraise the analytical implication of conduct metrics when the “F-test” was taken to the Variance’s test. Classifiers used in this study are “All words” or “AW”, “Highly Informative Words” or “H-IW”, and “Highly Informative Words plus Bigram” or “H-WB”. Identical validation and Vexed validation techniques were used to calculate the effectiveness of machine learning tools. The results of this research denote that the instrument is competent for definitive Composition Error Summary and other Composition choices for definite Error Summary. This research determines the practicality of machine learning techniques in corrective issues relevant to Error summary. The result of this study also explained that Composition/non-Composition Error Summaries have contrasting aspects that can be accomplished by machine learning devices. The advanced tool could be upgraded in some areas to create it more powerful. The array identification section of the current study has limitations, an array with different words and Composition recognition tools tend to prefer Compositions with more words, so improvements to this could implicate consideration of the semantics of Error Summary, equivalent, and use of n-grams. Also, in using the technology of machine learning and Natural Language processing some advancements to be made to the present characterization structure so for future research it is highly recommended to clear up the first’s Error Summary before operating several operations in the present study.Composition Error Summary  

This study aims to analyze the effectiveness and the economic side of the process of manual or semi-automatic bending signs. The data required for both types of marking methods are data on the number of operators, company working hours, effective working hours, and the results of the production process. After that it is done by data processing, data processing in this study uses the Experimental Method, in the Manual marking process, the process is done by directly drawing on the multiplex material according to the drawing design using 2 operators, in the Semi Automatic marking process, the use of a CNC machine is done by using only 1 operator and also using a setting on a CNC machine of 50 Amperes. So from the data that has been managed later, it shows that the use of CNC machines as a substitute for manual marking has a positive effect on the company. Which can reduce the amount of operator usage, get more results from the Manual marking process, and use less electricity. So overall, the marking process using the Semi Automatic method is better from all sides compared to the marking process using the Manual method.  

PT. Sejin Lestari Furniture is a company engaged in the production of furniture such as doors, tables, chairs, cabinets, bookcases and other interior equipment. As one of the companies that have succeeded in producing finished goods in large quantities, PT. Sejin Lestari Furniture relies on the help of an electric motor at every stage of its production, including one of the wood pellet production machines, namely the biofuel pelletizer molding machine which has four different types of motors. In order to work optimally, electric motors need a protection system that can protect them from threats. In this study, the protection system used is Thermal Overload Relay (TOR) which functions as an overload controller so that the electric motor is not easily burned or damaged. In this case, the TOR must be adjusted according to the capacity of each induction motor so that when an overload occurs, the TOR will automatically cut off the current flowing in the motor so that damage can be avoided. The TOR trip standards in this study uses the NEMA (National Electrical Manufactures Association) standard with the method used, namely Research and Development (R&D) where this method is carried out to produce certain products and test the effectiveness of these products. The data collection was obtained through journals and books.

Developments in the industrial world are increasingly rapid, resulting in increased competition in the industrial world, so that industrial companies are competing to improve production efficiency and effectiveness. Especially companies in the manufacturing sector, increasing productivity in a production system is an absolute thing that must be done. One of the main indicators in increasing productivity is the level of rehabilitation of production machines at the company. In measuring how good the rehabilitation of a production machine is, an effective and efficient maintenance maintenance process is needed for the company. Continuous use of the machine will decrease the level of readiness of the machine itself. In an effort to maintain the level of machine readiness so that production results are guaranteed due to continuous use of the machine, regular machine maintenance and maintenance activities are needed which are expected to prolong the condition of production machines so that they are not damaged, in case of machine damage it can disrupt production activities.

Property price determination is a complex challenge influenced by various factors, thus requiring an effective method for accurate prediction to support investment decision-making. In the current digital era, conventional approaches are being replaced by data-driven and artificial intelligence methods, where Linear Regression remains a popular choice due to its simplicity and effectiveness in modeling linear relationships. This study aims to analyze the relationship between the physical characteristics of a house and its selling price, and to build an accurate predictive model using the Linear Regression algorithm. A quantitative method was used, focusing on Building Area , Number of Rooms, and Building Age  against the House Selling Price. Correlation analysis results show that Building Area has the strongest correlation (0.81) with price, while Building Age shows a negative correlation (-0.52). The Linear Regression model demonstrated very strong and stable performance. The model achieved an R² Score of 0.9396 on the testing data, meaning 93.96% of house price variability can be explained by the model. Furthermore, the low MAE of only 11.31 million rupiah indicates a small prediction error, and the consistency of R² scores confirms that the model does not suffer from overfitting. This study concludes that the Linear Regression model provides excellent, stable, and reliable prediction performance for projecting house selling prices

PT GMP Sukses Makmur Indonesia is a company engaged in the production of Bubble Wrap (bubbled plastic). This company produces 818,680 rolls with an average monthly production of 68,223 rolls. The problem that often arises at Pt Gmp Sukses Makmur Indonesia is the high production machine delay time with a downtime of 705.43 hours and a breakdown of 663.33 hours in one year, so the engine performance is less effective. This can lead to not achieving production targets which cause losses. The purpose of this study is to determine the effectiveness of the Bubble Wrap production machine using the Overall Resource Effectiveness (ORE) measuring tool and to find the causes of the problems that have been carried out using the ORE method on Bubble Wrap production machines during the period September 2021 – August 2022. ORE is a method that is useful in measuring the effectiveness of production time by taking into account the resources which include humans, machines, materials and methods. In the ORE method there are seven factors that are measured including readiness, availability of facility, changover efficiency, availability of material, availability of manpower, performance efficiency, dan quality rate. The results of measuring machine effectiveness using the Overall Resource Effectiveness (ORE) method for Ektruder machines were 76.41%, Crushers 99.59%, Cooling 98.99%, Mixers 99.79%, and Air Compressors 98.88%. Based on the method used, it is known that in the Bubble Wrap production process, the extruder machine does not meet the ORE standard of 85%, while the influencing factors are performance efficiency and quality rate. where the value of performance efficiency and Quality rate does not meet the standard so that it affects the high value of Overall Resource Effectiveness (ORE).

The management of production and operations in factory machinery is an optimal management effort in the use of resources consisting of labor, machinery, equipment and raw materials. The production and operations manager directs various inputs in order to produce various outputs or results in units of quantity, quality, price, specific time and location according to consumer demand. Efficiency and effectiveness of the production process to be achieved requires proper scheduling management so that the production output produced can be completed according to target. Management of production and customer orders is still simple, besides that the status of raw material inventories that cannot be directly known can often result in delays in the production process and even production cannot be done as soon as possible. Scheduling orders that enter using the system first in first out where the first order that comes is done, but has not considered the number of orders, priority of customers, maturity of workmanship and engine capacity so that the estimated completion of the order can not be known. To make it more efficient and directed, the Earliest Due Date method is used by sorting the production work based on the closest due date. This method can be used for scheduling on one machine or for scheduling on several machines (parallel machine) so that the scheduling management information system can manage effective production scheduling and produce production reports automatically as needed.