Journal La Multiapp

Logistics Distribution Route Design Using Taboo Search Method

Yaskie Cahyana Septiviandra Ade Nugraha — 2025-03-24

Logistics, as the science and art of managing goods, energy, information, and other resources, plays a vital role in the economy through shipping, receiving, warehousing, and distribution. Optimal routes can reduce travel distances and maximize transport capacity, optimize resource use, and reduce the environmental impact of logistics activities. PT Spectrum Indo Transport is a logistics company serving the Java-Bali region with 40 trucks, including wingbox, colt diesel, and colt diesel long. However, the company often experiences delays in delivery. The largest demand is for 275 gram cereals distributed to the East Java region. The East Java region includes Malang (3,100 kg), Mojokerto (2,500 kg), Gresik (2,500 kg), Lamongan (2,300 kg), Nganjuk (2,700 kg), and Surabaya (2,300 kg). The fleet chosen is a colt diesel with a capacity of 5 tons due to high consumer demand. The company wants to optimize the distance of the delivery route to prevent delays in the distribution of goods. The purpose of this study is to improve the efficiency of the delivery of the Colt Diesel Box fleet in East Java carried out by PT Spectrum Indo Transport through the application of the Tabu Search method in determining the distribution route. It is expected that by implementing Tabu Search, the company can achieve more efficient delivery. With the shortest route, it will provide an optimal influence on the delivery of goods and can meet delivery on time.

Optimization of Production Capacity Jumbo Bag by Method Theory of Constraints

Delinda Brilian — 2025-03-24

PT. XYZ is a company engaged in production jumbo bag with a capacity of 500–2000 kg. Increased demand occurred in jumbo bag with a capacity of 500 kg which causes problems in the production process, especially at work stations Cutting (SK-4), which experienced obstacles in the form of bottleneck due to limited resource capacity. This condition hampers efficiency and productivity, so that companies are unable to meet production targets during the period May to October 2024. This research aims to optimize production capacity jumbo bag by applying Theory of Constraints (TOC) through approach Drum-Buffer-Rope And Linear Programming which is processed using software POM-QM. Research stages include identification constraint, exploitation constraint, non-subordinatin constraint, as well as elevation constraint. From the results of this research, a solution was obtained in the form of addition shift work at the station Cutting (SK-4) through reduction shift at the station Needle Loom (SK-3) without reducing the number of operators, managed to increase the efficiency of the production process significantly. The implementation results show an increase in production time capacity from 129511 minutes to 291399.8 minutes (an increase of 26%) as well as an increase output production of 1,476 unit to 3,320 unit. Thus, the company managed to overcome obstacles bottleneck, increase operational productivity, and achieve predetermined production targets more optimally.

Integration of House of Risk and Analytic Network Process Methods in Mitigation of Risk in the Sugar Production Process

Nanda Riska Dewi — 2025-03-24

PT.XYZ is a company engaged in manufacturing, specifically in the production of granulated sugar. The problem that occurs in this company is the increasing number of gravel sugar defects by 18% or 1,019.52 tons. This number far exceeds the defect limit set by the company of 5% or 283.2 tons per month. Therefore, the purpose of this study is to identify the risks of the sugar production process using the House of Risk (HOR) method and determine the best alternative for priority risk mitigation through the Analytic Network Process (ANP) method at PT.XYZ. The HOR method is used to identify and classify risks based on severity and likelihood. Furthermore, the ANP method is used to determine the priority of risk mitigation strategies by considering the interdependence between risks. The results of the study showed that through the Focus Group Discussion (FGD) approach, 25 risk events and 25 risk sources were obtained which will be used to determine the mitigation strategy in the House Of Risk (HOR) method. Based on the calculation results of the rate agreement for alternative (W) of 0.777778, it shows a fairly high level of conformity among respondents' answers in selecting risk mitigation alternatives. Based on the calculation results on the super decision software, the highest normalized value was obtained, namely in the Desuperheater alternative of 0.51145. With this research, it is expected to help companies in overcoming company problems and can increase profits for the company.

Analysis Defect Products in Silencers Using the New Seven Tools Method

Alifia Rizky Annandita — 2025-03-24

Quality is important for a product so that it can have more value than other products. To maintain quality, quality assurance (quality) is required. As is the case with silencer products produced by CV. Nyoto Plastik, silencer is a product that must maintain its quality value. Therefore, many defects in silencer products must be repaired by analyzing the causes of the defects. Based on research, the average defect in this silencer product reaches 10% within one year. New Seven Tools is a method for finding out the causes of defects based on humans, methods, materials, machines or the environment by using the seven tools contained in it. Therefore, this research aims to help maintain the quality of silencers by analyzing the causes of defects and providing suggestions for improvements to minimize defects that occur using the New Seven Tools method.

Analysis of Waste in the Production System with the Approach Lean Manufacturing Method

Prastyo Utomo — 2025-03-24

Improving the production process needs to be done continuously and continuously in order to minimize all activities that consume time but do not contribute directly to the value of the products produced. PT Cipta Oggi is a manufacturing company that makes furniture products which has a major problem, namely the low value of Process Cycle Efficiency, which the company should have a PCE value of at least 50%. The use of lean manufacturing methods with the help of VSM is used to reduce waste in order to increase the PCE value. There are 22 non-added value activities with a time range of 427 minutes that can be cut. Where the production time was originally 1353 minutes to 926 minutes which had an impact on increasing Process Cycle Efficiency which was originally 46.8% to 68.4%. Based on Failure Mode and Effect Analysis (FMEA), it is known that the main root cause that has the highest RPN value is the Overprocessing waste with a score of 420 and the proposed improvements in order to reduce this waste are the need for safe storage management that can reduce the risk of damage to goods which requires additional processes, namely rework and additional human resources are needed in the inspection process in order to reduce the number of rework.

Sentiment Analysis of Bamboo Charcoal: Comparing Machine Learning Algorithms for Effective Insights

Giovanni Ega Agustine — 2025-03-24

This research aims to analyze sentiments toward bamboo charcoal on social media, with a focus on public perception in the global market in English. Using data collected from the social media platform X, this study applies various machine learning algorithms, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Deep Learning, Naïve Bayes, Decision Tree, and Gradient Boosted Trees, with TF-IDF as the text representation. The analysis reveals that the SVM model achieved the most accurate result of 92.33%, demonstrating its effectiveness in sentiment detection. The study also found that the KNN model performed well, achieving an accuracy of 92.26%, although slightly lower than SVM. These findings highlight the growing interest in bamboo charcoal as a sustainable product, reflecting positive sentiments in the data. Additionally, the Deep Learning model also showed promising results, although it was slightly less effective than SVM and KNN. However, there were also notable concerns regarding the environmental impact of bamboo harvesting, which were primarily expressed in posts. The Decision Tree model, while useful, did not perform as well as the other models, indicating the need for further refinement. Future research could explore a broader range of social media platforms, models, and languages to gain a more comprehensive understanding of global perceptions. Furthermore, integrating sentiment analysis with real-time monitoring could help stakeholders respond more effectively to shifts in public opinion.

Design of a Web-Based Citizen Complaint System in Increasing Citizen Participation in the Village Development Process

Diah Ayu Cahya Ningrum — 2025-03-24

Jekulo Village faces challenges in managing citizen complaints and grievances due to limited physical infrastructure and time. The conventional complaint mechanism is often ineffective in accommodating citizen aspirations in a transparent and responsive manner. The manual complaint recording and resolution process also causes complaint handling to be slow and less organized. This paper aims to discuss the importance of implementing a web-based citizen complaint system to increase community participation in village development. This system is designed so that residents can submit complaints, grievances, and suggestions more easily and efficiently, while also allowing the village government to respond to complaints more quickly and accurately. With a technology-based system, communication between residents and the village government can run more transparently, and increase accountability in handling complaints. The results of the development of a web-based citizen complaint system in Jekulo Village show increased efficiency in handling public complaints. This system accelerates the process of receiving, verifying, and resolving citizen complaints, so that problems can be handled more quickly and accurately. In addition, transparency in resolving complaints increases, which has an impact on increasing citizen trust in the village government. With the real-time complaint status monitoring feature, residents feel more involved in the village development process. The system also provides data that can be analyzed to identify patterns of problems that often occur, so that village governments can take preventive steps to resolve recurring issues. Overall, the system is an effective tool in strengthening community participation and building villages that are more inclusive and responsive to the needs of their citizens.

Evaluation of the Implementation of ISO 9001:2015 Quality Management System on HR Performance

Maritza Nawwar — 2025-03-26

This research was conducted to measure the extent to which the implementation of the ISO 9001:2015 Quality Management System influences the HR performance of PT XYZ using the ISO 9001:2015 clause, namely Organizational Context, Leadership, Operations, Performance Evaluation, and Improvement as measuring tools. This research is quantitative in nature and the data was distributed to 56 PT XYZ staff using Google Form. The data obtained will be processed using SPSS software with the multiple linear regression analysis method. From the results obtained, it is known that the regression analysis equation is Y = 0.428 + 0.040X1 + 0.099X2 – 0.110X3 + 0.078X4 + 0.122X5. It can be said that the variables Leadership (X2), Performance Evaluation (X4), and Improvement (X5) have a significant influence on HR performance. This is supported by the linear regression equation which obtained a positive value on HR performance. Apart from that, with the linearity test it can be seen that the data produced is linear in relation to HR performance because the results obtained are greater than 0.05. Therefore, this research can be a consideration for improving the ISO 9001:2015 Quality Management System on HR performance.

Implementation of Machine Learning-Based Classification Model in Employee Recruitment Decision Prediction

Muhammad Fauzan Nur Adillah — 2025-04-09

Employees are vital assets for any organization, and accurate recruitment decision-making is crucial for the organization's long-term success. Incorrect decisions can lead to high costs due to re-hiring processes, onboarding, and decreased productivity. This study aims to develop a recruitment decision prediction model using data obtained from the Final Results of the 2024 CPNS Recruitment in the Ministry of Finance. The data includes attributes such as educational background, age, GPA, SKD Score, and SKB Score. To understand the relationships between variables, correlation analysis was conducted using a correlation matrix and heatmap visualization. Additionally, data exploration was performed using histograms to show the influence of attributes on recruitment decisions. This study employs five machine learning algorithms for prediction: Linear Support Vector Machine, Decision Tree (C5.0), Random Forest, k-Nearest Neighbor (k-NN), and Naïve Bayes Classifier. The results indicate that some attributes significantly influence recruitment decisions, and machine learning models can identify candidates who are more suitable for the available positions. Among the five models tested, Naïve Bayes proved to be the most effective, achieving an accuracy of 88% and an AUC of 0.97, demonstrating its strong performance in distinguishing positive and negative classes. The key factors contributing to the model's success include relevant feature selection, data quality, as well as appropriate preprocessing and validation techniques. This model is expected to enhance objectivity, efficiency, and accuracy in employee recruitment processes, thereby assisting organizations in making more precise and fair decisions.

Posyandu Information System in Payaman Village to Optimize Web-Based Health Services for Toddlers and the Elderly

Elmalia Risma Putri Sugiarto — 2025-04-09

The information system at Payaman Village Posyandu is currently still done manually by recording data using paper, making it vulnerable to errors in data processing and storage. This approach is less efficient, especially in managing registration data for toddlers and the elderly, as well as recording health history which is often not well documented. To overcome this problem, a website-based information system was developed which aims to increase efficiency and accuracy in the Posyandu administration process. This system is designed to make it easier to register participants, monitor the growth of toddlers and the health of the elderly in a more structured and well documented manner and carried out well by staff. The main users of this system are posyandu officers and village midwives who have access to health data and patient examination history. This system was built using the Waterfall software development method, with PHP and MySQL-based technology as the database. This system allows online registration to be carried out by Posyandu officers, central management of health data, and automatic creation of health history recapitulation reports. With this system, the Posyandu service process can be carried out more effectively, efficiently and transparently. In addition, the data stored in the system can be used for more accurate analysis of village community health. The implementation of this system is expected to improve the quality of health services at the Payaman Village Posyandu, reduce the risk of recording errors, and provide benefits for health workers in monitoring patient conditions on an ongoing basis.

Prediction of Electrical Energy Needs for Capital City of Central Java Based on Backpropagation and Linear Regression

Alfin Aulia Akbar — 2025-04-20

This study discusses the prediction of electricity needs according to population growth. The model is determined by knowing the population and electricity needs. The parameters determined include: population, number of electricity consumers, energy consumption growth and electricity load factor for eleven years (2012-2023). The back propagation (BP) method and linear regression are used to help predict electricity needs for the next five years (2025-2030) with the BP architecture determined by three hidden layers and the number of neurons 12, 10 and 1. The object of the study was determined to be Semarang City, Indonesia. The results show that BP and linear regression can be used to predict electricity consumption needs in various sectors accurately. This is evidenced by the MAPE value below 10% and the MSE value of 2,65 x10-10 in the household sector, MSE 3,83 x 10-10 in the business sector, MSE 2,41 x 10-7 in the industrial sector, and MSE 3,6 x 10-12 in the public sector. The BP model produces predicted outputs of electrical energy in 2030 in the household sector of 1.104.140 MWH, the business sector of 843.757 MWH, the industrial sector of 1.027.790 MWH and the public sector of 375.974 MWH. The predicted increase in all sectors of electrical energy results in a total percentage of 54.21% for power sufficiency in 2030, so a thorough planning study is needed to meet electrical energy needs in that year.

PROMETHEE Method for Prioritization of Electrical Energy Audit In Industry

Gellen Twin Agiantoro — 2025-04-20

The problem of electricity in industry is the increasing consumption of electrical energy which results in increasing electricity consumption costs. The solution is how to reduce electrical energy consumption in the factory. The first step is to conduct an electrical energy audit. This research discusses electrical energy audits in industry with the aim of providing recommendations for electrical energy efficiency. The model is determined as an industrial electrical system complete with an electrical loading system. Parameters determined: production machinery, lighting systems, office support equipment, clean water pumps and air conditioners. The PROMETHEE method was used for prioritization in selecting from several alternative energy-saving opportunities. The research stages began with an initial energy audit, calculation of energy consumption intensity (IKE), recapitulation of electrical energy consumption, determination of energy saving opportunities, and PROMETHEE calculation. The PROMETHEE method used with 3 (three) alternatives, namely energy saving opportunities at no cost, low costs energy saving opportunities and high costs energy saving opportunities. As the research object, only the production building at PT Hop Lun Indonesia (HLI), Semarang, Indonesia was determined. The results show that PROMETHEE can be used for electrical energy audits in industry. This is evidenced by the prioritisation of the best recommendation results from the three alternatives, namely the first order in low-cost energy saving opportunities by replacing the type of LED lamp that has the potential to save energy by 2,723 KWH/day, the second order of energy saving opportunities at no cost by socializing to all employees regarding energy savings and conducting periodic maintenance and the last order in high-cost energy saving opportunities by replacing AC Inverters which have the potential to save energy by 390 KWH/day and replacing motors on sewing machines using servo motors which have the potential to save energy by 38,286,400 KWH/day.

A Study of Rain Station Network Distribution Using Artificial Neural Networks

Aradila Tiara Putri — 2025-05-05

Hydrological analysis is an important component in water resources management, especially for planning and controlling water infrastructure. This study evaluates the effectiveness of the rain station network in the Upper Ciliwung Watershed and identifies rain station with maximum accuracy in representing the study area conditions. Rainfall and discharge data were tested using statistical tests to ensure the absence of trends, stationary, persistence, and outliers. The evaluation of the rain station network density was conducted based on WMO guidelines, which determined the Upper Ciliwung Watershed met the criteria with a density of 37.981 km² per rain station. Analysis of rain station network distribution patterns using Artificial Neural Networks (ANN) was conducted with three data divisions (70-20-10, 60-25-15, 50-30-20) and tested at 100, 500, and 1000 epochs. The best results were obtained at 70-20-10 composition with 1000 epochs, showing the smallest relative error of 9.880% and NSE value of 0.983. The most effective rain station combinations are Gadog, Cilember, and Gunung Mas. This research provides recommendations in rain station network optimization to improve the accuracy of hydrological data.