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  • Measurement Of Maturity Model Using The Service Operation Of SIAKBA in ITIL V3 At KPU

    Authors: Tria Khaerunisa; Irfan Dwiguna Sumitra

    DOI: 10.1109/INCITEST59455.2023.10396859

    Abstract

    The General Election Commission (KPU) is utilizing the advancing information technology (IT) to construct an Information System for KPU and the Ad hoc Board members known as SIAKBA. This IT system is progressively becoming more sophisticated as it undergoes further development. This study aims to assess the maturity model of the information system developed by the KPU and the Ad hoc Agency, employing the ITIL framework and adopting a Service Operation methodology. This study employs pertinent facts and information to assess the maturity of the information system. The research methodology employed in this study encompasses the utilization of questionnaires administered to relevant stakeholders, employing the RACI framework, as well as quantitative analysis to assess practices and procedures within the respective domain. Moreover, many metrics are utilized to assess performance within the KPU and Ad hoc Agency information systems. These metrics include incident resolution time, problem management effectiveness, change success rate, and service availability. The findings from the study on the KPU Information System and the ad hoc agency indicate a maturity level of 2.36, explicitly corresponding to level 2 as per the repeating table. Hence, it is imperative to conduct additional identification processes for the KPU to enhance its operational capabilities and deliver high-quality IT services. © 2023 IEEE.

    Author keywords

    General Election Commissions; ITIL V3; RACI; Service Operation; SIAKBA

    This article can be accessed at: https://www.scopus.com/pages/publications/85185197729

  • Comparison of K-Nearest Neighbor and Multiple Linear Regression for Beauty Product Sales Prediction

    Authors: Ade Ismayani Rahman, Rahma Wahdiniwaty

    DOI: 10.1109/INCITEST64888.2024.11121461

    Abstract

    Sales predictions constitute a critical component in establishing and expanding a business entity. Accurate sales projections enhance the quality of decisionmaking processes, increase profitability, and improve customer service results. The main objective of this research is to assess the efficacy of K-Nearest Neighbor and Multiple Linear Regression methodologies in predicting beauty product sales. The methodological approach employed in this research is of a quantitative method. The dataset incorporated encompasses variables such as product specifications, pricing, stock, and number of sold. Data preprocessing methodologies are employed to clean the data, handle missing data and detect outliers. The research findings show that both methods can predict product sales. However, the Multiple Linear Regression model has a better advantage than K-Nearest Neighbor. The Multiple Linear Regression method has smaller average values of RMSE, MAE, and MAPE than K-Nearest Neighbor, which are 4.637, 3.990 and 0.077. Meanwhile, the average value of R2 generated by Multiple Linear Regression is greater than K-Nearest Neighbor, which is 0.820. This proves that the Multiple Linear Regression method is more suitable for predicting beauty product sales. © 2024 IEEE.

    Author keywords

    data mining; k-nearest neighbor; multiple linear regression; sales prediction

    This article can be accessed at https://www.scopus.com/pages/publications/105015857048

  • Prediction of Raw Material Usage for Hijab Production Using k-Nearest Neighbors and Random Forest Methods

    Authors: Luthfi Rifqi Zulfiqar, Geraldi Catur Pamuji, Hengky Saputra, Fithrah Syawaludin

    DOI: 10.1109/INCITEST64888.2024.11121429

    Abstract

    Accurate demand forecasting will optimize the supply chain management in the hijab industry, since demand forecasting is highly required for maintaining efficient and effective production and inventory processes. This study investigates the k-Nearest Neighbors (k-NN) and Random Forest (RF) for the purpose of predicting demands regarding raw materials in the hijab production process. Key performance metrics considered for evaluating both models include the RMSE, MAE, MAPE, and R2. These findings indicate that k-NN outperforms RF in terms of superior prediction accuracy with K=2. In this respect, the root mean square error from k-NN was 1.768 against the RF’s RMSE of 1.801, proving that it is always more reliable in capturing local variations in data. On top of this, even incomplete data or those occasions when demand shifts all over the place were handled well by k-NN with more consistent and thus more accurate results. These results offer valuable insights for hijab manufacturers, allowing them to optimize their supply chain management by optimizing material shortages and overstock inventory. This research further highlights the importance of choosing the right predictive model according to the context and data characteristics to improve operational efficiency and enhance customer satisfaction. © 2024 IEEE.

    Author keywords

    Hijab Production; k-Nearest Neighbors; Prediction; Random Forest; Supply Chain Optimization

    This article can be accessed at https://www.scopus.com/pages/publications/105015887825

  • UI/UX Design Using User-Centered Design Method on Staycation Website

    Authors: Herry Saputra, Mohamad Algipani, Alfi Naga Mulya, Halena Fauziah Zafira

    DOI: 10.1109/INCITEST64888.2024.11121477

    Abstract

    This study focuses on creating a UI/UX design for a web-based Staycation (You Can Stay Plus Staycation) application. This research is a UI/UX design for Staycation, which can help users find hotels, apartments, and houses. The method used for the staycation application is User-Centered Design (UCD) method. UCD method focuses on user needs which the designs developed through can be optimized and focus on end-user needs. This research aims to create a friendly design that is easy to use and comfortable. This design is expected to make it easier for users to find vacation spots and places to stay. It can be concluded that with this application, users can find it easier to find vacation spots and places to visit. © 2024 IEEE.

    Author keywords

    – ; UCD; UI/UX design; web-based staycation

    This article can be accessed at https://www.scopus.com/pages/publications/105015654435

  • Prediction Models of Acceptance Student Scholarships in University for Students K-12 Using Decision Tree Learning

    Authors: Agus Nursikuwagus, Tono Hartono, Agus Setiana, Muhammad Agil Alfariski, Refaldi Satria Gumelar, Muhammad Rizdky Maulady

    DOI: 10.1109/INCITEST64888.2024.11121501

    Abstract

    Receiving scholarships for high school students is one of the key processes in obtaining scholarships at a university; therefore, the process of monitoring and evaluating scholarship recipients at the high school level is very necessary. We haven’t used machine learning for prediction yet. A fair and suitable process is crucial to support the justification candidate. This research aims to provide prediction learning models based on the proposed dataset. We leveraged classification techniques like decision trees base, naive bayes learning, and support vector learning. As a result of the task, we received a variety of accuracy levels, ranging from tree learning to support vector machine. Each model presents accuracy values of 1.00,1.00, 1.00,0.81, and 0.99, in that order. SVM outperforms the other models, particularly in reducing false predictions. The dataset consists of 547 instances, with 70% of them in the training dataset and 30% in the testing dataset. Future research can leverage deep learning methods or large language machines. Providing the tuning parameter is crucial for enhancing classification accuracy. © 2024 IEEE.

    Author keywords

    classification learning; confusion matrix; decision tree; models; prediction

    This article can be accessed at https://www.scopus.com/pages/publications/105015870242

  • The Utilization of Data Visualization in Klinik Keluarga within the Cianjur District

    Authors: Alif Finanditha, Dian Dharmayanti, Juliadit Syahputra

    DOI: 10.1109/INCITEST64888.2024.11121481

    Abstract

    The objective of this research is to develop effective and efficient visualizations to assist the Promkes & Marketing Coordinator, and Warehouse Coordinator in acquiring the necessary information in a more quick and precise manner to improve the decision-making process. Based on the results of interviews conducted with the Promkes & Marketing Coordinator, the Warehouse Coordinator said that the current form of information presentation is a graph. However, this form of presentation has not helped the Promkes & Marketing Coordinator and Warehouse Coordinator perform their duties and misinterpret the information. With so much data that needs to be processed, this results in the Promkes & Marketing Coordinator and Warehouse Coordinator taking longer to get information, which hinders the tasks of the Promkes & Marketing Coordinator and Warehouse Coordinator. In this research, the method used adapts from 7 steps of data visualization adapted to the research to build a good visualization according to the needs. The knowledge extraction process is performed using descriptive statistics and data mining techniques. The implementation of the visualization design is done through the creation of a prototype, which is then tested through the usability testing method. The test results indicate that the data visualization prototype enables health promotion & marketing and warehouse coordinators to grasp the necessary information more swiftly. © 2024 IEEE.

    Author keywords

    data mining; data visualization; usability testing

    This article can be accessed at https://www.scopus.com/pages/publications/105015855737 

  • Exhaust Gas Detection System in Cabin Car Based on Internet of Things

    Authors: Dedena Hirawan, Audy Revi Nugraha

    DOI: 10.1109/INCITEST64888.2024.11121491

    Abstract

    Driving a car is a daily transportation activity carried out by the Indonesian people. Currently, there is no feature on the car to check and provide warnings about the air quality in the cabin. The purpose of this study is to build an exhaust gas detection system in the car cabin by analyzing the air using the MQ-135 and MQ-7 sensors. This study uses a prototyping model for software and hardware development. The prototyping model is quite effective at every stage, from data collection stage to system testing and implementation stage. This study focuses on developing a system to detect exhaust gases inside car cabins, as there is currently no feature in cars to monitor cabin air quality. The system uses MQ-135 and MQ-7 sensors to analyze air quality and detect harmful gases such as Carbon Monoxide (CO), Carbon Dioxide (CO2), Ammonia (NH3), and Nitrogen Oxides (NOx), which result from fossil fuel combustion. If gas levels exceed safe limits, the system provides a warning and recommends opening the windows to improve air circulation. The goal is to help maintain healthy air quality in car cabins, preventing negative health impacts on passengers, and allowing car owners to monitor vehicle conditions for safe daily use. © 2024 IEEE.

    Author keywords

    Car Cabin; Detection System; Exhaust Gas; Internet of Things

    This article can be accessed at https://www.scopus.com/pages/publications/105015892910

  • Utilization of Database Replication for Performance Improvement and Backup of Higher Education Academic Information System Database

    Author: Andri Heryandi

    DOI: 10.1109/INCITEST64888.2024.11121494

    Abstract

    Currently, a higher education institution has an Academic Information System to support the implementation of educational processes. Over time, as the information system is used, the amount of stored data also increases. This leads to slower database services, which subsequently affects the performance of the information system. In terms of security, a database must also be prepared to face potential attacks or disruptions that could be damaging. Therefore, the database used in an information system must support steps to minimize the impact of such attacks or disruptions, for example, through backup measures. However, implementing backup procedures in a database can impact the overall performance of the information system, such as causing downtime during the backup process. This research creates a database server architecture that can enhance database performance (availability, speed, and security), thereby improving the performance of the academic information system while also supporting the backup process without compromising the performance of the system information using database replication technology. © 2024 IEEE.

    Author keywords

    academic information system; backup; database; replication; security

    This article can be accessed at https://www.scopus.com/pages/publications/105015890567

  • Tempeh Chips Production Optimization System using the Simplex Method

    Authors: Gentisya Tri Mardiani, Rina Susanto

    DOI: 10.1109/INCITEST64888.2024.11121428 

    Abstract

    In producing tempeh chips, the company has limitations such as the availability of raw materials and daily production capacity. The simplex method is used to calculate the optimal amount of production in one day and find out how many products are produced. The aim of this research is to assist Production Managers in calculating the amount of production time carried out and knowing the optimal number of products produced from each production. Based on the results of this analysis has been implemented then the application of the Simplex Method can be implemented to produce 1x production of salty tempeh chips and 2x production of sweet tempeh chips with a total production of 3500 kilos per day so that the company can maximize the use of raw materials, machines and production capacity and optimize the number of products produced. The system to be built has three users, namely Production Manager, Production Staff, and Warehouse Staff. The output of the system that will be built is to assist the Production Manager in calculating optimal production requirements every day, as well as assisting in making production plan decisions for each day. © 2024 IEEE.

    Author keywords

    decision making system; optimization; production; raw material; simplex method

    This article can be accessed at https://www.scopus.com/pages/publications/105015779398

  • Comparative Analysis of Interactive Map Data Loading Using the GeoJSON Method for West Java New Entrepreneurs

    Authors: Angga Setiyadi; Eko Budi Setiawan

    DOI: 10.1109/INCITEST59455.2023.10396976

    Abstract

    The purpose of this study is to analyze interactive maps using the GeoJSON method and without using GeoJSON for new entrepreneurs in West Java. The flow of the stages of this research consists of three parts, namely analyzing the code, implementing the code, and testing the code that has been created using GeoJSON and without GeoJSON. The results obtained in this study are using GeoJSON to display data on an interactive map faster than without using GeoJSON. The downside of using GeoJSON on an interactive map is that the app developer is more likely to write down the source code on loading the interactive map. © 2023 IEEE.

    Author keywords

    Comparative Analysis; GeoJSON; Interactive Map; New Entrepreneurs

    This article can be accessed at: https://www.scopus.com/pages/publications/85185197883