Engineering Headway Vol. 27

Abstract: This research examines the use of machine learning to group a collection of data related to the tax compliance of manufacturing firms in the Greater Jakarta area of Indonesia. The data set was obtained through a survey and was able to collect data from 209 respondents who represented the finance department of the companies. The k-means algorithm is applied to develop machine learning. The clustering aims at dividing the data set on the basis of similarity into three clusters. The result showed that the machine learning model was able to cluster the data into three groups. An evaluation was presented by comparing the clustering result with a classification result based on the average survey score that has been studied previously. The evaluation shows a small correlation between the clusters and the average survey score. Compliance of tax payers is a complex system and cannot be merely indicated based in the survey score. The clustering technique demonstrated its usefulness in uncovering intriguing patterns, distributions, and the fundamental structure of the data.

Abstract: Business Process Model and Notation (BPMN) is a modelling approach for business processes that connects communication between business analysts and system development teams with visual notation. Not only does BPMN model the notation diagram, but there is also contextual information about who is involved in the process, or more precisely, the role of an organizational unit responsible for fulfilling each task sequence. Considering how much study is being done on automatic generation of BPMN, a review is needed when seeking to know the latest information about this field. The main objectives of this study are to enhance understanding and knowledge in the field of automated BPMN generation, and to assess the level of achievement of previous researchers. Several studies encounter restrictions in dealing with intricate sentences and input data requirements. Certain research is centered on particular components or features of BPMN, including the interconnection of tasks, gateway rules, and hierarchical structures, while others strive to enhance the precision and comprehensiveness of BPMN models.

Abstract: Selecting the right modeling approach for Enterprise Architecture (EA) is pivotal for optimizing organizational effectiveness. This paper investigates the use of ArchiMate as a standalone tool versus its integration with UML and BPMN through comprehensive SWOT and SPACE analyses. The SWOT analysis highlights that ArchiMate’s strengths include its simplicity and ease of learning, which streamline user training and implementation. However, its limited scope may not suffice for more intricate scenarios, potentially leading to user dissatisfaction due to its narrower capabilities. Opportunities for improvement lie in leveraging ArchiMate's learnability and exploring hybrid approaches that combine its simplicity with the strengths of other languages. Threats include the growing complexity of enterprise environments and potential resistance from users accustomed to integrated systems. The SPACE analysis underscores ArchiMate's financial efficiency and specialized expertise, making it a valuable choice for less complex modeling needs. In contrast, the integrated approach offers a more robust solution for handling complex environments, providing a strategic advantage through enhanced versatility and detail. This integration supports more nuanced and detailed modeling, aligning better with sophisticated EA requirements. These findings equip organizations with insights to make informed decisions about whether to adopt ArchiMate exclusively or integrate it with UML and BPMN, ensuring alignment with their specific EA needs and strategic objectives.

Abstract: This paper examines the integration of digital twin (DT) and simulation technologies in warehouse management to enhance efficiency and competitiveness in the logistics industry, specifically for third-party logistics (3PL) providers. It uses a case study to benchmark current practices and identify areas for improvement. The research also analyses industry trends and competitor strategies, revealing a growing adoption of DT and simulation for warehouse optimization. The findings highlight significant benefits of DT and simulation integration, including improved operational efficiency, enhanced decision-making, optimized inventory management, and increased supply chain visibility. The paper proposes a practical framework for implementing these technologies, emphasizing a phased approach to ensure smooth integration and maximize impact for 3PL providers.

Abstract: This study presents a global digital marketing framework, developed through a case study of a UK-based B2B industrial flooring company and the review of relevant literature, focusing on enhancing its Southeast Asian market presence. Using a mixed-method approach, it combines qualitative and quantitative analyses to examine digital marketing strategies, trends, and competitor practices. Findings highlight the effective use of social media by leading competitors and emphasize the importance of digital marketing in optimizing B2B operations and cross-cultural interactions. The study recommends some actionable insights for companies aiming to remain competitive and grow in the dynamic market.

Abstract: Tempeh is a traditional Indonesian food popularly consumed for its high nutritional value. Determining the quality of tempeh is an important step in improving quality control of tempeh products. The aim of this research is to develop a low-cost spectrometer using multispectral sensor AS7265x to assess the quality of tempeh maturity over a period ranging from 1 to 5 days. The system comprises the AS7265x sensor chipset, a light source, an Arduino microcontroller, and a personal computer. The sensor covers 18 wavelengths in the 410 to 940 nm spectrum. The results showed that the low-cost spectrometer functioned properly and could read light reflectance. By combining SVM models and optimizing feature selection using filter-based and metaheuristic feature selection algorithms, we were able to classify tempeh with high accuracy. After using wavelengths selection based on mutual information, the accuracy of tempeh can reach 95%, compared to 90% accuracy when not using feature selection. From the accuracy performance results and evaluation metrics obtained, it can be concluded that the combination of SVM-based machine learning with low-cost spectrometer can be effectively and economically used to assess the quality of tempeh maturity.

Abstract: Keratoconjunctivitis, commonly known as "pinkeye" disease, significantly impacts the health and productivity of cattle and goats. This study conducts a comparative analysis of three pre-trained convolutional neural network (CNN) models - MobileNetV2, VGG16, and DenseNet - for classifying pinkeye disease images in livestock. The research comprises four phases: data collection, data preparation, modeling, and evaluation. Data collection involves a dataset of 1425 images gathered from various sources. Preprocessing includes resizing, augmentation, and dataset stratification. The study creates two CNN models tailored for cattle and goats using pre-trained CNN. MobileNetV2 consistently demonstrates superior generalization, surpassing 95% accuracy. In contrast, VGG16 and DenseNet201, while achieving higher overall accuracy, show overfitting. MobileNetV2 is identified as the most proficient model for pinkeye disease classification, advancing automated disease diagnosis in cattle and goat farming. Further validation in diverse operational contexts is recommended.

Abstract: In order to accelerate the development of smart cities in Indonesia, the government launched a program called "100 Smart Cities Movement". This program was implemented from 2017 to 2019 by guiding 100 selected cities/regencies in stages. In 2018 (2nd year), Boyolali Regency has become one of the regencies selected to receive technical guidance regarding smart cities. This research aims to identify and analyze the implementation of smart city in Boyolali Regency, especially the priority programs for each dimension. This research uses a qualitative descriptive analysis method using data in the form of in-depth interviews with key figures and documentation results. Boyolali Smart City has six priority programs, including: SIPAD, SIPP, SIMAPI, Indrokilo Botanical Garden, Si Teri Lapar, and i-Boyolali. Each of the priority programs has been running well and has had a positive impact on the community, especially the people of Boyolali Regency. However, in the implementation of the smart city programs there are still several obstacles and shortcomings, so evaluation and improvement is still needed so that the Boyolali smart city can be more optimal.

Abstract: Rapid growth of information and communication technology (ICT) in Indonesia has led to an increased utilization of ICT, such as public service and information center application. Bantulpedia is an application established by Bantul Regency Government with aim to meeting the demands of Bantul residents regarding public services and information center within the Regency. Bantulpedia serves as a platform intended to integrate public and government services from various local government agencies in Bantul Regency, with a specific focus on developing the smart governance dimension. This research particularly aims to (1) identify types of smart governance-based services integrated with Bantulpedia application, (2) analyze the utilization of Bantulpedia application in supporting the implementation of smart governance, and (3) analyze the needs of Bantulpedia application development in supporting the implementation of smart governance. Qualitative methods have been used for conducting this research. Primary data were obtained through observation and in-depth interviews, while the secondary data were sourced from the development plan, Bantul smart city masterplan, and supporting documents. Based on the result of this research, Bantulpedia has 20 features for public service. Tourism, news, and CCTV services are among the most frequently used due to their user-friendly interfaces and accessibility, which make them easily accessible and popular among people. The development of Bantulpedia, in collaboration with various government agencies in Bantul regency, aims to integrate various public services across multiple agencies. However, the restrictive use of the National Identity Number (NIN) limits its functionality.

Abstract: Transportation is one of the crucial factors in the development of a country. This can be observed from the increasing needs of transportation in supporting human activities in residential and urban areas. Therefore, it is essential to maintain the infrastructure that supports the needs of transportation. One of the infrastructures that need to be considered as the main factor of transportation is road condition. Damaged road conditions can cause obstacles to the transportation system. One way to detect road damage is by measuring the vibration values that occur under different road conditions using an accelerometer as a vibration recorder. The vibration data is classified into three groups based on the road conditions where the recordings took place, namely good road condition, speed bumps (bump), and potholes. A total of 52 data samples were collected for each road condition in Yogyakarta using a motorcycle, which were then processed into vibration data in the frequency domain. The vibration data processing was carried out using Jupyter Notebook software with Python programming language and the algorithms used in this research were Fast Fourier Transform (FFT), SG Filtering, and Power Spectral Density (PSD) to determine the strength of the vibration signal. After that classification was performed by applying supervised machine learning using the multiclass classification algorithm on Support Vector Machine (SVM) other than that, cross-validation process was implemented to know the performance of the machine learning model. The classification results show an accuracy value of 92.31% for predicting road condition labels in the training model and 97.44% for the testing model. Both models are calculated using 75% of the total data for the training model and 25% of the total data for the testing model.