Infrastructure development plays a crucial role in ensuring the sustainable economic growth of a country. For instance, Indonesia saw a 29.532% rise in infrastructure investments between 2015 and 2019, showcasing their heavy investment in infrastructure project. The Public Private Partnership (PPP) project in Indonesia is a collaborative effort between the government and private entities to enhance infrastructure project development. The Makassar-Parepare Railway Public Private Partnership (PPP) project is an example of a national strategic project on transportation sector, especially train transportation, which aims to improve the quality of national infrastructure and connectivity. However, the project faces various risks such as delays in determining the railway route and a lack of understanding of the Government Contracting Agency (GCA/PJPK) regarding the PPP concept. In order to ensure the success of this project, it is important to identify and manage these risks effectively. Utilizing the likert scale, a literature review uncovered 29 relevant risk factors, while the probability impact matrix (PIM) analysis aided in ranking the 18 main risks in the Makassar-Parepare Railway PPP project. This research can contribute to the development of more effective risk management strategies for similar infrastructure projects in the future.
This study proposes a geolocation microservices integration framework for immigration surveillance using UML and the sidecar pattern. The prototype was evaluated through multi-location subject testing, GNSS-based positional classification, descriptive error metrics, and comparative architectural experiments. Positional error was calculated using the Haversine formula and interpreted by subject rather than by a generalized percentage claim. The results show that six subjects were classified as Very High, one as High, and one as Moderate, with MAE of 43.34 m, RMSE of 75.20 m, median error of 16.75 m, and SD of 65.70 m. The comparative experiment indicates that microservices offer stronger modularity and fault isolation than a monolithic baseline, while the sidecar pattern improves observability and auditability without modifying the primary geolocation service. The framework is feasible as a decision-support layer for real-time immigration monitoring, subject to legal governance and further load testing.
Heart disease is a specific abnormal condition that includes heart-related and bloodstream-related illnesses. It can range from heart rhythm issues to blood vessel disorders. Medical practitioners use a patient''s medical history and tests like blood pressure, blood sugar, or cholesterol to make a diagnosis of heart disease. The Behavioral Risk Factor Surveillance System is the greatest telephone survey program in the United States for learning about Americans'' health-related risk behaviors, chronic illnesses, and use of preventative drugs (BRFSS). We may use the data to identify a variety of illness-related traits and forecast an individual's likelihood of developing a specific condition. Following that, we compare and contrast the accuracy of the outcomes obtained using the methods described in the journal, earlier methods for previous projects, and hybrid algorithms. The objective of this research is to surpass the result of the journal reference which is the minimum accuracy of 78% and the maximum of 90%. Meanwhile the results of our Hybrid Algorithm methods are 90% for XGBoost+Randomize Search and 91% for the other algorithms. Based on the results, we can conclude that by combining the classification algorithm with the hyperparameter optimization method, we may enhance classification accuracy. Because the presence of hyperparameters makes the limit more optimal and optimal for classification, resulting in more accurate findings. It can be seen that the results obtained from our project are slightly higher compared to the Journal. The highest accuracy from the Journal is 90% while our result is 91%.
OD matrix estimation under limited traffic count data in Indonesian Traffic Impact Analysis (Andalalin) remains fundamentally underdetermined, as the number of unknown OD pairs far exceeds the available traffic count observations. This study develops and empirically evaluates an integrated OD estimation system combining gravity model calibration, maximum entropy (MaxEnt) updating, and user equilibrium assignment within a Design Science Research framework. A stratified-coverage subsampling procedure (31 runs across four data availability scenarios) was applied to two datasets with contrasting characteristics. On the Magelang benchmark dataset (7 zones, known ground-truth OD), three key findings emerged: (1) reducing observed links from 29 to 18 via representative stratified selection improved GEH pass rate from 37.9% to 62.4%, indicating an optimal constraint count for small underdetermined networks; (2) GEH improvement did not coincide with improved OD accuracy (RMSE_OD increased 49%), empirically confirming the inherent limitation of flow-fit metrics as OD quality proxies; (3) result variability increased sharply below ~15 observed links, indicating a data threshold for reliable estimation. On the RSU Tabanan case study (4 zones, field data), all 31 runs produced identical outputs regardless of subset selection, revealing a prior-dominated regime caused by a 2.57× demand-volume structural inconsistency. These findings constitute a two-regime diagnostic framework applicable to Andalalin survey planning.
One promising solution to the global energy crisis is the utilization of biomass waste as an alternative fuel in the form of biobriquettes. A critical factor influencing the quality of biobriquettes is the type of binder used. Inorganic and composite binders often present drawbacks such as high ash content, low calorific value, and elevated production costs. To address these issues, this study explores the use of cassava peel waste as an organic binder. Cassava peel contains approximately 20% starch by dry weight, which acts as a natural polymer with adhesive properties when hydrolyzed into dextrin. The binder preparation involves washing, drying, and sieving the cassava peel to extract starch, followed by hydrolysis using 15 mL of 1N hydrochloric acid (HCl) at 110°C for 10 minutes to produce dextrin. The resulting solution is mixed with casein, cold water (15°C), and triethanolamine, then heated to 60°C and stirred until homogeneous. The resulting binder demonstrates high viscosity and strong adhesion, making it suitable for biobriquette production. The biobriquettes produced with this binder exhibit higher density (0.99–1.01 g/cm³) compared to commercial biobriquettes (0.97 g/cm³), indicating improved compactness and uniformity, which are essential for enhancing fuel quality and performance.
This paper explores the practical implementation of a Chatbot designed to connect natural language communication with database interactions by generating Structured Query Language (SQL) queries. Utilizing the OpenAPI framework, a widely-used specification for building APIs, our approach aims to enhance the flexibility and interoperability of the ChatBot. The system not only interprets user queries expressed in natural language but also transforms them into syntactically correct and semantically meaningful SQL commands. This paper provides an overview of the current landscape of natural language processing (NLP) Chatbots, discusses the architecture and design considerations of our implementation, and highlights the methodology for training and fine-tuning using state-of-the-art NLP models. Real-world use cases are presented to showcase the practical applicability of the Chatbot, demonstrating its ability to handle various complexities inherent in natural language queries. This work contributes to the field by presenting a scalable and robust solution that combines NLP and database interactions through the utilization of OpenAPI, with potential implications for improving human-computer interactions within database systems.
Not only the exchange of information between IoT devices but integration between IoT devices and cloud servers has also brought IoT to a higher level. Security is the main problem for IoT devices with limited resources for exchanging information. Digital envelopes are one method that uses encryption to secure data. However, lim- ited resources on constrained devices necessitate selecting the proper encryption. This systematic literature review (SLR) was conducted on 11 of 43 studies that have used encryption algorithms for IoT devices. In addition to using the algorithm, it will also explain the device used and the safety factor. A suitable encryption algorithm for the digital envelope has been found. In addition, Some of the safety factors, IoT devices, and specifications are also shown.
The development of chatbots is currently quite significant, considering the trend of interactive customer services 24/7. One advantage of using chatbots is reduc- ing queues and customer waiting times. However, previous research stated that 87% of users prefer interaction with humans to solve complex problems. Therefore, this study introduced a hybrid human-contextual chatbot with sustainable model devel- opment using Recurrent Neural Network (RNN) and threshold optimization. The research proposes a cooperation framework between Artificial Intelligence (AI) and humans to optimize the workforce while maintaining the quality of the company’s services. The system has a monitoring website and continuous model development to ensure the continued growth of the model. The system trial was conducted in XYZ company’s IT management division in Indonesia for four weeks. The performance evaluation process uses accuracy, hand-off rate, average execution time, and average response time. Weekly performance evaluation results obtained accuracy and hand- off rate score increased, but average execution time and response time decreased every week. The decrease in execution and response time indicates a faster model performance. The highest accuracy and hand-off rate values were 0.99 and 0.98, respectively. Execution and response times get the lowest seconds at 0.39 seconds and 0.85 seconds, respectively.
Public transportation has an important role in everyday life, namely reducing conges- tion and avoiding traffic accidents. However, in reality, public transportation tends to be abandoned by the people of Surabaya. The tendency of public transportation to be abandoned by the people of Surabaya City is due to reduced public satisfac- tion with the quality of public transportation services. This study aims to determine the factors that affect the satisfaction of passengers in public transport in the city of Surabaya using ordinary logistic regression and to identify what service quality needs to be improved and maintained in urban transportation using the Importance Perfor- mance Analysis method. The results of this study indicate that 70% of passengers in public transport in Surabaya are dissatisfied with the quality of the public transport service. Variables that affect passenger satisfaction are reliability (the ability of the public transportation to provide the promised service immediately), responsiveness (the ability of the driver to assist passengers and provide responsive and fast service), assurance (the knowledge and ability of the driver to maintain passenger trust), empa- thy (care driver to passenger needs), direct evidence (passenger attractiveness to the physical facilities of the public transportation) and transportation fares. The variable of service quality that becomes the main priority for improvement is the ability of the driver to provide the promised service immediately, the knowledge, ability, cour- tesy, and safety of the driver in carrying out their duties, as well as attractiveness to physical facilities.
The healthcare industry is currently advancing, so it is necessary to integrate the technology used by hospitals. Hospital technology integrates data collection, data management, and information presentation. In fact, in the current hospital supply chain technology, it has not been integrated so it requires further research. This research discusses hospital supply chain technology that uses several technologies, namely blockchain, Internet of Things (IoT), Radio Frequency Identification (RFID), Artificial Intelligence (AI), telemedicine, and Hospital Information System (HIS). The technology used needs to be integrated so that it can help the entire system in the hospital properly. In the technology used, there are several variables that affect technology integration so that it needs to be considered, such as the level of integration, level of accuracy, data security, trust between stakeholders, information quality, system quality, efficiency level, perceived ease of use, perceived usefulness, reliability level, and real time. Then proceed to use the Causal Loop Diagram to visualize the relationship between variables that affect the level of integration between technologies. With integrated technology, the hospital supply chain process will become more efficient and also increase trust between stakeholders in the hospital in using the technology. This study highlights the limitations of the research and suggests areas for future research, such as using the SFD (Stock Flow Diagram) method to further investigate the supply chain technologies used by hospitals.
This paper evaluates the feasibility and market viability of a domestically produced rehabilitation exoskeleton in Indonesia. The research incorporates a comprehensive economic analysis, including Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PP) to determine the investment potential of different production models: factory construction, building rental, and factory purchase. The findings indicate that while renting a building may yield a high IRR, the negative PP suggests a lack of long-term asset accumulation, making it unsustainable. Meanwhile, factory construction and purchase offer stronger long-term economic benefits. Additionally, market analysis highlights the cost advantage of the locally developed exoskeleton compared to imported models, with a 75%-85% price reduction, making it significantly more accessible for patients and medical institutions. The domestic production strategy not only reduces import dependency but also ensures adaptability to local patient needs, positioning the exoskeleton as a highly viable and impactful medical technology for Indonesia’s rehabilitation sector.
Featured data in a dataset can affect the data processing, either for the better or for the worse. In addition, feature data can also affect the time of data processing. Selection of the right feature data may need to be done where the feature data can represent the whole of a dataset. In this study, a search for feature data will be carried out that can result in better data processing. The classification process will be carried out on an Iris dataset with the KNN algorithm. The iris dataset has 4 feature data (Sepal Length, Sepal Width, Petal Length, Petal Width) and the exact feature data variation will be determined in this classification. The dataset will be broken down into 7 variations of data and tested with a comparison of the training data and test data, namely 90:10, 80:20, 70:30, 60:40, 50:50, 40:60, 30:70, 20:80 and 10:90. The KNN algorithm used has parameters with the number of n neighbors 5 and the Minkowski metric. In this study, the highest accuracy value was 96% and the lowest accuracy value was 71%. The highest accuracy value is obtained from the variation of the Petal Length and Petal Width data features while the lowest accuracy value is obtained from the variation of the Sepal Length and Sepal Width data features.
Wind is a term for moving air. Considering that fossil fuel sources are dwindling, wind has the potential to be used as a source of electricity. A generating system is needed to convert wind into energy. A turbine or often called a windmill is a device that can utilize wind power by converting mechanical energy into electrical energy. Wind turbines are classified as horizontal axis turbines or vertical axis turbines depending on the direction of rotation of the rotor. One of the vertical type turbines is the Savonius turbine. This turbine is very suitable for application in Indonesia because it has several advantages, including being able to rotate at relatively low wind speeds, being able to receive wind from all directions, as well as ease and low construction costs. This research aims to analyze the Savonius wind turbine prototype with 2, 3 and 4 blades. The turbine blade material used is PLA (Polylactic Acid), which is a biodegradable thermoplastic made from cane sugar or cornstarch which is environmentally friendly. Blade dimensions 100 mm x 102 mm x 2 mm. Data collection was carried out by testing at wind speeds of 3 m/s and 4 m/s. Wind speed is kept constant through the windtunnel. Experimental results show that the number of rotor rotations is inversely proportional to the number of turbine blades. Likewise for the turbine power coefficient. The turbine with 2 blades has the best performance compared to the other two turbines.
Currently, the public interest is still quite high for fossil fuels as indicated by the increase in fuel consumption which requires imports to meet domestic fuel needs. the negative impact of the use of fossil fuels is the availability of crude oil that continues to decline and the problem of exhaust emissions is increasingly concerning. the use of fossil fuels must be reduced by substitution of alternative fuels, especially in diesel engines. One alternative fuel is biodiesel which can be used directly in diesel engines without having to modify engine components. Biodiesel is a fuel produced from the transesterification process. Biodiesel can be produced by utilizing vegetable oils, animal fats, used cooking oil, and algae so that biodiesel can be said to be a renewable, biodegradable, non-toxic, and environmentally friendly fuel because the exhaust emissions produced are relatively cleaner. In applications in everyday life diesel engines are often used to drive generators. In use in Indonesia, the government encourages to mix biodiesel with diesel oil. Now it has reached B30 biodiesel, which is 30% biodiesel and 70% diesel oil. Diesel motors or diesel engines are required to use B30 by the government. Therefore, this study aims to create a setup machine as a learning medium for biodiesel applications in diesel engines and to facilitate several types of research on diesel engines with biodiesel fuel, because the trainer form is simple but still has the same function. The method used in this research consists of 3 stages, namely design, manufacture, and performance testing using halogen lamps as an output. The results of this study is that this simple diesel engine design can work properly shown by halogen lamps which all 4 were lit during testing process.
Stunting is a condition affecting children's growth and development due to chronic malnutrition and recurring infections, characterized by a height below -2 standard deviations on the WHO growth curve. It remains a major global nutritional issue, with 149.2 million children (22%) affected worldwide in 2020. In the same year, 276,069 children in West Java (24.5%) were classified as stunted. Addressing this issue can involve predictive approaches, such as supervised machine learning. The methods compared include Polynomial Regression (PR), Support Vector Regression (SVR), and Linear Regression (LR) in four treatments. The models analyzed include PR, SVR, LR, PR-XGB (Polynomial Regression with XGBoost), SVR-SGB (Support Vector Regression with Stochastic Gradient Boosting), LR-XGB (Linear Regression with XGBoost), PR-XGB2, SVR-SGB2, LR-XGB2 (the previous models with double boosting), PR-XGB2-Opt, SVR-SGB2-Opt, and LR-XGB2-Opt (double boosting with hyperparameter optimization). The novelty of this study lies in improving the performance of the models through a double-boosting technique using Extreme Gradient Boosting (XGBoost) with hyperparameter optimization via GridSearchCV. Among models without boosting, LR achieved the best performance with MSE 0.018217, MAE 0.130036, MAPE 0.314071; with single boosting, SVR-XGB performed best with MSE 0.031485, MAE 0.162925, MAPE 0.344510; with double boosting and hyperparameter optimization, both models LR-XGB2 and LR-XGB2-Opt maintained the best performance with the same value, MSE 0.016474, MAE 0.124677, MAPE 0.309293. These results suggest that double boosting with proper tuning significantly enhances model performance in predicting stunting prevalence.
Many studies in the literature have a premise that design patterns improve the quality of object-oriented software systems. Considerable research has been devoted to re-designing the system to improve software quality, mainly on its maintainability and reliability. Less attention has been paid to evaluating the impact of the performance efficiency quality factor. This research investigates the impact of design patterns on application performance and complexity. It is, therefore, beneficial to evaluate whether the design patterns may improve its performance and complexity or even decrease it. The research demonstrates scientific evidence in quantitative values through experimentation on a case study to present its influences. This paper uses an object-oriented enterprise project named SIA as a case study. Some issues related to design patterns are addressed. The selection of the design pattern is based on the application context issue. Three attributes related to performance efficiency are evaluated: time behavior, resource utilization, and capacity measures. The complexity is also evaluated. We use Apache JMeter and Java Mission Control as tools to support experimentation. The experiment results show that design patterns may decrease the quality of time behavior and resource utilization whilst they may increase the quality of capacity measures and complexity to a significant degree.
Hospitals strategically provide quality health services to the surrounding community. One of the strategic roles is realized in inpatient and outpatient services. Based on the background above, the author examines patient segmentation based on Customer Lifetime Value analysis using the RFMT model. The research stages include calculating the RFMT score, clustering using the K-Means and DBSCAN algorithms, and patient segmentation based on CLV analysis. The dataset in this study was obtained from inpatient and outpatient visits at a hospital from January to December 2022. The research results show four segmentations of outpatients and four inpatients based on CLV values: Champions, Loyal Customers, Potential Loyalists, and Lost Customers. Inpatient segmentation includes the Champion category, 473 (2%) patients belonging to 14 clusters (15.56%). The Loyal Customer Category is 1,727 (7%) patients who are members of 31 clusters (34.44%). The Potential Loyalist category is 3,874 (16%) patients who are members of 31 clusters (34.44%). The Lost Customer Category was 18,516 (75%) patients from 14 clusters (15.56%). Outpatient segmentation includes: Champion category, 3,512 (8%) patients belonging to 10 clusters (10.10%). The Loyal Customer Category is 5,661 (13%) patients who are members of 24 clusters (24.24%). The Potential Loyalist category is 11,070 (25%) patients who are members of 34 clusters (34.34%). In the Lost Customer Category, 23,678 (54%) patients belong to 31 clusters (31.31%).
Rainfall has a strong negative impact on television (TV) signals, most especially at the receivers’ end. This is due to the propagation effect caused by atmospheric rain absorption of the wave signal. Television signals may reach the TV receiver unstable due to interferences caused by heavy rainfall, which creates undesirable poor-quality reception and noise. The effect of rain-induced attenuation on television signal reception is not enviable, especially when it is heavy. Consequently, this work aims to analyze the correlation between received signal strength and frequency of transmission during rainfall. The received signal strength measurements and rainfall data were collected concurrently during dry days and rainy days to achieve this aim. These readings were taken with a signal strength meter and other mobile phone software such as the rain gauge app, compass, etc.). Results show that rainfall leads to a noticeable degradation in the quality of received signals. Specifically, the data obtained were simulated, and it was observed that attenuations increase sharply as the rain rate increases. In particular, when the frequency is about 1080 GHz and the wavelength is low, there tends to be a disturbance between the drops of rainfall, which causes attenuation and results in low signal strength. To conclude, this work proposes a possible solution that is favorable to all television subscribers during rainfall.
The severity of lung cancer can be used to determine appropriate treatment measures and reduce the risk of death. The severity identification is monitored based on the size and location of the nodule. However, previous studies still focused on determining the location of nodules without identifying their severity. In this study, the severity of lung cancer is detected based on the size of its nodules. This research contributes to the annotation of severity to the Lung Image Database Consortium image collection (LIDC-IDRI) dataset and the development of automatic severity detection using You Only Look Once (YOLO) methods. The data is given a severity level based on the nodule size calculated based on the number of pixels in the nodule length. Automatic detection is done using YOLO methods, which consist of several versions, namely YOLOv5, YOLOv7, and YOLOv8. YOLO methods can properly detect the location and severity of cancer nodules with the IoU evaluation results obtained using YOLOv5, YOLOv7, and YOLOv8, which are 0.86, 0.6, and 0.87, respectively. From the experiment, it can be concluded that determining the location and severity of cancer based on nodule size using YOLO methods is proven effective and can be done in real-time.
Excitation at low-rise reinforced concrete building had occurred within the first-year post-construction phase. It is found that the structures laying on thick soil layer while performing up to 4 kPa water transport activity. Three approaches have been adopted to investigate the dynamic behavior and the interaction the phenomenon commonly called fluid-soil-structure interaction. Applying the finite element computation to represent the dynamic of the soil-fluid and structure, existing and ideal-fixed base condition are modeled and compared each. It was found that the structure’s modes frequencies, much depend on the rigidity of the base and the fluids traffic on the pump station. Time history string of displacements at the arbitrary point shows that the vibration does occurs and it tendentious increase by time
