Jurnal Techno Nusa Mandiri

PREDICTING STUNTING IN TODDLERS IN WEST JAVA USING LINEAR REGRESSION BASED ON POVERTY LEVELS

2024-10-18T07:58:46+00:00

Children's growth is disrupted by stunting, a chronic nutritional condition brought on by a prolonged shortage of nutrient intake. Under-five stunting is a major issue that affects many nations, particularly those with high rates of poverty. The aim of this research is to use the linear regression method based on the proportion of poverty to predict the risk of stunting in children under five in West Java. Growing children are particularly vulnerable to stunting, which can have long-term effects on their development and health. The research site was selected in West Java Province due to the region's high stunting rates and nofigur poverty rate. Precise forecasts are required to surmount the current issues. The research methodology employed is the descriptive quantitative technique. The data, which was projected using percentage values, covered the years 2014–2020. This study uses linear regression as its algorithm. According to the study's findings, there will be an 8.55% chance of toddler stunting in West Java in 2024. It is hoped that the government would be able to lower the risk of stunting by estimating the proportion of risk.

COMPARISON LINEAR REGRESSION AND RANDOM FOREST MODELS FOR PREDICTION OF UNDERGROUND DROUGHT LEVELS IN FOREST FIRES

2024-10-18T07:58:55+00:00

The increase in forest fires poses a significant risk due to its impact on underground dryness, which can cause long-term environmental damage and challenge fire suppression efforts. This research aims to develop a prediction model for underground drought levels in the context of forest fires using machine learning techniques. The methodology used in this research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. This study analyzes a forest fire dataset, applies encoder labels to transform categorical variables, and uses linear regression and random forest models to predict underground drought levels. The goal is to create a predictive model that can help inform wildfire risk management strategies by anticipating underground drought levels. The results showed that the random forest model achieved higher prediction accuracy than the linear regression, with an R-squared value of 0.97. This suggests that the random forest model is a more robust tool for predicting underground drought levels, providing valuable insights for forest fire management. This research contributes to the understanding of underground drought levels, aiding the development of effective wildfire risk management strategies.

LOGISTICS SERVICE INFORMATION SYSTEM AUDIT USING COBIT 5 FRAMEWORK

2024-10-18T07:54:08+00:00

KBN Prima Logistik is a subsidiary affiliated with PT. Kawasan Berikat Nusantara which operates in the field of logistics services. In carrying out its business processes, it has implemented an Information System in the form of using desktop-based supporting business applications (Client Server). Some of the current problems are that the applications used are not functioning optimally, IT Governance procedures are not well defined by standards, there is no IT Division in the Organizational Structure that is responsible for IT management and there is a shortage of competent human resources in the IT field. Therefore, it is necessary to measure the Maturity Level and Capability Level of IT governance. So, research was carried out using the COBIT 5 framework as an Information System Audit model. The research method goes through the stages of problem identification, literature study, and domain determination which focuses on the DSS and MEA domains. The data collection method was through interviews and distributing questionnaires filled in by 20 respondents. The research results show that the Maturity Level value in the DSS and MEA domains shows an average value of 1.94 or 194%. Capability Level is still at Level 2 (Managed Process) from the expected Target Level 3 (Established Process). This indicates that although the IT processes has been run and implemented regularly with planning and monitoring according to business process objectives, but the management is not yet optimal and not well standardized. The results of the gap assessment show that the average gap value is 1.06.

QUALITY ANALYSIS OF THE ELECTRONIC GOVERNMENT PROCUREMENT ORDER SYSTEM USING WEBQUAL AND EUCS METHODS

2024-10-18T07:59:15+00:00

Within district of Hulu Sungai Selatan there is a website for the community to offer goods or services, namely SIOPEN, SIOPEN functions as a forum for the HSS community and umkm to offer their goods or services to the local government. The goal of this research is to evaluate SIOPEN's quality using the Webqual approach and users' degree of satisfaction using the EUCS approach. The Webqual approach, which has three dimensions information quality, usability, and interaction service was employed in this research together with the EUCS approach. The EUCS approach has some variables in partikular ease of use, accuracy, format, content, and timeliness. The approach of gathering information data through respondents via sending out online questionnaires to SIOPEN users and determining the sample using the formula from Slovin obtained 88 users then obtained by Webqual Index and Average Satisfaction. The results of research measuring the three dimensions of Webqual show that are in very good interpretation, The information quality dimension obtained 0.8392, the usability dimension obtained 0.8292, then the service interaction dimension obtained 0.8284. The findings from assessing the level of the satisfied user three variables of EUCS in particular content at a score of 4.27, ease of use at a score of 4.21, and timeliness at a score of 4.28 are at very satisfied level, then the other two variables are accuracy at a score of 4.15 and format at a score of 4.11 at a satisfied level.

PODCAST ROOM RESERVATION APPLICATION USING EXTREME PROGRAMMING METHOD

2024-09-24T03:17:37+00:00

In the era of globalization, information technology has become crucial in enhancing the productivity of information processing across various industries. Data management ensures smooth operations and informed decision-making. Podcasts have emerged as significant mediums for information consumption and entertainment, with an increasing number of listeners seeking high-quality content. Producing quality podcasts requires adequate facilities such as advanced recording equipment and acoustically suitable studio spaces. Development methodologies like Extreme Programming emphasize improving functionality and technical implementation. Reservations for podcast studios play a crucial role in scheduling recordings, with online booking systems enabling easy and efficient reservations, albeit requiring intensive individualized services. The capability of reservation systems to provide quick, accurate, and precise information can influence customer experiences and purchasing decisions. The integration of information technology and online reservations is key to optimizing podcast production and distribution, enhancing operational efficiency for service providers, while providing a satisfying experience for listeners. Although the system has yet to be fully implemented, it is designed to streamline booking processes and reduce manual intervention, which is anticipated to lead to significant improvements in both operational efficiency and customer satisfaction.

PROTOTYPE PARKING SENSOR ON A SHIP AT THE DOCK USING SENSOR BASED ON ARDUINO UNO R3

2024-09-24T03:04:33+00:00

The main issue that needs to be addressed in this research is to enhance the safety and efficiency of the vessel berthing process by reducing the risk of collisions with the dock. With the advancement of technology in the maritime industry, there is still a reliance on various parties such as tugboats, ship crew, and dock authorities to ensure safe vessel berthing. Therefore, it is necessary to design a modern technology-based parking sensor system to provide assistance in the vessel berthing process. By using a parking sensor system based on the Arduino Uno R3 and ultrasonic sensors JSN-SR04T and AJ-SR04M, this research aims to address the risk of collisions with the dock, which can result in serious damage to the vessel, dock, and the surrounding environment. This parking sensor system is designed to reduce this risk by providing early warnings to the users. Furthermore, the research aims to reduce dependence on human factors and minimize human errors. Lastly, the research also aims to improve the efficiency of the vessel berthing process. By designing and implementing this prototype, the research aims to provide a technological solution to address the above-mentioned issues, enhance safety, and optimize the vessel berthing process around Motor Vessel BIN NO.2 EKS. SANYO MARU NO.8.

PERFORMANCE COMPARISON OF CLASSICAL ALGORITHMS AND DEEP NEURAL NETWORKS FOR TUBERCULOSIS PREDICTION

2024-09-24T03:04:33+00:00

This study compares the performance of several classical machine learning algorithms and deep neural networks for the prediction of tuberculosis in the Democratic Republic of Congo (DRC), using a sample of 1000 cases including clinical and demographic data. The sample is divided into two sets: 80% for training and 20% for testing. The algorithms evaluated include Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest and Convolutional Neural Networks (CNN). The results show that the CNN has the best overall performance with an accuracy of 94%, an AUC of the ROC curve of 93%, an accuracy of 90%, an accuracy of 95%, a sensitivity of 88%, an F1-score of 91.3% and a Log Loss of 0.0386. The Random Forest follows closely behind with an accuracy of 92% and an AUC of 86%. The SVM and KNN models also performed strongly, but slightly less well. The Decision Tree obtained acceptable results, but inferior to the other algorithms evaluated. These results indicate that deep neural networks, and in particular the CNN, are superior for predicting tuberculosis compared with conventional machine learning algorithms. This superiority is particularly marked in terms of accuracy, sensitivity and reliability of predictions, as shown by the performance metrics obtained.

MULTILEVEL MODAL VALUE ANALYSIS FOR INTERPRETING CATEGORICAL K-MEDOIDS CLUSTERS DATA

2024-09-24T03:04:34+00:00

Consumer segmentation plays a crucial role for business owners in developing their enterprises. K-Medoid is commonly used for segmentation functions due to its low computational complexity. However, K-Medoid has limitations, such as the variability in cluster sizes across different iterations and the challenge of determining the optimal number of clusters. The Davies-Bouldin Index (DBI) is a metric used to evaluate the number of clusters by calculating the ratio between the within-cluster distance and the between-cluster distance. Most segmentation studies typically stop at the formation of clusters without further interpretation, particularly when dealing with categorical data. This study aims to modify the use of K-Medoid and propose a method for interpreting clusters with categorical data. The research began with questionnaire design and the data collecting from 100 respondents, which was normalized in the second stage. Clustering used K-Medoid with variations K values from K=2 to K=10, with each K value tested 10 times. The clustering results were evaluated using the DBI to select the optimal clusters. Data interpretation conducted using modal values, calculated as the ratio of the number of times a specific attribute variable was selected by respondents to the total number of data points in the cluster. Utilization and hierarchical visualization of modal values proposed in this study offer insights into the dominant variables within an attribute and also depict the relationships between attributes based on the ranking of modal values. These advantages facilitate business analysts in labeling clusters for developing consumer-driven business strategies.

UNVEILING GENDER FROM INDONESIAN NAMES USING RANDOM FOREST AND LOGISTIC REGRESSION ALGORITHMS

2024-09-30T09:21:03+00:00

Gender detection can be done in many ways, some of these ways by using image identification such as the process of image identification based on faces or image shapes, on the other hand image identification and detection can also be done based on text or written data. The usefulness of gender identification can be used in various aspects of life, ranging from greetings such as ladies and gentlemen, which will certainly make the person concerned feel more appreciated by the accuracy of the pronunciation of the name. This gender identification and detection process can be done by making class predictions on predetermined gender label classes. Of course, each name in various languages has different characteristics in identifying and representing each gender, as well as Indonesian names that have diversity and unique levels of variation. The purpose of this study is to test the results of the algorithm in classification based on class labels. The application of this detection uses two algorithms, namely Random Forest and Logistic Regression. Both of these algorithms can predict classes with perfect accuracy in 6 experimental data, then the results of 526 experimental data resulted in a final accuracy of 0.94 for logistic regression and 0.93 for random forest. The advantage with a thin difference in this case is in the Logistic Regression algorithm.

LITERATURE REVIEW: ADVANTAGES AND DISADVANTAGES OF BLACK BOX AND WHITE BOX TESTING METHODS

2024-10-08T05:03:12+00:00

Software testing methods play a crucial role in ensuring the quality, security, and performance of applications. The two main approaches often used are black box and white box testing. This article presents a comprehensive literature review of the characteristics, advantages and disadvantages of both testing methods with the aim of providing in-depth insights for practitioners and researchers in the field of software engineering. The research method used is Systematic Literature Review (SLR), which is a technique of collecting information through the process of searching for literature based on research questions, filtering search results, evaluating, and analyzing relevant research from various academic database sources to gain a comprehensive understanding. Through the analysis of various literature studies, the results of this research are the characteristics of each testing method that is more effectively used in best practice to achieve efficient testing with the coverage of literature review sources in the range of 2020 to 2024. The review of these characteristics is based on seven criteria, namely: test focus, test base, tester's point of view, test case design, program error identification method, program code knowledge, and test effectiveness. In addition, this article presents a comparison of the two methods so that the differences in testing methods can be clearly recognised in terms of how they are implemented. Based on the results of review literature, both methods are better used together in order to ensure that the functional and internal code structure of the software is functioning properly.

DESIGNING A MICROSERVICES BASED ENTERPRISE ARCHITECTURE USING TOGAF 10: A CASE STUDY APPROACH

2024-10-15T02:13:21+00:00

PT Umbara Karya Sejati, operating in the home living and industrial product sectors, faced significant digital transformation challenges, marked by inefficiencies due to system integration issues and vendor dependencies that incurred cost penalties of 10-15% each month. Addressing these challenges, this research developed an Enterprise Architecture using TOGAF 10's Microservices Architecture (MSA) from the Preliminary Phase to Phase F: Migration Planning. This approach aimed to enhance system integration and operational efficiency, thereby improving modularity and scalability while reducing reliance on external vendors. A pivotal component of this architecture, an API gateway, provided robust monitoring capabilities for integration processes, enabling quick identification and prioritization of critical issues, which will assist the internal IT team's workflow. Furthermore, the research planned the establishment of a DevOps team, incorporating Agile methodologies, and scheduled IT governance and data security training to prepare for future policy development and strengthen internal controls. This strategic design equips the organization to navigate the volatility, uncertainty, complexity, and ambiguity (VUCA) of market demands with agile and effective responses, projecting a 10% increase in both operational and cost efficiency for PT Umbara Karya Sejati.