Sinkron : jurnal dan penelitian teknik informatika
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Using Fuzzy Tsukamoto Method In Forecasting The Amount Medication Requrementsat In The Hospital
No full textThe pharmaceutical installation as one of the hospital service locations is an inseparable part of the hospital health service system which is oriented towards patient service, including pharmaceutical services needed by patients such as consumable medical equipment that is affordable for all levels of society and the provision of quality medicines. The problem that arises is the uncertain number of patients and the medicines needed by each patient are different and often the supply of medicines that are currently needed by the community is empty, while medicines that are less needed are in abundant stock. Mistakes in ordering medicines can cause shortages or excesses of medicine stock. They tend to only use estimates of the amount of remaining stock without any special methods being used. Even hospital pharmacies tend to buy too many medicines because of uncertain demand and fear of shortages. A method that can help in predicting the amount needed for medicines is by applying the fuzzy Tsukamoto method. The prediction process begins with testing drug data in 2022 to predict the amount needed for medicines in 2023 before finally the drug data for 2023 is used to predict the amount needed in 2024.The prediction process will use drug sales data in the form of the amount of inventory, the amount needed and remaining stock to build a prediction model that projects the amount of drug need in the year 2023. This approach will involve analyzing historical data and applying the Tsukamoto method to produce predictions of the amount needed for all drugs in the following year
Proposed Implementation uses TOGAF ADM and ArchiMate - Enterprise Architecture in Retail Industry
No full textAs the growth rate of the retail industry in Indonesia continues to increase, leveraging information technology (IT) to support business operations has become increasingly crucial for achieving effectiveness and efficiency. Retail companies must manage interconnected business systems, such as inventory management, supply chain, e-commerce, and customer service. Without a clear architecture, integrating these systems becomes challenging, leading to operational inefficiencies, difficulties in decision-making, and an inability to respond quickly to market trends. A comprehensive Enterprise Architecture (EA) is therefore essential for managing all core processes within a company. Implementing EA using the TOGAF (The Open Group Architecture Framework) methodology is an optimal choice, as it is widely recognized and adopted. Technology Architecture, Data Architecture, Application Architecture, and Business Architecture are the four primary domains of TOGAF. Business Architecture improves cross-departmental integration and streamlines Business Process, while Application Architecture facilitates automation and optimizes application systems for more efficient operations. Data Architecture focuses on structured data management, ensuring accurate and accessible information for decision-making. Meanwhile, technology architecture provides a flexible and adaptable technological infrastructure that responds to business changes. By implementing Enterprise Architecture (EA) through TOGAF ADM, the retail industry can streamline Business Process, integrate various systems, adopt new technologies, and optimize the supply chain more effectively. This approach not only enhances operational efficiency but also strengthens competitiveness in the retail sector by fostering innovation and providing responsive services
Complete Kernel Fisher Discriminant (CKFD) and Color Difference Histogram for Palm Disease
No full textPalm oil plantations play a significant role in the economy of Indonesia, supporting 16.2 million people. However, plant diseases pose a major threat to the productivity and health of palm oil crops. Early detection of these diseases is essential to prevent yield losses and mitigate damage. This study proposes the application of the Complete Kernel Fisher Discriminant (CKFD) method combined with Color Difference Histogram to classify diseases affecting oil palm fronds and leaves. The CKFD method uses a non-linear kernel transformation to improve the performance of Fisher Linear Discriminant Analysis (FLDA), while the Color Difference Histogram enhances sensitivity to color variations in different lighting conditions. Experimental results demonstrate that the CKFD method achieves superior accuracy in disease detection compared to traditional Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The proposed approach showed an average accuracy of 94.5% for detecting diseases like Curvularia sp and Cochliobolus carbonus. The combination of CKFD with Color Difference Histogram significantly reduces the impact of lighting variations on the classification results, making it a robust solution for practical deployment in palm oil plantations. This research provides an effective tool for early disease detection and management in the palm oil industry
The Design of a Good Data Processing App Applying QR Code
No full textDesigning a good data processing application using QR Codes aims to increase efficiency and accuracy in data management. QR codes are a fast and easy technology for storing and scanning data, allowing users to quickly access information without entering data manually. This application is designed for various purposes, such as inventory, time and attendance management, and document tracking. The application design process includes user needs analysis, interface design, back-end development, and integration with QR code technology. The prototype method is used during the system development stage. The programming language used is a hypertext preprocessor with a MySQL database, and a framework is used to ensure that the application behaves as expected and can provide effective solutions in data processing. The results of this design are expected to significantly contribute to operational efficiency, reduce human error in data entry, and increase the speed of accessing and managing information. Apart from that, implementing QR codes in data processing applications is hoped to be widely used in various industries and business fields
Analytical Study Forecasting Students Using Random Forest and Linear Regression Algorithms
No full textForecasting new student admissions essential for higher education institutions as it helps them plan for staffing and budgetary needs. Accurate predictions are difficult due to factors like economic conditions, government policies, and University competition. This study aims to analysis forecasting at Nasional university using Random Forest and Linear Regression algorithms. By examining historical admission data, the research seeks to identify key factors influencing the number of accepted students. Methodology involves collecting data from past admissions and applying both Random Forest and Linear Regression to compare their performance. Preliminary results, based on parameters such as application form purchases from 2015 to 2023, form prices, accreditation, and leading study programs, suggest that Random Forest offers more stable and realistic predictions. Analysis for MAE, MSE, RMSE, MAPE, MAD suggests that Linear Regression is more accurate for this data. predicts closer to actual values with lower overall errors. This makes Linear Regression preferable as it provides more reliable predictions with less deviation compared to Random Forest. Looking at admissions forecasts for the next 5 years, Random Forest predicts a steady decrease from 4224 in 2024 to 4129 in 2028. In contrast, Linear Regression suggests a stable trend with slight annual dips, going from 4954 in 2024 to 4941 in 2028. Therefore, Linear Regression is a more stable and realistic choice compared to Random Forest for this forecasting task in this research
The News Classification Using Bidirectional Long Short Term Memory and GloVe
No full textThe dissemination of information and news via online media encompasses not only established news platforms but also contributions from internet users, lacking oversight. News constitutes fact-grounded insights into ongoing occurrences. This research employed Bidirectional Long- and Short-Term Memory with Hyperparameter tuning on GloVe for news classification. This research aims to optimize news categorization through hyperparameter tuning on GloVe. GloVe facilitated the transformation of words into vector matrices, exploring its efficacy in news classification with hyperparameter tuning and Bi-LSTM for text analysis. Experiments encompassed untuned and hyperparameter-tuned approaches, employing GloVe's hyperparameters using Gridsearch and manual methods. GloVe's hyperparameter tuning reveals the potential for enhancing word vector representations. Surprisingly, non-hyperparameter tuned news classification yielded superior evaluation results compared to the hyperparameter approach. The untuned experiment achieved an accuracy of 0.98, while the gridsearch method yielded 0.85 accuracy, and hyperparameter tuning generated a 0.88 precision in the -11 model. These findings underscore the nuanced interplay of hyperparameters in optimizing text classification models like GloVe
Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate
No full textThis study proposes utilizing the machine learning technique CART to classify the spread of dengue hemorrhagic fever (DHF). To expand the features used, the CART classification model was developed based on data collected over the previous 2 to 4 years. The data sources included the Bandung City Health Office for the cases of DHF, the Bandung Meteorology, Climatology and Geophysics Agency for the climate data, the Bandung City Central Statistics Agency for population and educational history data. The top-performing CART classification model over the past 2, 3, and 4 years achieved accuracies of 93%, 93%, and 90%, respectively. The models that exhibited the highest accuracy values and optimal number of feature extensions were chosen as the best ones. CART is among several machine learning techniques that can effectively measure the most impactful features during the classification process. The meteorological parameters were found to be irrelevant in the classification process. This study reveals that the population size, male population proportion, and educational attainment levels are the most impactful features in the classification of DHF spread in Bandung City. The research provides valuable insights into the classification of DHF spread in Bandung City through feature expansion
Imperceptible and Robust Encryption: Salsa20 Stream Cipher for Colour Image Data
No full textData security has become crucial, especially in today's era, therefore we need to protect our personal data to avoid unwanted incidents. The primary objective of this research is to empirically demonstrate the viability of our proposed methodology for encrypting color images using the Salsa20 algorithm, renowned for its stream cipher characteristics, which inherently afford it a swift processing speed. The encryption method we use is to take each pixel from the original image and convert it into bytes based on the RGB value in it, then encrypt it using a keyword that has been converted using a hash function. In this study, we carried out several evaluations to evaluate the performance of the encrypted and decrypted images to test the method we propose, including histogram analysis and compare patterns, visual image testing, and key space analysis. Through this experiment, it has been proven that Salsa20 is effective in maintaining confidentiality and image integrity. Histogram analysis reveals differences in pixel distribution patterns between the original and encrypted images. Visual testing shows that the encrypted image maintains good optical quality. Keyspace analysis ensures the security of encryption keys. The performance evaluation resulted in an NPCR above 99%, UACI had been reached 69.28%, MSE was closes to 0, and the highest PSNR was around 61.89dB, this shows that encrypted images recovered with high accuracy
Revolutionizing Sustainable Public Transportation: The Go-Bus Mobile App Journey With Design Thinking
No full textBus Rapid Transit (BRT) has become a popular solution to address traffic congestion in Indonesia, including in Banyumas Regency. However, the supporting services provided by the BRT system still require improvement. This study focuses on designing the Go-Bus application, by integrating gamification elements to encourage the usage of Trans Banyumas. The Design Thinking method is used, encompassing the empathy, definition, ideation, prototype, and testing stages. This prototype undergoes User Satisfaction Testing and Single Ease Question (SEQ). the average score of 84.84% has been reached from the evaluation of 11 tasks by six respondents. Then, satisfaction score of 6.73 indicates Go-Bus as a user-friendly and satisfying application. This research aims to address challenges in motivating and altering user behavior to utilize public transportation. By incorporating gamification into the UI/UX design of the application, Go-Bus offers a solution that enhances user motivation, satisfaction, and encourages a shift towards public transportation usag
Methods for Development Mobile Stunting Application: A Systematic Literature Review
No full textStunting is a growth disorder in children. Stunting is one of the indicators of failure to thrive in toddlers caused by a chronic lack of nutritional intake in the first 1,000 days of life, from a fetus to a child aged 23 months. Based on data from the Asian Development Bank (ADB), in 2022 the percentage of stunting prevalence occurring in children aged <5 years in Indonesia reached 31.8%. So Indonesia is ranked 10th in Southeast Asia. The object of this review is to review the current literature and help researchers to find out what methods have been used in making stunting prevention applications. In a systematic search of the literature using quality databases including SpringerLink, ScienceDirect, and IEEE Xplore. The paper included in this review is a stunting prevention application information system by describing the methods most often used by researchers in making the stunting prevention application information system. There were 41 results based on the exclusion of titles and abstracts, based on the introduction and exclusion of conclusions there were 35 results, so we included 12 results for the full-text exclusion in the final analysis. So that the popular method used by researchers in Android-based stunting applications is the prototype method. Compared to other methods, prototyping is more suitable for systems that are made based on user needs