bluuebunny/crossref_metadata_2025 huggingface dataset:
Prototyping Model for Self-Appraisal Employee Performance Application Development in Cooperative
Authors: Shobrun Shobrun , Anisa Gia , Suakanto Sinung , Kusumasari Tien Fabrianti
Published: October 2023

Employee performance appraisal which is currently still being implemented cannot yet describe transparency. In addition, the process is still manual, which is far from effective. This study aims to develop a web-based employee performance appraisal application system that can increase the transparency of assessments in microfinance cooperatives. The focus is on using self-appraisal techniques to promote transparency in the appraisal process. Transparent assessments are critical to building trust and fairness during performance evaluations. Using the self-appraisal method, individuals can evaluate their own performance, skills, and achievements in a transparent and unbiased manner. This study investigates the process of making an employee performance appraisal system with the prototyping model method as a development method. The findings of this study contribute to existing knowledge about performance evaluation in microfinance services, particularly in relation to self-appraisal techniques, and offer practical insights for organizations wishing to increase the transparency of appraisals through web-based application systems.
Analysis of User Adoption Levels of JAKI Application Using the Government Adoption Model (GAM)
Authors: Kirani Zahra Anadya , Utomo Rio Guntur , Fathoni Muhammad Faris
Published: October 2023

This study delves into an analysis of the adoption patterns within the Jakarta Today e-government application (JAKI) through the dual lenses of the Government Adoption Model (GAM) and the Structural Equation Model (SEM). Encompassing JAKI users aged 17 years and above, the research encapsulates a substantial sample size of 384 individuals. The research findings underscore the pivotal role of key factors in driving e-Government adoption within the context of JAKI. Notably, Perceived Service Response, Perceived Trust, Perceived Uncertainty, Perceived Security, and Privacy collectively wield a significant and affirmative impact on the Adoption of e-Gov. However, intriguingly, factors including Perceived Awareness, Computer-self Efficacy, Availability of Resources, Perceived Ability to Use, Perceived Compatibility, Perceived Functional Benefit, Perceived Image, Perceived Information Quality, and Multilingual Option do not exert a notable influence on the Adoption of e-Gov. These insights proffer invaluable guidance for the Jakarta City Government, facilitating an enhanced understanding of user perceptions and needs. By meticulously addressing the determinative factors that engender a favorable adoption environment, the government stands poised to elevate the efficacy and reach of its e-government service, thus fostering greater citizen engagement and interaction with the JAKI application.
Paraphrase Generation For Reading Comprehension
Authors: Januarahman Faishal , Romadhony Ade
Published: October 2023

Reading comprehension is an assessment that tests readers understanding of a concept from the given text. The testing process is conducted by providing questions related to the content within the context of the text. The purpose of this research is to create new question variations from existing questions, and one of the methods to achieve this is by paraphrasing questions through the task of paraphrase generation. This can help ensure that readers have fully grasped a concept of a text. This study employs a traditional approach known as the thesaurus-based approach, in which the process involves substituting synonyms using the Indonesian Thesaurus dictionary. The data used consists of a list of Indonesian language reading comprehension assessment questions ranging from elementary to high school levels. To measure the quality of the generated paraphrased questions, two evaluation processes are conducted which are automatic evaluation with the scores ranging from 0-1 and human evaluation with score ranging from 1-4. The automatic evaluation includes the BLEU-4 metric, resulting in a score of 0.044, and the ROUGE-L metric, resulting an F1-score of 0.421. As for human evaluation, the obtained relevancy score is 2.533, and the fluency score is 3.186. The results from both evaluation metrics indicate that the generated paraphrased questions exhibit diverse new word choices but tend to have slightly different meanings compared to the reference questions.
Social Media Based Film Recommender System (Twitter) on Disney+ with Hybrid Filtering Using Support Vector Machine
Authors: Ramadhan Helmi Sunjaya , Budi Setiawan Erwin
Published: October 2023

In the current era, the culture of watching TV shows and movies has been made easier by the presence of the internet. Now, watching movies on platforms can be done from anywhere, one of which is Disney+. At times, people find it challenging to decide which film to watch given the multitude of genres and film titles available on these platforms. One solution to this issue is a recommendation system that can suggest films based on ratings. The recommendation system to be utilized involves Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering. This is because Collaborative Filtering and Content-Based Filtering encounter issues like cold start, sparsity, and overspecialization. Thus, the objective of this study is to develop a recommendation system using Hybrid Filtering combined with Support Vector Machine (SVM). In this research, classification will be carried out using poly, linear, and RBF kernels with varying parameters. Techniques such as TF-IDF, RMSE, tuning, and data balancing with SMOTEN will be implemented to enhance accuracy during the classification process. The evaluation employed in this study utilizes the confusion matrix. Support Vector Machine, when tuned and combined with SMOTEN, achieves noteworthy results, particularly with the RBF kernel which attains a Precision score of 0.94. Recall produces a value of 0.93 with the Poly kernel, while the highest Accuracy, at 0.93, is achieved with the RBF kernel. Furthermore, the RBF kernel also demonstrates the highest F1-Score of 0.93. These findings illustrate elevated precision, recall, accuracy, and F1-Score within the context of hybrid filtering, achieved through the application of Support Vector Machine for classification and the implementation of the SMOTEN technique.
Comparison Analysis of C4.5 Algorithm and KNN Algorithm for Predicting Data of Non-Active Students at Prima Indonesia University
Authors: Zai Ferman , Sirait Janiali , Nainggolan Dicky Wijaya , Sihombing Nissi Grace Dian , Banjarnahor Jepri
Published: October 2023

Education is important nowadays because universities need to improve their students' skills so they can compete in the globalization era. Education can be obtained through both formal and informal channels, and knowledge is available everywhere, especially in today's world where information tools are rapidly evolving. Inactive students are students who do not participate in a course for a maximum of two consecutive semesters. Students who are not active have the opportunity to drop out of university studies. Students who drop out of college are usually motivated by economic factors, and the cessation of the lecture process can cause inactivity and administrative costs. Therefore, this research was conducted using the C4.5 algorithm method and the K-Nearest Neighbor (KNN) algorithm to compare and predict data on inactive students at Universitas Prima Indonesia. The research continued with the data collection and data preprocessing stages, after which the data mining process was carried out to get the final results of this research. The testing process follows the process of comparing the C4.5 algorithm and the K-Nearest Neighbor (KNN) algorithm with K-fold crossing. This evaluation step is compared by considering the comparison values of the confusion matrix (precision, precision, recall). The accuracy results obtained by each algorithm provide information about the effectiveness of using these techniques in processing the specified dataset. The accuracy of the Decision Tree C4.5 algorithm is 99.12% and the K-Nearest Neighbors algorithm is 99.14%. Based on research conducted using the K-Nearest Neighbors and C4.5 algorithms to predict inactive students, the KNN algorithm is more accurate than the C4.5 algorithm.
Ontology-based Nutrition Recommender System for Stunting Patients
Authors: Ramadhani Nur Laili , Baizal Z. K. A.
Published: October 2023

Stunting is a growth disorder that occurs in early childhood. This condition occurs because the child has a chronic nutritional problem which triggers the child to have a height below normal. The indicator used as a standard for whether a child is stunted or not is height for age. If a child has a z-score value less than -2 standard deviations, then the child is said to suffer from stunting. Poor nutritional intake is one of the factors causing children to suffer from stunting. Most Indonesian people think that the genetics of both parents causes children to be shorter than their age, but genetics is a minimal factor that causes stunting. In 2020, Indonesia ranks second in the prevalence of stunting in Southeast Asia, according to the Asian Development Bank (ADB) report. Based on the results of the Indonesian Nutritional Status Survey (SSGI) in 2021, the stunting prevalence rate in Indonesia 2021 is 24.4%, but in 2022, the stunting prevalence rate will drop to 21.6%. One way to treat stunting in children is by providing daily nutritional intake according to the child's condition. In this study, we used the Telegram chatbot with an ontology and the rules Semantic Web Rule Language as a knowledge base. The accuracy performance of our system is 93.3% which shows that our system can provide nutritional recommendations for stunting patients.
The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification
Authors: Fawwaz Insidini , Yennimar Yennimar , Dharsinni N P , Wijaya Bayu Angga
Published: October 2023

Marine fauna are all types of organisms that live in the marine environment. Marine fauna is also an important part of the marine ecosystem that has an important role in maintaining environmental balance. However, the survival of marine fauna is threatened due to activities carried out by humans, such as pollution, overfishing, industrial waste disposal into marine waters, plastic pollution and so on. Therefore, efforts are needed to monitor and protect marine fauna so that marine ecosystems can remain stable. One way to monitor marine fauna is by using classification technology. One of the technologies that can be used in marine fauna classification technology is Convolutional Neural Network (CNN).  CNN is one of the classification methods that can be used to classify objects in images with a high level of accuracy. The CNN architecture models used are MobileNet, Xception, and VGG19. Furthermore, the method used to improve the performance of the CNN algorithm is the Transfer Learning method. The test results show that the MobileNet architecture model produces the highest accuracy value of 91.94% compared to Xception and VGG19 which only get an accuracy value of 87.64% and 88.42%. This shows that the MobileNet model has a more optimal performance in classifying marine fauna.
Ontology-Based Food Recommender System for Nutrition in School-Age
Authors: Wulandari Dinda Atikah , Baizal Z. K. A.
Published: October 2023

Nutrition plays an important role in the body and child development. Therefore, it is very important for parent to understand the nutritional needs of children to grow healthy and smart. If nutritional intake is not met, malnutrition can occur in children it interferes whit their growth and development process. The food recommendation system in this study is based on knowledge modeling. The focus of the research is to develop a recommendation system using ontology with Semantic Web Rule Language (SWRL) and form a knowledge base according to the guidelines proposed by Recommended Nutrient Intakes (RNI). Additionally, an Artificial Intelligence (AI) telegram chatbot named NutritionChildreBot was developed for this purpose. The recommended food menu is following the nutritional needs of children aged 7-9 years. The acquired knowledge base will be managed to provide information to users. The results of this research evaluation are in the form of recommendations for selecting foods that meet children’s nutritional needs based on information obtained from reliable sources.Based on this value, the calculation of precision, memory, and F_Score obtained is 97,9% of the accuracy of the results recommended by the system
LSTM and Bidirectional GRU Comparison for Text Classification
Authors: Asrawi Hannan , Utami Ema , Yaqin Ainul
Published: October 2023

Although the phrases machine learning and AI are frequently used interchangeably and are frequently discussed together, they do not have the same meanings. While all artificial intelligence (AI) is machine learning, not all AI is machine learning, which is a key distinction. In the beginning, machine learning and natural language processing (NLP) are related since machine learning is frequently employed as a tool for NLP tasks. The advantage of NLP is that it can perform analysis, and examine a lot of data, including comments on social media accounts and hundreds of online customer evaluations. Text classification is essentially what needs to be done. This study compares Bidirectional GRU and LSTM as text classification algorithms using 20,000 newsgroup documents from 20 newsgroups from The UCI KDD Archive.

After using the suggested model, we compare it to the long short-term memory and bidirectional GRU models for accuracy and validation. The results of the two comparisons show that the bidirectional GRU model performs better than the long short-term memory model. And this is a successful classification of text using a deep learning algorithm that uses a bidirectional GRU.
Identification of Public Library Visitor Profiles using K-means Algorithm based on The Cluster Validity Index
Authors: Asriningtias Salnan Ratih , Wulandari Eka Ratri Noor , Persijn Myro Boyke , Rosyida Novita , Sutawijaya Bayu
Published: October 2023

The existence of a public library in the Gampingan village has a positive impact, such as increasing the literacy culture of the village community. However, the library collection is not sufficient for the needs of visitors.  Therefore, it is necessary to add library collections to fulfill the requirement.  One of the solutions is mapping the library needs of visitors. The mapping can be done by identifying visitor profiles by grouping visitors based on the criteria of age, gender, type of visitor, and category of book library. One of the methods that can be used in the process of grouping visitors based on criteria is to use the K-Means Clustering method. Determining the number of K cluster centers at K-Means Clustering method that are not appropriate will give bad results, it is necessary to test the number of K cluster centers using the Cluster Validity index by measuring the clusters with cluster variance, within-cluster variance, and between-cluster variance. From the grouping process using K-Means Clustering with Cluster Validity index, we get 3 clusters of visitor profiles with a cluster variance value of less than 0.1. This shows that this method was able to identify the visitor profiles with high grouping accuracy values.
Sales Conversion Optimization Analysis Using the Random Forest Method
Authors: Nugroho Kristiawan , Wismarini Th. Dwiati , Murti Hari
Published: October 2023

Sales conversion is a challenging field of work in sales and business. Companies are competing to be winners by improving their services and hoping that their product sales can increase in various ways, including by using optimization theory. However, the lack of data analysis is a problem that is often encountered in optimizing sales conversions. Various machine learning-based methods have also been used to help analyze sales conversion optimization. This research uses the Random Forest method which is one of the more robust machine learning methods compared to other methods, namely Adaptive Booster (AdaBoost) and K-Nearest Neighbor (KNN) in analyzing sales conversion optimization. The results showed that the Random Forest method had the best performance in classifying data, by using the 10 cross validation technique the results were obtained with a Mean Squared Error (MSE) value of 0.928 and a Root Mean Square Error (RMSE) of 0.963, better than the Adaptive Booster method. and K-Nearest Neighbor which has lower performance. Sales conversion optimization processing using Random Forest is proven to have the best performance as evidenced by the small Mean Squared Error and Root Mean Square Error which means it has an accurate level of performance compared to other methods.
Breast Cancer Detection in Histopathology Images using ResNet101 Architecture
Authors: Istighosah Maie , Sunyoto Andi , Hidayat Tonny
Published: October 2023

Cancer is a significant challenge in many fields, especially health and medicine. Breast cancer is among the most common and frequent cancers in women worldwide. Early detection of cancer is the main step for early treatment and increasing the chances of patient survival. As the convolutional neural network method has grown in popularity, breast cancer can be easily identified without the help of experts. Using BreaKHis histopathology data, this project will assess the efficacy of the CNN architecture ResNet101 for breast cancer image classification. The dataset is divided into two classes, namely 1146 malignant and 547 benign. The treatment of data preprocessing is considered. The implementation of data augmentation in the benign class to obtain data balance between the two classes and prevent overfitting. The BreaKHis dataset has noise and uneven color distribution. Approaches such as bilateral filtering, image enhancement, and color normalization were chosen to enhance image quality. Adding flatten, dense, and dropout layers to the ResNet101 architecture is applied to improve the model performance. Parameters were modified during the training stage to achieve optimal model performance. The Adam optimizer was used with a learning rate 0.0001 and a batch size of 32. Furthermore, the model was trained for 100 epochs. The accuracy, precision, recall, and f1-score results are 98.7%, 98.73%, 98.7%, and 98.7%, respectively. According to the results, the proposed ResNet101 model outperforms the standard technique as well as other architectures.
Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset
Authors: Rahman Rizki , Fauzi Abdulloh Ferian
Published: October 2023

The background is the increasing cybersecurity threats in the form of phishing attacks that can be detrimental to individuals and organizations. The purpose of this research is to compare the performance of four Naive Bayes variants in classifying phishing emails with a method that involves a data pre-processing stage, phishing emails are collected, cleaned, and converted into appropriate numerical features. Next, the GridSearch approach was used to find the best parameters. This research objective is to understand how each Naive Bayes variant works on phishing email datasets. This phishing detection task is based on the following performance evaluation criteria such as accuracy, precision, recall, and F1-score. In this study, Bernoulli got the best accuracy of 97.34% but when the results obtained a hyperparameter, the results showed an increase with the most optimal results and the best performance is Bernoulli 97.38%. The research results are to provide an in-depth insight into the effectiveness of each variant of Naive Bayes in dealing with phishing email datasets and researchers in selecting the most suitable Naive Bayes variant for phishing detection tasks. In addition, the applied GridSearch method can guide how to find the best parameters for Naive Bayes models in other contexts. In summary, this study focuses on analyzing the performance of four variants of Naive Bayes Gaussian, Multinomial, Complement, and Bernoulli with the best algorithms Bernoulli 97.38%.
Expert System for Diagnosing Learning Disorders in Children Using the Dempster-Shafer Theory Approach
Authors: Nugraheni Murien , Nuraini Rini , Tonggiroh Mursalim , Nurhayati Siti
Published: October 2023

Learning disorders can occur in children where a child experiences difficulty mastering important skills such as reading, writing, or arithmetic. Learning disorders can have an emotional impact on children, such as low self-confidence, anxiety, or frustration. Therefore, it is important for parents and educators to recognize the signs of learning disorders so that appropriate intervention can be given. The aim of this research is to develop an expert system that can diagnose learning disorders in children using the Dempster-Shafer Theory algorithm to make it easier to diagnose and produce the right diagnosis. The Dempster-Shafer Theory approach has the ability to provide probability values in evidence based on the level of belief and reasoning in accordance with logic and then combine it with information from certain events. This research produces an expert system built on a website that can diagnose based on symptoms and display diagnosis results, definitions of types of learning disorders, and treatment options. The accuracy test results show a value of 92%, which means that the system built using the Dempster-Shafer Theory approach is able to diagnose learning disorders in children well.
Ambon Banana Maturity Classification Based On Convolutional Neural Network (CNN)
Authors: Nisa Yuha Aulia , Sari Christy Atika , Rachmawanto Eko Hari , Mohd Yaacob Noorayisahbe
Published: October 2023

The banana (Musa paradical), is an excellent fruit produced nationally and high in vitamins. In Indonesia, banana production is at a higher level than other fruit products. However, one of them is the issue with bananas' post-harvest, which arises when they are produced in huge quantities on a large scale or by an industry that sorts bananas. So far, the determination of the maturity level of bananas is done by relying on visual analysis limited to the color of the skin by the human eye. However, this identification approach has several drawbacks. First, this method requires significant effort in the banana sorting process. In addition, the perception of the fruit's maturity level can vary, because humans can experience fatigue and lack of consistency in judgment. In addition, human judgment is also influenced by subjective factors that can affect the final result. Considering this problem, developed a system to classify the ripeness level of Ambon bananas. This system utilizes image enhancement features to increase contrast, which is implemented using a Convolutional Neural Network (CNN). The classification process is carried out through image processing using MATLAB R2022a software, which forms the basis of a classification system with 4 classes which include 486 images of unripe Ambon bananas, 235 images of half-ripe Ambon bananas, 309 images of perfectly ripe Ambon bananas, 184 images of rotten Ambon bananas. The dataset analyzed in this study totaled 1214 data divided into 1093 training data and 121 test data. The CNN method is used in this data classification, and the results show an accuracy rate of 95.87%.
Prediction of the Human Development Index for Equitable Development in West Sumatra Province Using the C4.5 Algorithm
Authors: Sirait Weri , Azizah Nur
Published: October 2023

Unequal development in Indonesia can be seen from the Human Development Index. The Human Development Index is a tool used to measure the attainment of the quality of life of a region or country and as material for economic policy on quality of life. It contains components of health level, education level and welfare level. In 2022, West Sumatra Province achieved the 9th highest Human Development Index in Indonesia, namely 73.26, with this figure the West Sumatra Province Human Development Index is above the national average. However, there are still regencies/cities in West Sumatra Province that have achievements below the national average. This factor causes the development conditions in West Sumatra Province to be uneven. Uneven human development conditions will make it difficult for the government to improve Human Resources (HR). In this research, the C45 Data Mining Algorithm was implemented to predict the Regency/City Human Development Index in West Sumatra Province. As is the method of the Central Bureau of Statistics in measuring the Human Development Index, the variables used from the Human Development Index indicators are Life Expectancy, Years of School Expectation, Average Length of Schooling, and Per Capita Expenditures. The Central Statistics Agency data used in this research covers all regencies/cities in West Sumatra during the period 2018-2022. Range levels are grouped into three groups, namely, low, medium, and high. Based on testing using RapidMiner software with the Cross Validation operator, an accuracy value of 86.61% was obtained.
Linear Regression Analysis To Measure The Correlation Between Poverty Rate And Stunting Rate
Authors: Suhaerudin Suhaerudin , Sumardi Ade , juliane Christina
Published: October 2023

Children's stunting or growth disorders are becoming major global health issues, particularly in impoverished nations. It is characterized by short height for children and affects future economic potential, health, and cognitive development over the long run. Stunting has a detrimental effect on cognitive growth, schooling, and future economic production in addition to being a sign of dietary deficiencies. This study aims to analyze the relationship between poverty levels and stunting rates. Using secondary data from health surveys and population censuses, this study analyzed the rate of stunting in children aged 0-5 years and correlated it with poverty indicators at the household and community levels. Correlation analysis methods were used to assess the relationship between these variables, while controlling for confounding variables such as parental education, access to health services, and nutrition. The multiple linear regression test results prove that the incidence of stunting is influenced by the poor population variable by 34.1%, so there are other factors that influence it by 64.9%. The results of the analysis show that there is a significant positive correlation between the poverty rate and the prevalence of stunting. This finding underscores the importance of cooperation between the health and economic sectors in efforts to reduce stunting and poverty.
Analysis of the Level of Public Satisfaction on the Tiktok Application as an E-Commerce
Authors: Irmayani Deci , Sinaga Fransisco Alexander , Masrizal Masrizal
Published: October 2023

Abstract: Online shopping has become one of the alternatives used by society today. This happens because doing online shopping saves a lot of time. There are many online shops in Indonesia and are used by many people, such as Shopee, Lazada and Tokopedia. But now there is an application that initially only acted as a social media platform, but now also doubles as an E-commerce application, namely TikTok. TikTok has now become an E-commerce application. The prices given are also very cheap and there are lots of promotions given to customers. But there are still some people who don't want to shop online on TikTok on the grounds that the goods are not good. So from these 2 things, research needs to be made to determine the level of public satisfaction with the TikTok application as an E-commerce. The aim of this research is to see how many people are satisfied shopping on TikTok. This research was carried out using a classification model in data mining using the K-Nearest Neighbor (kNN) method and the Decision Tree method. The classification results obtained were 119 community data (for representation of 96.74%) and for people who were dissatisfied with the TikTok application as an E-commerce it was 4 community data (for representation of 3.25%). These results provide the conclusion that many people are satisfied shopping on the TikTok application as E-commerce
Embarking on Comprehensive Exploration of Classification System of Fruits and Vegetables
Authors: Wedha Bayu Yasa , Vasandani Michael Sagar , Wedha Alessandro Enriqco Putra Bayu
Published: October 2023

This research thoroughly investigates the fruit and vegetable classification system, emphasizing exhaustive investigation. This research aims to comprehend, analyze, and document numerous facets of this classification with MobileNetV2. This investigation included a comprehensive literature review, field investigation, and review of relevant scientific documents. In this investigation, we divide the classification of fruits and vegetables into various levels, ranging from the most general, such as kingdom and division, to the most specific, such as order and family. We also investigate the central role of taxonomy in comprehending the evolutionary and phylogenetic relationships between various categories of fruits and vegetables. This research enables us to identify and understand the taxonomic relationships between multiple varieties of fruits and vegetables and classify them into the appropriate botanical families. In addition, we investigate the global diversity of fruit and vegetable varieties, emphasizing the significance of conservation and genetic management to preserve the diversity of these precious commodities. In their efforts to comprehend, manage, and maintain the genetic variety of fruits and vegetables, this research provides researchers, botanists, and producers valuable insights. The findings of this study indicate that investigating fruit and vegetable classification systems is a crucial step in comprehending and conserving this irreplaceable natural resource, which provides direct benefits to humans in the context of global biodiversity conservation. MobileNetV2 research results accuracy epochs(5) = 94.84%, epochs(10) = 98.35%, epochs(15) = 98.69%.
Innovative Role of Blockchain Pharmaceutical Supply Chain Digital Transformation: Enterprise Architecture Perspective
Authors: Wedha Bayu Yas , Vasandani Michael Sagar , Wedha Alessandro Enriqco Putra Bayu
Published: October 2023

The advent of the Fourth Industrial Revolution has compelled numerous industries to undergo digital changes or transformations. One example is the pharmaceutical industry, which is responsible for providing medicinal products. The pharmaceutical supply chain assumes a crucial position within the pharmaceutical industry as it enables the secure, streamlined, and dependable transportation of medications from producers to individuals in need. The issue of prioritizing digital transformation within the pharmaceutical supply chain has emerged as a significant problem for hospitals and pharmaceutical businesses. Integrating different components inside the system is effectively supported by the substantial function fulfilled by Enterprise Architecture in this specific context. The objective is to minimize errors, enhance inventory management, optimize product distribution, and guarantee the safety and quality of pharmaceuticals. However, the process of adequately monitoring and verifying data has its challenges. However, these challenges can be efficiently addressed through implementing blockchain technology. In addition to this, Blockchain technology has the potential to enhance industrial efficiency. Utilizing blockchain technology enables the facilitation of transparency, immutability, and data integrity across the entirety of the supply chain. Integrating Enterprise Architecture electronic automation with Blockchain technology enables pharmaceutical enterprises to establish robust systems facilitated by Smart Contracts. The use of this system is expected to significantly enhance automation and regulatory adherence within supply chain processes, leading to notable advancements in operational efficiency, security, and data accuracy. Integrating blockchain technology and smart contracts enables pharmaceutical enterprises to enhance their product offers to hospitals and patients at reduced expenses, facilitating notable advancements in Enterprise Architecture.
Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis
Authors: Hindarto Djarot
Published: October 2023

This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets. A dataset comprising text reviews with positive or negative sentiment labels was evaluated. All evaluated models demonstrated an extremely high accuracy, ranging from 99.81% to 99.99%. Apart from that, the loss generated by these models is also low, ranging from 0.0043 to 0.0021. However, there are minor performance differences between the evaluated architectures. The Long Short-Term Memory and Gated Recurrent Unit models mainly perform marginally better than the Simple Recurrent Neural Network, albeit with slightly lower accuracy and loss. In the meantime, the Bidirectional Recurrent Neural Network model demonstrates competitive performance, as it can effectively manage text context from both directions. Additionally, One-Dimensional Convolutional Neural Networks provide satisfactory results, indicating that convolution-based approaches are also effective in sentiment analysis. The findings of this study provide practitioners with essential insights for selecting an appropriate architecture for sentiment analysis tasks. While all models yield excellent performance, the choice of architecture can impact computational efficiency and training time. Therefore, a comprehensive comprehension of the respective characteristics of Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks is essential for making more informed decisions when constructing sentiment analysis models.
LITERATURE REVIEW: GREEN TEA EXTRACT POTENCY (Camellia sinensis) in LOWERING GLYCEMIC INDEX OF A WISTAR RAT
Authors: Fajrin Filda Nisrina , Prabowo Imam , Safira Lisa
Published: October 2023

The aim of the research is to determine the potency of green tea extract (Camellia sinesis) to decrease blood glucose levels in diabetes mellitus model Wistar rats. A systematic review based on PRISMA-P 2020 with a literature search strategy using Google Scholar, PubMed, Science Direct, and Scopus databases. Literature quality was assessed using the JBI Critical Appraisal Checklist which produced 12 comprehensive potential literatures based on selection with inclusion and exclusion criteria. Analysis of 12 journals found that there were 11 studies that showed a significant decrease in blood glucose levels after administration of green tea extract (Camellia sinensis). Green tea extract (Camellia sinensis) has the potential to reduce blood glucose levels in Wistar strain rats with diabetes mellitus model. The mechanism underlying the antihyperglycemic effect is proven to be due to the protective and antidiabetic effects of the compounds contained in green tea extract, especially the flavonoid group, namely catechins. The minimum dose of green tea extract observed to cause an antihyperglycemic effect in experimental animals was 40 mg/kgBW.
POTENCY OF DEVELOPMENT GRAPTOPYLLUM AS ONE OF INDONESIAN ORIGINAL HERBS; A BIBLIOMETRIC STUDY
Authors: Makkiyah Feda Anisa
Published: October 2023

Indonesia is home to thousands of herbal medicine, and one of the species that would gain more popularity is the Graptophyllum species. This indigenous Papua island plant grows as a shrub plant in the garden of many houses. This study aims to map the research available about this species and explore the potential effects of this plant. Methods. The bibliometric study was performed to map the articles. Scopus databases and Vosviewer were used in this study. Results. 53 articles were withdrawn from Scopus databases. Indonesia has the highest number of publications and many Indonesian researchers collaborated with authors from Japan, Switzerland, and Sweden. The keywords were divided into three clusters and most of the composition discussed was flavonoid. The species discussed was the Graptophyllum pictum. Conclusion. As one of the Indonesian herbal medicine, Graptophyllum pictum is more popular in its own country. Hopefully, the development of chemical drugs from Graptophyllum pictum will be in its own country.
PREVALENCE OF POTENTIAL DRUG-DRUG INTERACTIONS AMONG PROLANIS TYPE 2 DIABETES PATIENT WITH HYPERTENTION IN PRIMARY HEALTH CARE: CROSS SECTIONAL STUDY
Authors: Ilmi Primayanti Nurul , Hilda Fauziah  , Annisa Farida Muti
Published: October 2023

Potential drug–drug interactions (pDDIs) are important factors resulting in adverse drug reactions or therapeutic failure. Therefore, pDDIs need to be identified to prevent the related risk and improve drug safety. The objective of this study was to assess the prevalence of pDDIs among Prolanis type 2 diabetes patients with hypertension. Additionally, this study aims to categorize and rate the identified pDDIs according to mechanism, severity and level of significance. This cross-sectional study was conducted at Kramat Jati District primary health care. Patient medical records from January to June 2018, were analysed using Drug Interaction Facts and Stockley’s Drug Interaction for pDDIs with a total 138 patients identified. pDDIs were detected in 35 patients (25.4%), with a total 57 interactions. They were clinically relevant with major (42.1%) in severity and refer to level one of significance (42.1%). The interaction type was unknown (64.9%), pharmacodynamic (64.9%) and pharmacokinetic (12.2%) respectively. The most common interaction was amlodipine-simvastatin in 19 cases (33.3%). Prolanis type 2 diabetes patients with hypertension were at risk to pDDIs, particularly to major pDDIs. Screening of prescriptions and medical records for pDDIs also monitoring of pharmacotherapy in terms of response and associated adverse drug events will contribute to patient safety.
Edukasi Penerapan Protokol Kesehatan bagi Anak Usia Sekolah Sebagai Strategi Tangkal Penyebaran Virus Corona
Authors: Bahar Meiskha , Irmarahayu Agneta , Faranita Tri , Kolib Abdul , Sandra Oktania
Published: October 2023

Perubahan tatanan kehidupan selama masa pandemi Covid-19 memaksa kita untuk menghadapi berbagai tantangan baru dalam kehidupan sehari-hari. Pada saat seperti ini perubahan perilaku serta kesadaran masyarakat sangatlah penting. Kepatuhan menjalankan protokol kesehatan  dapat memutus rantai penularan dan menekan angka penyebaran Covid-19. Upaya menjaga protocol Kesehatan pada anak usia sekolah dibutuhkan sejak dari rumah dana lingkungan keluarga. Untuk itu dibutuhkan berbagai upaya penyebarluasan informasi kepada masyarakat. Kegiatan ini meliputi pemberian informasi dan pengetahuan kepada ibu-ibu di wilayah Komplek UPN Meruyung melalui media power point dan video secara luring dengan menjaga protocol Kesehatan bagi semua peserta. Setelah itu ada sesi diskusi dan tanya jawab. Diharapkan dari kegiatan ini dapat memberikan tambahan pengetahuan dan menciptakan perubahan perilaku hidup lebih sehat dan siap memasuki masa endemi.  Kata kunci: Anak usia sekolah, Covid-19, Protokol kesehatan
Knowledge Sharing Covid-19: Waspada Harus, Panik Jangan!: Penguatan Peran Orangtua Dalam Pencegahan Penularan Covid-19 Di Sekolah
Authors: Citrawati Mila , Susantiningsih Tiwuk , Selvester Thadeus Maria
Published: October 2023

Covid-19 adalah penyakti pandemi sejak tahun 2020. Covid-19 adalah penyakit yang disebabkan oleh virus corona. Pada saat sekolah, anak anak lebih rentan tertular Covid-19. Tingkat pengetahuan dan ketrampilan anak anak dan orangtua dalam menyikapi Covid-19 masih rendah, maka perlu dilakukan Knowledge Sharing mengenai Covid-19 terutama kepada orangtua dan tenaga pendidik di SDN 11 Pondok Labu Jakarta Selatan. Knowledge sharing ini bertujuan untuk memberikan informasi mengenai langkah-langkah yang harus dilakukan saat seseorang mengetahui dirinya positif Covid-19. Kegiatan ini diikuti 90 orang peserta. Diberikan pretest dan postest untuk mengetahui tingkat pengetahuan dan ketrampilan peserta mengenai Penyakit Covid-19. Dari kegiatan pengabdian ini didapatkan kesimpulan bahwa tingkat pengetahuan dan ketrampilan peserta webinar Knowledge Sharing Covid-19 mengalami peningkatan sebesar 18% pada opsi penyebab penyakit Virus-19. Terjadi peningkatan sebesar 14% pada opsi cara penularan penyakit Covid-19. Terjadi peningkatan sebesar 16% pada opsi Tanda dan gejala Covid-19. Terjadi peningkatan sebesar 23% pada opsi isolasi mandiri dilakukan selama 10 hari dan terjadi peningkatan sebesar 18% pada opsi tanda dan gejala memberatnya penyakit Covid-19.
Pelatihan Cuci Tangan Pakai Sabun (CTPS) Untuk Pencegahan Dan Pengendalian Infeksi Diare dan Thyphoid Anak di Desa Pangkalan Jati Cinere Depok
Authors: Selvester Thadeus Maria , Susantiningsih Tiwuk , Simanjuntak Kristina , Yuliyanti Retno , Citrawati Mila
Published: October 2023

Cuci Tangan Pakai Sabun (CTPS) adalah salah satu bentuk Perilaku Hidup Bersih dan Sehat (PHBS) untuk mencegah dan mengendalikan infeksi diare dan thyphoid terutama pada anak anak. Insiden infeksi diare dan thyphoid pada anak masih tinggi. Kebersihan tangan menjadi salah satu faktor pencegahan penularan infeksi diare dan thyphoid melalui makanan yang dipegang dengan tangan yang kotor. Tim Pengabdian Kepada Masyarakat Fakultas Kedokteran UPN Veteran Jakarta telah melakukan pelatihan Cuci Tangan Pakai Sabun (CTPS) sebagai sarana edukasi dan pelatihan pencegahan dan pengendalian infeksi diare dan thyphoid pada anak di Desa Pangkalan Jati Cinere Depok. Responden yang mengikuti kegiatan ini sebanyak 40 anak usia 8-12 tahun. Pelatihan menggunakan media powerpoint dan brosur CTPS serta praktek langsung CTPS. Hasil penilaian nilai pretest sebesar 70,25% dan setelah dilakukan penyuluhan dan pelatihan, kemudian dilakukan posttest terjadi peningkatan nilai posttest sebesar 22% menjadi 92,25%. Disela sela pelatihan responden diminta untuk melakukan CTPS agar ketrampilan CTPS semakin baik. CTPS adalah salah satu ketrampilan yang dapat diterapkan pada anak anak untuk pencegahan dan pengendalian infeksi diare dan thyphoid pada anak.
Edukasi Nyeri Punggung Bawah Pada Masyarakat Desa Jonggol, Jawa Barat
Authors: ANISAH MAKKIYAH FEDA
Published: October 2023

Nyeri punggung bawah merupakah masalah yang kedua terbanyak pasien datang ke poli rawat jalan dan mengganggu kualitas hidup seseorang. Kebanyakan nyeri  ini disebabkan postur  duduk, berdiri ataupun perilaku lain yang menyebabkan nyeri punggung bawah.  Metode. Penyuluhan ini dilakukan dalam bentuk edukasi, pengisian kuesioner oleh responden dan interview pendek, mengenai  perilaku yang memperberat nyeri punggung bawah yang dilakukan kepada masyarakat yang berkumpul di Puskesma jonggol, Kecamatan Jonggol, Jawa Barat, tanggal 6 Juli 2023. Hasil. Kebanyakan responden belum memahami nyeri punggung bawah dan belum mendapatkan informasi mengenai penyakit ini. Perilaku yang memperberat nyeri punggung bawah banyak dilakukan oleh responden. Kesimpulan. Pengabdian kepada masyarakat ini berguna dalam meningkatkan kegiatan promosi penyakit kepada masyarakat yaitu dengan memberikan edukasi mengenai nyeri punggung bawah dan bagaimana mencegahnya.
Vegetable oils used as vitamin E vehicle affect the electrical activity of rat heart.
Authors: Ozdemir S , Ayaz M , Tuncer T , Ugur M , Turan B
Published: October 2023

The aim of this study is to define the possible effects of vegetable oils used as vitamin E vehicle on the electrical activity of the rat heart. To test the possible effects of vitamin E vehicles we studied the effect of i.p. injected corn oil, hazelnut oil or peanut oil on the action potential parameters recorded in both papillary and left atrial muscle strips. Four experimental groups were used. The control group was injected (i.p.) with distilled water, while the three remaining groups received injections of corn oil, hazelnut oil, or peanut oil for five weeks (in a dose of 0.4 ml/kg/day – minimum amount of oil in which vitamin E could be dissolved). We used borosilicated (15-20 M) capillary electrodes and intracellular action potentials (AP) were recorded in isolated papillary and left atrium muscle strips. While administration of three different types of vegetable oil had no significant effect on AP parameters of papillary muscle, they significantly prolonged the repolarization phase of AP in atrial strips. These results show that vegetable oils used as vitamin E vehicles may alter the electrical activity of the heart in a tissue-dependent manner. The present data indicate that the possible effect of vegetable oil vehicles should be kept in mind while evaluating the possible effects of in vivo vitamin E administration.
Tissue factor, tissue factor pathway inhibitor and cytoadhesive molecules in patients with an acute coronary syndrome.
Authors: Malý M , Vojáček J , Hraboš V , Semrád M , Mates M , Kvasnička J , Salaj P , Durdil V
Published: October 2023

The tissue factor plays a crucial role in initiating blood coagulation after plaque rupture in patients with acute coronary syndrome. It is abundant in atherosclerotic plaques. Moreover, P-selectin, some cytokines, endotoxin and immune complexes can stimulate monocytes and induce the tissue factor expression on their surface. The aim of the study was to compare plasma levels of the tissue factor, tissue factor pathway inhibitor, P-selectin, E-selectin and ICAM-1 in patients with acute myocardial infarction, unstable angina pectoris, stable coronary artery disease and normal control subjects. In addition, plasma levels of the tissue factor, tissue factor pathway inhibitor, P-selectin, E-selectin and ICAM-1 were measured in the blood withdrawn from the coronary sinus in a subgroup of patients with unstable angina pectoris and stable coronary artery disease in which the difference between concentrations in the coronary sinus and systemic blood was calculated. A significant increase in tissue factor pathway inhibitor plasma levels was detected in patients with acute myocardial infarction (373.3±135.1 ng/ml, p<0.01) and unstable angina pectoris (119.6±86.9 ng/ml, p<0.05) in contrast to the patients with stable coronary artery disease (46.3±37.5 ng/ml) and normal subjects (45.1±14.3 ng/ml). The plasma levels of tissue factor pathway inhibitor were significantly increased both in the coronary sinus and systemic blood in the patients with unstable angina pectoris. There was only a non-significant trend to higher plasma levels of the tissue factor in patients with acute myocardial infarction and unstable angina pectoris as compared to the patients with stable coronary artery disease and normal subjects, the values being 129.1±30.2 pg/ml, 130.5±57.8 pg/ml, 120.2±45.1 pg/ml and 124.9±31.8 pg/ml, respectively. Plasma levels of soluble P-selectin was only slightly, but non-significantly higher in patients with unstable angina pectoris and stable coronary artery disease (184.2±85.4 ng/ml and 201.6±67.9 ng/ml, respectively) than in patients with the acute myocardial infarction (157.4±88.4 ng/ml) or normal subjects (151.4±47.1 ng/ml). The difference in plasma levels of soluble ICAM-1 between the blood withdrawn from the coronary sinus and systemic circulation correlated significantly with the corresponding difference in plasma levels of soluble P-selectin and E-selectin. In conclusion, the tissue factor and the tissue factor pathway inhibitor play a crucial role in the initiation of arterial thrombosis. The tissue factor pathway inhibitor levels are increased both in the systemic blood and in the coronary sinus of patients with the acute coronary syndrome.