bluuebunny/crossref_metadata_2025 huggingface dataset:
Perancangan Keamanan Pengguna Cardless dari Ancaman Cyber Crime Menggunakan Kriptografi Curva Elliptic
Authors: Samantha Nevy Bella , Destya Senie , Ashari Wahid Miftahul
Published: July 2024

In this era, technology has discovered new innovations by creating practical ways of making transactions. Cash withdrawal transactions are now much easier because a new way has been discovered, where people can make transactions without using a card. This is certainly very useful for people whose wallets are often left behind. This ease of carrying out transactions is called Cardless. Cardless itself is a cardless cash withdrawal service that offers a fast and time-saving transaction process. Apart from that, cardless cards also have several advantages, including ATM cards being safer from the risk of being swallowed or left behind by the ATM, and avoiding the risk of fraud. Like technology in general created by humans, cardless certainly also has disadvantages. This transaction has the potential for identity theft, uncontrolled spending, system compromise, and not everyone uses cardless cash withdrawal transactions due to lack of socialization of the use of cardless transactions. In this research, the researcher wants to recommend designing additional security in the m-banking application. Different from previous research, this time the researcher used the Elliptic Curve method. The subjects of this research are cardless users. The goal to be achieved in this research is that the security of the m-banking application when using cardless transactions becomes safer because the calculation of the algorithm has been added to randomize or encrypt the PIN. The function of encrypting the PIN itself is aimed at making it more difficult for criminals to successfully carry out crimes. . If the level of security is not added as recommended by the author, the bank will have to think of other ways to increase security when carrying out cardless transactions because then more customers or users will become victims. From the encryption results in this research, the PIN was previously 312143, after the encryption process was carried out, the PIN changed to DUDSDTDSDVDU. Of course, in the future technology will continue to develop and perhaps even bigger flaws will be created, but up to now Elliptic Curve cryptography is still cryptography that is difficult to solve. With so little chance that cybercrime perpetrators can break in.
Perbandingan Matriks Loss Pada Model Deep Learning Resnet50 dan Xception dalam Deteksi Objek
Authors: Herimanto Herimanto
Published: July 2024

The implementation of deep learning has expanded into various fields, not confined solely to the field of education, particularly in computer science. It has also integrated technology into various other domains, including geospatial, remote sensing, and even the medical field. This development has made a significant contribution to reshaping the way humans understand and tackle challenges across different sectors. In this context, deep learning is employed for object detection and classification. Despite the considerable progress facilitated by the application of deep learning, object detection remains a challenge that is not entirely resolved. Constraints such as variations in lighting conditions, angles of view, and object diversity make achieving high-accuracy object detection a difficult task. Therefore, further research is required to comprehend and compare the performance of various deep learning models in addressing this issue. This research focuses on the comparison of two deep learning models, namely ResNet50 and Xception, in terms of loss metrics when detecting an object, in this case, a chair. The models are provided with input images of chairs and predict whether the chairs are empty or occupied. The results obtained from this research indicate that the ResNet50 model has a lower total loss value of 0.19422098, while the Xception model has a total loss value of 1.1822930. The lower the loss value, the better the model's performance. Based on the comparison results, the author has developed a web application simulator using Flask, utilizing the model with the lowest loss, which is the ResNet50 model.
Sentiment Analysis of the Jakarta - Bandung Fast Train Project Using the SVM Method
Authors: Dhiyaulhaq Muhammad Daffa , Gunawan Putu Harry
Published: July 2024

Web growth contributes greatly to user-generated content such as user feedback, opinions and reviews. The construction of the Jakarta-Bandung High Speed Train is both an icon and a momentum for Indonesia to modernize mass transportation in an era of continuous progress. Sentiment analysis is one of the text-based research field solutions suitable for addressing satisfaction issues based on user reviews. In this research, the system will be made with review sentences from users and produce output in the form of positive and negative classes. The method used by the author is classification using the Support Vector Machine (SVM) method and Word2Vec extraction features. In addition, a comparison of the accuracy value between the Support Vector Machine method, Naïve Bayes method and TF-IDF extraction features is carried out. The data studied came from several news websites containing user reviews of the Jakarta-Bandung High Speed Train. This method is used because it represents words in a vector, besides that the training process is faster when compared to other extraction features. This research resulted in the performance of accurasy, precision, recall, and f1-score, namely accurasy of 82.74%, precision of 75.68%, recall of 97.67%, and f1-score of 85.28%. These results were obtained using the best tuning hyperparameters, namely ('C': 10, 'gamma': 0.1, 'kernel': 'rbf'). Then in the second scenario a comparison is made with the Naïve Bayes method. It was found that the accuracy of the Support Vector Machine method using the TF-IDF extraction feature obtained better and stable performance results than the other three performance results, which amounted to 86.90%. So the author concludes that the Support Vector Machine method using the TF-IDF extraction feature is better when compared to the Naïve Bayes method and the Word2vec extraction feature.
Perbandingan Prediksi Obat Berdasarkan Pemakaian Menggunakan Algoritma Single Moving Average dan Support Vector Regression
Authors: Tillah Said Nurfan Hidayad , Nazir Alwis , Iskandar Iwan , Budianita Elvia , Afrianty Iis
Published: July 2024

To ensure the availability and quality of drugs, Public Health Centers (PHC) must pay attention to the planning and procurement process. The problem that often arises is the increase in drug stock due to the stable use of drugs each month, resulting in excess and expired drugs that are not used. In addition, it is necessary to avoid inappropriate drug demand, which affects stock availability. Drug usage prediction is done with several methods such as the Single Moving Average (SMA) algorithm in the data mining method and the Support Vector Regression (SVR) algorithm in the machine learning method. This algorithm was chosen because the drug data of Diazepam 5 mg and Mefenamic Acid 500 mg is sustainable from January 2020 to June 2023 (42 months). Implementation using the Phyton programming language. Testing using the Mean Absolute Percentage Error (MAPE) method, this study aims to measure the accuracy of predictions in each algorithm. In research with Diazepam 5 mg and Mefenamic Acid 500 mg drugs, with a division of 80% in training data and 20% in test data. With a calculation of 3 periods, the SMA algorithm produces MAPE values of 4.10% and 4.29%, in the "very good" range. The SVR algorithm, which uses an RBF kernel with a complexity parameter of 1.0 and an epsilon parameter of 0.1, produces MAPE results of 7.35% and 9.52%, in the "Very Good" range. Thus, the SMA algorithm predicts better than the SVR algorithm.
Clustering Analysis of Poverty Levels in North Sumatra Province Using the Application of Data Mining with the K-Means Algorithm
Authors: Andriyani Widyastuti , Nasyuha Asyahri Hadi , Syahra Yohanni , Triaji Bagas
Published: July 2024

North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention and in-depth analysis. Thus, research on poverty levels in this province becomes very relevant and urgent. Therefore, a more in-depth analysis is needed regarding poverty levels in various regions within this province using data mining methods. The data mining approach is a way to gain understanding from large amounts of data. In the context of the problem of poverty levels, data mining has the potential to help reveal patterns that may be hidden in economic and social data. One algorithm that is often applied in clustering analysis is the K-Means algorithm. The K-Means algorithm is a clustering method that is widely used in data analysis and allows grouping data based on similar characteristics, so that it can be used to classify areas with similar levels of poverty. The results of this research show that data mining with the application of the K-Means algorithm can help produce more effective decisions in analyzing clustering of poverty levels in North Sumatra Province involving the use of data over a ten-year period, namely from 2013 to 2022, which records the number of poor people based on District and city. 3 groups were produced, namely 3 levels of poverty, including relatively stable, very vulnerable and vulnerable. Data from 33 districts or cities in North Sumatra resulted in a poverty level clustering of 1 city that was very vulnerable, 4 cities that were vulnerable and 27 cities that were relatively stable.
Penerapan Metode CRISP-DM untuk Optimalisasi Strategi Pemasaran STP (Segmenting, Targeting, Positioning) Layanan Akomodasi Hotel, Homestay, dan Resort
Authors: Singgalen Yerik Afrianto
Published: July 2024

The challenge in maintaining the business continuity of hotel, homestay, and resort accommodation services is the use of technology and marketing methods that are effective and efficient. This study aims to apply the Cross Industry Standard Process for Data Mining (CRISP-DM) method to optimize hotel, homestay, and resort marketing strategies. This research uses case studies on hotel, homestay, and resort accommodation services based on the context of the service provider's business operational area in North Toraja Regency, South Sulawesi Province. The results of this study show that TF-IDF results and sentiment extracts can be utilized for segmenting and targeting processes. Based on the results of TF-IDF, it is known that the main emphasis of hotel, homestay, or resort guests (Toraja Herigate, Toraja Misiliana, Rosaliana Homestay, Hotel Pison, and Luta Resort Toraja) with business operational areas in Rantepao sub-district, North Toraja Regency, South Sulawesi Province, is as follows: staff (97), Rantepao (98), clean (108), breakfast (139), nice (147), rooms (165), toraja (177), good (189),  room (268), hotel (331). In addition, modeling results using the SVM algorithm with SMOTE operators can be used in positioning strategies to improve the competitiveness of accommodation services based on business operational areas. Specifically, the performance of the SVM algorithm using SMOTE operators resulted in an accuracy value of 97.64%, a precision value of 100%, a recall value of 95.37%, an f-measure value of 97.56%, an AUC value of 100%). Compared to the DT algorithm, the performance of SVM algorithms using SMOTE operators has better performance in the classification of review data. Thus, the CRISP-DM method is able to optimize the marketing strategy of hotels, homestays, and resorts.
Penerapan Data Mining Untuk Klasifikasi Penerima Kredit Dengan Perbandingan Algoritma Naïve Bayes dan Algoritma C4.5
Authors: Librado Dison , Nasyuha Asyahri Hadi
Published: July 2024

Credit is the process of borrowing money from customers to be paid over a certain period of time and with a payment agreement. In general, credit is provided by companies operating in the financial sector such as banks, cooperatives, business credit and finance. In the implementation process, providing credit to customers must be appropriate. In reality, the process of granting credit is still given to the wrong people. The problems faced must be resolved immediately and well, if the problems continue and giving credit not to the right customers will be very detrimental to the company. The settlement process can be done by looking at customer data that has previously received credit. Data mining is a technique that can be used to help solve these problems. In the process of resolving credit granting problems, data mining can be used to process previous credit customer data to obtain a pattern of which customers are eligible for credit. Classification is a method used in data mining to solve various kinds of problems. In this research, research will be carried out using the Naïve Bayes algorithm and the C4.5 algorithm. The method comparison process carried out in the research was carried out to obtain more definite results. This is based on the importance of giving credit to the right person so that there are no problems in the process of completing credit bill payments. Completion of data mining by applying the Naïve Bayes and C4.5 algorithms has been successfully carried out and classification can be carried out for decision making, both algorithms have the same decision making result, namely "Accepted". However, there are differences in the level of accuracy obtained. In the Naïve Bayes algorithm the accuracy level is 86.67%, while in the C4.5 algorithm the accuracy level is 100%.
Perbandingan Metode K-Nearest Neighbor dan Support Vector Machine Untuk Memprediksi Penerima Beasiswa Keringanan UKT
Authors: Novianto Enggar , Hermawan Arief , Avianto Donny
Published: July 2024

Scholarships are financial assistance provided to individuals, pupils, or scholars to extend their education. These may be provided by government agencies or the colleges themselves to students. One component that ensures quality human resources is formal education. The purpose of scholarships is to help disadvantaged or underprivileged students. Scholarship providers usually give some consideration to the student's level of difficulty, such as parents' salary and number of siblings. Due to the large number of applications for relief scholarships and strict assessment criteria, not all students who apply can be accepted. Scholarship application selection officers often have difficulty determining which students are worthy of receiving a scholarship. While the quota for scholarship recipients for this study program is always limited, applications for student UKT relief scholarships continue to increase every semester. This application came from students with poor economic conditions. To select UKT relief scholarship application documents, you have to consider various criteria and use manual methods which are less effective and require more time to determine the results. This research aims to make a comparison between the K-Nearest Neighbor and Support Vector Machine classification algorithms in determining recipients of UKT relief scholarships for undergraduate students in the Legal Sciences Study Program, Faculty of Law, Sebelas Maret University using the RapidMiner application. The accuracy results obtained using the RapidMiner application that have been carried out, the K-NN method produces an accuracy of 92.92%, while the SVM method produces an accuracy of 85.84%, so the K-NN method is the best method in classification for predicting recipients of UKT relief scholarships for students in the program. Bachelor of Law studies.
Deteksi Serangan DDOS Pada Jaringan SDN dengan Metode Random Forest
Authors: Ekawijana Ardhian , Bakhrun Akhmad , Kurniawan M Teguh
Published: July 2024

Distributed Denial Service (DDoS) Attack is an attempt by an attacker to paralyze a network system by flooding it with requests. A busy system can cause a drop in performance and even crash. Software Defined Service (SDN) is a new paradigm in creating a network in a certain area. SDN, with all its advantages and flexibility in implementation, is attractive to implement, but still leaves major security problems, especially being vulnerable to DDoS attacks. This research will detect whether a particular request is DDoS or not. Random Forest is a method for developing Decision Trees to classify whether a request or data packet is an attack or not. Random Forest as a development method from the previous method covers the weakness of overfitting. The results of this research were 98% for accuracy values.
Medical Image Classification of Brain Tumors using Convolutional Neural Network Algorithm
Authors: Muis Alwas , Sunardi Sunardi , Yudhana Anton
Published: July 2024

Brain tumor is a highly dangerous and deadly disease. It can occur due to the abnormal growth of cells or tissues in the head. Treatment for brain tumor is done with surgery and chemotherapy aimed at killing or destroying the cells that affect the growth process of brain tumor. Diagnosis of brain tumor is done using medical scans such as MRI, CT Scan, and PET Scan by analyzing the resulting images. Another method used to detect brain tumors is through biopsy, which is a process of taking cells or tissue from the body for examination in the laboratory. However, this method takes a long time because the cells taken from the patient will be examined in the laboratory. Therefore, a technique is needed to speed up accurate brain tumor diagnosis in order to obtain quick treatment. Machine learning can solve this problem with the classification of images produced by MRI. The classification technique that can be used is the GoogLeNet architecture in CNN. Because GoogLeNet is the algorithm that won the ImageNet Large Scale Visual Recognition Challenge (ILSVC) in 2014 The purpose of this study is to classify brain images using the GoogLeNet architecture. The dataset used in this study consists of 7023 images, consisting of 6320 images for training the model and 703 for testing the model. The results of this study obtained an accuracy percentage of 96%. This result is higher than previous studies that obtained an accuracy value of 94%.
Perbandingan Optimasi Metode Grid Search dan Random Search dalam Algoritma XGBoost untuk Klasifikasi Stunting
Authors: Pramudhyta Nirvan Adam , Rohman Muhammad Syaifur
Published: July 2024

Stunting is a condition of stunted physical growth in children due to chronic nutritional deficiencies with serious impacts on health and psychological aspects. The impacts include decreased self-esteem, learning difficulties, impaired concentration, critical thinking problems, and lower economic contributions as adults. This study aims to optimize the XGBoost classification model using the Grid Search and Random Search methods, thereby improving the accuracy of detecting stunting and obtaining an accurate and efficient diagnosis. Seeing the danger and alarming prevalence rate of stunting, signaling the urgency of handling this problem for the welfare of future generations, an automatic classification model is needed to avoid subjectivity and potential errors in the manual decision-making process. XGBoost needs optimization because it has parameters that require adjustment to maximize accuracy. Comparison of two optimization models is important to understand the advantages and disadvantages of each because they have different approaches in finding the best combination. The study used 10,000 data from Krobokan Health Center with attributes of gender, age, birth weight, birth height, weight at measurement, height at measurement, and category. The largest increase in accuracy was obtained by the Grid Search model with an increase in XGBoost accuracy of 5.81% from 83.28% to 89.09%. The Random Search model increased the accuracy by 5.43%, reaching an accuracy of 88.71%. The choice of both models depends on time and resource preferences. Random Search provides higher time efficiency than Grid Search. This research can contribute to identifying children at risk of stunting so that intervention actions can be carried out more efficiently.
Malware Detection Using K-Nearest Neighbor Algorithm and Feature Selection
Authors: Supriyanto Catur , Rafrastara Fauzi Adi , Amiral Afinzaki , Amalia Syafira Rosa , Al Fahreza Muhammad Daffa , Abdollah Mohd. Faizal
Published: July 2024

Malware is one of the biggest threats in today’s digital era. Malware detection becomes crucial since it can protect devices or systems from the dangers posed by malware, such as data loss/damage, data theft, account break-ins, and the entry of intruders who can gain full access of system. Considering that malware has also evolved from traditional form (monomorphic) to modern form (polymorphic, metamorphic, and oligomorphic), a malware detection system is needed that is no longer signature-based, but rather machine learning-based. This research will discuss malware detection by classifying the file whether considered as malware or goodware, using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features was reduced using the Information Gain and Principal Component Analysis (PCA) feature selection methods. The performance of kNN with PCA and Information Gain will then be compared to get the best performance. As a result, by using the PCA method where the number of features was reduced until the remaining 32 PCs, the kNN algorithm succeeded in maintaining classification performance with an accuracy of 95.6% and an F1-Score of 95.6%. Using the same number of features as the basis, the Information Gain method is applied by sorting the features from those with the highest Information Gain score and taking the 32 best features. The result, by using this Information Gain method, the classification performance of the kNN algorithm can be increased to 96.9% for both accuracy and F1-Score.
Klasifikasi Kondisi Kendaraan Berpotensi Kecelakaan Berbasis Android Menggunakan Long Short Term Memory
Authors: Nabila Puspita Aliya , Soim Sopian , Handayani Ade Silvia
Published: July 2024

Traffic accidents are a severe problem that often results in loss of life and property damage. Efforts to overcome this situation require real-time vehicle monitoring with the capture and collection of relevant data to provide information about the driver and family at home to reduce the risk of accidents by identifying potentially dangerous vehicle conditions automatically and quickly. This research utilizes Long Short Term Memory technology to analyze sensor data installed in the vehicle to an android device to be classified according to three conditions that recognize vehicle conditions as safe, alert, or dangerous. The Long Short Term Memory model used achieved a high level of accuracy with a value of 99.96% when training on data. After testing, this model still has a good level of accuracy with a value of 93.3%. In the test, the precision value of each class is 83.33% for the safe class, 80% for the danger class, and 100% for the alert class. In indicating that Long Short Term Memory in this study is very efficient in identifying and classifying vehicle conditions to reduce potential accidents. The information processed by Long Short Term Memory will be transmitted to an Android application capable of delivering up-to-date insights into the vehicle's condition. This app incorporates cautionary alerts in the presence of potential accident indicators to aid in vigilance and accident prevention. The integration of this system aims to enhance road safety and diminish the occurrence of accidents resulting from suboptimal vehicle conditions or hazardous driver conduct. This application can provide convenience for vehicle owners to know the state of the vehicle in real-time remotely in optimal conditions.
Prediction of Indonesian Presidential Election Results using Sentiment Analysis with Na�ve Bayes Method
Authors: Firdaus Asno Azzawagama , Yudhana Anton , Riadi Imam , Mahsun Mahsun
Published: July 2024

Social media serves as a solution for politicians as a campaign tool because it can save costs compared to conventional campaigns. The 2024 Indonesian Presidential Election has drawn public attention, especially among social media users. Twitter, as one of the widely used social media platforms in Indonesia, functions as an effective campaign forum. However, the problem that arises is how to automatically collect social media data related to presidential discussions and provide conclusions on the analysis results. Of course, this is not easy if done manually. Sentiment analysis is one approach that can be used for this in order to draw conclusions and analysis related to the available data. Data was collected shortly after the registration of presidential and vice-presidential candidates in November 2023. This study aims to obtain sentiment results from the latest data obtained, get the best model from the Naive Bayes method, to conduct analysis in predicting presidential election results based on sentiment. However, at the time of data collection, candidate numbers had not been assigned by the Election organizers. The obtained data amounted to 11,569 records using the Valence Aware Dictionary for Sentiment Reasoning (VADER) library for labeling. After removing duplicated tweets, the data was reduced to 4,893 records, with each candidate pair having 1,631 data points. The sentiment analysis classification model was determined using the Nave Bayes method with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Based on the data, the highest percentage of positive sentiment was found in Ganjar Pranowo - Mahfud MD data at 69.16%, and the highest negative sentiment was in Prabowo Subianto - Gibran Rakabuming Raka data at 52.12%. Common words in positive sentiment for Ganjar Pranowo - Mahfud MD include "strong," "corruption," "support," "reward," and others. Meanwhile, frequently appearing negative sentiment words for Prabowo Subianto - Gibran Rakabuming Raka include "child," "eldest," "mk," "young," and others. This research achieved an average accuracy of 76.67% using the Naive Bayes method on the entire dataset, indicating its reliability in similar cases.
Pengaruh Oversampling dan Cross Validation Pada Model Machine Learning Untuk Sentimen Analisis Kebijakan Luaran Kelulusan Mahasiswa
Authors: Rahayu Mufida , Luthfiarta Ardytha , Cahyaningrum Lailatul , Azzahra Alya Nurfaiza
Published: July 2024

The Minister of Education, Culture, Research and Technology issued a new policy on graduation standards for undergraduate and postgraduate students. This policy was delivered on August 29, 2023, on live streaming YouTube Kemendikbudristek at the Merdeka Belajar seminar episode 26: Transformation of National Standards and Higher Education Accreditation. The policy has caused various kinds of positive and negative responses in the community. Based on this problem, this research analyzes the sentiment of how the attitude and response of the community regarding this matter, so that it can be useful for the community in the future. This research uses two algorithms Nave Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) with data collection done through YouTube video comments getting a total dataset of 1085 data. After that, enter the data pre-processing which is then labeled using the Lexicon-based method with the stemming Sastrawi method. Datasets are grouped into positive sentiment and negative sentiment where the labeling results show unbalanced label data. Then the oversampling method Synthetic Minority Over-sampling Technique (SMOTE) is performed so that the data can be balanced and produce good accuracy. The test results after the SMOTE technique show that the NBC algorithm has the highest accuracy compared to KNN. The accuracy results are 74%, precision 74.6%, recall 74% and f1-score 73.9%. While KNN produces an accuracy of 50.2%, precision of 75.2%, recall of 50.2%, and f1-score of 34.5%.
Prediksi Kegagalan Transformator Daya dengan Metode DGA (Dissolved Gas Analysis) Menggunakan Random Forest Berbasis TDCG
Authors: Sugiman Marcelino Maxwell , Purnomo Hindriyanto Dwi
Published: July 2024

Transformers are critical components, and early detection of potential failures plays an important role in the reliable operation of the power system. This article describes a novel approach for power transformer failure prediction based on dissolved gas analysis (DGA) by applying the TDCG method with Random Forest algorithm. DGA data from operational transformers are used to train and test the predictive model. The Random Forest method based on TDCG enables comprehensive analysis of dissolved gas changes in transformer oil, thus enabling early detection of failure conditions. Experimental results show that the predictive model using the model created by applying hyperparameter tuning for optimal parameter tuning to have high accuracy, the accuracy obtained reaches 96% in detecting potential failures, the standard used for accuracy presentation uses confusion matrix as the accuracy of the predictive model. In addition, it can optimize time efficiency in analyzing failures and prevent human error when calculating gas fault identification or potential failures.
Prediksi Banjir Berdasarkan Indeks Curah Hujan Menggunakan Deep Neural Network (DNN)
Authors: Fafaza Safira Alya , Rohman Muhammad Syaifur , Pramunendar Ricardus Anggi , Sri Winarsih Nurul Anisa , Saraswati Galuh Wilujeng , Saputra Filmada Ocky , Ratmana Danny Oka , Shidik Guruh Fajar
Published: July 2024

Floods are natural disasters that often occur and are among the most destructive because they have significant economic and social impacts. Accurate flood predictions are essential to manage risk and organize emergency response planning effectively. This research uses Deep Neural Network (DNN) to build a flood forecasting model that relies on rainfall index indicators and captures complex and ever-changing patterns obtained from rainfall index data. Using historical information from flood disaster events in Kerala, India, an analysis was conducted to assess the impact of various factors, particularly in learning rate and optimizer type, on model performance. The experimental results show that the type of optimizer is a crucial factor in determining the model's effectiveness, as shown in the ANOVA statistics with a P-value of 0.008493, much lower than the general threshold of 0.05. This is because this type of optimizer can significantly improve prediction accuracy. With the Adam optimizer type, the learning rate range is between 0.1 and 0.4, showing an accuracy level of up to 100%. However, the choice of learning rate does not significantly impact, indicating that the main emphasis on parameter adjustment should be determined accurately. Therefore, by carrying out appropriate parameter adjustments and thorough validation to find the optimal configuration that can increase accuracy in predicting flood disasters based on rainfall indices, the DNN model has the potential to become a tool that can assist in flood risk planning and management.
Decision Support System Using a Combination of COPRAS and Rank Reciprocal Approaches to Select Accounting Software
Authors: Erkamim Moh. , Handayani Nurdiana , Heriyani Nofitri , Soares Teotino Gomes
Published: July 2024

Accounting software plays an important role in carrying out accounting processes that are fast, efficient, accurate and in accordance with applicable standards. With the emergence of various accounting software that offers a variety of features, users, both individuals and companies, often experience difficulty in determining the software that best suits their needs. The aim of this research is to develop a decision support system that makes it easier to choose accounting software through the application of the COPRAS approach and the Rank Reciprocal weighting technique. The Rank Reciprocal approach is used to rank or weight the criteria given by the decision-maker. The COPRAS (Complex Proportional Assessment) approach focuses on cognitive aspects so that it can accommodate the preferences and subjective assessments of decision-makers. Based on the case study that has been carried out, the highest to lowest utility value results are obtained, namely Zahir Online (A2), which obtained a score of 100. Since the decision support system's output yields a result that is identical to that of computations made by hand, it is deemed legitimate. Apart from that, the usability test obtained an average score of 91%, which proves that the system is in accordance with its usability and what is needed by its users.
Evaluasi Performa Oversampling dan Augmentasi pada Klasifikasi Penyakit Kulit Menerapkan Convolutional Neural Network
Authors: Iskandar Deo Andrianto , Salam Abu
Published: July 2024

The skin is the largest outer part of the human body. Maintaining skin is very important. The appearance of unusual things on the skin will raise concerns because it is possible that the skin could be affected by fatal diseases. Limited specialist doctor examinations in Indonesia add to the difficulty in preventing skin diseases. Therefore, this research was conducted to facilitate the classification of skin diseases. Skin disease classification must have good accuracy or precision in classifying each type. This study classifies skin diseases accurately and precisely by evaluating the performance of Oversampling and Augmentation techniques. This research uses the Convolutional Neural Network (CNN) approach. Using the HAM10000 dataset which contains dermoscopic images with a total of 10015 images. This study applies Oversampling to overcome data imbalance and applies image augmentation to improve model training performance. The performance of the model is evaluated using accuracy, recall, precision, f1-score, specificity, sensitivity, gmean. Comparisons are obtained from testing the original dataset, the dataset with oversampling and various augmentation techniques. The evaluation results show that the third test, namely classification using the CNN approach with oversampling and augmentation rotation, zoom, width, height, vertical_flip, gets the best results, namely accuracy 0.98, recall 0.98, precision 0.98, f1-score 0.98, specificity 0.99, sensitivity 0.98, gmean 0.98.
Penerapan Monitoring dan Controlling Suhu Ruang Server Berbasis Internet of Things (IoT)
Authors: Syafii Olynda Mufariihana Nur , Astutik Ika Ratna Indra , Hindarto Hindarto
Published: July 2024

The server room is a space owned by every company that serves as the control center for database systems in both small and large companies. Operational activities of the server are carried out by administrators (operators) who can monitor and manage the database system on the server. The temperature in the server room is crucial for maintaining the facilities and regulating the temperature when operators are inside. From the above issues, a system can be implemented that is currently widely used by everyone, namely the use of the Internet of Things (IoT). The Internet of Things (IoT) is a concept designed to enable electronic devices to communicate autonomously and exchange data through network connections. The implementation of IoT has the potential for monitoring and control at specific locations. With the presence of IoT, it can integrate IoT, temperature sensor devices, and output applications such as Blynk and WhatsApp. Blynk facilitates the NodeMCU microcontroller and DHT22 to receive and send temperature values to users of the application. In addition to Blynk, another monitoring system uses a WhatsApp Bot that sends temperature notifications in the server room. The IoT with this concept yielded research test results, where the system facilitates users or administrators in monitoring and controlling temperature using Blynk and WhatsApp applications, benefiting from a more user-friendly interface. In conclusion, this research facilitates users or owners of server rooms to manage facilities for longer durability, and users or administrators can monitor the server room temperature without having to be inside.
Pengembangan Chatbot Kesehatan Mental Menggunakan Algoritma Long Short-Term Memory
Authors: Zakariya Fajarudin , Zeniarja Junta , Winarno Sri
Published: July 2024

Mental health has now become a crucial aspect of contemporary society, especially in Indonesia. This reflects the emotional, psychological, and social well-being of individuals, encompassing the ability to cope with stress in daily life. A comprehensive understanding of mental health has become highly important for the community to prevent the occurrence of mental health problems or disorders. The objective of this research is to design a chatbot as an information and solution hub for maintaining mental health, with the hope that the development of this chatbot can help reduce the risk of mental health-related issues. In the development process of this chatbot, the author applies the AI Project Cycle and utilizes a deep learning approach for the chatbot model. The development involves the Flask platform, and to achieve high accuracy, the model employs the Long Short-Term Memory (LSTM) architecturea type of recurrent neural network (RNN) specifically designed to handle long-term dependency issues common in complex mental health contexts. LSTM enables the model to store and access long-term contextual information, which can be highly beneficial in providing accurate solutions and understanding emotional condition changes. The trained LSTM model demonstrates an accuracy of 93%, validation accuracy of 82%, a loss of 0.3%, and validation loss of 1.6% after 200 epochs. Therefore, it can be concluded that using the LSTM algorithm for the chatbot model in this development is quite effective.
Perbandingan Efektivitas Na�ve Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube
Authors: Azzahra Tarissa Aura , Winarsih Nurul Anisa Sri , Saraswati Galuh Wilujeng , Saputra Filmada Ocky , Rohman Muhammad Syaifur , Ratmana Danny Oka , Pramunendar Ricardus Anggi , Shidik Guruh Fajar
Published: July 2024

Advancements in the field of Natural Language Processing (NLP) have opened significant opportunities in sentiment analysis, particularly in the context of disaster response. In today's digital era, YouTube has emerged as a primary source for the public to acquire information regarding critical events. This study explores and compares two dominant sentiment analysis techniques, namely Naive Bayes and Support Vector Machine (SVM). It utilizes YouTube comment data related to natural disasters to test the effectiveness of these algorithms in identifying and classifying public sentiment as neutral, positive, or negative. The process involves collecting comment data, pre-processing the data, and applying Term-Frequency-Inverse Document Frequency (TF-IDF) weighting to prepare the data for analysis. Subsequently, the performance of both models is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results indicate that while both algorithms have their strengths and weaknesses, SVM tends to show better performance in sentiment classification, especially in terms of accuracy and precision, with an accuracy result of 92% and precision of 89% for negative predictions and 94% for positive predictions. On the other hand, Naive Bayes only achieved an accuracy of 79% and a precision of 91% for negative predictions and 73% for positive predictions. This study provides significant insights into the application of machine learning algorithms in sentiment analysis.
Sentiment Analysis on Twitter(X) Related to Relocating the National Capital using the IndoBERT Method using Extraction Features of Chi-Square
Authors: Arista Dufha , Sibaroni Yuliant , Prasetyo Sri Suryani
Published: July 2024

Sentiment analysis or commonly referred to as opinion mining is a field of science that can be used to get the percentage of positive sentiment and negative sentiment towards a person, company, institution, product, or even an issue or topic. Various topics are discussed on social media, one of which is Twitter (X). Starting from the economy, politics, social, culture, law and others. One of the most discussed topics on Twitter (X) is the transfer of Indonesia's capital city to East Kalimantan Province, which has drawn various opinions from netizens on Twitter (X). In this study, data regarding the transfer of the national capital taken by the author was taken from social media, namely from the social media Twitter (X) with a date range of January 1, 2022 to February 28, 2022. The method used in this research is IndoBERT using Chi-Square. Based on the experiments that have been carried out, the performance of IndoBERT with Chi-square selection features shows good results with an overall accuracy value of 94%, a precision value of 85%, a recall value of 91%, and an f1 value of 88.4% for all datasets.
Perbandingan Metode Na�ve Bayes dan Support Vector Machine Pada Klasifikasi 22 Bahasa Daerah
Authors: Rakajati Bima , Hidayat Erwin Yudi
Published: July 2024

Indonesia boasts a rich cultural diversity, encompassing over 1300 ethnic groups and 2500 regional languages. The challenge arises due to the multitude of regional languages in Indonesia, making language identification in textual form difficult. This research compares Machine Learning methods for classifying 22 regional languages in Indonesia, aiming to provide a deep understanding of the relative performance of each method. The study successfully addresses the primary difficulty, which is the identification of regional languages in Indonesia. The main constraint of this research lies in the complexity of regional languages in Indonesia, with various characteristics, variations in grammar, and differing sentence structures, resulting in accuracy not yet reaching perfection. This factor opens opportunities for future research through parameter optimization or exploration of alternative methods. Evaluation results indicate that the Support Vector Machine achieves the highest accuracy, reaching 89.41%, making it the preferred choice for model implementation. Although Nave Bayes yields good results with an accuracy of 82.08%, Support Vector Machine remains the preferred option. The application of the model using Streamlit demonstrates the effectiveness of the Support Vector Machine in accurately predicting Javanese song lyrics. This research has the potential to assist users in identifying regional languages based on text and contributes significantly to understanding Machine Learning methods for classifying regional language texts. Despite its limitations, this study can be extended to other regional languages, enhancing model accuracy through parameter improvements.
Sentimen Analisis Terhadap Fitur Tiktok Shop Menggunakan Na�ve Bayes dan K-Nearest Neighbor
Authors: Hasibuan Sandi Saputra , Angraini Angraini , Saputra Eki , Megawati Megawati
Published: July 2024

One of the features introduced by the TikTok platform is TikTok Shop, where sellers and buyers can interact easily through the live broadcast feature and promote products within the TikTok application without having to switch to other applications, as well as offering products at very cheap prices. However, this causes dissatisfaction from small traders who find it difficult to compete. Opinions about the TikTok Shop feature have created various responses from the public. This research aims to analyze public sentiment towards the TikTok Shop feature using Twitter as a data source. Applying the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms and dividing data using 10-Fold Cross Validation. Labeling was carried out using the clustering method using the K-Means algorithm, divided into three categories positive, negative and neutral. The addition of qualitative data analysis techniques with thematic analysis method in this research aims to find patterns or themes in the data. The labeling results show that 75% of the total data expresses negative sentiment towards the TikTok Shop feature. And the results of the thematic analysis found that the main theme, namely "Inappropriate regulations", covers 32% of the data, with a total of 754. This research concludes that the KNN method is superior to NBC, with better accuracy, precision and recall. This is in line with previous research that KNN is superior to nave Bayes, but other research shows the opposite where nave Bayes is superior to KNN. Further research can be carried out to improve the performance of these two algorithms in sentiment analysis, for example by using more sophisticated preprocessing methods, more representative feature extraction, or more efficient optimization techniques.
Comparative Analysis of ARIMA and LSTM Models for Predicting Physical Fatigue in Bandung Workers
Authors: Prasetyo Kiki Dwi , Wijaya Rifki , Wulandari Gia Septiana
Published: July 2024

In today's era of rapid economic growth, there is an increasing demand for workers to increase productivity by working longer and harder. However, these demands often lead to irregular and excessive working hours, which can potentially lead to negative consequences, such as physical fatigue-a state in which the body feels tired after physical activity. Factors that influence this fatigue include age, gender, health conditions, workload and work environment. Physical fatigue poses a significant challenge in ensuring workplace safety, especially in the transportation and industrial sectors, as it can reduce overall performance, productivity and quality of work. In addition, physical fatigue also increases the likelihood of decision-making errors and workplace accidents. Predicting physical fatigue is crucial to addressing these challenges. Heart rate serves as a parameter to measure fatigue, given its proven efficacy as a marker to predict physical fatigue, which is derived from the electrocardiogram and regulated by the autonomic nervous system. This research utilizes two machine learning algorithms - ARIMA and LSTM - with heart rate (bpm) and number of steps as variables. Performance evaluation, using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), showed that the LSTM model outperformed the ARIMA model. The LSTM model showed better performance, with MSE of 0.1108 and RMSE of 0.3329, compared to the ARIMA model with MSE of 0.2397 and RMSE of 0.4895.