bluuebunny/crossref_metadata_2025 huggingface dataset:
Single-Label and Multi-Label Text Classification using ANN and Comparison with Naïve Bayes and SVM
Authors: Karsana M. Mahfi Nurandi , L. Kemas Muslim , Astuti Widi
Published: July 2024

Machine learning has become useful in daily life thanks to improvements in machine learning techniques. Text classification as an important part in machine learning. There are already many methods used for text classification such as Artificial Neural Network (ANN), Naïve Bayes, SVM, Decision Tree etc.  ANN is a branch in machine learning which approximate the function of natural neural network. ANN have been used extensively for classification. In this research a simple architecture of ANN is used. But it needs to be pointed out that the architecture used in this research is relatively simple compared to the cutting edge in ANN development and research to show the potential that ANN have compared to other classification method. ANN, Naïve Bayes and SVM performance are measured using f1-macro. Performance of classification model is measured of multiple single-label and multi-label dataset. This research found that in single-label classification ANN have a comparable f1-macro with 0.79 compared to 0.82 for SVM. In multi-label classification ANN have the best f1-macro with 0.48 compared to 0.44 in SVM.
Penerapan Aplikasi Sistem Pakar Untuk Mendiagnosa Penyakit Kucing Berbasis Web Menggunakan Metode Forward Chaining Dan Certainty Factor
Authors: Nurrachma Febrina Shanti , Azizah Nuril Lutvi , Mauliana Metatia Intan
Published: July 2024

Cats are animals that are loved and admired by the public because of their beautiful physical shape and adorable behavior. But most cat keepers do not know how to treat and do not know the disease experienced by their pets just by looking at the symptoms of the disease that occurs in cats. So many cat owners are not aware if their cat has a disease and is late in providing treatment. From these problems, an expert system is needed that can help in solving problems, an expert system application that adopts artificial intelligence in the field of veterinary medicine that can diagnose diseases in cats. The purpose of this research is to build an expert system that is useful as a tool for diagnosing diseases in cats based on the web using forward chaining and certainty factor methods. This system is built using PHP and MySQL programming languages and based on the results of system functional testing using black box testing which states that the system is running well and all components produce the expected results and are able to assist users in diagnosing cat diseases, the results of disease diagnosis and the confidence value is in accordance between manual calculations and calculations on the system by bringing up the results of certainty value calculations  factor in each possible disease detected on the website. From the day of diagnostic testing, it was concluded that 33% of cats were diagnosed with feline calciirus, 33% of cats were diagnosed with feline panleuopenia, 40% of cats were diagnosed with feline rhinotracheitis, 37% of cats were diagnosed with helmathiasis, 80% of cats were diagnosed with otitis, 36% of cats were diagnosed with ringworm, 68% of cats were diagnosed with scabies.
Optimasi Convolutional Neural Network NASNetLarge Menggunakan Augmentasi Data untuk Klasifikasi Citra Penyakit Daun Padi
Authors: Zulfa Afiana Nabilla , Jasril Jasril , Irsyad Muhammad , Yanto Febi , Sanjaya Suwanto
Published: July 2024

Diseases that attack rice are one of the elements that can reduce rice production. Rice diseases include Blast, Brown Spot, Leaf Smut, and so on. Distinguishing rice disease from sight has a weakness because rice disease has similar symptoms and characteristics. Farmers lack knowledge in identifying rice disease types so that technology is needed that can help distinguish rice diseases. The method used for rice image classification in this study is the Convolutional Neural Network NASNetLarge architecture. There are two classification processes, namely the classification process using data augmentation and without data augmentation. The data consists of 4 classes, namely Healthy, Leaf Smut, Blast, and Brown Spot with a total of 440 original images and 1320 augmented images. This study uses data augmentation, namely Horizontal Flips, Vertical Flips, and Contrast. The results for the classification process without data augmentation obtained the highest accuracy, namely 94.31%, 100% precision, 100% recall, and 100% f1-score at a ratio of 80:20, learning rate 0.1, dense 256, batch size 32, and optimizer Adam. While the accuracy obtained in the classification process using data augmentation is 98.73%, 96.11% precision, 100% recall, and 98.01% f1-score at a ratio of 70:30, learning rate 0.1, dense 16, batch size 128, and the Adagrad optimizer. The accuracy results show that the data augmentation and hyperparameters used can increase the accuracy in classifying rice leaf disease images.
Penerapan Metode MOOSRA dalam Rekomendasi Platform Investasi Emas Online Terbaik dengan Pembobotan ROC
Authors: Ekawati Hanifah , Yunita Yunita
Published: July 2024

In the current era, technology is considered more practical in doing all kinds of things, for example, making investments that initially could only be done in person and were considered complicated, but now with technology, investments can be made online, making it more practical. Investments can be made by the younger generation, not only by their parents, the younger generation needs to learn about various things about investing with the aim of preparing for a brighter future. Investments that can be made by the younger generation can be in various forms, one of which is gold investment. Gold investment can be done by buying physical gold such as jewelry or gold bars, or by means of non-physical gold investment such as through the futures market, gold mutual funds, or online gold investment platforms. The online gold investment platform offers competitive gold prices and gold storage services in a secure warehouse to make it easier for investors to buy and sell gold online. However, because there are so many gold investment platforms, young gold investors are confused about determining the best online gold investment platform to use. The application of a decision support system is used in this study to solve problems in the recommendation of the best gold investment platform by applying the MOOSRA (Multi-Objective Optimization on the Basis of Simple Analysis) method and ROC (Rank Order Centroid) weighting. Then several criteria are used in the recommendations for the best gold investment platforms, namely Rating Reviews, Integrated E-commerce, Number of Payment Methods, Number of Partners and Minimum Purchases. By applying the MOOSRA and ROC methods, the best gold investment platform results with the highest preference value are obtained in alternative P2, namely BukaEmas with a value of 1151.88524.
Sistem Pakar Diagnosa Penyakit Guillain-Barre Syndrome dengan Menerapkan Algoritma Teorema Bayes
Authors: Arfyanti Ita , Fahmi Muhammad
Published: July 2024

One of the diseases of the peripheral nervous system is Guillain-Barre Syndrome, this disease usually affects adults aged 30 years and over but does not rule out the possibility of attacking at various ages. This disease usually occurs in someone who has a deficiency of vitamins including B1, B6, B12 and among those who are addicted to alcohol, exposed to poisons and do too many repetitive movements. There are various kinds of symptoms that mark a patient with Guillain-Barre Syndrome including muscle pain, tingling, difficulty swallowing, difficulty breathing, loss of response to motor movements, increased pulse, indigestion, excessive sweating, unstable blood pressure. Then a study was carried out that utilized an expert system with the Bayes theorem algorithm. This system can make it easier for an expert to diagnose the patient's disease easily and more efficiently with the advantage of not having to meet directly between the patient and the doctor. Based on these problems, the researcher applied the algorithm above to produce a diagnosis with a percentage of the possibility that the patient suffered from this disease based on existing symptom data of 77.2%.
Penerapan Metode EDAS Dalam Pemilihan Wirausaha Muda Terbaik dengan Pembobotan ROC
Authors: Salmon Salmon , Harpad Bartolomius , Andrea Reza
Published: July 2024

An entrepreneur is a person or actor who has created a new business by taking on most of the risks that will occur and enjoying most of the rewards. Entrepreneurship aims to create or create a job field so that it can help another person's work and to train a job so as to create other people's welfare. A young entrepreneur is someone who is skilled in taking advantage of an opportunity to develop his business in order to improve life in a good direction. Young entrepreneurs should have criteria in running an entrepreneur such as: optimistic, innovative, creative and leadership. So the calculations from the research above use the Evaluation Based On Distance From Average Solution or EDAS method of weighting Rank Order Centroid (ROC) there are 10 alternatives and 4 criteria so that a higher decision value can be produced by Saiful (A1) first rank with value 1.
Data Clustering Mining Applying the K-Means Algorithm, Cervical Cancer Behavior Risk
Authors: Lubis Ridha Maya Faza , Huang Jen-Peng , Wang Pai-Chou , Khoifin Kiki , Sigiro Mula , Panjaitan Joel
Published: July 2024

Nowadays, cancer is often heard as a topic of conversation for both men and women in Indonesia and even in the world, in addition to the symptoms that are not too significant and also the lack of public awareness to carry out periodic health checks, which has a negative impact on health. This lack of care is also caused by several factors, namely the lack of the community's economy, too busy with work (other matters) and even some people are not ready to know and accept the disease they are suffering from. Based on all the factors causing the reluctance of medical examinations, of course, it requires us to carry out examinations so that we can prevent and treat them early if they are diagnosed with certain diseases. There are several cancers with predominant sufferers and even only suffered by women, one of which is cervical cancer. In 2020 it is estimated that cases of cervical cancer will increase by 3.4% from 6.6% in 2018 to 9% and even cervical cancer will also become the third deadly disease in women after breast cancer and lung cancer. From this it can be seen that the percentage of deaths caused by cervical cancer is always increasing. Therefore, to reduce the high mortality rate, a clustering technique was carried out to group the data into their respective clusters based on the similarity of characteristics between one data and another. The algorithm used is K-Means with the rapid miner tester application. The final result obtained is that cluster 1 has more data and it is stated that out of 72 data on Cervical Cancer only 28 are declared as sufferers of Cervical Cancer and 44 other data are not.
Penerapan Algoritma Support Vector Regression dalam Memprediksi Produksi dan Produktivitas Kelapa Sawit
Authors: Widiarni Adyah , Mustakim Mustakim
Published: July 2024

Palm oil is a plantation crop that provides the highest economic value in Indonesia. Riau is currently the highest palm oil producing province in Indonesia with a state-run palm oil company, PTPN V. However, palm oil production is not always stable every month, whichexperiences ups and downs in the amount of production and productivity due to several factors including irregular rainfall, climate, soil fertility and most importantly fruit bunches that are not ready to harvest. So the data mining processing process is carried out by predicting the amount of production and productivity of oil palm applying the Support Vector Regression (SVR) algorithm with three kernels such as the Linear kernel, RBF kernel and Polynomial kernel. Experimental results on palm oil production and productivity show that the best kernel is the RBF kernel because the prediction results are close to the actual value. The accurate rate on palm oil production is 75.4% and palm oil productivity produces an accuracy value of 71%. It also produces an error value on palm oil production of 1.8%, for productivity of 2.1%. The results of the study can be used as an estimated picture in the company's future decision making.
Data Mining Klasifikasi Breast Cancer Menerapkan Algoritma Gradient Boosted Trees
Authors: Kraugusteeliana Kraugusteeliana , Muis Saludin , Nugroho Fifto , Karim Abdul , Siagian Yessica
Published: July 2024

Cancer is a deadly disease that is often experienced suddenly. Cancer is suffered not only among adults and the elderly, but even small children who are just born can also suffer from cancer. There are many types of cancer that have almost the same symptoms but different types and there are also levels of seriousness (danger) of these cancers, ranging from common cancers to malignant cancers that have significant changes to the body. There are many types of cancer, one of which is breast cancer, which is more common in women. This type of cancer often occurs in adult women and the elderly. In this study, to facilitate the diagnosis of breast cancer, a classification method was applied. By making an early diagnosis can reduce the mortality rate, previous diagnosis is done by utilizing image media (PET scan and CT scan) which takes a long time so it is considered less efficient. The classification algorithm used is gradient boosted trees. The test was carried out using the rapidminer application as a tester to determine the accuracy of the algorithm and also the AUC size obtained using information gain. The final result after applying the gradient boosted method produces an accuracy rate of 58.52%, this is considered less effective to use so this algorithm is not suitable to be used as a prediction of breast cancer. Precision of 64.25% and recall of only 69.44%.
Model Clustering Zona Kesesuaian Lahan menggunakan Kombinasi Algoritma Fuzzy C-Means dan Partition Coefficient Index
Authors: Susetyo Yerymia Alfa
Published: July 2024

The agricultural sector is one of the vital supporters of national development. The planning of a good agricultural system needs to be supported by looking at the characteristics of each region. The diversity of agricultural areas in Indonesia needs to be simplified by classification according to their similar characteristics. This study aims to group the area of land suitability in an agricultural area. Clustering is obtained using the Fuzzy C-Means algorithm that is validated using the Partition Coefficient Index. Agriculture zone clusters are obtained from the identification of the characteristics of the slope, height, and rainfall of each region. It produced three clusters of land-compatibility zones with almost identical degree of membership. The Partition Coefficient Index algorithm is used to validate the resulting cluster. The results of these three clusters are valid, with PCI membership degrees already grouped according to each cluster. There are two points in the cluster 1, seven points in cluster 2, and eight points on cluster 3.The three clusters that have been generated can facilitate the identification of suitable agricultural land according to their respective characteristics.
Penerapan Metode Preference Selection Index (PSI) Dalam Menilai Kinerja Dosen Saat Pembelajaran Daring Dimasa New Normal
Authors: Mulyani Neni , Azhar Zulfi , Hutahaean Jeperson , Hadanyani Fitri , Kurnia Indah
Published: July 2024

The performance of lecture during brave learning has drastically decreased due to the lack of communivation between lectures and students. As a result of bold learning. Lecturers often do not do learning using zoom meetings but only provide material without explaining it. Then the risk that will arise for lecturers who do not provide student learning reports will result in students not being enthusiastic in lectures, so that they can be reviewed in an assessment of lecturer performance. Therefore a decision support system is made to facilitate the calculation process of all criteria for assessing lecturer performance. The purpose of this decision support system is to determine the criteria for a problem using the Preference Selection Index (PSI) method.
Sentiment Analysis on Tweets of Kanjuruhan Tragedy Using Deep Learning IndoBERTweet
Authors: Maulana Adhyaksa Diffa , Lhaksmana Kemas Muslim
Published: July 2024

The incident that occurred in Indonesian football at the Kanjuruhan Stadium was caused by unscrupulous supporters who entered the field and unscrupulous officers who fired tear gas into the stands. With this incident, many responses and opinions were given by the Indonesian people through social media Twitter in the form of positive, negative, and neutral opinions. This difference in opinion occurred because of the many victims who died or were injured, with many supporters who did not like the actions taken by the authorities during the riots. With this incident, the government must make decisions to ease the concerns of the community. Therefore, research will be conducted to analyze the sentiment of public opinion regarding the Kanjuruhan tragedy using the IndoBERTweet method with a comparison using naive Bayes. The results of this study using the IndoBERTweet method get better results than naive Bayes method. With the results of the IndoBERTweet method 88% accuracy, 82% precision value, 85% recall value, and 84% f1-score value, naive the Naive Bayes results are 62% accuracy, 59% Precision Value, 61% Recall Value, and f1-Score of 59%.
Bank Central Asia (BBCA) Stock Price Sentiment Analysis On Twitter Data Using Neural Convolutional Network (CNN) And Bidirectional Long Short-Term Memory (BI-LSTM)
Authors: Nugraha Mansel Lorenzo , Setiawan Erwin Budi
Published: July 2024

Stock investing has become popular among the public. Although this stock investment has significant risks, every year, investors keep increasing because the return from stocks is also quite promising. Social media also supports this stock investing, which can give information extensively and very quickly, so it can affect the stock price. The Efficient Market Hypothesis (EMH) theory defines that market information reflects stock prices. In this research, sentiment analysis uses a dataset crawled from Twitter to process the sentiment into helpful information. All the tweets related to stock prices are collected for sentiment analysis according to the appropriate sentiment type, whether it's a positive or negative sentiment. Many believe that sentiment influences stock price movements. This sentiment analysis process uses a hybrid method named Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) with feature expansion Word2Vec. Afterwards, the hybrid method analysis will establish the final accuracy obtained. This research uses 27.930 data and shows the hybrid CNN Bi-LSTM method result is 95.74%.
Implementasi K-Means Clustering untuk Analisis Tingkat Pemahaman Computational Thinking Siswa Sekolah Dasar
Authors: Herlina Herlina , Ernaningsih Zeny
Published: July 2024

The rapid development of technology impacted the world of education worldwide, including in Indonesia. Good problem-solving skills are needed in this era. Computational thinking (CT) or computational thinking is considered capable of training students in problem-solving skills. This study aims to group students based on CT abilities to help teachers more easily determine learning methods that suit the characteristics of students. The data processing uses the K-Means algorithm with data taken from the results of the Bebras Challenge 2022 for the elementary school level. In the clustering process, the most optimal number of clusters was determined using the Elbow method. The optimal cluster is 3 clusters: namely high, medium and low levels of understanding of CT. The cluster for the SiKecil category has an average value of 81.52 and a duration of 15.07 minutes for the high cluster, an average value of 43.02 and a duration of 24.59 minutes for the medium cluster, and an average value of 34.96 and a duration of 15.28 minutes for the low cluster. The clusters formed for the Siaga category are clusters with a high level of understanding of CT with an average value of 67.13 and a duration of 23.88 minutes, an average value of 56.91 and a duration of 34.76 minutes for the medium cluster, and low cluster with an average value of 32.14 and a duration of 20.79 minutes.
Penerapan Algoritma Fuzzy C-Means Pada Segmentasi Pelanggan B2B dengan Model LRFM
Authors: Zahrani Putri Aufa , Afdal M , Monalisa Siti , Permana Inggih
Published: July 2024

PT. XYZ is one of the major pharmaceutical industries in Indonesia by marketing its products through B2B (Business to Business) customers. PT. XYZ doesn't understand what customers need. PT. XYZ also implements a cashback system for B2B customers. This study aims to determine customer segmentation, analysis of customer characteristics, firmgration and proposed strategies provided by researchers to PT. XYZ. Loyalty and customer characteristics are very influential on a company. To show which customers are loyal to the company, the Fuzzy C-Means algorithm is used to cluster and the Davies Bouldien Indeks (DBI) is used for the clustering algorithm results. The algorithm used is according to the Length, Recency, Frequency and Monetary (LRFM) model to classify purchasing behavior. It can be seen from the frequency variable which customers are loyal to which companies are not. Then determine the firmography using the attributes of business entity type, customer type, and location. After determining loyal and non-loyal customers, the analysis of customer characteristics is divided into 4 parts, namely the Superstar Segment or the best customer, which is located in cluster 2 where customers in cluster 2 can have a long-term relationship with the company, then the Golden Segment or which has the second highest value (monetary) is located in cluster 4, then the Average Value Segment or the customer who has the average value of all segments is located in cluster 5 and the Dormant Segment or the lowest customer is located in cluster 3 where customer 3 has little relationship with the company.
Analisa Market Basket Analysis untuk Melihat Pola Transaksi Customer Menggunakan Algoritma Apriori dan FP-Growth
Authors: Pabutungan Griya Jitri , Purnomo Hindriyanto Dwi
Published: July 2024

Online sales are considered an alternative approach that can positively impact product marketing. But the more online shops, the greater the competition in the business world. From the problems above, so that the store can survive among busy competitors, a strategy that is qualified as an effort to attract customer attention is needed. One effort that can be done is to look at customer patterns or tendencies during transactions. Knowing this is expected to provide additional information to stores to increase loyalty and meet customer needs so that online business stores that are built can last a long time. Based on the description that has been explained, the purpose of writing this research is to find customer spending patterns so that it can support XYZ company as one of the retail application managers in creating business strategies that can be used to attract customer interest using the market basket analysis method and the help of the a priori algorithm and fp-growth as a comparison. The market basket looks at product categories often purchased together in one receipt by customers. This study uses transaction data of 34,159,477, a minimum confidence value of 0.2, and a minimum support of 0.01 for both algorithms. The results of the two algorithms using the same minimum value give the same result in the form of 10 association rules itemset.
Analisis Sentimen Berbasis Aspek Ulasan Aplikasi Mobile JKN dengan Lexicon Based dan Naïve Bayes
Authors: Roiqoh Salsabila , Zaman Badrus , Kartono Kartono
Published: July 2024

Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan is a legal entity that provides social health insurance programs for the public released application called Mobile JKN to support various health services activities using users devices. Mobile JKN has not fully received a positive public perception and still has many shortcomings. It is necessary to conduct a deeper evaluation and analysis of the Mobile JKN. This study focuses on aspect-based sentiment analysis of user reviews on the Google Play Store to evaluate the Mobile JKN. The review data used are the last two versions, 4.2.3 and 4.3.0. This study was carried out by modeling aspects/topics using the Latent Dirichlet Allocation method and sentiment analysis using Naïve Bayes and Lexicon-Based methods. This research resulted in 3 aspects, namely Services and Features, Register and Login, and User Satisfaction. This was obtained based on the model with the highest coherence score of 0.6392 obtained in the model looping with the number of topics from 1 to 9, random state = 42, passes =50, and iteration = 60. Meanwhile, based on the sentiment analysis results, the Naïve Bayes method is better than the Lexicon-Based (Inset Lexicon) method. This is evident from performance of the Naïve Bayes with the highest accuracy score of 94.75% and Lexicon Based with Inset Lexicon obtained an accuracy score of 59.99%.
Analisis Sentimen Publik Terhadap Elektabilitas Ganjar Pranowo di Tahun Politik 2024 di Twitter dengan Algoritma KNN dan Naïve Bayes
Authors: Sandi Dede , Utami Ema , Kusnawi Kusnawi
Published: July 2024

The political year for 2024 has now increasingly entertained all Indonesian people to hold a democratic party. Various political parties have become quite dramatic in expressing their coalitions and declaring their alignment with several presidential candidates that are known to the whole community. The electability of each presidential candidate that is determined is increasingly interesting and hotly discussed, which often makes anyone take action to voice their partisanship between the pros and cons. One of them is Ganjar Pranowo, who is a political figure for the governor of Central Java. Recently, in the middle of 2023, a political party has proposed him to advance to the seat of head of state as a presidential candidate for the upcoming 2024 election. With the existence of various polemics of opinion from various layers of society, this is the right moment to carry out an analysis as a form of polarization unanimity which is presented from various public opinions as a general description and an outline in sentiment in the form of information on the conclusions of public opinion. The stages in this research began with conducting a literature study and exploring studies related to opinions and alignments with public sentiment regarding the electability of Ganjar Pranowo as a presidential candidate, and then collecting opinion data from Twitter on the electability of Ganjar Pranowo. At the experimental stage, the authors divided the data with a percentage of 80% training data and 20% testing data. The modeling used is K-Nearest Neighbor (KNN) and Naïve Bayes to classify text data as well as make comparisons of the two. In the implementation process, the author uses python as a programming language in building the model. Confusion Matrix is used for every performance evaluation related to model accuracy in each algorithm. The results showed that the division of training data and testing data and the value of k in the K-Nearest Neighbor (KNN) model greatly affect the accuracy of the model. From the test results on the comparison of the two models, the K-Nearest Neighbor model has the best accuracy with an accuracy value of 99% of the K-Nearest Neighbor with an accuracy value of 96%. The percentage of sentiment with a comparison of 96.6% positive sentiment and 3.4% negative sentiment concluded that most people still dominate positive sentiment.
Sentiment Analysis of Simobi Plus Mobile Application Using Naïve Bayes Classification
Authors: Hardi Stevan Hamonangan , Hartomo Kristoko Dwi
Published: July 2024

Sinar Mas Bank is one of many banks operating in Indonesia. Quite a few people use Sinar Mas Bank's services as their bank of choice for their day-to-day transactions. By popular demand, Sinar Mas Bank serves users of banking services by creating an M-banking application. The M-banking application created by Bank Sinar Mas is called Simobi Plus Mobile Banking. There are already 52.3 thousand reviews regardings this application on the Google Play Store platform. Among these are positive and negative reviews from customers who use the application for their daily transactions. In reviews that use 1-5 star ratings, many people are misled by giving different ratings than the given stars. Many customers who leave 5-star app reviews, but comments on these reviews contain negative words. As a result, the application developer becomes confused because the comments given do not match the rating given by the user. Comments that are not in accordance with the rating given can involve the developer of the application to make improvements or development for the application. Therefore, Research should be conducted using techniques and analytics to categorize the user comments into several groups. This study uses sentiment analysis using the Naive Bayes method to capture positive and negative sentiments for comments on the Simobi Plus mobile banking application on the Google Play store, so that these sentiments have the appropriate value. The accuracy scores for the negative class, positive class, recall, and mood analysis are used to evaluate the test. The resulting value has an accuracy of 99%, which is almost perfect. The precision value was 100%, whereas the recall class produced a value of 98% (positive class: negative). And the AUC value is 0.980.
Identification of Regional Origin Based on Dialec Using the Perceptron Evolving Multilayer Method
Authors: Nugroho Okvi , Sitompul Opim Salim , Suherman Suherman
Published: July 2024

Voice detection is very important for the world of information technology that can be used for voice processing, biometrics, human computer interfaces. Voice identification carried out in this study is based on speech or dialect using a prototype that has been designed using the Raspiberry Pi device and other supporting devices. In its application, the regional identification prototype uses sound feature extraction, namely Mel Frequency Cepstral Coefficients (MFCC) and uses an artificial neural network method with a multilayer perceptron (secos) developing algorithm. The purpose of this study is to identify regional origins based on dialect or speech using the Mel Frequency Cepstral Coefficients (MFCC) extraction technique and the Evolving Multilayer Perceptron method. The results of the regional recognition test produce a good level of accuracy, with testing as an example of the Aceh area with test data of 10 voice samples, the results obtained by the prototype can identify voices with a success rat of being able to recognize 7 voices out of 10 samples tested in the Aceh region. From all the tests on the areas of Aceh, Karo, Nias, Simalungun, the accuracy was 88%
Perbandingan Algoritma NBC, KNN, dan C4.5 Untuk Klasifikasi Penerima Bantuan Program Keluarga Harapan
Authors: Dina Aulia , Permana Inggih , Muttakin Fitriani , Maita Idria
Published: July 2024

One of the strategic programs in Indonesia to tackle poverty is the Family Hope Program (PKH) which is carried out by the government by providing cash to very poor families. The problem that occurs in PKH is the distribution of aid that is still not on target. Therefore this study aims to create a classification model for PKH beneficiaries to overcome these problems. The algorithms used to create a classification model are the Naïve Bayes Classifier (NBC), K-Nearest Neighbor (K-NN), and C4.5. The validation method used is K-Fold Cross Validation (K = 10). The number of attributes used is 33 attributes. The data used to construct the classification model (data after pre-processing) is as much as 378 data on prospective PKH beneficiaries. Based on the experimental results the NBC algorithm produces an accuracy value of 77.51%, the K-NN algorithm (K = 3) produces an accuracy value of 76.72%, the C4.5 algorithm produces an accuracy value of 80.16%. In addition, the C4.5 algorithm succeeded in reducing the number of attributes, from 33 attributes to just 8 attributes, namely: number of household members, fasbab, other houses, gold, fridge, number of rooms, walls, and excreta disposal. This reduces the complexity of the classification model generated by the C4.5 algorithm.
Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor
Authors: Warraihan Dzul Asfi , Permana Inggih , Mustakim Mustakim , Novita Rice
Published: July 2024

Online transportation is a form of internet-based transportation that covers all aspects of the transaction process, including booking, route tracking, payment, and service assessment of the online transportation. Maxim is one of the popular online transportation providers in Indonesia so it will continue to improve its services to serve the needs of the entire community. In making developments, Maxim needs user opinions regarding its application or services. This research conducts sentiment analysis of Maxim users' opinions on Instagram using Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Opinions are divided into 3 classes: negative, neutral, and positive. This research also uses the Random Over Sampling method and data sharing with 10-Fold Cross Validation. The accuracy results on sentiment data related to applications using the NBC algorithm are 81.03% and in the KNN algorithm with a value of k = 3 which is 80.72%. Meanwhile, sentiment data related to services produces an accuracy value in the NBC algorithm, namely 94% and the KNN algorithm with k = 3, namely 84%. It can be concluded that the NBC model is better than the KNN model in testing application-related sentiment data and service-related sentiment data after the Random Over Sampling method.
Otoritas Guru Dalam Prestasi Belajar Siswa Menggunakan Fuzzy Mamdani
Authors: Desyanti Desyanti , Suarlin John , Faisal Rudi
Published: July 2024

Education is a systematic effort to create good learning conditions in increasing one's potential to have good knowledge, but in the teaching and learning process one often finds students who cannot achieve achievements in accordance with their intelligence and also the teacher's attitude in teaching and educating. The teaching and learning process is sometimes less effective, teachers tend to provide material using books and blackboards, thereby reducing students' interest in learning and affecting their learning achievement. Teachers have a huge influence on each student's development, but not all teachers are able to exercise their authority in the classroom. Teachers will be able to exercise their authority if they have good teaching skills. The use of fuzzy mamdani is able to calculate the extent of the teacher's authoritative relationship to student learning achievement based on the assessment data carried out. Based on the research results, the level of authority is divided into 3 membership functions (Bad, Fair, Good), from this data the highest value is 0.74 in the good membership function, 0.25 is sufficient and 0.00 is bad. So the category of the level of relationship between teacher authority and student learning achievement with a score of 84.95 is in the Good category. The value obtained from the calculation results can be a benchmark for the teacher's attitude and upbringing towards students.
Sentiment Classification of Fuel Price Increase With Gated Recurrent Unit (GRU) and FastText
Authors: Rahim Aditya Andar , Sibaroni Yuliant , Prasetiyowati Sri Suryani
Published: July 2024

The government usually implements a policy of increasing fuel prices and reducing subsidized fuel every year. Rising fuel prices have had a mixed impact on society. The rapid development of information technology has led to easy access and an increase in the number of internet users. Social media platforms, such as Twitter, are widely used by people to express themselves in everyday life. Through this social media, the public can submit reviews regarding public policies implemented by the government regarding fuel prices. The reviews submitted varied, ranging from positive, neutral to negative. Sentiment analysis can analyze the types of reviews submitted by people, including positive, negative, or neutral. This research uses Gated Recurrent Unit and FastText feature expansion to classify sentiments related to rising fuel prices on Twitter. This system was developed through several stages, namely data crawling, data labeling, data initial processing, feature expansion, classification, and evaluation. This study aims to determine the classification performance using Gated Recurrent Unit and FastText. The data used was 8,635, and the highest accuracy reached 90.15% with an F1 score of 90.06%. The research results may help the government in determining how individuals feel about fuel price increases. By understanding public sentiment, the government can reevaluate its policies or even establish new ones that serve the public interest.
A Comparison of C4.5 and K-Nearest Neighbor Algorithm on Classification of Disk Hernia and Spondylolisthesis in Vertebral Column
Authors: Handayani Irma , Suprapto Suprapto
Published: July 2024

Good spinal health is needed to carry out daily activities. Trauma to the vertebral column can affect the spinal cord's ability to send and receive messages from the brain to the body's sensory and motor control systems. Disk hernia and spondylolisthesis are examples of pathology of the vertebral column. Research on pathology or damage to bones and joints of the skeletal system is rare. Whereas the classification system can be used by radiologists as a "second opinion" so that it can improve productivity and diagnosis consistency from that radiologist. This study compared the accuracy values of the C4.5 and K-NN algorithms in the classification of herniated disc disease and spondylolisthesis as well as a comparison of the speed of time in the classification process. Tests were carried out using data from 310 patients with normal conditions (100 patients), herniated disks (60 patients), and spondylolisthesis (150 patients). The results showed that the accuracy of the C4.5 classifier was 89% and the K-NN classifier was 83%. The average time needed to classify the C4.5 classifier is 0.00912297 seconds and the K-NN classifier is 0.000212303 seconds.
Hate Speech Detection in Indonesia Twitter Comments Using Convolutional Neural Network (CNN) and FastText Word Embedding
Authors: Puteri Fadhilah Nadia , Sibaroni Yuliant , F. Fitriyani
Published: July 2024

Hate speech is a problem that is often present in Indonesia, including on social media platforms such as Twitter. Refers to any form of communication, whether oral, written, or symbolic, that may offend, threaten or insult an individual or group based on attributes such as religion, race, ethnicity, sexual orientation, or other characteristics. The existence of freedom of expression and communication on social media triggers the spread of hate speech quickly and widely. To avoid this, a system is needed that can detect hate speech on social media. Deep learning is potentially better at recognizing and analyzing language patterns that reflect hate speech in text. In the previous study, the accuracy obtained was 73.2% using the Convolutional Neural Network method. This study proposed a hate speech detection system using Convolutional Neural Network model and FastText word embedding. The performance of Convolutional Neural Network classification model and FastText as word embedding provide excellent performance results in detecting hate speech, by involving the K-Fold Cross Validation process to the appropriate dropout value is able to achieve an accuracy value of 80%. The resulting accuracy value can be a benchmark that the model that has been built is able to avoid the spread of hate speech on social media.
Expert System for Pineapple Fruit Diseases Using Bayes' Theorem
Authors: Pujianto Ade , Wardhana Raditya , Sidauruk Acihmah , Andriani Ria
Published: July 2024

Expert systems are a branch of artificial intelligence that utilizes specialized knowledge to solve problems at the level of an expert. In the field of agriculture, expert systems are used for diagnosing plant diseases. In this research, an expert system was designed and developed with the aim of assisting pineapple farmers in determining the diagnosis of diseases based on the main symptoms observed in the plants. To overcome knowledge uncertainty, the Bayesian probability method was employed in this expert system. The diagnostic process begins with a consultation session, where the system asks relevant questions to the farmers based on the observed symptoms in the pineapple plants. The ultimate outcome of this study is an expert system capable of diagnosing diseases in pineapple plants and providing effective solutions with an accuracy rate of 93.34%. Additionally, the system provides probability values for each diagnosed disease, indicating the system's confidence level in the identified diseases, and offers treatment recommendations or solutions to the pineapple farmers.
Analisis Perbandingan Teorema Bayes dan Case Based Reasoning Dalam Diagnosis Penyakit Myasthenia Gravis
Authors: Triaji Bagas , Azanuddin Azanuddin , Rusydi Ibnu , Mariami Ita , Nasyuha Asyahri Hadi
Published: July 2024

The medical industry faces several obstacles due to illness. Treatment of any condition, including myasthenia gravis, relies heavily on an accurate and precise diagnosis. Myasthenia gravis is an autoimmune disease that affects the neuromuscular junction and is characterized by sudden muscle weakness and fatigue due to the loss of acetylcholine receptors (AChRs) at the neuromuscular junction. Successful treatment planning and providing a good prognosis to the patient is highly dependent on accurate and rapid diagnosis. To diagnose Myasthenia Gravis, this study compares and contrasts Case Anthology with Bayes' Theorem. The neuromuscular condition called myasthenia gravis is characterized by a variable decrease in muscle strength. Correct and timely diagnosis is essential to start a successful course of therapy. Data from patients with Myasthenia Gravis symptoms and clinical indicators were collected for this study. To obtain an accurate diagnosis, the dataset was analyzed using Bayes' Theorem and Case Anthology techniques. Based on the current symptoms, Bayes' Theorem is used to estimate the probability of the condition, while Anthology of Cases is used to diagnose the patient. Based on symptoms, Bayes' Theorem predicts disease outcome probabilistically, but requires reliable initial assumptions and is susceptible to prior probabilities. On the other hand, Case Anthologies use information obtained from previous situations, but may be limited by the availability of relevant data and may experience difficulties in dealing with unique or unusual situations. This study helps us understand the benefits and limitations of each technique in diagnosing Myasthenia Gravis. A more accurate and effective diagnosis can be made by combining the two methods. These studies can serve as a foundation for creating more sophisticated diagnostic techniques integrated into clinical practice. The following is a summary of the percentages obtained using the Bayes Theorem and Case Anthology methods: For the diagnosis of Myasthenia Gravis, the Bayes Theorem technique produces a percentage value of 55% while the Case Anthology method only produces a percentage value of 26%. Therefore, the Bayes Theorem technique is better and more reliable in diagnosing Myasthenia Gravis.
Misogyny Text Detection on Tiktok Social Media in Indonesian Using the Pre-trained Language Model IndoBERTweet
Authors: Zakaria Perwira Hanif , Nurjannah Dade , Nurrahmi Hani
Published: July 2024

Social media is a popular communication and information platform due to its ease and speed of access. By using social media, one can express himself freely. This triggers irresponsible individuals to utter hate speech with the aim of bringing down a person or group of people. Misogyny is a form of hate speech directed at women. The problem of misogyny should not be underestimated because misogyny can be one of the main reasons women feel miserable. In this study, a model will be built to detect misogyny text on the Indonesian language TikTok social media using the IndoBERTweet pre-trained model. IndoBERTweet is a pre-trained model based on the BERT model, which has been trained using Indonesian language datasets taken from the previous Twitter social media, resulting in a good performance for detecting misogynous texts on social media by classifying them. The dataset used is in the form of text data taken from misogyny comments by focusing on forms of misogyny in the form of stereotypes, dominance, sexual harassment, and discredit in short video content on women's TikTok social media accounts. The performance of built model performs hyperparameter settings which include batch size 16, epochs 10, and learning rate 7e-5 and is evaluated using a confusion matrix with the best accuracy results of 76.89%.
Study of Feature Extraction Method to Detect Myocardial Infraction Using a Phonocardiogram
Authors: Malik Ashydiki , Mandala Satria , Pramudyo Miftah
Published: July 2024

Myocardial Infraction is one of the most dangerous and often fatal cardiovascular diseases. To detect this disease early, non-invasive methods based on Phonocardiogram (PCG) signals have become a significant focus of research. However, to present, research on feature extraction from PCG signals is still limited. In this research, we propose a study of feature extraction algorithms using Discrete Wavelet Transform (DWT), Mel Frequency Cepstral Coefficients (MFCC), and Entropy methods to detect heart attacks. In the pre-processing stage, we applied noisereduce to remove noise in the PCG signal. Further, we perform feature extraction using DWT, MFCC, and Entropy methods on the processed PCG signal. Following that, we used a detuned KNN with hyperparameters as the classification algorithm to classify the features into two categories: heart attack and non-heart attack. The test results show that DWT, MFCC, and Entropy-based feature extraction methods can make a significant contribution in detecting Myocardial Infraction. In comparison with other feature extraction algorithms, the test results show that the Entropy-based feature extraction method provides the best accuracy of 99%, with 99% sensitivity and 99% specificity. This research makes an important contribution to the development of heart attack detection methods using PCG signals. With promising results, the Entropy-based feature extraction method can be an effective and efficient approach in detecting coronary heart disease early, which in turn can improve patient prognosis and treatment.