bluuebunny/crossref_metadata_2025 huggingface dataset:
Pemilihan Paket Wisata One Day Tour Menggunakan Model Pendukung Keputusan TOPSIS
Authors: Singgalen Yerik Afrianto
Published: July 2024

The development of Labuan Bajo tourism attracts foreign and domestic tourists to explore various natural beauties through fun activities. Tour agents provide a variety of interesting activities and tourist visits to the islands around Labuan Bajo that can provide new experiences for tourists. The activities are sold through One-day tour packages in Labuan Bajo. Still, depending on the service provider, the itinerary description, admission ticket,  and order of visit to the destination are very complicated. Considering this, this study uses the TOPSIS decision model in choosing One Day Tour tour packages in Labuan Bajo by considering the criteria of price, destination, duration, admission ticket, and service rating. Based on the results of this study, it can be seen that the highest preference value from the ranking results is the Full Day Trip to Explore 6 Destinations in Labuan Bajo and Komodo tour package, with a value of 0.673056111. Furthermore, the tour package that occupies the second position from the ranking results is the 1-day Komodo island Tour hopping around by Speed Boat with a preference value of 0.628303746. Meanwhile, the tour package that occupies the third position from the ranking results is One day Komodo trip with Bintang Komodo Tours with a  preference value of 0.53181476. This shows that the TOPSIS method produces recommendations for One Day Tour packages in Labuan Bajo for tourists by considering the price of tour packages, the number of destinations visited, the length of time or duration of tourist time, admission tickets in tour packages, and ratings The services of the travelers were previously related to the tour package. Thus, the selection of TOPSIS-based One Day Tour tour packages can minimize the risk that causes misunderstandings or tourist dissatisfaction related to the tour packages prepared by each travel agent.
Analisis Sentimen Ulasan Pengguna Game Pubg Di Google Play Store Menggunakan Algoritma Naïve Bayes
Authors: Wibowo Fajar Iqbal , Febriandirza Arafat
Published: July 2024

In today's digital era, technological development is very rapid and sophisticated. The online gaming industry has also evolved. Online games are a variant of video games that are played online via the internet. When users connect with other users, users can interact and work together. Battle rolaye games, such as Player Unknown's Battlegrounds (PUBG) have become one of the most popular of the many online games available. PUBG games offer a large-scale gaming experience that creates a dynamic gaming experience. One of the advantages of the PUBG game is that it has an attractive visual design and high quality graphics so that the game feels more realistic. However, this cannot guarantee satisfaction for users. To find out user sentiment towards the PUBG game, sentiment analysis using the Naïve Bayes Algorithm is carried out which aims to find out how accurate the Naïve Bayes Algorithm is used in classification. Data is taken using web scrapping techniques as many as 1000 user reviews in the Google Play Store review column. After going through preprocessing, the data is divided into 50% training data and 50% testing data. Prediction results tend to be positive with 578 positive sentiments and 232 negative sentiments. Based on evaluation using confusion matrix, the results are 83.95% for accuracy, 88.10% for precision, and 89.62% for recall.
Perbandingan Metode ARAS dan MOORA dalam Seleksi Penerimaan Pegawai Baru Non ASN
Authors: Susanto Susanto , Ningrum Setya , Cahyono Yudi
Published: July 2024

The acceptance of new non-ASN candidates is a major problem in recruiting new employees, this happens because of the large number of new prospective employees who register and the number of vacancies that are inadequate, therefore research is carried out that aims to create a decision support system that can speed up the process of recruiting candidates. new employee. The methods used in this research are ARAS (Additive Ratio Assessment) and MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) methods. The selection of this method is able to filter out the most alternatives based on weighting and this method is able to choose goals based on different criteria, namely benefits and costs. The results obtained from the ARAS method are the calculation rankings from the comparison of alternative utility functions with the optimal utility function values, while the results obtained from the MOORA method are the calculation rankings of the maximum and minimum values. From this value, an alternative that meets the criteria through calculations using the ARAS and MOORA methods, the employee alternative chosen is Yuniar Pilakso Angkasa with a final result of 0.935 for the ARAS method and 0.163 for the MOORA method. The results of the Spearman Rank coefficient test obtained a value of 0.9357% for the ARAS method and 0.7428% for the MOORA method, meaning that the two methods have a strong correlation level and can be used in recruiting new non ASN employees.
Sentiment Classification of Food Influencer Content Reviews using Support Vector Machine Model through CRISP-DM Framework
Authors: Singgalen Yerik Afrianto
Published: July 2024

The research problem revolves around the challenges in effectively marketing culinary tourism aligned with tourist preferences in Indonesia, necessitating a substantial exploration of consumer sentiments related to culinary diversity through the lens of food influencer content. Food influencers are crucial in stimulating tourists' interest in gastronomy through culinary tourism in Indonesia. This research reveals challenges in culinary tourism marketing aligned with tourist preferences, necessitating substantial exploration of consumer sentiments related to culinary diversity through food influencer content. The sentiment classification method employed is the Cross-Industry Standard Process for Data Mining (CRISP-DM) using the Support Vector Machine (SVM) algorithm and the SMOTE operator. The data source is derived from a video with the ID PMhfLy_buV8, containing 114,422 comments. This study collects and processes 30,000 comments, resulting in 9,323 data points. The findings highlight the vital performance metrics of SVM models, both with and without SMOTE, showcasing high accuracy, precision, recall, and F-measure values. Specifically, SVM without SMOTE achieves 95.28% accuracy, while SVM with SMOTE achieves 98.67%. Despite some limitations in discerning positive and negative sentiments, indicated by moderate Area Under the Curve (AUC) values (0.608 to 0.658), the overall efficacy of SVM in sentiment analysis for food influencer content is apparent. Drawing from a dataset of 30,000 comments, these insights contribute to advancing sentiment analysis methodologies and offer practical implications for understanding consumer perceptions and behaviors in digital media and influencer marketing. Additionally, the prominence of frequent words such as "bang" (1322), "nonton" (1064), "makan" (921), "yang" (801), "puasa" (711), "tahun" (484), "ngiler" (448), "lagi" (384), "tanboy" (311), and "enak" (315), as extracted from RapidMiner analysis, underscores the significance of language patterns in the realm of food influencer content.
Rancang Bangun Alat Informasi Penjemputan Siswa Berbasis Mikrokontroller ESP32
Authors: Maulana Irfan , Bachtiar Moh. Muaz , Fadlun Wira , Sakti Fredi Prima
Published: July 2024

In daily life, the phenomenon of traffic density increases significantly during school hours. There are many ways to overcome this phenomenon, one of which is making a student pickup information tool based on the ESP32 microcontroller. The tool created is in the form of a student pick-up information display, which will display the student's name and class when the parent has correctly entered the code via the web application. Student pickup information tools use hardware and software. The hardware consists of an ESP32 microcontroller as a manager for input data and output data which is the link between the web server and the P10 LED display and the P10 DMD panel to display the name and class data of the students being picked up. Meanwhile, the software uses Arduino IDE software and MySQL database. The results of testing from a series of research shows efficiency and activeness in picking up students with a success percentage of 100% with testing 15 times.
Klasifikasi Kanker Payudara Menggunakan Metode Convolutional Neural Network (CNN) dengan Arsitektur VGG-16
Authors: Idawati Idawati , Rini Dian Palupi , Primanita Anggina , Saputra Tommy
Published: July 2024

Breast cancer classification is a process to determine the type and characteristics of breast cancer based on the characteristics of cancer cells. In this research, a system is designed to classify breast cancer using ultrasound images which are then processed using the Convolutional Neural Network method with the VGG-16 architecture. The aim of the research is to develop a breast cancer classification system using Convolutional Neural Network (CNN) and evaluate the classification results using Convolutional Neural Network (CNN) with the VGG-16 architecture. In breast cancer classification, three classes are considered: normal, benign, and malignant. The steps in the classification process include image input, filtering, resizing, data augmentation, and data digitization. The best results were obtained in this test using the SGD optimizer hyperparameter, learning rate 0.001, epoch 20 and batch size 32 producing an accuracy value of 78.87%, a precision value of 75.69%, an AUC value of 79.85% and an f1 score value of 74.67%.
Penerapan K-Means Untuk Clustering Kondisi Gizi Balita Pada Posyandu
Authors: Rahmat Candra Adi , Permatasari Hilda , Rasywir Errissya , Pratama Yovi
Published: July 2024

Malnutrition in children is a major public health problem in developing countries, including Indonesia. National data show that 36.8% of children under five years of age (toddlers) are stunted (short and very short, measured by height for age). To be able to know the nutritional condition of the toddler, can use analysis and a calculation method. In this study, the authors utilize an analysis and calculation of data, namely data mining. One of the techniques in data mining is clustering. K-Means Clustering is one of the algorithms in the Clustering technique in data mining. In this study the authors used as many as 20 data on toddlers. From the 20 data on toddlers, the authors determined the cluster center randomly as much as 3 data and resulted that, 4 toddlers were malnourished, 7 toddlers were well nourished, and 9 toddlers were obese.
Analisis Metode Ensemble Pada Klasifikasi Penyakit Jantung Berbasis Decision Tree
Authors: Aziz Mochammad Ilham , Fanani Ahmad Zainul , Affandy Affandy
Published: July 2024

Heart disease is one disease that is not easy to predict early on and maybe some people are not aware that they have the disease. Data obtained by WHO More than 17 million people worldwide died of heart attacks in 2016. If thesymptoms of heart disease or heart attack are known, prevention of heart disease can be anticipated and even minimized mortality. Analysis of heart disease aims to reduce mortality from the disease. In writing this research, a decision tree algorithm method is used, the algorithm still has weaknesses in making prediction accuracy. So we need a way to improve the accuracy of the classification learning outcomes. This study aims to improve the learning outcomes of heart disease classification by using ensemble learning methods, namely Boostrap Aggregating (Bagging) and Adaptive Boosting (Adaboost). Both methods were tested by predicting deaths caused by heart disease.
Penerapan Algoritma C4.5 Untuk Klasifikasi Tren Pelanggaran Kendaraan Angkutan Barang dengan Metode CRISP-DM
Authors: Purnomo Novie Hari , Pamungkas Bayu , Juliane Christina
Published: July 2024

Road damage due to ODOL (Over Dimension Over Loading) increases the road maintenance budget significantly, namely an average of IDR 43.45 trillion per year. In addition, many accidents involving ODOL trucks or overloading and dimensions have occurred. The level of violations caused by ODOL vehicles is still high, so technology is needed that is able to manage data and serve as a reference to find out the hidden approaches in the data set, as well as analyze the grouping between data and attributes to facilitate decision making and policy making. This study applies the CRISP-DM methodology using a decision tree model with the C4.5 algorithm. The purpose of this research is to classify trends in freight transport violations based on violation data in the UPPKB. The research data is primary data obtained from the Directorate of Road Transportation Infrastructure of the Ministry of Transportation through the online weighbridge system (JTO). The expected result of this research is to be able to find out the pattern of classification trends for freight vehicle disturbances based on the results of the C.45 algorithm decision tree, so that the research results can be used as a reference in making decisions and making policies. The results of this study indicate that the accuracy performance in data mining tests for the classification of trends in freight vehicle disturbances with 10 fold cross validation linear sampling produces an accuracy of 86.31% +/- 1.23% (micro average: 86.31%), shuffled sampling produces an accuracy of 86.34% +/ - 0.67% (micro average: 86.34%) and stratified sampling produces an accuracy of 86.34% +/- 0.67% (micro average: 86.34%).
Performa Support Vector Machine Pada Klasifikasi Lahan dan Air Tanah
Authors: Angellina Angellina , Herwindiati Dyah Erny , Hendryli Janson
Published: July 2024

Groundwater is one of the sources of water in the world. The availability of groundwater is one of the factors that plays an important role in carrying out daily life activities, including for drinking, cooking, washing, irrigating rice fields, and many others. One source of groundwater in Jakarta is obtained from the Ciliwung River which is passed by the Bogor and Depok areas. However, the existence of springs and groundwater continues to decrease until now. The purpose of this paper is to discuss the first stage of the classification of groundwater availability in several sub-districts in the Bogor and Depok areas. The results of phase one will present a mapping of green areas along with their classification. Data taken from Landsat 8 Satellite Imagery - United States Geological Survey (USGS). The Support vector Machine (SVM) method is used to classify the availability of groundwater. The input data for the training process are the Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Enhanced Vegetation Index constants. The results of the evaluation using linear kernel produced a green F1 score of 89.58%, half green 65.62%, and dry 83.44%. While the results of the evaluation using the polynomial kernel produced a green F1 score of 83.58%, half green 25.68%, and dry 66.59%.
Aplikasi Augmented Reality Geometri Sekolah Dasar Untuk Bangun Datar dan Ruang Menggunakan Metode Marker Based Tracking
Authors: Putra Syahrizal Dwi , Aryani Diah , Syofyan Harlinda , Yasin Verdi
Published: July 2024

Geometry is an important part of students' mastery of mathematics. In its application, students are still constrained in imagining objects abstractly. It takes the right learning media that is used by teachers and keeps abreast of current technological developments. This study aims to describe flat and spatial geometric objects in teaching geometry using augmented reality learning media based on Android applications so that learning becomes more interesting, concrete and visual equipped with a quiz game feature that contains quizzes that are puzzle games. This application was built using Unity3D and Vuforia SDK and 3D objects created using Blender. This application utilizes the marker method used to determine the point of emergence of 3D objects. The results of application testing using the black box method state that the detection of markers on objects, features and the speed level of devices using the application is running well. The Marker Based Tracking method can be used to recognize flat object markers and spatial planes very well.
Penerapan Metode SMARTER pada Sistem Pendukung Keputusan Pemilihan Lahan Kayu Putih
Authors: Maharrani Ratih Hafsarah , Abda’u Prih Diantono , Hastuti Hety Dwi
Published: July 2024

The productivity of eucalyptus in Indonesia is included in the low productivity group, the lack of raw materials resulting in a deficit in eucalyptus production is the cause of this. Meanwhile market demand is increasing. The large area of land in the Cilacap Regency area is not matched by its utilization, this is due to a lack of knowledge about potential land uses. There are 5 criteria for land, namely land "S1 (Highly Suitable), S2 (Quite Suitable), S3 (marginally suitable), N1 (currently not suitable) and N (not suitable forever). “Decision support methods can be used in determining land suitability, one of which is the SMARTER method. The method was chosen because determining the suitability of eucalyptus land was processed by "rank order centroid (ROC) weighting" where the existing criteria have weight based on the priority level adjusted to the ranking process. Of the 84 processed data, it was found that around 98% or 83 of the data stated that the Cilacap district was included in the S2 suitability range (Quite Appropriate). In addition, based on the results of the accuracy testing carried out, the percentage of accuracy was 80.95%. Seeing the high percentage, the decision support system using the SMARTER method is appropriate to be used to determine the suitability of eucalyptus land. “
Penerapan Metode WASPAS Pada Sistem Pendukung Keputusan Penentuan Tempat Wisata Kuliner
Authors: Hamdana Elok Nur , Saputri Dina Risky Alin , Ikawati Deasy Sandhya Elya
Published: July 2024

Culinary tourism is one of the tourist attractions when visiting an area with the aim of enjoying food with a certain taste. Culinary tourism in Batu City and Malang City offers a variety of culinary products, ranging from filling food to snacks such as typical and legendary dishes. So this creates its own problems for the community and tourists because they feel confused about making choices. Therefore, there is a need for a solution to this problem, namely by creating a decision support system that can help the community and tourists in determining culinary tourism spots appropriately according to their needs. This study uses the WASPAS (Weighted Aggregated Sum Product Assessment) method to calculate which culinary tourism spots are suitable based on the needs of the user or users. Based on this research, the system calculation using the WASPAS (Weighted Aggregated Sum Product Assessment) method on a culinary tourism decision support system can provide the right recommendation for users and users by using a Likert scale calculation obtained from the questionnaire results getting a final score of 88.95 %. From the test results using Spearman Correlation, correlation values are obtained of 0.6 and 0.8 which indicate that there is a relationship between the ratings generated by the system and the calculated rankings of people who like to eat.
Analisis Penerapan Metode WASPAS dan MOORA dalam Kelayakan Pengangkatan Karyawan Tetap
Authors: Kraugusteeliana Kraugusteeliana , Alam Sitti Nur , Triwahyono Bambang , Wibisono Muhammad Bayu , Fatmayati Fryda
Published: July 2024

In determining the considerations for determining employees, there are many criteria that must be owned by an employee. Each company has different criteria for determining considerations and considerations, to make employees more active and get a clear position. To determine the decisions of employees who deserve to be permanent employees, a decision support system is needed. This study aims to determine the best alternative based on predetermined criteria by applying the Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA) method and the Weight Aggregated Sum Product Assessment (WASPAS) method. The research was carried out by finding the values for each attribute, then a ranking process was carried out which would determine the optimal alternative, namely determining the considerations to be carried out. As for those who are eligible to become permanent employees, alternative A6 with the acquisition value of the WASPAS method is 7.175147 and the acquisition value of the MOORA method is 0.37077. So it can be said that the methods that are suitable for application in WASPAS and MOORA are methods that are one of the appropriate methods WASPAS and MOORA are suitable methods for calculating an event.
Klasifikasi Sentimen Publik Terhadap Jenis Vaksin Covid-19 yang Tersertifikasi WHO Berbasis NLP dan KNN
Authors: Arsi Primandani , Prayoga Iphang , Asyari Muhammad Hasyim
Published: July 2024

The corona virus epidemic became an epidemic at the end of 2019 in the world. Some people are going through a pandemic with fear or even just being normal. The expression of fear, they discussed on social media. Now, social media is a means for some people to express their emotions namelly twitter. In order to end this pandemic the company is trying to develop a covid-19 vaccine, such as Pfizer, AstraZeneca, and Moderna which have obtained licenses from the World Health Organization (WHO). However, the discovery of the vaccine was not welcomed by some people. This is because of the post-vaccine impact and the vaccine development period which is considered too short. In this study, sentiment analysis was carried out based on public sentiment on Twitter social media about the covid-19 vaccine that has obtained a license from WHO uses NLP (Natural Language Processing) and machine learning algorithms. The purpose of this research is to find out the sentiment circulating on Twitter towards WHO-certified vaccines such as Pfizer, Moderna and AstraZeneca based on NLP as decision makers and sources of reference for the general public. Based on the research results, the highest positive sentiment was the Pfizer vaccine then Moderna, namely 47.30% and 46.20%. Meanwhile, the AstraZeneca vaccine received the lowest sentiment rating of the three, namely 40.09%.
Fisher Kolmogorov Equation Theory Simulation Using Deep Learning
Authors: Shafira Conny Tria , Gunawan Putu Harry , Ihsan Aditya Firman
Published: July 2024

Neural Networks (NNs), a powerful tool for identifying non-linear systems, derive their computational power through a parallel distributed structure. The Physics-Informed Neural Network (PINN) technique can solve the Partial Differential Equation (PDP) in the Fisher Kolmogorov equation. By testing several hyperparameter changes, the formula is correct, and the visualization results can be consistent. Shows that an accurate value can be obtained from the results of the Mean Squared Error (MSE) on the formula loss value (loss f) and data loss (loss u). In experiment 1 the MSE obtained was 0.00001657 (Loss f) and 0.00000038 (loss u), as well as the MSE values obtained in experiment 4, is 0.00005865 (Loss f) and 0.00000216 (Loss u). It can be said to be accurate if the MSE value is close to 0. A formula is proven correct if it displays consistent data in random input data, but with the condition that it uses the same parameters. The author conducted research to simulate the Fisher-Kolmogorov equation with deep learning using the PINN technique. So the purpose of the research conducted was to simulate the Fisher-Kolmogorov equation with the deep learning method using the PINN technique. From the research, it can be concluded that Fisher-Kolmogorov's equation proves to be true if the simulation is carried out in deep learning and produces a visualization that is consistent with the functions used for visualization.
Anti Cheat of Computer Based Test Application in Enterpreneurship Exams using The Multiplicative Random Number Generator Method
Authors: Anwar Badrul , Ganefri Ganefri , Yulastri Asmar , Nofriansyah Dicky , Nasyuha Asyahri Hadi
Published: July 2024

STMIK Triguna Dharma is one of the best private universities in North Sumatra in the field of ICT. To realize an entrepreneurial university, it is necessary to have maximum efforts from all elements of management and the foundation. This reflects the quality of learning and good higher education governance. So far, there have been many test implementation techniques in several schools, such as conventional and CBT concepts. Based on the observed phenomena, the CBT that was implemented had problems including fraud in the implementation. Referring to this problem, STMIK Triguna Dharma innovates by building a web-based anti-cheat CBT application by adopting the Multi Random Number Generator Method. The advantage of this method is that it is able to randomize questions and answers with the available question packages so that this effort can properly reduce cheating, for example on semester exams. The results of this study are a web-based anti-cheat application which adopts the MRNG method which is expected to be a solution and can be used by STMIK Triguna Dharma in conducting semester exams, especially in entrepreneurship courses.
Analisis Sentimen Pengguna Twitter Terhadap Kenaikan Harga Bahan Bakar Minyak (BBM) Menggunakan Metode Logistic Regression
Authors: Nurhusen Muhammad Raja , Indra Jamaludin , Baihaqi Kiki Ahmad
Published: July 2024

In Indonesia itself, fuel is a very important raw material for society, especially for the industrial sector. The fuel price hike policy sparked controversy on social media, one of which was Twitter. After the increase in fuel prices was passed, every day on Twitter was filled with tweets with the hashtag (#bbmnaik). The pros and cons that exist in the community regarding the increase in fuel prices is an interesting research material. This study aims to analyze public sentiment whether it is negative or supportive. The method used is Logistic Regression assisted by the Confusion Matrix for evaluation calculations. The advantage of this method compared to other methods is that the Logistic Regression method is often used to create a predictive model whose result values are in the form of yes/no, true/false, thus this method is very suitable for this research. The data used is 3000 data with keywords (increase in fuel prices). The results of the analysis that has been carried out show that positive sentiments get an accuracy value of 38% and negative sentiments of 80%. Classification performance of the Logistic Regression method gains 73%. The results of evaluation calculations with the Confusion Matrix using data testing as many as 600 data get an accuracy rate of 77%, a precision value of 95%, a recall value of 79%, and an f1 score of 86%. So it can be concluded from the results of the sentiment analysis that has been done that the public is more pro against the rejection of the increase in fuel prices.
Prediksi Volume Sampah di TPSA Banyuurip Menggunakan Metode Backpropagation Neural Network
Authors: Santoso Wahyu , Maimunah Maimunah , Sukmasetya Pristi
Published: July 2024

The current waste problem is an important issue in many big cities, including Magelang City. The increasing rate of population growth and the decreasing land area for Banyuurip TPSA also makes it difficult for the government to handle waste, causing a negative impact on the environment around the TPSA. Therefore it is necessary to have a prediction of the volume of waste that enters TPSA every day using the Backpropagation Neural Network method so that it can assist the government in preparing budgets, preparing cleaners and estimating the capacity of TPSA in the future. The data used is time series data in the form of waste volume at Banyuurip TPSA from 2019 to 2022. From the results of the Backpropagation Neural Network method with parameters 30-7-1 and 1000 epochs, the best MSE value is 0.018870. The results of the training will then be used to predict the volume of waste the next day.
Clustering Prevalensi Stunting Balita Menggunakan Agglomerative Hierarchical Clustering
Authors: Anggraeni Maulina Rizky , Yudatama Uky , Maimunah Maimunah
Published: July 2024

Indonesia is a country that still has a high stunting prevalence rate of around 36%, ranking 5th with the highest stunting prevalence rate. According to the WHO (World Health Organization) this figure has not reached the expected rate, which is below 20%. Out of 180 countries in the world, nutrition problems in Indonesia are ranked 117th which is still far behind neighboring countries, such as Malaysia which is ranked 44th, Vietnam is ranked 58th, Thiland is ranked 64th, and Singapore is ranked 12th. have a stunting prevalence rate above 20%. Clustering is the process of analyzing data to group similar data into one class and different from other classes. This research was conducted using the Agglomerative Hierarchical Clustering Average Linkage method with a bottom-up approach. The data used is the prevalence of stunting in Tegalrejo using data totaling 2397 in January for toddlers and 3256 in February. The results of the clustering form 3 levels of stunting prevalence each month which can be translated into low prevalence, moderate prevalence, high prevalence, these labels are obtained based on the mean value in each cluster. From the clustering results, there were 5 villages with a low prevalence of stunting in January and February. In villages with a moderate prevalence of stunting, there were 12 villages in January and 10 villages in February. In villages with a high prevalence of stunting in January there were 4 villages and in February 6 villages. This means that there are additional villages with a high prevalence of stunting.
Part-of-Speech Tagging Implementation on Telkom University News using Bidirectional LSTM Method
Authors: Putra Rheza Ramadhan , Richasdy Donni , Ihsan Aditya Firman
Published: July 2024

News is a tool used to disseminate information through various media, one of which is the internet. Various kinds of news articles have words that are not recognized in the dictionary such as slang words and have foreign words that do not exist in the corpus. How can a POS tagging model built on the corpus be able to handle word class labeling in Indonesian news. The research was conducted to check the results of POS tagging on a collection of news about Telkom University which was selected manually. By using the bidirectional LSTM model, three test scenarios were attempted to improve the performance of the built model, the test scenarios were applying the best padding for the corpus, comparing the performance results of the modified corpus model with the original corpus model, and determining the dimensions of the Word2vec vector. Then the selected model from each corpus is implemented on the news that has been labeled manually. One of the best scenario tests is obtained by modifying the corpus by removing double words in the word class "X" and changing some of the word classes "X" which are more likely to be foreign words so that they are changed to the word class "FW". The best performance results in the implementation of news about Telkom University using the bidirectional LSTM model which was built based on the modified corpus get accuracy values of 92.74%, precision of 92.85%, recall of 92.74%, and F1-score 92.48%.
Studi Bibliometrik Jurnal Media Informatika 2018-2022
Authors: Watrianthos Ronal , Sagala Ahmad Habin , Syafriyeti Rahmi , Yuhefizar Yuhefizar , Mesran Mesran
Published: July 2024

This study aims to conduct a bibliometric analysis of the Budidarma Informatics Media Journal (MIB) for the 2018-2022 period using the Dimensions database. The period produced 683 publications which then became the dataset in this study. The dataset was analyzed using Bibliometrix R-package Biblioshiny, and research trends were mapped using VOSviewer visualization. The analysis results show that the collaboration index of MIB journals has a moderate value and tends to be low, although the annual growth rate of 79.08% looks very significant. The number of citations over five years only amounted to 157, so the average citation per article was 0.22. The average FCR is 0.16, which indicates a citation that is well below average. The results of testing Lotka's postulates for author productivity in this period showed a significant difference between the analysis results and the theory in Lotka's postulates. As for trends, research related to "algorithms" became the most dominant word and the center of the cluster connected to other clusters, becoming the most widely used research topic.
Internet of Things Sebagai Alat Penentuan Lokasi Budidaya Rumput Laut Gracilaria Sp
Authors: Afif Moh Thoriq , Nur Utomo Aulia Desy , Zafia Anggi
Published: July 2024

Seaweed or commonly called seaweed is one of the abundant underwater biological resources in Indonesia, with around 8.6% of the total marine biota. Seaweed has various benefits such as for food ingredients, cosmetic ingredients and others. However, in seaweed cultivation, failure often occurs due to various factors, one of which is the presence of diseases or pests that attack seaweed. Gracilaria sp seaweed in Gerongan ponds in Pasuruan, East Java often experiences different responses when planted in different ponds. Existing environmental factors come from ponds where seaweed is planted to cause damage. This tool is made using a microcontroller to prevent crop failure and damage to seaweed in the Gerongan pond, Pasururan, East Java, namely the design of a tool for determining the location of Gracilaria Sp seaweed cultivation based on IOT. This tool uses the NodeMCU ESP8266 with the help of sensors, namely a distance sensor for water depth, a temperature sensor, a strong current sensor, and a pH sensor. The results of this study show that seaweed farmers can monitor seaweed by stabilizing the pond environment with data that has been obtained from sensor results that they can see on the Android application. This tool makes it easier for farmers to cultivate seaweed and helps improve the quality of the Grasilaria sp. seaweed harvest.
Disaster Management Sentiment Analysis Using the BiLSTM Method
Authors: Yahya Rachdian Habi , Maharani Warih , Wijaya Rifki
Published: July 2024

Indonesia is a country prone to natural disasters. Natural disasters occur due to the process of adjustment to changes in natural conditions due to human behavior or biological processes. Community responses through tweets on Twitter are crucial for decision-making and action in disaster management and recovery processes. From the many public reactions via Twitter, sentiment analysis can be carried out. Classification using the BiLSTM method can be carried out to determine the categories of positive and negative responses after previously being compared using the SVM, which resulted in an accuracy of 82.73% and a BERT of 81.78%. After the classification process, the testing process is carried out with Word2Vec. From a total of 2,686 Twitter data, it was concluded that there were around 2,081 positive sentiments and 605 negative sentiments related to disaster management in Indonesia. At the same time, the test results obtained accuracy reached 84%, precision 88%, recall 92%, and f1-score reached 90%.
Personality Classification on Twitter Social Media using BERT
Authors: Maulino Yantrisnandra Akbar , Maharani Warih , Gani Prati Hutari
Published: July 2024

In the modern era, social media is a platform often used to interact with people. Twitter is a popular social media, especially for human interaction. Using tweets on Twitter can describe how a person's personality and can also describe characteristics of a person. Humans themselves based on the Big Five Model Nursing Theory (Big Five Personality), have five general personalities, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism. Personality itself influences a person's judgment of many things, knowing the personality of a person can make it easier to know the characteristics, habits, and ways of that person in their daily activities. In addition, understanding someone's personality can be a reference in seeing how someone can interact with others. It can also be used when looking for a job according to their personality. Thus, this research builds a system to classify personality using the BERT model with the dataset used in the form of tweets from Twitter users by making several changes such as parameters and using tests with several ratios in determining test data and also training data. The results acquired in this study are 50%.
Studi Komparasi Metode Analisis Sentimen Naïve Bayes, SVM, dan Logistic Regression Pada Piala Dunia 2022
Authors: Anbari Muhamad Zaki , Sugiantoro Bambang
Published: July 2024

The world cup is the most popular sporting event in the world. The 2022 World Cup will be held for the first time in the Middle East, in the country of Qatar to be precise. Its implementation was colored by various controversies ranging from human rights issues, LGBT+ issues, issues of alcoholic beverages, and so on which were so busy in the mainstream media. Various sentiments and opinions have emerged on social media regarding the implementation of the world cup, some have positive opinions and some have negative ones. Sentiment analysis was carried out to find out the main opinions that are developing in society regarding the 2022 world cup, the results can then be used as input and consideration for policy makers. This study uses the snscrape library running on the Python programming language to collect tweets related to the 2022 World Cup on the Twitter social media platform on the first day of the World Cup. The collected data then enters the pre-processing, splitting, TF-IDF stage, before it is ready to be used for modeling. The method used in this research is Bernouli Naïve Bayes, Support Vector Machine, and Logistic Regression. The evaluation results show that the Bernouli Naïve Bayes method produces a precision parameter value of 71%, a recall parameter of 99%, and an accuracy of 76%. While the Support Vector Classifier method produces precision parameter values of 94%, 93% recall parameters, and 92% accuracy. The Logistic Regression method produces a precision parameter value of 93%, a recall parameter of 93%, and an accuracy of 92%.
Penerapan Metode VIKOR untuk Menentukan Kelayakan Perpustakaan Sekolah Diakreditasi
Authors: Sukamto Sukamto , Fitriansyah Aidil , Jukris Avisha Delinda
Published: July 2024

School libraries should be accredited with the aim of improving the quality of libraries. Pekanbaru City Library and Archives Service (DISPUSIP) in determining a school library that deserves to be accredited is still done manually. For this reason, a decision support system (DSS) is needed. This study uses the VIKOR method by steps of making a decision matrix, normalizing the criteria weight, determining the normalization matrix, determining the utility, determining the VIKOR index, and ranking. The alternative used is nine (9) junior high schools, both public and private. The criteria used refers to the school library accreditation instruments issued by the National Library that consist of six (6) criteria, namely collections, library facilities and infrastructure, library services, library staff, library administration and management, and reinforcement. The results obtained for the SMP library are Sek 4, Sek 7 and Sek 5 which are eligible for accreditation.
Aspect-Based Sentiment Analysis on Twitter Using Bidirectional Long Short-Term Memory
Authors: Sholehat Rizki Annas , Setiawan Erwin Budi , Sibaroni Yuliant
Published: July 2024

Twitter as one of the social media with the most users in the world, is often used as a medium for sharing opinions that can be positive or negative. Movie reviews containing many complex explanations and judgments will be challenging to classify. Therefore a sentiment analysis process based on aspects is needed to analyze the polarity of film review opinions based on predetermined aspects. This research aims to analyze the polarity of film review opinions based on aspects using the Bidirectional Long Short-Term Memory method and GloVe feature extraction. This study uses plot, acting, and director aspects with a total dataset of 17.247 data. Bidirectional Long Short-Term Memory is proven to produce relevant and accurate results for sentiment analysis with the greatest accuracy of 56,29% in the plot aspect, 87,07% in the acting aspect, and 85,55% in the director aspect. GloVe feature extraction is proven to increase the performance value of this research by up to 13,57% in the plot aspect, 4,16% in the acting aspect, and 10,48% in the director aspect.
Komparasi Hasil Optimasi Pada Prediksi Harga Saham PT. Telkom Indonesia Menggunakan Algoritma Long Short Term Memory
Authors: Enriko I Ketut Agung , Gustiyana Fikri Nizar , Putra Rahmat Hardian
Published: July 2024

To invest or buy and sell on the stock exchange requires understanding in the field of data analysis. The movement of the curve in the stock market is very dynamic, so it requires data modeling to predict stock prices in order to get prices with a high degree of accuracy. One of the steps to achieve this can be using a prediction system based on machine learning. There are several algorithms that can be used to predict stock values, one of which is the Long-Short Term Memory (LSTM) algorithm. This study aims to compare several optimization models, namely the Adam, SGD and RMSprop optimization models to analyze the accuracy of the LSTM algorithm in predicting stock price data and analyzing the number of epochs in forming an optimal model. The results of our research show that the LSTM algorithm has a good level of accurate prediction as shown in the Mean Absolute Percentage Error (MAPE) value and the data model obtained on variations in epochs values. Adam's optimization model shows that the higher the epoch value, the lower the loss value. The lower the loss value, the higher the prediction accuracy of the resulting stock data. Adam's Optimization Model is also the model with the highest accuracy value of 98.45%.
Random Oversampling, Chi-Square, dan AdaBoost dalam Penanganan Ketidakseimbangan Kelas pada Klasifikasi C5.0
Authors: Tanti Tanti
Published: July 2024

In data mining, there is a classification method. One of the problems often experienced in data mining classification is class imbalance. Class imbalance is a condition where the distribution of the dataset is uneven, meaning that it is divided into the majority class and the minority class with varying degrees of severity. The minority class is often misclassified because the majority class will be overclassified. This problem makes the classification process difficult and results in sub-optimal classification performance. Due to an imbalance, the classification will produce much higher accuracy for the majority class than for the minority class. This study aims to apply Random Oversampling, Chi-Square, and AdaBoost in overcoming class imbalances to optimize the performance of the C5.0 classification. In dealing with unbalanced datasets, performance appraisal needs to focus more on the positive class. So that the metric that is more suitable for assessing the classification results of unbalanced datasets is recall/sensitivity/TPR. The results showed that the application of Random Oversampling alone was able to improve the recall/sensitivity/TPR performance of standard C5.0. The application of Chi-Square alone has not been able to improve the performance of the C5.0 classification, but it has increased after the application of Random Oversampling. The combination of the three, namely Random Oversampling, Chi-Square, and AdaBoost able to increase the recall/sensitivity/TPR value of the standard C5.0.
Implementasi Arsitektur EfficientNetV2 Untuk Klasifikasi Gambar Makanan Tradisional Indonesia
Authors: Citra Erin Eka , Fudholi Dhomas Hatta , Dewa Chandra Kusuma
Published: July 2024

Indonesia has many variations of traditional food and interesting tourist destinations. The large number of tourist destinations make people like traveling and try to enjoy their traditional food. However, when trying traditional foods, especially foods that are new to them, they must be more careful, because the various ingredients contained in them have an impact on health. This research will try to make an application that can recognize Indonesian traditional food. The hope is that it can provide complete information, so that it can be used to develop calorie counter applications in the future. This study aims to design a system that can classify Indonesian traditional food images to help recognize food names with a certain level of accuracy using the EfficientNetV2 architecture. EfficientNetV2 is a new family of deep learning that excels in training as well as parameter efficiency. Deep Learning is a method often used to classify complex images. The EfficientNetV2 used in this study consists of four different architectures namely EfficientNetV2_S_21k, EfficientNetV2_M_21k, EfficientNetV2_L_21k, and EfficientNetV2_XL_21k. The dataset used comes from three types of data source categories, namely from Google Images, direct image capture using a Smartphone camera, and a combination of both. Each dataset category consists of 18 classes with a total of 1,800 images from Google Images, 1,800 images from Smartphone cameras, and 3,600 images from a combination of Google Images and Smartphone cameras. The dataset is taken from three categories to compare the level of accuracy and get the best accuracy value. The results of this study indicate that EfficientNetV2 can classify images of Indonesian traditional food with the highest test accuracy value of 99.4% from the EfficientNetV2-L(21k) model and the results obtained do not occur overfitting.
Klasifikasi Pengenalan Wajah Siswa Pada Sistem Kehadiran dengan Menggunakan Metode Convolutional Neural Network
Authors: Kurniawan Henri , Kusrini Kusrini , Kusnawi Kusnawi
Published: July 2024

The student attendance system is useful for monitoring student attendance. The current technology is technology capable of detecting an object, such as fingerprints, voice, eye retinas, and faces. The author will create a model that can be used to detect student faces. In this study the authors used a modified Convolutional Neural Network (CNN) algorithm. The complexity of the CNN designed is in accordance with the specifications of the hardware and software used. Face data is taken directly from students in class (private dataset). Recording of students' faces using a standard quality webcam camera. The images produced by each student are 126 images with a total of 20 classes (labels). Taking pictures with various angles of the face, namely from above, below, front, left side and right side. The augmentation techniques used are flip, random rotation and affine techniques to enrich the data. Regularization techniques, such as dropout are also used. This is in order to increase accuracy, speed of model training and avoid overfitting of the built model. The evaluation results with the confusion matrix on the modified Convolutional Neural Network (CNN) algorithm produce a faster model training process with 5.31 hours and accuracy reaching 97.78%, the loss value is stable at 0.1177, loss validation with the number 0.0192, with as many iterations (epochs) as 60. The resulting model will be developed on a prototype of the student attendance system.