bluuebunny/crossref_metadata_2025 huggingface dataset:
Penerapan Metode Saving Matrix Dalam Optimasi Rute Perjalanan Kunjungan Mitra Sekolah Untuk Peningkatan Strategi Marketing Kampus
Authors: Agustina Nani , Martini Martini , Sutinah Entin
Published: July 2024

Bina Sarana Informatika University has a lot of campuses, one of which is in the Jatiwaringin area. In marketing activities, UBSI has a team called Markom. One of the success factors of UBSI's marketing is determined by visits to high schools around the UBSI Jatiwaringin campus, where this activity is carried out as a form of cooperation between UBSI and the school in carrying out promotions for campus events whose target is class XII students. In carrying out campus marketing activities, UBSI Jatiwaringin has 15 target schools around the campus. In carrying out the visit, Markom had problems in determining which schools to visit first, because in carrying out campus marketing activities, Markom only provided time with a duration of 08:00-12:00 by making visits starting from the UBSI Jatiwaringin campus and returned to the UBSI Jatiwaringin campus. So it is necessary to have an optimal route pattern to determine the minimum route in the school visit process. To overcome these problems need to apply a method. In this study the Saving Matrix method was used to determine the path and distance of visits to schools with optimal visiting times. The data obtained is in the form of school names, distances, and travel routes from UBSI Jatiwarigin to the schools to be visited. From the results of data processing, it was obtained that the visit time was 6 days with 6 routes with a travel time of 233 minutes which previously did not have rules for using routes but only based on estimates and urgency which sometimes took 8 days. The results of this study indicate that by using the Saving Matrix method the time needed is more optimal because the steps taken are appropriate in solving travel route problems and easy to implement.
Klasifikasi Citra Stroke Menggunakan Augmentasi dan Convolutional Neural Network EfficientNet-B0
Authors: Putri Nadila Handayani , Jasril Jasril , Irsyad Muhammad , Agustian Surya , Yanto Febi
Published: July 2024

A stroke is a sudden onset of brain dysfunction, lasting for 24 hours or longer, resulting from clinically focal and global brain dysfunction. As many as 15 million people die from stroke each year. The stroke patients need an immediate treatment to minimize the risk of brain damage. One of the proponents for the stroke diagnosis is through a computed tomography (CT) image. In recent years, the image processing techniques capable to detect stroke patterns in a brain image, it can be useful for doctors and radiologists in doing diagnosis and treatment. This study aims to compare the level of accuracy using augmentation and without augmentation and hyperparameters using the Convolutional Neural Network in the EfficientNet-B0 architecture to classify ischemic, hemorrhagic, and normal brain stroke images. The data augmentation is produced by rotating, horizontal flipping, and contrast tuning of the original data. Testing data is provided as much as 20% of the portion of the original and augmented data, and the other 80% is used for the training process to find the optimal model. The model search is based on the composition of the training and validation data with a ratio of 70:30, 80:20 and 90:10. The experimental results show that the best performance is obtained for the combined original and augmented images, with accuracies of 97%, 93%, and 94%, respectively, for the three types of data-test: original, augmented, and combined. The merging of original and augmentated images for training data has shown that the model is robust enough in producing high accuracy results.
Seleksi Penerima Bantuan Pangan Istimewa Menggunakan Metode Simple Multi Attribute Rating Technique
Authors: Urva Gellysa , Desriyati Welly
Published: July 2024

BPI  is a routine program distributed by PT Amandika Permana once a year to workers with criteria determined by the company. The increasing number of workers makes it difficult for companies to select workers who are eligible for food assistance. In order for the distribution of food aid to be channeled on target, the company needs an analysis and quick calculations in the selection process. The implementation of the SMART (Simple Multi Attribute Rating Technique) method with a large number of criteria is able to solve existing problems. The implementation of the SMART method is determined by applying ten criteria in the form of employment status, income, number of dependents, home ownership status, residential land status, building area, movable assets, sources of drinking water, sources of house lighting and other assistance program cards. The final results of the study show that the use of the SMART method is able to assist users in determining BPI beneficiaries. The decision results are divided into three categories, where values 0.5 – 0.8 are recommended, 0.3 – 0.4 are considered, and values 0.1 – 0.2 are not recommended. Attachments to the selected data make it easy for users to make decisions efficiently and effectively. Based on the results obtained, the beneficiary who is most entitled to assistance also occupies the first position with a value of 0.75. Determining the appropriate weight value for each criterion affects the selection results to get the best results.
Data Mining Penentuan Jurusan Siswa Menggunakan Metode Agglomerative Hierarchical Clustering (AHC
Authors: Aldisa Rima Tamara
Published: July 2024

There are various students who experience problems during the learning process because the chosen major does not match their abilities because choosing a major is only influenced by other people so that it is not in accordance with their abilities and a teacher is also difficult to adjust the abilities of one student to other students. In order for these students to be grouped with students who have the same level of knowledge, a grouping is carried out using data mining techniques in order to obtain new information in a database that has a large size or large amount of data to make it easier for users to obtain this information. The method used is AHC which is utilized as clustering with the single linkage method, the single link method is considered more effective than other methods because the problem is very suitable where the grouping process is carried out based on each criterion of the distance between all alternatives. The criteria used were three criteria (Science Score, IPS Score, TPA Score) with a total of 121 students. The application of the AHC method is carried out by utilizing rapidminer so that the results obtained are more efficient and effective. The results that can be used by the school are cluster 0 totaling 93 students, cluster 1 totaling 10 students, cluster 2 totaling 10 students and cluster 3 totaling 8 students.
Perbandingan Metode MADM dalam Memilih Pegawai Terbaik dengan Pembobotan Objektif
Authors: Lubis Andre Hasudungan , Lubis Juanda Hakim , Aprillya Dinda Rizky
Published: July 2024

Nowadays, MADM or Multi-Attribute Decision Making as the part of decision-making theory has been used in various studies to examine decision making problems. Several methods such as SAW, ARAS, and MABAC are the most popular method to be selected to solve these decision-making problems, especially for personnel selection in a company or institute. However, these methods will certainly present various results. Hence, it is necessary to perform a comparison of the most optimal ranking results between these methods. The study focused on comparing those three methods in handling the personnel selection problem through the objective weighting by using SWARA method. The RSI method also employed to ensure the proper method to be used to solve the MADM problem. Five attributes are selected as the references to select best personnel among 38 of them, including Attendance, Discipline, Performance, Punishment, and Achievement. The study reveals that all of the three methods have the identical of RSI score. The results showed that the three methods had almost the same RSI values. The SAW method has the highest RSI value compared to other methods, namely 0.999489; the MABAC method has an RSI value of 0.999416, and the ARAS method with the lowest RSI value, namely 0.999052. Theoretical and practical implications are presented and discussed, along with suggestions for future research.
Rancang Bangun Keamanan Kotak Amal dengan Akses Fingerprint Menggunakan ESP32-Cam dan Telegram Berbasis IOT
Authors: Hermawan Diky , Jufrizel Jufrizel , Ullah Aulia , Faizal Ahmad
Published: July 2024

The charity box is one of the objects that is often the center of attention by thieves, the security of the charity box in current technological developments can be further developed, the creation of a charity box security tool with Fingerprint access using an IOT-based ESP32-cam is one of the developments that aims to improve security in charity box and reduce the rate of theft that occurs. The tool is made using Arduino UNO as the main microcontroller that processes data in this design and ESP32-cam as the recipient of commands from Arduino Uno and Telegram after which it sends data in the form of notifications in the form of photos and sentences describing the images that have been sent. Using 2 sensor inputs, namely the Fingerprint sensor to open the lid of the charity box door, if the fingerprint is registered, it sends a command to the Solenoid to open the charity box door, whereas when the fingerprint is not registered, the Solenoid will not open after that, ESP32-cam gets an order to capture the photo. sent to the telegram application and the Limit Switch sensor functions to detect the state of the charity box door when there is a forced opening that occurs at the door it will send data to Arduino UNO that there is a threat then the buzzer will sound. After testing the results obtained have a success rate of 100%, the ESP32-cam can receive orders to take pictures properly if one of the sensors used is active and can send data in the form of photos to the telegram application properly then the sensor used also works well well, the buzzer will sound when a forced opening is detected by the Limit Switch sensor. In this research it is hoped that it can improve the security of the charity box by capturing photos through the applied ESP32-Cam.
Penerapan Metode TOPSIS Dalam Sistem Pendukung Keputusan Seleksi Penerimaan Asisten Dosen Berbasis Web
Authors: Kho Richard William , Purnomo Hindriyanto Dwi
Published: July 2024

Technological developments in the field of education in tertiary institutions help and facilitate students in carrying out lecture processes such as conducting online classes, making it easy to obtain material provided by lecturers and various administrative processes that can be taken care of online. In the classroom, lecturers in teaching need teaching assistants as companions so that one of the success factors in the lecture process is based on the interaction between teaching assistants and students where the teaching assistant acts as a liaison between students and lecturers. At the Faculty of Information Technology, Satya Wacana Christian University, Informatics Engineering Study Program, to register as a teaching assistant, various processes are needed to meet the criteria that help in the selection process for teaching assistants, but in the selection process for accepting teaching assistants, it is still influenced by personal preferences which causes injustice in assessment and decision making. , various selection processes are complicated and time-consuming due to the many criteria with different levels of importance, the selection process is still done manually so that the selection process for hiring assistant lecturers is not effective and efficient. And in the process of storing data that is also ineffective, it is possible that it can be damaged or deleted. By using a decision support system and the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) it can assist in calculating all criteria simultaneously and giving weight to each criterion so that criteria can be identified that have more influence on the selection process and produce mathematical calculations so that the results which can be determined at the best value. The purpose of this study is to speed up the process of selecting teaching assistants by implementing the TOPSIS method to assist in selecting admission assistant lecturers. The results obtained are websites using the PHP programming language, Laravel framework, Bootstrap framework and MySQL database which are running well and have an attractive appearance. black box testing, validation of manual calculations where the results of the calculations are in accordance with the results on the website made with brandon which gets the highest prevalence value of 0.58.
Market Basket Analysis Menggunakan Algoritma Apriori dan FP Growth untuk Menentukan Pola Pembelian Konsumen
Authors: Noviana Reski , Hermawan Arief , Avianto Donny
Published: July 2024

An increase in sales transaction data every day will cause a very large amount of product sales transaction data to be stored. In most cases, data relating to very large sales transactions are only stored and used for archival purposes, not exploited adequately. In retail marketing, association data mining is used to investigate product purchasing patterns. However, if the company does not know the customer's purchasing patterns, it can have impacts such as inappropriate marketing strategies, decreased customer retention, lost business opportunities, lack of personalization, tough competition, stock/production inefficiencies, loss of customer trust. Knowing consumer purchasing patterns, the company can develop sales strategies and make the right decisions. In this study using Market Basket Analysis using the Apriori Algorithm and FP-Growth to determine consumer buying patterns. The results of this study resulted in two itemset combinations. The first combination is that if the buyer buys yogurt and sausage, the buyer also buys whole milk. The resulting support value is 0.00147 (0.0147%), the confidence value is 0.255814 (25.58%) and the lift value is 1.61986. the second combination, namely if the buyer buys sausage (sausages) and rolls/buns (bread rolls), then also buys whole milk (milk), this combination produces a support value of 0.001136 (0.0113%), a confidence of 0.2125 (21.25%) and a lift of 1.34559 . In addition to the combination of the itemset produced in this study, it also measures computational speed in processing Groceries data for Market Basket Analysis. The computational speed produced by the Apriori Algorithm is 3.1765 seconds, while the FP-Growth algorithm is 0.15892 seconds. The difference in computational speed between the Apriori Algorithm and FP-Growth is 3.0176 seconds.
Handling Unbalanced Data Sets Using DBMUTE and NearMiss Methods to Improve Classification Performance of Yeast Data Sets
Authors: Wirawan Bima Mahardika , Purbolaksono Mahendra Dwifebri , Nhita Fhira
Published: July 2024

Yeast vacuole biogenesis was chosen as a model system for organelle assembly because most vacuole functions can be used for vegetative cell growth. Therefore it is possible to generate an extensive collection of mutants with defects in unbalanced vacuole assembly. With this in mind, we must find the structural balance of data in yeast. Imbalanced data is when there is an unbalanced distribution of data classes and the number of data classes is either more or lower than the number of other data classes. Our method uses the f1score performance matrix method and the balanced accuracy on DBMUTE and NearMiss undersampling. Previously, only a few studies explained the results of using a performance matrix and balanced accuracy. Then, find out the performance results of the f1 score and balanced accuracy and get the best score from the yeast datasets. In the study, a comparison between the imbalanced datasets using the undersampling method. Furthermore, to obtain the performance matrix results, use the f1 score and balance accuracy. After testing five yeast datasets, we performed an average f1 score and balance accuracy with the highest average NearMiss f1 score of 62.23% and the highest average balanced accuracy of 78.59%.
Tourism Recommendation System using Weighted Hybrid Method in Bali Island
Authors: Pratama Diffo Elza , Nurjanah Dade , Nurrahmi Hani
Published: July 2024

Tourism is a promising sector for global economic growth, as it has shown resilience during the global crisis. In Bali, tourism is a leading sector alongside agriculture and industry, making a significant contribution to regional and community development. However, Bali's popularity as a sought-after tourist destination also raises the need for an information system that can provide destination recommendations. To overcome the problem of information overload, a recommendation system is needed. This study tested the tourism recommendation system in Bali using the Weighted Hybrid technique which combines two methods, namely Collaborative Filtering and Content-Based using the weighted value technique. Collaborative Filtering, Content-Based, and Weighted Hybrid approaches will be compared in this study to improve the performance and accuracy of current recommendation systems. Utilizing the MAE, MSE, and RMSE values, the evaluation is carried out by comparing the evaluation matrices of the three Collaborative Filtering, Content-Based, and Weighted Hybrid methods. With MAE, MSE, and RMSE values of 0.4854, 0.4034, and 0.6351 respectively, the evaluation findings show that the Weighted Hybrid technique beats Collaborative Filtering and Content-Based with a weight value of 0.4.
Perbandingan Algoritma SVM, KNN dan NBC Terhadap Analisis Sentimen Aplikasi Loan Service
Authors: Nurmalasari Dewi , Hermanto Teguh Iman , Nugroho Iman Ma'ruf
Published: July 2024

According to data on the number of credit card users in Indonesia, it has decreased from late 2019 to 2021 oneThe reason is because of the Covid-19 pandemic that hit. Because of this condition, many people are starting to switch to digital credit because they are considered to minimize transmission of viruses and the process is felt to be more efficient than having to use a credit card. This study aims to compare the level of accuracy between the three algorithms, namely the naïve Bayes classifier, k-nearest neighbor and support vector machine for digital credit applications or often called loan services, namely Kredivo. Akulaku, and Indodana in Indonesia by classifying it into two classes namely positive and negative by using the help of the Python programming language to analyze a sentiment by going through text preprocessing and weighting processes said TF-IDF. The results for the accuracy of the Kredivo application using K-NN get a score of 84%, Naïve Bayes 88%, and SVM get 89%. For the application of the K-NN method, it gets 79%, Naïve Bayes 86%, and SVM 87%. As for the indodana application, the K-NN method gets 81%, Naïve Bayes 88%, and SVM 88%. From the results of this accuracy it can be concluded that the Support Vector Machine method has better accuracy results compared to the K-Nearest Neighbor and Naïve Bayes methods.
Klasifikasi Jenis Mangga Menggunakan Algoritma Convolutional Neural Network
Authors: Yati Risma , Rohana Tatang , Pratama Adi Rizky
Published: July 2024

The name of the mango is Mangnifera IndicaL. It originated in India and spread to Indonesia. There are various types of mango variations with different shapes and colors according to the type. To distinguish each mango is seen by its shape and color. However, if in the harvest process mango farmers have to choose manually it takes a long time and potentially mistaken in determining the type. So it needs technology that can make it easier to differentiate the type of mango based on its shape. The study aims to create models with the best accuracy on the process of classifying 5 types of mango based on its shape. The data used in the research this time there are 5 types of mango that will be classified, namely Mangga Apel, Arumanis mango, Mangga Gedong Gincu, Golek mango and Mangga Manalagi. Used 375 images of mango as data sets. The data set before entering the previous training process is undergoing a pre-processing phase that includes the augmentation and resize process. The number of images increased to 2250. The data set is divided into three parts: 70% training data, 20% validation data, and 10% test data. Next is the process of segmentation, the segmentation used in this research is otsu segmentation. The classification process uses the Convolutional Neural Network (CNN) architecture with 3 layers of convolution 16,32 and 64, also using the Adam optimizer. 4 experimental scenarios were performed to find the best accuracy value by distinguishing between learning rate and batch size. From the confusion matrix test results, the best accuracy values were obtained from the input hyperparameter size100x100, epoch 100, learning rate 0,001 and batch size 15 with accurate values of 99.56%, precision 100%, recall 100%, and f1-score 100%.
Implementasi Metode MAUT dengan Menerapkan Pembobotan ROC Dalam Pemilihan Ketua Himpunan Mahasiswa
Authors: Faran Jhiro , Aldisa Rima Tamara
Published: July 2024

The election of the head of the student association is an important process in maintaining the sustainability and progress of the organization. However, determining the best association chairperson can be a complex and subjective task. Therefore, we need an effective decision support system to assist in the selection process. This study aims to develop a Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT) method with ROC weighting in selecting the best association chairman. The MAUT method is one of the methods used to overcome complexity in decision making by considering several relevant attributes. The results of this study are expected to provide objective recommendations in the process of selecting the best chairman of the association. The results of this study using the MAUT method are with a value of 0.775 as the highest alternative chosen as chairman of the association. By utilizing the MAUT method, this system can assist the relevant election committee in obtaining better and more informed decisions. This proposed decision support system can also be used as a guide for other schools facing similar challenges in selecting association leaders
Analisis Sentimen Opini Pengguna Twitter Terhadap Tragedi Kanjuruhan Malang dengan Metode Support Vector Machine
Authors: Herlambang Fahri Putra , Avianto Donny
Published: July 2024

The Kanjuruhan tragedy on October 1, 2022, strongly impacted Indonesian football stadium safety. At the Kanjuruhan Stadium, a Persebaya vs. Arema FC match resulted in the deaths of 135 supporters. Due to the significant number of fatalities, there is ongoing debate regarding the responsible parties for the tragedy. Since there are expected to be 18.45 million active users in Indonesia by 2022, Twitter research helps determine popular attitudes. Support Vector Machine is used in this work to evaluate tweets and identify whether they include positive or negative emotions. The categorization outcomes may influence how the public views those responsible for the tragedy. On October 6, 2022, specific Twitter data on tear gas riots, oppressive government, rivalry between supporters, and violence against authorities were taken into account. The sentiment classes are negative, neutral, and positive. The study attained a 95.55% f1-score, 95.16% accuracy, 97.56% precision, and 95.16% recall.
Perbandingan Algoritma K-Means Dan K-Medoids Untuk Pemetaan Hasil Produksi Buah-Buahan
Authors: Prasetyaningrum Eka , Susanti Puji
Published: July 2024

In general, in 2019-2020 fruit production in Kotawaringin Timur district has decreased. Based on the data on fruit production, the amount of fruit production decreased, resulting in scarce fruit stocks and expensive fruit prices. Based on these problems, fruit production will be grouped according to the type of production in East Kotawaringin district using data mining techniques with clustering techniques using the K-Means algorithm and K-Medoids algorithm in order to optimize and increase fruit production. The results of grouping fruit production will be divided into 3 clusters, namely the highest cluster, the medium cluster, and the lowest cluster, making it easier for the Food and Agriculture Security Service in East Kotawaringin district to calculate and increase agricultural yields, especially in the horticulture sector. Based on the test results using data in 2019-2022, totaling 29 data in the Rapidminer application version 9.9 by comparing the DBI (Davies Bouldin Index) values of the two algorithms with so that the conclusion in determining the best value for the number of clusters (K) is that the fourth experiment shows 0.296 DBI (Davies Bouldin Index) values with six clusters. If the DBI value is smaller or closer to 0, then the cluster results obtained are more optimal. The results obtained in the K-Means algorithm get a smaller DBI (Davies Bouldin Index) value with a value of 0.296 while the K-Medoids algorithm results with a DBI (Davies Bouldin Index) value of 0.507. The best algorithm for clustering fruit production in Kotawaringin Timur district is the K-Means algorithm based on the DBI values obtained.
Implementasi Data Mining dengan Algoritma Apriori dalam Menentukan Pola Pembelian Aksesoris Laptop
Authors: Soepriyono Gatot , Triayudi Agung
Published: July 2024

Consumer purchasing patterns are an important factor in the business world, which influences marketing strategies, stock management and company profits. In the context of the laptop accessories business, a deep understanding of consumer purchasing patterns is very necessary to increase operational efficiency and customer satisfaction. Data mining, as a powerful data analysis method, has become an effective tool in uncovering these patterns. One of the data mining algorithms that is often used to analyze association patterns is the Apriori algorithm. This research applies the Apriori algorithm to identify and analyze purchasing patterns for laptop accessories from transaction data obtained from a retail store. By analyzing this data, we can identify items that are frequently purchased together and purchasing patterns that may not be immediately apparent to humans. The results of this analysis provide valuable insight into consumer preferences, helping retail stores to design more effective marketing strategies. The results of this research can also be used to manage stock more efficiently. By knowing deeper purchasing patterns, retail stores can predict stock needs more accurately, reduce the risk of excess inventory, and optimize operational expenses. Thus, this research can help increase company profits and satisfy customers by providing accessories that suit their preferences. In the increasingly developing information era, the use of data mining and algorithms such as Apriori is becoming increasingly important. This research is an example of how data analysis can be used in the real world to support smarter and more efficient decision making in the laptop accessories business. As a result, a better understanding of consumer behavior and purchasing patterns can provide a strong foundation for developing successful business strategies.
Analisis Sentimen Masyarakat Terhadap Perilaku Korupsi Pejabat Pemerintah Berdasarkan Tweet Menggunakan Naive Bayes Classifier
Authors: Syakir Abdul , Hasan Firman Noor
Published: July 2024

The corrupt behavior of government officials is a problem that worries the public and threatens the integrity of the government system. In today's digital era, social media is an important means for the public to voice their opinions and sentiments on social issues, including the corruption of government officials. This study aims to analyze public sentiment toward the corrupt behavior of government officials based on tweet data on social media using the Naïve Bayes Classifier method. Tweet data is taken from social media Twitter related to corruption cases involving government officials within a certain period. The data is then processed to remove irrelevant elements and extract the sentiments contained in the tweets The Naïve Bayes Classifier method is applied to classify these tweets of positive, negative, or neutral sentiment toward corrupt behavior from government officials. The results of this study conclude that the public is very angry, disappointed, and has a low level of trust in corrupt behavior committed by government officials. Proven by the most dominant sentiment category is negative sentiment with 224 data and 95 data fall into the positive sentiment category.
Penerapan Metode Elimination Et Choix Traduisant la Realite (ELECTRE) dalam Proses Segmenting, Targeting, dan Positioning Layanan Akomodasi
Authors: Singgalen Yerik Afrianto
Published: July 2024

One of the challenges in marketing marketing strategy is determining the appropriate segments and target markets and increasing competitiveness through solid positioning. To determine the STP (Segmenting, Targeting, Positioning) marketing strategy in the accommodation service business, it is necessary to support decision-support models relevant to resource availability and the market environment. This study applies the Elimination et Choix Traduisant la Realite (ELECTRE) method in segmenting, targeting, and positioning hotel, homestay, and resort accommodation services. The stages in the ELECTRE method are as follows:  the stage of determining criteria and alternatives; the stage of normalization of the decision matrix; the weighting stage of the normalization matrix; the stage of determining the set of concordance and discordance on the index; the calculation stage of the concordance and discordance matrices; the calculation stage of the dominant matrix of concordance and discordance; Determination stage aggregate dominance matrix; the stage of elimination of less favorable alternatives. Based on the results of this study, it is known that the ELECTRE method can provide a systematic decision-making structure and framework, especially in segmenting, targeting, and positioning, to optimize results by business goals. Based on the results of implementing the ELECTRE method, it can be seen that A1 has an E value of 63.52, so it occupies the first position. In addition, A2 has an E value of 53.74, so it occupies the second position. Also, A3 has an E value of 49.74, occupying the third position. Based on the application of the ELECTRE method, hotels (A1), homestays (A2),  and resorts  (A3) need to optimize the 9P marketing mix (product, price, place, promotion, people, process, physical evidence, performance, partnership) in the process of Segmenting, Targeting, and Positioning for the sustainability of the accommodation service business. However, there is a challenge in this method, which is the integration of subjective preferences of capital owners in the decision-making process using ELECTRE, which can lead to distortion of results due to subjectivity and individual interests.
Analisis Sentimen Terhadap Penggunaan Aplikasi Astro Menggunakan Algoritma Naïve Bayes
Authors: Prasetyo Stevan Amadeo , Atmojo Wahyu Tisno
Published: July 2024

Astro is a quick commerce application that focuses on providing fast groceries delivery services within less than an hour. It was established in 2021 under PT Astro Technologies Indonesia. Quick commerce applications are relatively new in Indonesia, and customer sentiments play a crucial role in determining the acceptance of this category within the society. Through reviews available on Google Play Store, we can gauge whether the majority of the populace holds a positive or negative sentiment towards it. The Astro app has been downloaded by over one million users, with a rating of 4.6 and more than 11.2 thousand reviews on Google Play Store. A total of 1,820 reviews were successfully obtained, and after preprocessing and data cleaning, 1,646 reviews remained. Subsequently, the data was labeled using the Naïve Bayes algorithm, categorizing sentiments into positive and negative. The results showed 1,213 reviews, or 74%, expressed a positive sentiment, while 433 reviews, or 26%, conveyed a negative sentiment. Using the confusion matrix, the classification performance achieved an accuracy of 63%, with a class recall of 61.58% for True Positive (TP) and 69.97% for True Negative (TN). The class precision for True Positive (TP) was 83.93%, and for True Negative (TN) was 38.36%. Based on the analysis, it can be concluded that the majority of Astro app users have a positive sentiment. However, a notable portion holds a negative sentiment, providing valuable insights to address concerns raised by users with negative sentiments.
Metode MICE Support Vector Machine (MICE-SVM) untuk Klasifikasi Performance Mahasiswa Merdeka Belajar Kampus Merdeka
Authors: Hermawan Angga Apriano , Saraswati Galuh Wilujeng , Kartikadarma Etika
Published: July 2024

The Ministry of Education and Culture established a Merdeka Belajar Kampus Merdeka (MBKM) program with the aim of improving the competency of student graduates, both soft skills and hard skills, so that they are better prepared and relevant to the needs of the times, preparing graduates as future leaders of the nation who are superior and have personality. However, the MBKM program is not always effective in improving the quality of a student because there are still several shortcomings. It is also felt that some students have not received maximum results when participating in the MBKM program. In fact, not all programs offered by MBKM partners receive an assessment in the form of soft skills scores. The aim of this research is to classify whether the MBKM program influences the performance of MBKM program students by applying the Multivariate Imputation by Chained Equation (MICE) method to overcome missing values in the classification of MBKM student performance at the Faculty of Computer Science, Dian Nuswantoro University. The qualification of MBKM student performance is very important because we need to know whether the program is deemed effective or not to be continued in the future. In this study, researchers used a dataset originating from the MBKM report from students at the Faculty of Computer Science, Dian Nuswantoro University. Researchers obtained data by collecting data from MBKM student certificates and reporting the results. The data taken was 277 pieces for training and 69 pieces for testing. Next, the researchers used the Support Vector Machine (SVM) algorithm for the classification process. The research results show that the performance of the Support Vector Machine (SVM) algorithm model with MICE missing value handling has better accuracy results, with an accuracy value of 98.07% compared to using the Mean Imputation method, which only obtains an accuracy of 97.34%.
Analisis Optimasi Algoritma Decision Tree, Logistic Regression dan SVM Menggunakan Soft Voting
Authors: Pratama Yosiko Aditya , Budiman Fikri , Winarno Sri , Kurniawan Defri
Published: July 2024

Agriculture constitutes a fundamental pillar of a nation's economy. One key to success in agriculture is the selection of suitable land. The prediction of whether land is fertile or not can be efficiently accomplished through a data mining approach. This is because data mining offers several algorithms for extracting crucial information from vast datasets through classification. However, classification algorithms in data mining often encounter the challenge of data imbalance, which can lead to low accuracy rates. Processing data with calculation models that have low accuracy rates can result in numerous erroneous predictions (fail predictions). To address this issue, this research conducts testing and comparative analysis of the confusion matrix results from four calculation models: the Decision Tree algorithm, Logistic Regression, SVM, and the combination of these three algorithms using the Soft Voting ensemble technique. The test results indicate that processing data using the Decision Tree, Logistic Regression, and SVM algorithms, along with the optimization of the Soft Voting ensemble model, achieves the highest accuracy rate of 91.53%. This accuracy rate is higher compared to the other three calculation models: the Decision Tree algorithm with a difference of 3.83%, Logistic Regression with a difference of 2.66%, and SVM with a difference of 1.36%. This research makes a significant contribution by identifying an efficient solution to improve the accuracy of identifying fertile agricultural land, which is a crucial step in supporting the success of the agricultural sector in the country's economy.
Analisis Data Mining dalam Komparasi Average Linkage AHC dan K-Means Clustering untuk Dataset Facebook Live Sellers
Authors: Faran Jhiro , Aldisa Rima Tamara
Published: July 2024

Facebook Live is a social media platform owned by Facebook that allows users to broadcast videos directly or live stream via the internet. Users can share moments in real-time with friends, followers, or members of certain groups. The platform allows anyone with a Facebook account to create live video broadcasts from a mobile device or computer equipped with a webcam. Many Micro, Small and Medium Enterprises (MSMEs) use Facebook Live as a tool to sell products or services directly to their audience. This strategy is increasingly popular in direct marketing on social media, especially in countries such as China and Thailand. Sellers on Facebook Live, known as Facebook Live Sellers, broadcast live on the platform to introduce products or services. They explain all the features offered, answer questions from viewers, and encourage them to make a purchase immediately. To increase buyer interest, they often offer special offers or discounts. Facebook Live Sellers can also be considered a form of influencer marketing, where individuals or businesses build a loyal following and use their influence to promote products and services. Despite the potential benefits, Facebook Live Sellers also face challenges. They interact directly with potential buyers, who may sometimes be dissatisfied with the product offered or the way the seller promotes it. Therefore, evaluations such as comments, reactions (such as like, unlike, angry), and other interactions during broadcasts are important. This research aims to group potential buyers' reactions during Facebook Live broadcasts as a strategy to overcome several problems in direct sales via this platform. In addition, grouping by the number of likes and comments can help sellers identify the most active groups of buyers and have the potential to become loyal customers. The number of data samples was determined using the Solvin method so that the dataset that became the data sample was 341 data. The methods used for grouping are K-Means and AHC (Average linkage) with the final results showing that the amount of data grouped into three clusters by both methods is the same, with most of the data being in Cluster 0, namely 98.5% of the total data sample. . Cluster 1 has a small amount of data, namely 0.6%, while Cluster 2 has 0.9% of the data sample.
Analisis Sentimen Warganet Terhadap Keberadaan Juru Parkir Liar Menggunakan Metode Naive Bayes Classifier
Authors: Mukti Avis Tantra , Hasan Firman Noor
Published: July 2024

The still high rate of poverty in Indonesia causes many impacts, one of which is the emergence of illegal parking attendants. This condition continues to be supported by the creation of a parking area for business actors for their visitors. The parking areas provided are often free, and even have signs saying so. However, there are individuals who use the free parking space to earn income. There are many netizens' sentiments regarding the phenomenon of lying parking attendants on social media. Therefore, in this research, an analysis was used in the form of netizen sentiment towards illegal parking attendants on social media X using Naïve Bayes. The main objective of this research is to understand the public's feelings towards the existence of illegal parking attendants operating in the parking area. The dataset used in this analysis was 905 taken from social media The results of this research netizens felt very annoyed, angry and disturbed by the presence of illegal parking attendants operating. This is proven by the results of negative sentiment which dominates 93% of the total data or as many as 841 negative sentiments regarding this phenomenon.
Ensemble Klasifikasi Penyakit Tuberculosis Pada Hasil Pengobatan Menggunakan Metode Hybrid K-Nearest Neighbor (K-NN), Decision Tree dan Support Vector Machine (SVM)
Authors: Azzahra Alya Nurfaiza , Zeniarja Junta , Luthfiarta Ardytha , Rahayu Mufida
Published: July 2024

Tuberculosis (TB) is an infectious disease with the highest cause of death in the world. This disease can be transmitted through the air and attacks the pulmonary respiratory system. The increase in TB cases from year to year is due to little information about the treatment of this disease. This requires the process of diagnosing and treating TB requiring accurate data analysis. From these problems, classification of tuberculosis disease is needed to improve better treatment results. In this study, experiments were used with the Hybrid model classification algorithm with a method that combines three approaches, namely K-Nearest Neighbor (K-NN), Decision Tree, Support Vector Machine (SVM) to classify treatment results using the Ensemble classification method and aims to combine each method in order to create a stronger Ensemble model and increase accuracy in treatment results, using data from the Semarang City Health Service or what is called Tuberculosis Information System (SITB) data in 2020-2023 with 80% training data and test data 20%. Based on the results of testing and analysis using the confusion matrix, the highest accuracy value was obtained at 78.55% using K-Fold Cross validation, namely k equals 7 and the Ensemble model obtained high results for treatment outcomes.
Leveraging BiLSTM and LDA for Analyzing and Dashboarding User Feedback in Applications
Authors: Mutmainah Siti , Fudholi Dhomas Hatta
Published: July 2024

The idea of prioritizing customer satisfaction to uphold or improve the excellence of a product or service can incorporate the utilization of user feedback. However, to provide a comprehensive visual summary to application developers or stakeholders, it is important to provide a detailed description of user sentiment issues. In this study, the data source used is user feedback from local telemedicine applications in Indonesia. This research builds a framework of deep learning to perform user feedback analysis and applies topic modeling to sentiment clusters. then builds visual construction of research results effectively and efficiently, to facilitate stakeholders in making decisions. Build a framework to analyze user feedback utilizing deep learning BiLSTM + IndoBERT for sentiment classification and LDA to model topics in sentiment groups. The results show that most of the user reviews of the five telemedicine applications have a positive sentiment at 91%. The model used has good prediction performance with the accuracy of the BiLSTM model with IndoBERT 96.44%. The negative sentiment group comprises 12 topics (0.58446), the most significant topics being 35.4% about telephone broadcasting, 25.3% payments, and 8.5% about medicine purchase service. For the imbalanced data case, BiLSTM showed good accuracy and precision values. The classifications and topics generated by deep learning models are affected by improper data labeling, so it is necessary to explore the data labels generated.
Sistem Deteksi Anomali Pada Transformator Menggunakan Dissolved Gas Analysis Dengan Metode K-Nearest Neighbour
Authors: Kurniawan Andre , Purnomo Hindriyanto Dwi
Published: July 2024

The transformer is the most important part of the electric power system, therefore maintenance needs to be carried out to prevent the emergence of anomalies in the transformer. Dissolved Gas Analysis (DGA) is a method for detecting anomalies in transformers. DGA is used to test the condition of the insulating oil in transformers by taking samples of the insulating oil. If an anomalous event occurs in a transformer, the resulting gas concentration will vary depending on the type of anomalous event in the transformer. The main problem underlying this research is the inability of previously existing anomaly detection systems to provide an optimal level of accuracy, traditional methods or approaches used also face obstacles in interpreting complex data from dissolved gas analysis. The aim of the research carried out is to be able to design an anomaly detection system on Transformers using DGA and to see the level of accuracy of the existing DGA method using KNN. In this research, the anomaly detection system on the transformer resulted in the highest level of accuracy being 94% using the key gas method and the lowest level of accuracy being 79% using the Doernenburg Ratio method. The conclusion of this research is that it is able to create a system that can make it easier to analyze anomalies in transformers, and can be used as an alternative method for determining the condition of transformers.
Pengenalan Ekspresi Wajah Menggunakan Transfer Learning MobileNetV2 dan EfficientNet-B0 dalam Memprediksi Perkelahian
Authors: Handayani Ni Made Kirei Kharisma , Hidayat Erwin Yudi , Naufal Muhammad , Putra Permana Langgeng Wicaksono Ellwid
Published: July 2024

Expressions play an important role in recognizing someone's emotions. Recognizing emotions can help understand someone's condition and be a sign of their possible actions. Fighting is one of the violences that occur due to someone's negative emotions that need to be prevented and treated immediately. In this study, expression recognition is used to predict the possibility of a fight based on the expression shown by a person. The dataset used is FER-2013 which has been modified into two labels, namely "Yes" and "No". The data undergoes a preprocessing step which includes resizing and normalization. Model experiments using transfer learning from the MobileNetV2 and EfficientNet-B0 architectures have been modified by performing hyperparameter and fine tuning which includes freezing the layer by 25% in the first layers of each model and adding several layers such as flatten and dense. In the training process, some parameters used are 30 epochs, batch size 32, and Adam optimization with a learning rate of 0.0001. Model performance evaluation is measured using Confusion Matrix, then the results are compared and obtained the model that produces the best accuracy value is EfficientNet-B0 which is 82%. Meanwhile, based on the training time and model weight, MobileNetV2 is 1 hour 1 minute 43 seconds faster and 21.57 MB smaller than EfficientNet-B0.