bluuebunny/crossref_metadata_2025 huggingface dataset:
Sentiment Analysis of Practo Application Reviews Using Naïve Bayes and TF-IDF Methods
Authors: Putranto Rizal Adi , Purbolaksono Mahendra Dwifebri , Astuti Widi
Published: July 2024

Entering the 4.0 era, it seems that the healthcare industry is the one most likely to benefit from the combination of physical, digital and biological systems. Digital health applications or telemedicine have experienced significant growth in recent years. In the current era, the development of telemedicine is accelerating, one of which is the Practo application. As the number of users using this app increases, it is important to get their opinions in order to improve the health services provided by the app. Therefore, sentiment analysis of the comments regarding the health services on the app is necessary to find out the users' opinions. By utilizing sentiment analysis, it is possible to use the sentiment analysis results obtained as a sample that corresponds to both positive and negative comments. In addition, it can be revealed that there is a mismatch between the ratings and comments given by users. This information has the benefit of being able to improve the Practo application and improve the health services provided to more effectively meet the needs and expectations of users. This research employs the Naïve Bayes approach for sentiment analysis, utilizing TF-IDF feature extraction. Naïve Bayes was chosen because it is known as an efficient classification algorithm but has a high level of accuracy. This approach involves utilizing the Bayes rule formula to calculate probabilities and make classifications. It is applicable for solving classification problems that involve either numeric or nominal feature data. Meanwhile, TF-IDF was chosen because it can associate each word in a document with a numerical value that reflects its level of relevance to the document. TF-IDF is used to measure the weighting of words as features in the summary. In this study, the best model achieved a performance with an f1-score of 85.50%.
Penerapan Case Base Reasoning Pada Sistem Pakar Aspek Perkembangan Berdasarkan Standar Tingkat Pencapaian Perkembangan Anak
Authors: Tazkiya Nazwa Wanda , Atmojo Wahyu Tisno
Published: July 2024

Every child will develop in every aspect of development. But every child will experience different development in every aspect of their development. Many parents are less aware of the importance of early childhood development and pay less attention to child development in every aspect of development. The importance of aspects of child development at the age of 5-6 years where this age will be a provision for children in entering the next level such as elementary school. Efforts to overcome this problem with the development of expert systems become effective solutions. The expert system helps parents to make it easier to determine the more dominant aspects of development in children aged 5-6 years. In this expert system research, applying the Case Base Reasoning method is used to match new cases with previous cases that have been solved stored in the case base. Thus, it is expected that parents' awareness and attention to child development will increase so that parents can provide support and learning that is more optimal and in accordance with children's needs. Results obtained for more dominant aspects in the development of children aged 5-6 years based on indicators Standar Tingkat Pencapaian Perkembangan Anak (STPPA) RA which has been determined by the Decree of the Director General of Islamic Education No. 3331 of 2021 with 6 aspects of child development such as religious and moral values, physical motor, cognitive, language, social-emotional and art, test results were obtained using the case base reasoning method against expert analysis with 5 test data getting 4 accurate test data and 1 inaccurate test data so as to get an accuracy of 80%.
Effectiveness of Using Autoencoder Method on Recommender System in E-Commerce Domains
Authors: Agung Pramu Putra Jayana Citra , Baizal Z K A
Published: July 2024

The growth of large data in the online market can cause problems for users, one of which is in finding products that are to their liking. Recommender systems can overcome this problem by providing specific product recommendations to be promoted and offered to buyers, for example with Collaborative Filtering. The Collaborative Filtering Paradigm consists of Memory-based and Model-based techniques. Model-based techniques are considered to be able to complement the shortcomings of memory-based because of their high scalability, accuracy, and reduced dimensions. The type of model-based that is best known for having good results is Singular Value Decomposition (SVD) and what has been frequently used recently is deep learning, especially Autoencoder. The both models are very popular for use in dimension reduction so they are suitable for making recommendations. The advantage of Deep Learning is this method can be done without preprocessing so it can minimize the process that must be done. The evaluation results show that the errors produced by SVD and Autoencoder are lower compared to other studies. RMSE is 0.7 and MAE is 0.5. Even though the RMSE and MAE on the Autoencoder are greater than the SVD, the results of the T-Test show that there is no significant difference in the two error results. Autoencoders have been shown to have good results without preprocessing and are more effective with shorter processes and there are no significant differences with SVDs. Thus, Autoencoder can be said to be worthy of use and more effective in giving recommendations.
Hoax Detection of Indonesian News Media on Twitter Using IndoBERT with Word Embedding Word2Vec
Authors: Bhagaskara S M Pernanda Arya , Prasetiyowati Sri Suryani , Sibaroni Yuliant
Published: July 2024

Hoax is data that is added or deducted from the news that occurred. In the digital age, hoaxes are increasingly being spread, and people are very quickly affected by their spread, especially hoaxes circulating in Indonesian news media on social media. Disseminating information that has not been confirmed as accurate can cause public concern and anxiety. Virtual diversion has transformed into a correspondence key to begin thinking, talking, and moving around cordial issues. In this manner, exploration will be led by consolidating the IndoBERT model with the Word2Vec development highlight in arranging deception news in Indonesian news media. This model was constructed using K-Fold cross-validation to enhance model performance across extensive data sets. The information utilized comes from tweets shared on Twitter by the Indonesian public. The trials that have been carried out demonstrate that combining Word2Vec with IndoBERT is effective at detecting hoaxes, with an overall accuracy score of 88% for the entire dataset. This conclusion can be drawn from the classification results of Word2Vec with IndoBERT. Also, the best precision and incentive for every cycle is almost 99%. In addition, the study's objective is to identify hoax news in Indonesian news media disseminated via social media. This will encourage individuals to be more cautious when reading and disseminating news, as untrue information will significantly impact certain individuals.
Sistem Pendukung Keputusan Penerapan Metode MOOSRA dan Pembobotan ROC dalam Penilaian Kinerja Laboran Kimia
Authors: Rambe Deby Lorensyah , Ritonga Ali Akbar , Hasibuan Elysa Rohayani , Purnama Iwan
Published: July 2024

Laboran is a designation for technical personnel who operate in a laboratory environment or a place related to scientific, research or testing activities. A chemical worker is a technical worker who has specific knowledge and skills in the chemical field. In schools, chemical workers have a role as workers who supervise and manage chemical laboratories. His main duties include sampling, material testing, data processing and maintenance of laboratory equipment. The school needs to know the performance of each chemical worker, so an assessment of the performance of each chemical worker is needed so that the school knows the best performance of the chemical worker, an additional salary will be added. The performance assessment includes several criteria namely Technical Expertise, Professional Ethics, Problem Solving, Punctuality, Safety and Health, Maintenance of tools and materials, Report Quality, Self-Development, Accuracy, Teamwork and Communication. To simplify the evaluation system for the performance of chemical workers and find out who is the best, a decision-making system is needed, namely a decision support system (DSS). SPK is a system designed to assist decision makers in solving problems or choosing the best option from various available alternatives. In this study the authors used the MOOSRA (Multi-Objective Optimization on the Basis of Simple Analysis) method and ROC (Rank Order Centroid) weighting. The MOOSRA method is a stable, consistent, and reliable multi-criteria comparison method that can provide an accurate assessment of chemical workforce performance. From the application of the ROC and MOOSRA methods, it was obtained an assessment of the performance of the chemical workforce with the chemical workforce alternative with the highest performance, namely the LK07 alternative on behalf of Gueen with a value of 207.95261.
Perbandingan Metode Penyesuaian Kontras Citra Pada Pengenalan Ekspresi Wajah Menggunakan Fine-Tuning AlexNet
Authors: Sarif Akhmad , Gunawan Dadang
Published: July 2024

Research related to facial expression recognition (FER) has become a significant topic of interest in the field of computer vision due to its broad applications. Artificial intelligence technologies, such as deep learning, have been applied in FER research. The use of deep learning models in FER requires a dataset for training, which plays a crucial role in determining the performance of deep learning. However, the available FER datasets often require preprocessing before being processed using deep learning. In this study, a comparison of contrast adjustment preprocessing methods was conducted using Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, the dataset images were used with a fine-tuned deep learning model, specifically AlexNet, to classify them according to the categories of human facial expressions. The objective of this research is to determine the superior contrast adjustment method for FER dataset images in improving the performance of the deep learning model employed. The CK+ dataset (The Extended Cohn-Kanade) and KDEF dataset (The Karolinska Directed Emotional Faces) were used in this study. The results indicate that the CLAHE method outperforms HE in both the CK+ and KDEF datasets. In the CK+ dataset, the CLAHE method achieved an average accuracy of 93.21%, while the average accuracy of the HE method was 91.50%. For the KDEF dataset, the average accuracy of the CLAHE method was 88.35%, compared to 84.70% for the HE method.
Analisis Sentimen Terhadap Isu Resesi Tahun 2023 di Indonesia menggunakan Metode Naïve Bayes
Authors: Zakaria Naufal Fakhri , Wibowo Merlinda , Setya Nugraha Novanda Alim
Published: July 2024

Recession is a phenomenon in which the real GDP (gross domestic product) decreases for two consecutive quarters, meaning that economic activities such as distribution, investment, consumption, production will decrease, causing a domino effect that is detrimental to various parties, one of which is layoffs (termination of employment). The recession was initiated by the weakening of the global economy which had an impact on the domestic economy and countries in the world. The stronger the dependence of a country's economy on the global economy, the faster a recession will occur in that country. Indonesian President Joko Widodo predicts that in 2023 Indonesia will be a dark year due to the economic and energy crisis due to COVID-19 and the war between Russia and Ukraine Therefore a sentiment analysis is needed to see public opinion regarding the issue of the 2023 recession in Indonesia. The method used in this study is the Naïve Bayes classification method. Naïve Bayes is a classification algorithm that is widely used in Data Mining or Text Mining. This study aims to search for negative, positive, and neutral comments and to find out the accuracy of the Naïve Bayes method. Sentiment analysis was obtained by means of data cleaning, labeling, TF-IDF, split, Naïve Bayes classification, and evaluation. It is hoped that after making sentiment analysis using the Naïve Bayes method, negative, positive and neutral comments will be obtained and the accuracy of Naïve Bayes will reach 70%.
Pengembangan Analisis Teknikal Untuk Trading Bursa Saham dengan Long Short Term Memory
Authors: El Hakim Faris Abdi , Fariza Arna , Setiawardhana Setiawardhana
Published: July 2024

Stock price movements are difficult to predict and can change over time. Technical analysis is necessary to determine the right timing and company for stock investments. However, novice traders may face difficulties in analyzing stocks. Therefore, the author conducted research on the development of technical analysis for stock trading using Long Short-Term Memory (LSTM). The study utilized data from PT Bank Central Asia Tbk (BBCA.JK) stock prices, covering the period from July 1, 2004, to April 28, 2023. The LSTM method combined with technical analysis resulted in RMSE values of 136.759 for Open price, 126.52 for Close price, 317.968 for High price, 178.001 for Low price, and 189.669 for Adj. Close price, which outperformed the LSTM method without technical analysis. Furthermore, the LSTM method achieved better accuracy compared to Support Vector Regression (SVR) and K-Nearest Neighbors (KNN) using three different datasets. The RMSE values were 65.21 for LSTM, 313.56 for SVR, and 72.44 for KNN. The R2 values were 0.9919 for LSTM, 0.81 for SVR, and 0.990 for KNN. The results of the model were implemented in a web-based system using the Laravel framework and MySQL database.
Music Recommender System Based on Play Count Using Singular Value Decomposition++
Authors: Ramadhan Muhamad Elang , Wibowo Agung Toto
Published: July 2024

The availability of digital music content on various music streaming services, which is constantly growing, has increased the need for recommender systems (RS) to assist users in finding music that suits their taste without the need of searching manually. One of the commonly used paradigms is Collaborative Filtering (CF). In CF, the input used to predict ratings can take the form of explicit or implicit input from user feedback. In the music domain, implicit feedback such as the number of music plays can be utilized to predict a user's music preferences. Singular Value Decomposition++ is one of the Matrix Factorization (MF) algorithms that can leverage implicit feedback and address the sparsity issue. In this research, a music recommender system is built using the Million Song Dataset (MSD) Subset from The Echo Nest, utilizing SVD++ algorithm. Additionally, the performance of the built system is measured through k-fold cross-validation using the evaluation metrics RMSE and NDCG. The performance measurement results using RMSE and NDCG in 5-fold cross-validation yield an RMSE of 0.4423, NDCG@5 of 0.8232, and NDCG@10 of 0.8231 for the top 10 items.
BERTopic Modeling of Natural Language Processing Abstracts: Thematic Structure and Trajectory
Authors: Samsir Samsir , Saragih Reagan Surbakti , Subagio Selamat , Aditiya Rahmad , Watrianthos Ronal
Published: July 2024

The rapid growth in the academic literature presents challenges in identifying relevant studies. This research aimed to apply unsupervised clustering techniques to 13,027 Scopus abstracts to uncover structure and themes in natural language processing (NLP) publications. Abstracts were pre-processed with tokenization, lemmatization, and vectorization. The BERTopic algorithm was used for clustering, using the MiniLM-L6-v2 embedding model and a minimum topic size of 50. Quantitative analysis revealed eight main topics, with sizes ranging from 205 to 4089 abstracts per topic. The language models topic was most prominent with 4089 abstracts. The topics were evaluated using coherence scores between 0.42 and 0.58, indicating meaningful themes. Keywords and sample documents provided interpretable topic representations. The results showcase the ability to produce coherent topics and capture connections between NLP studies. Clustering supports focused browsing and identification of relevant literature. Unlike human-curated classifications, the unsupervised data-driven approach prevents bias. Given the need to understand research trends, clustering abstracts enables efficient knowledge discovery from scientific corpora. This methodology can be applied to various datasets and fields to uncover overlooked patterns. The ability to adjust parameters allows for customized analysis. In general, unsupervised clustering provides a versatile framework for navigating, summarizing, and analyzing academic literature as volumes expand exponentially.
Penerapan Data Mining Dengan Metode K-Nearest Neighbor Terhadap Klasifikasi Sarang Walet
Authors: Ismail Muhammad , Yulvianda Renaldi
Published: July 2024

Sungai Benuh Village is one of the areas where many swallow houses are made because it is able to produce a large number of swallow nests so that the purpose of this study was to make a classification by applying data mining to see the quality level of swallow nests, so that later it will become a reference in helping buyers and the seller obtains appropriate results and maintains the selling power of the swallow's nest. This is also based on a problem that is often encountered, namely sellers and buyers do not have a fixed standard of evaluation when a transaction takes place, so that unilateral judgments appear. In addition, there are differences in quality and quantity in different seasons. During the rainy season, swallow nests are larger, white, clean and numerous, while during the dry season, the opposite results are obtained. Classification results using the k-nearest neighbor method with Weka software show 90% accuracy for 45 out of 50 data samples, including comparative data samples or new data samples using a value of k = 7 with categorized attributes of cleanliness, color, size, shape and harvest time “Good” or “Bad”. Evaluation of the results with the confusion matrix results obtained accuracy of 80%, precision 80.49, recall 94.29% and F1 score 86.84%. So, this research was successfully carried out with high classification results so that it can be a reference to help buyers and sellers obtain a mutual agreement during transactions and maintain the selling power of the swallow's nest.
Penerapan Algoritma K-Medoids Pada Clustering Penerima Bantuan Pangan Non Tunai (BPNT)
Authors: Ramayanti Tiara , Haerani Elin , Jasril Jasril , Oktavia Lola
Published: July 2024

Bantuan Pangan Non Tunai (BPNT) is assistance distributed by the government to underprivileged communities to ease the financial burden that is increasingly burdening their lives. In a number of cases, it was found that the number of people who received BPNT was not properly targeted, so it was necessary to analyze the pattern of the characteristics of BPNT recipients so that the assistance was right on target. There are many criteria that must be considered to determine the people who are entitled to receive BPNT, so an appropriate algorithm is needed to determine the right cluster when analyzing characteristic patterns. This study applies the K-Medoids algorithm to classify BPNT data obtained from Firza Syahputra's research in 2020–2021, with a total of 732 attributes, so that the government can consider the factors that characterize beneficiaries. Perform tests using the Silhouette coefficient, which is useful for maximizing clustering results. The clustering result is three clusters, and the silhouette coefficient is 0.4439221599010089. The results of the analysis show that clustering performed using the K-Medoids algorithm can assume that clusters are grouped according to grouping: cluster 0 is eligible to receive BPNT, cluster 1 is considered, and cluster 2 is not eligible to receive BPNT.
Enhancing Fire Detection in Images using Faster R-CNN with Gaussian Filtering and Contrast Adjustment
Authors: Lazzaro Dwiki , Sthevanie Febryanti , Ramadhani Kurniawan Nur
Published: July 2024

A system is designed with an accurate and efficient model to detect fires, aiming to assist in fire prevention. Designing such a system poses a challenging task, as numerous aspects need to be considered, including model accuracy, parameter count, computational complexity, and more. Therefore, the research will incorporate techniques such as Image Smoothing Filtering and Contrast Adjustment to enhance the fire detection process. The primary objective is to develop a robust system that can effectively identify and detect fire occurrences. Accuracy is crucial to ensure reliable results, while efficiency plays a significant role in real-time fire detection. By implementing Image Smoothing Filtering, the system can reduce noise and enhance image quality, improving detection performance. Contrast Adjustment techniques will further contribute to the system's efficiency by emphasizing fire patterns and enhancing their visibility. The system's design encompasses careful consideration of various factors to strike a balance between accuracy, efficiency, and computational complexity. By utilizing Image Smoothing Filtering and Contrast Adjustment, the research aims to develop a comprehensive fire detection system that can aid in preventing fire incidents. This study endeavors to contribute to the advancement of fire detection technologies and pave the way for future innovations in this field.
Sentiment Analysis Netizens on Social Media Twitter Against Indonesian Presidential Candidates in 2024 Using Naive Bayes Classifier Algorithm
Authors: Ismail Ismail , Nugroho Mario Adi , Sulistiyowati Rini
Published: July 2024

The democratic system is highly respected in Indonesia. The Indonesian state government organizes its government in a democratic manner, namely, it is run by, for, and with the consent of the people. Democracy is held through general elections to occupy leadership and power seats, followed by political parties. The high enthusiasm of Twitter social media users with the electability of presidential candidates proposed by political parties in the 2024 general election and the existence of a survey of potential presidents from national institutions, as well as mass and social media coverage of presidential candidates suggested by the political parties, generates opinions, attitudes, and emotions among people from all walks of life through tweets. The availability of abundant data on Twitter and other social media can provide useful information. Tweet data is obtained by crawling data using the help of the Python library, namely snscrape. The sentiment analysis uses a mixed method, namely by using machine learning and Lexicon Based, through the process of fine-grained sentiment analysis using the technique, namely, knowing the level of opinion polarity by grouping netizen responses and opinions into three parts: positive, neutral, and negative, with the help of machine learning and natural language processing. The results of the study were carried out by experimenting with four scenarios by dividing test and training data by 60:40, 70:30, 80:20, and 90:10. Measurement of the accuracy value results in the classification of the Nave Bayes Classifier Algorithm as 68%, 67%, 70%, and 71%. From the tweet data, it is clear that positive sentiment is dominant on the research topic.
Penerapan Klasifikasi Algoritma C4.5 Dan Algoritma C5.0 Untuk Mengetahui Tingkat Kepuasan Mahasiswa Terhadap Website Sistem Informasi Terpadu Layanan Program Studi (SIPLO)
Authors: Nurfitrayani Nurfitrayani , Islamiyah Islamiyah , Azam Masa Amin Padmo
Published: July 2024

Integrated Information System for Study Program Services (SIPLO) is a website-based information system for academic services at the study program level, specifically designed for the Information Systems Study Program at Mulawarman University. Despite containing information that supports lectures, SIPLO's features and information have not met students' satisfaction, as indicated by data collected through interviews. Therefore, the objective of this study was to determine the level of student satisfaction with the SIPLO website.This study employed a data mining technique using the classification methods of the C4.5 algorithm and the C5.0 algorithm. The PIECES indicators, which include performance, information, economy, control, efficiency, and service, were used as attributes in the data mining application. The data utilized in the study consisted of questionnaires distributed to 182 students from the Information Systems Study Program at Mulawarman University in 2019, 2020, and 2021. The data was divided into a 80% training data and a 20% test data ratio. The research findings using the C4.5 algorithm revealed that the variables influencing student satisfaction are performance, control, information, efficiency, and service. Meanwhile, the C5.0 algorithm identified control, performance, efficiency, information, and service as the influential variables. Both algorithms yielded an accuracy value of 91.89%, precision value of 93.75%, recall value of 96.77%, F1-Score value of 95.24%, and an AUC value of 0.8172. These results indicate a good classification performance.
Kontrol Lampu Pada Pendeteksian Kantuk dengan Parameter Posisi Tidur Menggunakan Object Detection Effiecientdet Lite
Authors: Baharudin Nurul Humaera , Arda Abdul Latief , Achmad Andani
Published: July 2024

Drowsiness is a sign that the body is entering a phase towards rest. Integrated detection of sleepy activity light control where light control automation is carried out based on the results of object detection, namely humans when they are sleepy, will be needed to efficiently use electrical energy. Analysis of the sleep detection scheme and the effectiveness of the object detection method in detecting sleepiness with body and eye position parameters was carried out using the Efficientdet Lite object detection method as the architecture used in making sleep detection models where the model created is implemented on a Raspberry Pi which is integrated with light control resulting in detection drowsiness with a comparison of 3 sleepiness parameters (body position, eyes, facial expressions) is able to control lights in the form of automatic lights turning off and on based on the labels that appear in each drowsiness detection parameter class. The results obtained were that the metric evaluation produced a different Average Precision for 3 parameters but in the appearance of the bounding box the most precise in detecting objects was the sleeping position, for the eye parameter the bounding box was not appropriate in detecting drowsiness while in facial expressions the bounding box was difficult to appear. Accuracy testing was carried out on the sleep position parameter resulting in a detection accuracy of 80%.
Implementasi Kontrol PID untuk Percepatan Rotasi pada Robot Hexapod
Authors: Pratama Yovi , Bustami Muhammad Irwan , Toscany Afrizal Nehemia , Saputra Chindra , Sika Xaverius , Susilo Janu Hadi , Taupiq Arahmad , Pratama Cahyana Putra
Published: July 2024

In the development of robot technology, the hexapod robot has great potential to be used in post-disaster rescue because of its ability to adapt to difficult terrain. In this study the authors consider the effectiveness of PID (Proportional Integral Derivative) control based on related literature. Several previous studies have shown the successful use of PID in servo or robotics movement control in different contexts, such as approaching fire sources, controlling DC motor angles, controlling cleaning robot movements on glass, and so on. But the authors want to implement PID motion control with a different approach. The main objective of the hexapod robot is to save the victim by overcoming obstacles and delivering them to the safe zone. In the first test the authors use the PID control method in the middle position between the initial compass rotation value and the destination rotation. The results show that the time needed to reach the rotation goal is relatively fast. In contrast, the second test using the PID control method for all rotary motion resulted in a significant reduction in the error value compared to the first test. Although it requires more time, this method increases the robot's accuracy in achieving the rotation objective efficiently. The robot uses a CMPS03 compass sensor which can provide a direction value which will be input to the PID and servos that are installed as foot movers, each servo is controlled by a microcontroller with the inverse kinematic method to produce the appropriate movement. The results of this research are expected to be the basis for the development of more capable and reliable robots in the next Indonesian SAR robot contest.
Sistem Pakar Deteksi Penyakit Otitis dengan Perbandingan Metode Certainty Factor, Teorema Bayes, dan Dempster Shafer
Authors: Karim Abdul , Esabella Shinta , Hanafiah Mhd Ali , Nasution Muhammad Bobbi Kurniawan , Ernawati Andi
Published: July 2024

Otitis, especially otitis media, otitis externa, and otitis interna, is a common health issue in humans. Accurate identification and diagnosis are crucial for effective treatment. Expert systems have become an effective solution in this field as they can leverage medical knowledge to support decision-making processes. This research aims to develop an Expert System for Otitis Disease Detection using three different methods: Certainty Factor (CF), Bayes' Theorem, and Dempster-Shafer Method. The CF method is used to measure the level of confidence in decision-making, Bayes' Theorem utilizes probabilities to support diagnosis, and Dempster-Shafer matches previous cases with current symptoms to provide diagnostic recommendations. In this study, otitis symptom data were collected from previously diagnosed patients to train the expert system. The system was then tested with new cases to analyze the performance and accuracy of each method. The research results indicate that all three methods have the potential to detect otitis disease with varying levels of accuracy. In some situations, one method may outperform the others, suggesting that using a combination or integration of methods can enhance diagnostic accuracy. This research makes a significant contribution to the development of expert systems in the healthcare and medical services field. It is expected to assist doctors in the faster and more accurate diagnosis of otitis diseases. The calculation results show that the Certainty Factor method has the highest confidence level in diagnosing otitis media (86%), otitis externa (79%), and otitis interna (87%). While Dempster-Shafer has lower confidence levels in all cases, it still provides a significant contribution in certain situations.
Performa Metode Convolutional Neural Network Pada Face Landmark Untuk Virtual Make Up Try On
Authors: Angeline Dameethia , Jochsen Erico , Herwindiati Dyah Erny , Hendryli Janson
Published: July 2024

Make up or facial makeup, is an activity to change the appearance from its original form with the help of make up materials and tools. Make-up tools are beauty tools that are commonly used by most women to beautify the appearance of their faces with many shade choices. The shade on the make-up tool is the color usually used in make-up. Examples of make-up tools that are most often used include eyeshadow, blush on, and lipstick. These make-up tools are sold widely online and offline in physical stores. However, usually a tester is also needed so that those who want to buy can try the shade that suits them. When buying online, they often find it difficult to choose the right shade, while testers in physical stores are sometimes considered less hygienic because they have been used by many people. The aim of this paper is to measure the performance of the Convolutional Neural Network (CNN) method using the ResNet-50 architecture on facial landmarks for creating virtual make up try ons which can be an alternative to this problem. The facial image data source used is from the Kaggle site called Facial Keypoints Detection. The testing process produces 78.99% accuracy while the training process produces 95.12% accuracy. The evaluation results of this model use Root Mean Squared Error (RMSE) of 2.2577 and Mean Absolute Error (MAE) of 1.5389.
Peningkatan Akurasi Pembacaan Lembar Jawaban Komputer dengan Memperbaiki Ketidaksimetrisan Citra Hasil Pemindaian Menggunakan Transformasi Homografi
Authors: Prayitno Prayitno , Shidiq Guruh Fajar , Fanani Ahmad Zainal , Soeleman M. Arief
Published: July 2024

Answer Sheet Reading Computers are one of the technologies for converting images into information which continues to develop until now. Some of the applications of this technology include correcting various school exams, psychological tests, surveys and voting. Accuracy is something that is often a problem in various studies on reading computer answer sheets. Accuracy is greatly influenced by the scanned image. In the process of scanning computer answer sheets, images often produce asymmetry, such as tilting, shifting and dilatation.The process of scanning computer answer sheets often produces asymmetrical images, such as tilted, shifted and dilated.  This incident will affect the accuracy of the results of reading the computer answer sheet, due to deformation of the shape between the reference image and the scanned image. This study aims to improve asymmetric images to become symmetrical with the Homografi transformation in order to get better reading accuracy. The results showed that the improvement of image symmetry with Homografi transformation was better than the skew correction method. This is shown from the respective RMSE values, the Homografi transformation method produces an RMSE value of 51.54 and the skew correction method produces a value of 67.04. The results of the study also stated that the accuracy of reading computer answer sheets with the Homografi transformation method was better than skew correction. The skew correction accuracy is 95.8%, while the Homografi transformation is 99.3%.
Klasterisasi Jawaban Uraian Mahasiswa Menggunakan TF-IDF dan K-Means untuk Membantu Koreksi Ujian
Authors: Mashudi Irsyad Arif , Arief Sofyan Noor , E.I. Deasy Sandhya , Fatmawati Triana , Hani’ah Mamluatul , Alfarid Irfan Thalib
Published: July 2024

One way to ensure students understand a topic is by giving them essay questions. Essay questions provide a more accurate evaluation compared to other types of questions. However, this raises new problems where lecturers often have not found an effective way to assess answers to essay questions. The large number of students makes the assessment process take a long time. However, in reality, there are many similarities in the answers between students. These similar answers can be grouped and given the same grade. Unfortunately, if done manually, this grouping takes a very long time. Clustering is one way that can be used to determine variations in student answers as a whole. TF-IDF and K-Means are the clustering algorithms that are considered the strongest and most popular. By using TF-IDF and K-Means to help lecturers group students' descriptive answers, it turns out to be quite effective because with a percentage of conformity to the grouping results of 65%, lecturers can group descriptive answers in a much faster time than manually grouping descriptive answers.
Penerapan Deep Learning Dalam Pengenalan Endek Bali Menggunakan Convolutional Neural Network
Authors: Hendrawati Theresia , Wulandari Dewa Ayu Putri , Dharma I Gde Swiyasa Surya , Ginantra, M.Kom Ni Luh Wiwik Sri Rahayu , Yanti Christina Purnama
Published: July 2024

Endek Bali has been recognized as one of the Intellectual Property of Traditional Cultural Expressions, with registration number EBT 12.2020.0000085 on December 22, 2020. In the present era, many people find it difficult to distinguish between endek fabric and batik fabric because their patterns are quite similar. This research aims to help identify Bali's Endek fabric based on digital images. One of the approaches used is the Convolutional Neural Network method with ResNet50, which is a deep learning method used to recognize and classify objects in digital images. Evaluation result from testing the best model with new testing model using confession matrix get result of 90,69% accuracy, 90,69% recall, 90,60% precision and 90,68% f1-score. Thus, the model developed in this research demonstrates optimal performance in classifying images of Bali's Endek.
Hidroponik Pintar Menggunakan Fuzzy Logic Berbasis Internet of Things Pada Tanaman Selada
Authors: Kobar Aris Martin , Indra Jamaludin , Cahyana Yana
Published: July 2024

Changes in land use have become one of the drivers of innovation in modern agriculture, so research and development has been carried out on hydroponic systems. Hydroponic systems require special treatment on several parameters for optimal results and good quality. Manual verification is considered less effective because agents have to do it more often, so there are often delays in monitoring and adding nutrient solutions to the water. Previous researchers have controlled hydroponic systems using various methods. To regulate the nutrition of the hydroponic system in the hydroponic management system using Fuzzy Logic Control based on Electrical Conductivity (EC) used in the fuzzy input is the EC error and determining the duration of turning on the nutrient valve which shows the results that the fuzzy logic control can maintain the EC value range according to plant needs with water depth. In this research, monitoring and managing nutrition, temperature and water level in a hydroponic system is designed using Fuzzy Logic based on the Internet of Things so that monitoring and setting Parts Per Million (PPM) can be done automatically so that you can monitor lettuce plants remotely according to their age. can be viewed via the web application. The test results in this research obtained that the accuracy of the TDS sensor in detecting water TDS values was 97.78%, and the accuracy of the DS18B20 sensor in detecting water temperature conditions was 98.37%. The fuzzy test obtained an error value of 10%.
Optimalisasi JST dalam Memprediksi Kunjungan Wisatawan Mancanegara Untuk Perencanaan dan Pengembangan Pariwisata yang Efektif
Authors: Winanjaya Riki , Okprana Harly
Published: July 2024

Foreign tourist predictions assist the government and stakeholders in planning long- and short-term tourism strategies. Accurate information on the estimated number of tourists enables appropriate infrastructure development, efficient budget allocation and setting of relevant policies. Foreign tourists discussed in this study are foreign tourists based in ASEAN countries. This research will utilize historical data on foreign tourist arrivals from the Ministry of Tourism, the Ministry of Law and Human Rights (Directorate General of Immigration) and Mobile Positioning Data. The data that has been obtained will be processed and filtered to obtain relevant and accurate data before being used as input in the creation of an Artificial Neural Network (ANN) model. The algorithm proposed in this study is the Cyclical Rule algorithm with the optimization of the Bayesian Regulation algorithm, which can be used to solve data prediction problems. This study was analyzed using 10 (ten) architectural models, including 4-4-1, 4-5-1, 4-8-1, 4-10-1, 4-12-1, 4-15-1, 4-16-1, 4-20-1, 4-24-1, and 4-25-1. Based on the analysis, the results obtained from the 4-10-1 model with the optimization of the Bayesian Regulation algorithm as the best model with the smallest testing MSE compared to the other models, equal to 0.00786961. Based on the prediction results, foreign tourist arrivals from ASEAN countries in 2023 are expected to decrease compared to 2022. Tourism actors can take advantage of the results of this prediction to improve the quality and quantity of services provided to tourists, as well as adjust the needs of tourists with the resources available at tourist destinations.
Analisis Perbandingan Teorema Bayes dan Certainty factor dalam Mendiagnosa Von Hippel-Lindau Disease
Authors: Harianto Kusno , Azahari Azahari
Published: July 2024

This research aims to compare the effectiveness of two diagnostic methods in identifying Von Hippel-Lindau (VHL) disease, namely the Bayes' Theorem and Certainty Factor. VHL is a rare disease that affects organs such as the brain, eyes, and kidneys. Health is a precious asset for humans, and often the assistance of specialized doctors is required to diagnose complex conditions like VHL. In the era of modern technology, the fusion of medical science with artificial intelligence has provided a fresh impetus in the development of expert systems to support faster and more accurate medical diagnoses. The Bayes' Theorem method is a statistical technique used to calculate the level of certainty in medical data. It helps measure the probability of whether someone has VHL or not. On the other hand, Certainty Factor is another method that gauges the level of confidence in diagnosing a disease using specific metrics, such as how certain symptoms are related to the disease. This research will conduct experiments on both methods using existing medical data and VHL cases. We will compare the accuracy, efficiency, and speed of both methods in diagnosing VHL. The results of this study are expected to provide valuable insights into which method is better suited to support VHL diagnosis. The implementation of expert systems in the field of medicine is crucial as it can assist doctors in making better decisions. The findings of this research can contribute to the development of more advanced and accurate medical expert systems, which, in turn, will enhance the care and prognosis of patients with VHL and other diseases. Thus, this research has the potential for significant impact in the fields of health and computer science. The results of the study indicate two methods in diagnosing Von Hippel-Lindau disease: "Certainty Factor" with a certainty level of 97.44%, and "Bayes' Theorem" with a certainty level of 41.22%. This provides insights into the relative effectiveness of both methods in diagnosing the disease, with "Certainty Factor" appearing to be more reliable.
Prototipe Sistem Monitoring dan Kendali Suhu Box Kubikel 20 kV Berbasis Long Range (LoRa)
Authors: Ariandi Muhamad , Oktaria Yoza Risti
Published: July 2024

Electricity distribution often faces problems, one of which is interference with the 20 kV cubicle box caused by corona due to changes in temperature and condensation. This disturbance can cause arcing between the insulator and live parts. One solution is to use a heater and exhaust fan to maintain temperature and humidity. This study proposes a prototype to combine temperature and control heaters and exhaust fans automatically. Integrated sensors help monitor the temperature in the cubicle. Using a temperature sensor (DHT 22) shows high accuracy, and a current sensor (SCT 013) is suitable for exhaust fans and heaters. When the sensor is ready, the DHT 22 sensor will read the temperature in the room. If the reading temperature exceeds 40°C, then the exhaust fan will turn on to remove hot air in the cubicle box, whereas if the temperature is below 40°C the heater will turn on. If the detected temperature exceeds 43°C, and the exhaust fan does not turn on, a notification will appear on Blynk to immediately check the exhaust fan for errors. Meanwhile, if the temperature detects the cubicle box below the temperature of 37°C, a heater error notification will appear. The SCT 013 current sensor will detect the amount of current flowing in the exhaust fan and heater with the help of Esp32 as a microcontroller. This prototype utilizes LoRa technology for remote communication and sends notifications via the Blynk application. Tests show that this tool can help operators effectively monitor and control cubicle box temperature.
Penerapan dan Pemantauan Pakan Ikan Lele Otomatis Menggunakan Keypad Shield Berbasis IoT
Authors: Ariandi Muhamad , Karua Imam
Published: July 2024

Cultivating freshwater fish is a promising business in the long term. One type of freshwater fish that is widely cultivated by people is catfish. Catfish is a type of freshwater fish that does not require a lot of care, so it is very profitable and has high economic value. The problem that often occurs in catfish farming is that when feeding fish, farmers are often late in providing fish food, which causes catfish to die and experience cannibalism or eat other species. Based on the problems that occurred, research was carried out at Agrowisata Tekno 44 which aimed to implement an automatic catfish feeding device through the Internet of Things (IoT) based Blynk system which can be controlled using an LCD Keypad Shield which can provide a warning if the feed approaches a predetermined minimum limit. Addition of Mega+Wifi R3 Atmega 2560+ESP 8266 which functions as a link between the automatic fish feed and the Blynk application. The research method used in writing this article is a literature study by comparing the results of ultrasonic sensor measurements with a measuring tool in the form of a vernier caliper, and an interview method with cultivators to obtain information about feeding. Scheduled measurements at a specified time to obtain maximum results, an ultrasonic sensor was added to the fish feed container which functions to determine the contents of the volume of the RTC fish feed container which is used to store time information from the automatic fish feed tool, Push Button to measure the amount feed, the servo motor is used to open and close the fish food container that will exit into the fish pond. Testing the ultrasonic sensor with a vernier caliper to measure the distance that detects fish food in the fish food container produces a measurement ratio with a difference of 0.1 from the comparison between the measurements of the ultrasonic sensor and the vernier caliper. The results of testing carried out for 7 days with automatic fish feeding running according to a predetermined schedule, namely 06.00 and 18.00 with a success rate of 100% accuracy. And the success of the feed weight was 95.6% with an average accuracy of 96%, meaning that the fish did not lack food due to the lack of feed received.
Classification of Medicinal Wild Plant Leaf Types Using a Combination of ELM and PCA Algorithms
Authors: Alamsyah Dedy , Rossi Farli , Septarini Ri Sabti , Shalahudin Mohammad Imam
Published: July 2024

Despite their detrimental nature, it turns out that wild plants have many benefits for human health. Wild plants with a form of herbaceous vegetation contain ingredients that can be used as medicine, especially in their leaves. However, because the information is very similar and the form is similar, people don't know about it. For this reason, the aim of this research is to implement an artificial neural network algorithm using Extreme Learning Machine (ELM) and the Principal Component Analysis (PCA) algorithm to classify images of wild plant leaves with medicinal properties, especially in herbaceous vegetation. The feature extraction used in this research involves morphological features by considering the shape of the object. The PCA algorithm will reduce data complexity and identify hidden patterns in the data by changing the original feature space to a new and more concise feature space. Next, the ELM algorithm is used to recognize class grouping patterns when solving classification problems. Accuracy test results show a value of 90.667%.