Fatigue Detection Using Decision Tree Method based on PPG signal

Fatigue is a complex psychophysiological condition marked by sleepiness or fatigue, poor performance, and a range of physiological changes. A decision tree may be used to categorize weariness based on the subject's heart rate data. To begin the experiment, a dataset of the heart rate signal was obtained. The signal has already undergone preprocessing. The feature obtained through preprocessing is then used to construct the decision model. Four traits were discovered. The HF power, LF power, normalized HF power, and normalized LF power are the characteristics. This research has a 75.94% accuracy rating. The precision, recall, and F-measure scores for this study were 0.736, 0.736, and 0.736, respectively

Comparison Learning Model AIR and TAI Combined With Cognitive Conflict Strategy Againts Active Learning and Concept Understanding

Abstract:  There are several problems in the learning process of Basic Electricity and Electronics at SMK Negeri 8 Malang, including: (1) When learning takes place students do not pay attention and listen to the teacher when delivering material, (2) The teacher does not focus on learning activities to students, (3) Students are less active in asking and expressing his opinion about the material that has been taught. This study uses a variety of learning models and methods that can improve students' learning activeness and conceptual understanding, namely the Auditory, Intellectual, Repetition (AIR) learning model and the Team Assisted Individualization (TAI) learning model, each of which is combined with cognitive conflict strategies. The research design used a quasi experimental design with a non-equivalent control group design type. The data analysis technique consisted of normality test, homogeneity test, two mean similarity test, and hypothesis testing. The conclusion of this study is that the AIR learning model combined with cognitive conflict strategies is superior to the TAI learning model combined with cognitive conflict strategie

Home Energy Security Prototype using Microcontroller Based on Fingerprint Sensor

The globalization era brings rapid development in technology.The human need for speed and easiness pushed them toinnovate, such as in the security field. Initially, the securitysystem was conducted manually and impractical compared tonowadays system. A security technology that is developed wasbiometric application, particularly fingerprint. Fingerprintbasedsecurity became a reliable enough system because of itsaccuracy level, safe, secure, and comfortable to be used ashousing security system identification. This research aimed todevelop a security system based on fingerprint biometric takenfrom previous researches by optimizing and upgrading theprevious weaknesses. This security system could be a solutionto a robbery that used Arduino UNO Atmega328P CH340 R3Board Micro USB port. The inputs were fingerprint sensor, 4x5keypad, and magnetic sensor, whereas the outputs were 12 Vsolenoid, 16x2 LCD, GSM SIM800L module, LED, andbuzzer. The advantage of this security system was its ability togive a danger sign in the form of noise when the systemdetected the wrong fingerprint or when it detects a forcedopening. The system would call the homeowner then. Otherthan that, this system notified the homeowner of all of theactivities through SMS so that it can be used as a long-distanceobservation. This system was completed with a push button toopen the door from the inside. The maximum fingerprints thatcould be stored were four users and one admin. The admin’sjob was to add/delete fingerprints, replace the home owner’sphone number, and change the system’s PIN. The resultsshowed that the fingerprint sensor read the prints in a relativelyfast time of 1.136 seconds. The average duration that wasneeded to send an SMS was 69 seconds while through call was3.2 seconds

Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second

Penghapusan Kolom dan Baris Pertama pada Matriks Distance Untuk Optimasi Spell Checker Damerau-Levenshtein Distance

Damerau-Levenshtein Distance menentukan jarak atau jumlah minimum operasi yang dibutuhkan untuk mengubah satu string menjadi string lain, di mana operasi yang digunakan untuk menentukan tingkat kemiripian antar String adalah insertion, deletion, substitution dan transposition. Algoritma ini sendiri dapat juga digunakan untuk mengoreksi kesalahan kata. Namun, Algoritma Damerau-Levenshtein Distance mempunyai kelemahan, yaitu waktu pemrosesan yang lama. Pada perhitungan jarak antara dua string dengan algoritma Damerau-Levenshtein, setiap huruf dari kedua string akan dibandingkan dengan membuat matriks distance. Karena Kamus Bahasa Indonesia memiliki lebih dari 30.000 kata dasar, operasi perhitungan jarak akan dilakukan lebih dari 30.000 kali untuk setiap kesalahan. Penelitian ini mengusulkan peningkatan untuk mempersingkat waktu pemrosesan algoritma Damerau-Levenshtein dengan mengurangi baris dan kolom matriks distance. Hasil akhir yang diharapkan dari penelitian ini adalah waktu pemrosesan menjadi lebih cepat tanpa harus mengorbankan akurasi

Generating Javanese Stopwords List using K-means Clustering Algorithm

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable

Text classification of traditional and national songs using naïve bayes algorithm

In this research, we investigate the effectiveness of the multinomial Naïve Bayes algorithm in the context of text classification, with a particular focus on distinguishing between folk songs and national songs. The rationale for choosing the Naïve Bayes method lies in its unique ability to evaluate word frequencies not only within individual documents but across the entire dataset, leading to significant improvements in accuracy and stability. Our dataset includes 480 folk songs and 90 national songs, categorized into six distinct scenarios, encompassing two, four, and 31 labels, with and without the application of Synthetic Minority Over-sampling Technique (SMOTE). The research journey involves several essential stages, beginning with pre-processing tasks such as case folding, punctuation removal, tokenization, and TF-IDF transformation. Subsequently, the text classification is executed using the multinomial Naïve Bayes algorithm, followed by rigorous testing through k-fold cross-validation and SMOTE resampling techniques. Notably, our findings reveal that the most favorable scenario unfolds when SMOTE is applied to two labels, resulting in a remarkable accuracy rate of 93.75%. These findings underscore the prowess of the multinomial Naïve Bayes algorithm in effectively classifying small data label categories

Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm

Opinion Analysis is a research study needed to social media, since the content could become a trending topic and has a significant impact on social life. One of the social media that have a big contribution to cyberspace and information development is Twitter. In the Twitter application, users can insert images that represent emotions, facial expressions, or icons. Emoji is a graphic symbol in the form of an image to express a thing, with the Emoji, a text can be read and understood according to its meaning because the image represents it. Of the several things that have been mentioned then, the researchers conducted research on the classification of tweet content based on the use of Emojis. This study aims to determine the emotional uses of Twitter in one period. Every tweet on the Twitter timeline, which contains both text and Emojis, will be classified according to several categories. The algorithm used was Naïve Bayes. It calculated the probability of Emoji tweet to obtain the text classification with Emojis. The results of the classification of emotions are grouped with three categories, namely "angry," "joy," and "sad," it showed that the category "joy" had become the emotional trend of Twitter users where Emojis (x1f60a) dominate the most. Meanwhile, the accuracy of the algorithm used to reach 90% with a 70:30 holdout technique

Single Exponential Smoothing-Multilayer Perceptron Untuk Peramalan Pengunjung Unik Jurnal Elektronik

Jumlah kunjungan rerata pengunjung unik per hari pada jurnal elektronik menunjukkan bahwa hasil terbitan karya ilmiah website tersebut menarik. Sehingga jumlah pengunjung unik dijadikan indikator penting dalam mengukur keberhasilan sebuah jurnal elektronik untuk memenuhi perluasan, penyebaran dan percepatan sistem akreditasi jurnal. Pengunjung Unik merupakan jumlah pengunjung per Internet Address (IP) yang mengakses sebuah jurnal elektronik dalam kurun waktu tertentu. Terdapat beberapa metode yang biasa digunakan untuk peramalan, diantaranya adalah Multilayer Perceptron (MLP). Kualitas data berpengaruh besar dalam membangun model MLP yang baik, karena sukses tidaknya permodelan pada MLP sangat dipengaruhi oleh data input. Salah satu cara untuk meningkatkan kualitas data adalah dengan melakukan smoothing pada data tersebut. Pada penelitian ini digunkan metode peramalan Multilayer Perceptron berdasarkan penelitian sebelumnya dengan kombinasi data training dan testing 80%-20% dengan asitektur 2-1-1 dan learning rate 0,4. Selanjutnya untuk meningkatkan kualitas data dilakukan smoothing dengan menerapkan metode Single Exponential Smoothing. Dari penelitian yang dilakukan diperoleh hasil terbaik menggunakan alpha 0.9 dengan hasil akurasi MSE 94.02% dan RMSE 75.54% dengan lama waktu eksekusi 580,27 detik. The number of visits by the average unique visitor per day on electronic journals shows that the published scientific papers on the website are interesting. So that the number of unique visitors is used as an important indicator in measuring the success of an electronic journal to meet the expansion, dissemination and acceleration of the journal accreditation system. Unique Visitors is the number of visitors per Internet Address (IP) who access an electronic journal within a certain period of time. There are several methods commonly used for forecasting, including the Multilayer Perceptron (MLP). Data quality has a big influence in building a good MLP model, because the success or failure of modeling in MLP is greatly influenced by the input data. One way to improve data quality is by smoothing the data. In this study, the Multilayer Perceptron forecasting method was used based on previous research with a combination of training data and testing 80% -20% with a 2-1-1 architecture and a learning rate of 0.4. Furthermore, to improve data quality, smoothing is done by applying the Single Exponential Smoothing method. From the research conducted, the best results were obtained using alpha 0.9 with MSE accuracy of 94.02% and RMSE 75.54% with a long execution time of 580.27 seconds

Stemming javanese affix words using nazief and adriani modifications

Stemming is the process of finding a basic word with several stages of affix removal. The main reason for stemming is to check spelling and machine translation and to support the effectiveness of the retrieval process. This study uses the Nazief and Adriani algorithm for stemming Javanese-influenced words. The first step taken is data collection and making a basic word dictionary. Then do the stemming process. Before stemming, modifications are made to the rules. The rules of the Nazief and Adriani algorithm, which are based on the morphology rules of the Indonesian language, are modified to suit the morphological rules of the Javanese language. Of the 366 words that were tested, it produced 351 correct basic words and 15 basic words that experienced errors. The results show that this algorithm can be used for stemming Javanese with an accuracy value of 95.9%