2 10 12 https://repository.horizon.ac.id/files/original/77520216a8794b5606aecf6041ea3521.pdf ac9e9162f1a066ab146a3eee91d3762a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 8 No 5 (2024) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource ESP32 and MAX30100 with Chebyshev Filter for Enhanced Heart and Oxygen Measurement Subject The topic of the resource chebyshev filter; butterworthfilter; heart rate; PPG signal; MAX30100 sensor Description An account of the resource Health monitoring is important in the technology and information era. A health monitoring device must possess high accuracy in monitoring an individual's health. The MAX30100 sensor still exhibits low accuracy and requires improvements to enhance its precision.This study proposes a remote health monitoring system based on a MAX30100 sensor for heart rate and oxygen saturation detection. The digital signal processing method uses the Chebyshev II filter on PPG to reduce noise, and the RSA algorithm is employed to enhance data security. The results of testing the MAX30100 sensor value without a filter produced the lowest error value of 0.97%, the highest 6.59% for BPM, the lowest error value of 1.88%, and the highest error of 2.66% for SpO2. The MAX30100 sensor with the Chebyshev II filter that the author proposed has the highest level of accuracy with a low error value compared to previous tests, with the lowest error value of 0.23% and the highest 0.99% for BPM and the lowest error value of 0% and the highest error of 0.2% for SpO2. The RSA algorithmensures secure data transmission from data modification by eavesdroppers. The average total time required by the system is 542.9 ms Creator An entity primarily responsible for making the resource Naval Indra Waskita1, Hizkia Menahem Tandungan2, Ridhan Hafizh3, Syifa Jauza Suwaendi4, Magfirawaty Magfirawaty Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5945/975 Publisher An entity responsible for making the resource available Cryptographic Hardware Engineering, Cryptography Department, National Cyber and Crypto Polytechnic, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 19-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/370371a14b85d558c5b11bf0a9cd603e.pdf 13c5c7476702b16ed3f6fe12c5b3e73e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 8 No 5 (2024) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Investigation Towards Resampling Techniques and Classification Algorithms on CM1 NASA PROMISEDataset for Software Defect Prediction Subject The topic of the resource software defect prediction; machine learning; classificationalgorithm; imbalanced data; resampling Description An account of the resource Software defect prediction is a practical approach to improvingthequality and efficiency of software testing processes. However,establishing robust and trustworthy models for software defect prediction is quite challenging due to the limitation of historical datasets thatmostdevelopersare capable of collecting.The inherently imbalanced nature of mostsoftware defectdatasets also posed another problem. Therefore, aninsightinto howtoproperlyconstruct software defect prediction models on a small,yet imbalanced,dataset isrequired. The objective of thisstudyis thereforeto provide the required insightbyway ofinvestigating and comparinga number ofresampling techniques,classification algorithms, and evaluation measurements (metrics) for buildingsoftware defect prediction models on CM1 NASA PROMISE data as therepresentation of asmall yet unbalanceddataset. This study is comparative descriptive research.It followsapositivist(quantitative)approach. Data were collected through observation towards experiments on four categoriesof resampling techniques (oversampling, under sampling, ensemble, and combine) combined withthree categoriesof machine learning classification algorithms (traditional, ensemble, and neural network) to predictdefective software moduleson CM1 NASA PROMISE dataset. Trainingprocesses were carried outtwice, each of which used the5-fold cross-validationand the70% training and 30% testing data splitting(holdout)method. Our result shows that the combinedandoversamplingtechniques provide apositive effect on the performance of the models.In the context of classification models, ensemble-based algorithms,which extend the decision treeclassification mechanism such as Random Forestand eXtreme Gradient Boosting, achieved sufficiently good performance for predicting defective software modules.Regardingthe evaluation measurements, thecombined and rank-based performance metrics yielded modest variance values, which isdeemed suitable forevaluatingtheperformance of the models inthis contex Creator An entity primarily responsible for making the resource Agung Fatwanto1*, Muh Nur Aslam2, Rebbecah Ndugi3, Muhammad Syafrudin Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5910/973 Publisher An entity responsible for making the resource available Informatics Department, Facultyof Science and Technology, UIN Sunan Kalijaga, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 14-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT