4 research outputs found

    PENGEMBANGAN TRAINER FILTER ANALOG PASIF DAN AKTIF BERBASIS PENGUAT OPERASIONAL PADA MATA PELAJARAN PENERAPAN RANGKAIAN ELEKTRONIKA KELAS XI TEKNIK AUDIO VIDEO DI SMK NEGERI 3 JOMBANG

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    AbstrakPenelitian ini bertujuan untuk menghasilkan media pembelajaran berupa Trainer Filter Analog pasif dan aktif yang berbasis penguat operasional pada mata pelajaran Penerapan Rangkaian Elektronika kelas XI dengan kriteria valid, praktis dan efektif. Desain penelitian yang digunakan adalah penelitian pengembangan (R&D), dengan menerapkan desain uji coba One-Shot Case Study pada uji penerapan produk. Penelitian mengambil tempat di SMK Negeri 3 Jombang dengan subjek penelitian siswa kelas XI TAV dengan jumlah 31 siswa dari 33 siswa. Produk yang dikembangkan adalah Trainer Filter Analog pasif dan aktif berbasis penguat operasional dan lembar kerja siswa (Jobsheet). Instrument penelitian yang diguanakan adalah lembar validasi Trainer Filter Analog, lembar validasi jobsheet, soal evaluasi pengetahuan, lembar observasi kompetensi keterampilan peserta didik, serta angket respon peserta didik. Analisis kompetensi siswa menggunakan analisis deskriptif untuk mengungkapkan kompetensi pengetahuan dan keterampilan peserta didik. Hasil penelitian menunjukkan (1) tingkat kevalidan Trainer Filter Analog pasif dan aktif berbasis penguat operasional sebesar 88,88%, tingkat kevalidan jobsheet 90%. (2)Trainer beserta jobsheet dapat dikatakan efektif, merujuk pada rata – rata hasil belajar siswa sebesar 86.81. (3) Kepraktisan dari nilai respon siswa mendapatkan poin sebesar 84.08%. melihat dari hasil – hasil yang dikemukakan tersebut dapat disimpulkan bahwa penelitian ini menghasilkan Trainer Filter Analog pasif dan aktif berdasarkan penguat operasional yang layak sebagai media pembelajaran pada mata pelajaran Penenrapan Rangkaian Elektronika di SMK Negeri 3 Jombang. Kata Kunci: Trainer filter analog, jobsheet, respon peserta didik, hasil belajar.Abstract The aim of this study is to produce an analog filter passive and active base on operational amplifier trainer as learning media on electrical circuit subject on class XI with criteria: valid seen from the result of validation, practice in terms of student response result, and effectively assessed from student learning outcomes. Research and development design (R&D) was used in this study, by applying one shot case study design test on the trial of implementation product. The study took place in SMK Negeri 3 Jombang with 31 students from 33 students of class XI TAV as research subjects. Product that was develop is an analog filter passive and active base on operational amplifier trainer and jobsheet. The research instrument that was use are trainer analog filter validation sheet, jobsheet validation sheet, item test validation sheet, cognitive evaluation test sheet, psychomotor evaluation sheet and student response questionnaire. Student competency analysis uses descriptive analysis to express students knowledge and skill competencies. The study result indicate (1) the level of validity analog filter passive and active base on operational amplifier trainer is 88.88%, job sheet validity is 90%. (2) Trainer and job sheet can be said effectively referring to the outcomes from student learning 86,81 for the average. (3) While for the practicality from the student response value get 84.08% point. Referring from the result stated above it can be concluded that the study produce active passive analog filter trainer base on operational amplifier that are feasible to use as learning media for electronic circuits application subject in SMK Negeri 3 Jombang Keywords: Analog filter trainer, jobsheet, student response, learning result

    Performance of Ensemble Classification for Agricultural and Biological Science Journals with Scopus Index

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    The ensemble method is considered an advanced method in both prediction and classification. The application of this method is estimated to have a more optimal output than the previous classification method. This article aims to determine the ensemble's performance to classify journal quartiles. The subject of agriculture was chosen because Indonesia is an agricultural country, and the interest of researchers in this field shows a positive response. The data is downloaded through the Scimago Journal and Country Rank with the accumulation in 2020. Labels have four classes: Q1, Q2, Q3, and Q4. The ensemble applied is Boosting and Bagging with Decision Tree (DT) and Gaussian Naïve Bayes (GNB) algorithms compiled from 2144 instances. The Boosting meta-ensembles used are Adaboost and XGBoost. From this study, the Bagging Decision Tree has the highest accuracy score at 71.36, followed by XGBoost Decision Tree with 69.51. The third is XGBoost Gaussian Naïve Bayes with 68.82, Adaboost Decision Tree with 60.42, Adaboost Gaussian Naïve Bayes with 58.2, and Bagging Gaussian Naïve Bayes with 56.12 results. This paper shows that the Bagging Decision Tree is the ensemble method that works optimally in this subject classification. This result suggests that the ensemble method can still fail to produce an ideal outcome that approaches the SJR system

    Classification of engineering journals quartile using Various Supervised Learning models

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    In scientific research, journals are among the primary sources of information. There are quartiles or categories of quality in journals which are Q1, Q2, Q3, and Q4. These quartiles represent the assessment of journal. A classification machine learning algorithm is developed as a means in the categorization of journals. The process of classifying data to estimate an item class with an unknown label is called classification. Various classification algorithms, such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM) are employed in this study, with several situations for exchanging training and testing data. Cross-validation with Confusion Matrix values of accuracy, precision, recall, and error classification is used to analyzed classification performance. The classifier with the finest accuracy rate is KNN with average accuracy of 70%, Naïve Bayes at 60% and SVM at 40%. This research suggests assumption that algorithms used in this article can approach SJR classification system

    Boosting and bagging classification for computer science journal

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    In recent years, computer science has expanded significantly. The provision of literacy resources is required as a reference resource to balance these developments. Journals are an excellent place to find the sources of this literacy. SCImago Journal Rank (SJR), a very reliable journal ranking website, can assist in locating high-quality journals. Sadly, several papers have a nonlinear relationship between the SJR indicators and its quartile classification. The classification approach may tackle the problem. This research uses Gaussian Naïve Bayes as the primary learner and ensemble boosting and bagging with a decision tree approach. This study also used some different settings. In the decision tree (DT), just the value of the estimator is used as a scenario. However, in the Gaussian Naïve Bayes, the depth and the estimator value are used. Adaboost and XGBoost are the two types of boosting employed. It is well known that Bagging is the ensemble that performs best; additionally, the XGBoost style of boosting performs better. Additionally, modifications to the depth and estimator parameters impact
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