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All Journal ILKOM Jurnal Ilmiah Academic : Journal of Social and Educational Studies
Nuraisyah, Andi
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Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences Other

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Metode Total Physical Response dalam Peningkatan Keterampilan Berbicara Bahasa Jerman Nuraisyah, Andi; Saleh, Nurming
Academic : Journal of Social and Educational Studies Vol 3, No 2 (2025): Academic : Journal of Social and Educational Studies
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/academic.v3i2.67301

Abstract

Abstract. This study aims to improve the German speaking skills of Class XI.1 Students of SMA Negeri 24 Bone Regency using the Total Physical Response method. This study is a classroom action research (CAR) which includes planning, implementation, observation and reflection. The subjects of this study were 33 class XI.1 students. The data collection techniques used were speaking skills tests and observations. The results of the first cycle observation, the teacher had not applied the Total Physical Response method according to the steps, so that students were not yet fluent in speaking German, the results still did not reach the qualifications. However, the results obtained in cycle II, all steps of the Total Physical Response method had been implemented well and students were able to speak German clearly and fluently. Quantitative data were obtained through speaking skills tests in cycles I and II. The results of the students' German speaking test in cycle I obtained an average of 70.41 and increased in cycle II with an average value of 81.75. The results of this study indicate that the application of the Total Physical Response method can improve the German speaking skills of class XI.1 students of SMA Negeri 24 BoneKeywords: Classroom Action Research, Speaking Skills, Total Physical Response
Extreme learning machine with feature extraction using GLCM for phosphorus deficiency identification of cocoa plants Basri, Basri; Assidiq, Muhammad; Karim, Harli A.; Nuraisyah, Andi
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1226.112-119

Abstract

This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with data collection, algorithm development to validation, and measurement using ROC. It was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.
Co-Authors Assidiq, Muhammad Basri Basri K, Nurul Alifah Karim, Harli A. Michrun Nisa Saleh, Nurming Yusuf, Nurul Arfiyanti