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Abstrak :
Pembelajaran yang bermakna dan mendalam pada pembelajaran kolaboratif daring memerlukan suatu rancangan pemodelan pembelajaran. Kerangka kerja Community of Inquiry (CoI) berfokus pada memfasilitasi pengalaman belajar yang bermakna dan mendalam melalui tiga aspek kehadiran yaitu sosial, kognitif, dan pengajaran. Forum diskusi asinkron saat ini sudah banyak digunakan namun beberapa kekurangan masih dirasakan oleh peserta didik maupun pengajar. Penelitian ini bertujuan untuk melakukan implementasi dan evaluasi forum diskusi asinkron berbasis kerangka kerja CoI berupa aplikasi web yang dapat digunakan secara nyata berdasarkan hasil rancangan penelitian sebelumnya. Penelitian ini mengadopsi metode Waterfall sebagai metode Software Development Life Cycle (SDLC). Metode Waterfall yang dimodifikasi menjadi tiga tahapan yaitu design, implementation, serta verification & testing. Tahapan design menghasilkan definisi sistem secara teknis melalui rancangan Unified Model Language (UML) sistem. Tahapan implementasi menghasilkan aplikasi web yang dapat digunakan melalui kegiatan mempersiapkan tech stack, development backend, dan development frontend. Framework Django dan PostgreSQL digunakan untuk pengembangan backend dan Next.js untuk pengembangan frontend. Tahapan verification & testing menghasilkan bahwa tidak ditemukan error utama pada setiap fitur melalui pengujian User Acceptance Testing (UAT). Evaluasi dilakukan melalui survey daring yang berisi pertanyaan kualitatif dan kuantitatif untuk mengukur aspek usability dan keberhasilan memunculkan kehadiran pada model CoI. Hasil usability testing memperoleh tingkat complete success sebesar 81.08%. Evaluasi nilai System Usability Scale (SUS) memperoleh nilai 78,78 yang termasuk dalam kategori “Good” dan “Acceptable”. Hasil evaluasi terhadap aplikasi web menunjukkan bahwa fitur Reaction Post, Post, Thread Diskusi, Profil Pengguna, dan Text Editor dapat memunculkan kehadiran sosial. Fitur Post, Thread Diskusi, Ringkasan Diskusi, dan Referensi Diskusi dapat memunculkan kehadiran kognitif. Fitur Thread Diskusi, Panduan Diskusi, Referensi Diskusi, Week, dan Inquiry State dapat memunculkan kehadiran pengajaran. ......Meaningful and deep learning in online collaborative learning requires a learning design framework. The Community of Inquiry (CoI) framework focuses on facilitating meaningful and deep learning experiences through three aspects of presence: social, cognitive, and teaching. Asynchronous discussion forums are widely used, but some shortcomings are still felt by students and teachers. This study aims to implement and evaluate an asynchronous discussion forum based on the CoI framework in the form of a web application that can be used effectively based on the previous research design. This research adopts the Waterfall method as the Software Development Life Cycle (SDLC) method. The modified Waterfall method consists of three stages: design, implementation, and verification & testing. The design stage produces a technical system definition through Unified Modeling Language (UML) system design. The implementation stage develops a web application through activities such as preparing the tech stack, developing the backend, and developing the frontend. The Django framework and PostgreSQL are used for backend development, and Next.js is used for frontend development. The verification & testing stage confirms that no major errors were found in each feature through User Acceptance Testing (UAT). Evaluation is conducted through an online survey that includes qualitative and quantitative questions to measure usability aspects and the success of bringing presence to the CoI model. The results of the usability testing achieved a complete success rate of 81.08%. The System Usability Scale (SUS) evaluation obtained a score of 78.78, which falls into the "Good" and "Acceptable" categories. The evaluation of the web application showed that features such as Reaction Post, Post, Discussion Thread, User Profile, and Text Editor can evoke social presence. Features like Post, Discussion Thread, Discussion Summary, and Discussion References can evoke cognitive presence. Features like Discussion Thread, Discussion Guide, Discussion References, Week, and Inquiry State can evoke teaching presence.

Abstrak :
Forum diskusi asinkron adalah salah satu media pembelajaran kolaboratif daring yang mampu mendorong pemikiran kritis, pertukaran gagasan, dan pembentukan pengetahuan. Analisis konten merupakan metode ilmiah yang dapat digunakan untuk mengidentifikasi keterampilan berpikir kritis dari transkrip pada forum diskusi asinkron. Metode analisis konten konvensional membutuhkan tahapan pengodean manual yang membutuhkan banyak waktu dan tenaga. Hal ini dapat mengakibatkan pengajar terlambat dalam memberikan intervensi instruksional karena informasi keterampilan berpikir kritis tidak dapat diperoleh secara cepat. Penelitian ini mengacu pada kerangka kerja Community of Inquiry (CoI) di mana keterampilan berpikir kritis dioperasionalisasikan melalui empat level dalam kehadiran kognitif yaitu pemantik diskusi, eksplorasi, integrasi, dan resolusi. Tujuan penelitian adalah mengembangkan model klasifikasi berbasis machine learning yang mampu menganalisis secara otomatis kehadiran kognitif pada transkrip diskusi berbahasa Indonesia. Desain penelitian menggunakan metode campuran kuantitatif dan kualitatif. Data eksperimen berjumlah 1.200 pesan diskusi dari mata kuliah Aljabar Linear di lingkungan pembelajaran bauran. Hasil penelitian menunjukkan bahwa kesiapan mahasiswa dalam mengelola pembelajaran dan lingkungan e-learning berpengaruh signifikan terhadap pengembangan kehadiran sosial dan kehadiran kognitif. Dataset level kehadiran kognitif pada transkrip diskusi asinkron dibangun dengan metode analisis konten yang reliabel kategori hampir sempurna (Cohen’s kappa = 0,88). Eksperimen pengembangan model analisis kehadiran kognitif menggunakan sepuluh basis algoritma yaitu XGBoost, Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), IndoBERT-base, IndoBERT-large dan XLM-RoBERTa. Model berbasis IndoBERT-large memiliki performa terbaik dengan akurasi sebesar 0,825. Prototipe sistem Cognipresa (cognitive presence analytics) telah dikembangkan untuk memfasilitasi pengajar dengan menganalisis kehadiran kognitif mahasiswa dalam diskusi secara otomatis. Evaluasi sistem menunjukkan hasil yang menjanjikan dari sisi usability dengan nilai System Usability Scale (SUS) sebesar 80,83. ......The asynchronous discussion forum serves as a collaborative online learning platform capable of stimulating critical thinking, exchanging ideas, and shaping knowledge. Content analysis is a scientific method that can be employed to identify critical thinking skills from transcripts in asynchronous discussion forums. Conventional content analysis methods entail manual encoding stages, which consume a significant amount of time and effort. This may lead to instructors being delayed in providing instructional interventions due to the inability to swiftly obtain information on critical thinking skills. This study references the Community of Inquiry (CoI) framework, where critical thinking skills are operationalized through four levels of cognitive presence: triggering event, exploration, integration, and resolution. The research's objective is to develop a machine learning-based classification model capable of automatically analyzing cognitive presence in Indonesian-language discussion transcripts. The research design incorporates both quantitative and qualitative methods. The experimental data consists of 1,200 discussion messages from the Linear Algebra course in a blended learning environment. The research findings indicate that students' preparedness in managing learning and e-learning environment significantly influences the development of social presence and cognitive presence. The dataset for cognitive presence at the transcript of asynchronous discussions was constructed using a content analysis method with a reliably almost perfect category (Cohen’s kappa = 0.88). An experimental development of the cognitive presence analysis model was conducted using ten algorithmic bases, namely XGBoost, Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), IndoBERT-base, IndoBERT-large, and XLM- RoBERTa. The IndoBERT-large-based model demonstrated the best performance with an accuracy of 0.825. A prototype system called Cognipresa (cognitive presence analytics) has been developed to facilitate educators in automatically analyzing students' cognitive presence in discussions. The system evaluation indicates promising results in terms of usability, with a System Usability Scale (SUS) score of 80.83.