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"Perkembangan pesat kecerdasan buatan (AI) telah memengaruhi berbagai bidang, termasuk dalam AI-Assisted Language Learning (AALL). Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang memengaruhi adopsi teknologi AALL dengan menggabungkan kerangka teori UTAUT2, Self-Determination Theory (SDT), dan AI anxiety. Pendekatan kuantitatif diterapkan dalam penelitian ini dengan menggunakan data yang diperoleh dari kuesioner daring yang disebarkan kepada 557 responden. Data yang terkumpul kemudian dianalisis dengan metode Partial Least Squares Structural Equation Modeling (PLS-SEM) menggunakan perangkat lunak SmartPLS 4. Hasil analisis menunjukkan bahwa faktor-faktor yang signifikan mempengaruhi adopsi teknologi AALL meliputi autonomy, competence, performance expectancy, effort expectancy, social influence, habit, price value, AI anxiety, behavioral intention, dan actual use. Di antara faktor-faktor tersebut, habit terbukti memiliki pengaruh signifikan terhadap niat pengguna dalam mengadopsi teknologi ini. Sementara itu, faktor facilitating conditions dan hedonic motivation tidak menunjukkan pengaruh signifikan dalam konteks penelitian ini. Temuan ini memberikan wawasan lebih dalam tentang dinamika adopsi teknologi AALL, serta memberikan kontribusi terhadap pengembangan strategi implementasi teknologi yang lebih efektif di masa depan.

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The rapid development of artificial intelligence (AI) has influenced various fields, including AI-assisted language learning (AALL). This study aims to identify the factors that influence the adoption of AALL technology by integrating the UTAUT2 model, Self Determination Theory (SDT), and AI anxiety. A quantitative approach was applied in this research, utilizing data collected through an online questionnaire distributed to 557 respondents. The collected data was then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the SmartPLS 4 software. The analysis results indicate that significant factors influencing AALL technology adoption include autonomy, competence, performance expectancy, effort expectancy, social influence, habit, price value, AI anxiety, behavioral intention, and actual use. Among these factors, habit was found to have a significant impact on users' intention to adopt this technology. Meanwhile, facilitating conditions and hedonic motivation did not show significant effects in the context of this study. These findings provide deeper insights into the dynamics of AALL technology adoption and contribute to the development of more effective technology implementation strategies in the future."

"Penelitian ini bertujuan mengembangkan dan mengevaluasi chatbot asisten dosen yang memberikan layanan konsultasi akademik secara otomatis dan responsif kepada mahasiswa. Studi ini mengambil topik Scrum dalam mata kuliah Dinamika Tim Perangkat Lunak sebagai kasus uji, yang diajarkan di Program Magister Teknologi Informasi Universitas Indonesia (MTI UI). Pengembangan sistem dilakukan secara kolaboratif oleh dua penulis dengan peran berbeda: penulis pertama membangun aplikasi front-end dan back-end serta melakukan pengujian melalui User Acceptance Testing (UAT) dan API Testing; sedangkan penulis kedua mengembangkan model language model (LLM) yang mencakup persiapan data, fine-tuning model, evaluasi kinerja, dan deployment menggunakan FastAPI. Evaluasi chatbot dilakukan secara kuantitatif menggunakan metrik BLEU, ROUGE, dan BERTScore, serta secara kualitatif melalui wawancara dengan pengguna. Hasil evaluasi menunjukkan bahwa model berbayar ChatGPT unggul dalam seluruh metrik evaluasi, sementara model open-source seperti LLaMA dan Bloom masih menunjukkan potensi meskipun perlu peningkatan kualitas melalui penambahan data fine-tuning. Menariknya, proses fine-tuning tidak selalu memberikan peningkatan performa yang konsisten, bahkan pada beberapa kasus justru menurunkan akurasi model. Namun demikian, penggunaan model open-source tetap menjanjikan dari sisi efektivitas biaya, karena tidak bergantung pada token seperti model komersial.

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This study aims to develop and evaluate a teaching assistant chatbot designed to provide automated and responsive academic consultation services to students. The case study focuses on the Scrum topic within the Software Team Dynamics course at the Master of Information Technology program, Universitas Indonesia (MTI UI). The development process was a collaboration between two authors with distinct roles: the first author was responsible for building the front-end and back-end applications and conducting tests using User Acceptance Testing (UAT) and API Testing; the second author focused on developing the large language model (LLM), including data preparation, model fine-tuning, performance evaluation, and deployment using FastAPI. The chatbot's performance was evaluated using quantitative metrics such as BLEU, ROUGE, and BERTScore, along with qualitative evaluation through user interviews. The results show that the commercial ChatGPT model achieved the best performance across all evaluation metrics. In contrast, open-source models such as LLaMA and Bloom showed promising but still suboptimal results, requiring larger and more comprehensive fine-tuning datasets to improve. Interestingly, fine-tuning did not always lead to consistent performance improvement; in some cases, it even degraded the model's quality. Nevertheless, open-source models remain advantageous in terms of cost-effectiveness, as they do not incur token-based usage fees like ChatGPT."

"Perkembangan kecerdasan buatan (artificial intelligence/AI) bergerak semakin cepat dan mengalami kemajuan pesat dalam setiap bidang kehidupan manusia, tak terkecuali dalam dunia kendali industri. Sementara kendali industri mensyaratkan sistem pengendali yang mampu mengatasi perubahan karakteristik secara otomatis serta dapat beradaptasi dengan dinamika perubahan sistem yang diakibatkan adanya perubahan kondisi lingkungan kerja. Pengendali berbasis kecerdasan buatan dianggap mampu untuk beradaptasi dengan perubahan karakteristik dari sistem secara otomatis adalah pengendali berbasis neural network. Dalam penelitian ini disajikan desain dan simulasi sistem pengendali berbasis neural network dengan metode pembelajaran back propagation yaitu pengendali inverse langsung(direct inverse control/DIC), pengendali neuron tunggal(single neuron controller), serta pengendali PID pada plant modul training pressure process rig(PPR 38-417). Untuk pengujiannya, didesain sistem identifikasi berbasis neural network sebagai simulator plant. Hasilnya, semua sistem kendali yang didesain tersebut mampu mengendalikan plant sesuai dengan sinyal referensi yang dinginkan. Namun pengendali single neuron dan PID mampu mempertahankan keluarannya dengan baik saat diberi gangguan pada sinyal kendali maupun plant dibandingkan dengan pengendali inverse langsung(ANN-DIC). Hal ini dikarenakan kendali single neuron dan PID bersifat close loop sehingga mampu mengkoreksi kesalahan secara langsung. Sementara jika dibandingkan dengan kendali PID, kendali single neuron lebih adaptif untuk berbagai kondisi gangguan karena memiliki metode pembelajaran langsung, sementara kendali PID perlu dilakukan tunning untuk mendapatkan unjuk kerja yang handal.

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The development of artificial intelligence (AI) is moving faster and experiencing rapid progress in every area of human life, not least in the world of industrial control. While industrial control requires a control system that is able to overcome changes in characteristics automatically and can adapt to the dynamics of system changes caused by changes in working environment conditions. Artificial intelligence-based controllers are considered capable of adapting to changes in the characteristics of the system automatically is a neural network-based controller. In this study, the design and simulation of a neural network-based controller system with back propagation learning methods, namely direct inverse control (DIC) and single neuron controller, as well as PID controllers for the pressure process rig (PPR 38-417) training module.

For the test, a neural network-based identification system is designed as a simulator plant. As a result, all the control systems designed are able to control the plant in accordance with the desired reference signal. However, single neuron and PID controllers are able to maintain their output well when given interference with the control signal or plant compared to the direct inverse controller (ANN-DIC). This is because single neuron control and PID are close loop so that they can correct errors directly. Meanwhile, when compared to PID control, single neuron control is more adaptive for a variety of disruption conditions because it has a direct learning method, while PID control needs to be tuned to get reliable performance."