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The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.

Penelitian ini mengevaluasi efisiensi tiga algoritma sorting hybrid, yaitu TimSort, IntroSort, dan Merge-Insertion Sort, pada dataset skala menengah yang memiliki jumlah elemen antara 10.000 hingga 1.000.000. Tujuan utama penelitian adalah untuk menganalisis performa algoritma berdasarkan waktu eksekusi, konsumsi memori, dan stabilitas, dengan pengujian dilakukan pada berbagai jenis dataset, termasuk data acak, terurut, hampir terurut, dan data dengan banyak elemen duplikat. Pengujian dilakukan melalui simulasi komputasi menggunakan bahasa pemrograman Python dalam lingkungan terkontrol untuk memastikan hasil yang konsisten. Dataset sintetis dibuat untuk mencerminkan kasus dunia nyata, seperti pengolahan log sistem, pengurutan data pelanggan dalam aplikasi e-commerce, atau pengolahan data sensor dalam sistem Internet of Things (IoT). Hasil pengujian menunjukkan bahwa TimSort memiliki performa unggul pada dataset hampir terurut dengan waktu eksekusi rata-rata 0,12 detik untuk 1.000.000 elemen, sedangkan IntroSort lebih cepat pada dataset acak dengan waktu 0,09 detik dan konsumsi memori rendah sekitar 120 MB. Merge-Insertion Sort menonjol dalam hal stabilitas, tetapi memerlukan memori lebih besar, yaitu sekitar 180 MB untuk dataset yang sama. Analisis mendalam menunjukkan bahwa pemilihan algoritma yang optimal sangat bergantung pada karakteristik dataset dan kebutuhan aplikasi, seperti kecepatan untuk data acak atau stabilitas untuk pengurutan data berurutan. Penelitian ini merekomendasikan TimSort untuk aplikasi yang memerlukan stabilitas tinggi, seperti pengolahan data transaksi keuangan, dan IntroSort untuk aplikasi yang mengutamakan kecepatan pada data acak, seperti analitik data real-time. Untuk pengembangan lebih lanjut, penelitian ini menyarankan eksplorasi optimasi paralel atau implementasi algoritma pada perangkat dengan sumber daya terbatas guna meningkatkan skalabilitas dan efisiensi.

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication