<![CDATA[Analisis event log melalui process mining telah digunakan secara luas untuk mengeksplorasi pola perilaku dalam berbagai domain, terutama pendidikan dan game. Pendekatan ini memanfaatkan alat bantu seperti Disco, ProM, dan metode berbasis machine learning untuk menggali pola perilaku, memahami proses, dan mengevaluasi hasil. Penelitian ini menggunakan metode Systematic Literature Review (SLR) dengan teknik Critical Appraisal Skills Programme (CASP) untuk memastikan kualitas dan validitas studi yang dianalisis. Dari total 48 artikel yang ditemukan, sebanyak 30 artikel lolos tahap seleksi, dan 18 artikel dinyatakan layak untuk dianalisis lebih lanjut. Dalam domain pendidikan, analisis menunjukkan perbedaan signifikan dalam pola belajar siswa, seperti perilaku kelompok dengan nilai tinggi yang cenderung konsisten dibandingkan kelompok dengan nilai rendah. Pola akses ke Learning Management System (LMS) dihubungkan dengan capaian akademik, memberikan wawasan tentang hubungan antara aktivitas belajar dan hasil pembelajaran. Di sisi lain, dalam domain game, algoritma process mining digunakan untuk mengidentifikasi kesalahan pemain, mengevaluasi jalur normatif, dan menganalisis keterkaitan antara model proses dan performa pemain. Hasilnya menunjukkan bahwa algoritma seperti Inductive Miner, Heuristic Miner, dan Fuzzy Miner memberikan fitness model tinggi (0,9–1,0) dan mampu mengekstraksi pola perilaku yang signifikan. Penggunaan tools seperti ProM dan Disco memungkinkan visualisasi model proses yang intuitif, sementara algoritma berbasis machine learning seperti XGBoost memberikan akurasi tinggi dalam memprediksi hasil berdasarkan data event log. Secara keseluruhan, hasil studi menegaskan bahwa process mining berperan penting dalam mengungkap pola perilaku kompleks serta mendukung peningkatan efektivitas pembelajaran dan evaluasi performa pengguna di berbagai domain. Abstract Event log analysis through process mining has been widely used to explore behavioural patterns in various domains, particularly education and gaming. This approach utilizes Disco, ProM, and machine learning to examine behavioural patterns, understand processes, and evaluate results. This study employed a Systematic Literature Review (SLR) with the Critical Appraisal Skills Programme (CASP) technique to ensure the quality and validity of the analyzed studies. Of the 48 articles found, 30 passed the selection stage, and 18 were deemed suitable for further analysis. In the education domain, the analysis revealed significant differences in student learning patterns, such as the behaviour of high-scoring groups tending to be more consistent than that of low-scoring groups. Access patterns to Learning Management Systems (LMS) were linked to academic achievement, providing insight into the relationship between learning activities and learning outcomes. Process mining algorithms were used in the gaming domain to identify player errors, evaluate normative pathways, and analyze the relationship between process models and player performance. The results show that algorithms such as Inductive Miner, Heuristic Miner, and Fuzzy Miner provide high model fitness (0.9–1.0) and can extract significant behavioural patterns. Tools such as ProM and Disco allow for intuitive visualization of process models. In contrast, machine learning-based algorithms such as XGBoost provide high accuracy in predicting outcomes based on event log data. The study results confirm that process mining is crucial in uncovering complex behavioural patterns and supporting improved learning effectiveness and user performance evaluation across various domains. ]]>
