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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.

This study compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

. Ekspor memainkan peran penting dalam pertumbuhan ekonomi, memberikan kontribusi terhadap devisa negara dan meningkatkan daya saing di pasar internasional. Menentukan produk ekspor unggulan menjadi tantangan karena harus mempertimbangkan berbagai faktor, seperti nilai ekonomi, permintaan pasar, stabilitas harga, dan keberlanjutan produksi. Keputusan yang kurang tepat dapat berdampak pada efisiensi, daya saing produk lokal, dan bahkan ketergantungan pada pasar global. Oleh karena itu, dibutuhkan pendekatan yang lebih sistematis dan berbasis data untuk membuat keputusan yang tepat dalam pemilihan produk ekspor unggulan. Penggunaan sistem pendukung keputusan (SPK) dapat membantu mempermudah proses ini dengan menyediakan analisis berbasis data yang lebih objektif. Metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) dapat digunakan untuk menentukan bobot kriteria, membantu pengambil keputusan untuk menilai seberapa penting setiap faktor yang terlibat. Sementara itu, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) digunakan untuk memilih alternatif terbaik berdasarkan kedekatannya dengan solusi ideal yang diinginkan. Pendekatan ini bertujuan untuk memberikan rekomendasi yang lebih objektif dan akurat, mendukung pengambil kebijakan serta pelaku usaha dalam menetapkan strategi ekspor yang optimal. Dengan cara ini, daya saing produk lokal dapat ditingkatkan secara berkelanjutan, memastikan pertumbuhan ekonomi yang lebih stabil dan meningkatkan posisi negara di pasar global  

Decision Support System (SPK) can be used to select the best college in this journal, the author uses the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method which is expected to be a solution for consideration for prospective new students who want to pursue higher education. From the calculation results, it is found that the highest result value from the calculation formula can be used to be the best choice in choosing a college for prospective students. With confusion matrix accuracy reaching 90%.  

Decision support system of credit giving is a computer-based information system that can be used as a tool for manager of the credit department to decide received  whether or not the loan application that submitted by customers. In the decision support system of credit giving uses principles of assessment called the 5 C. The 5 C are character, capacity, capital, collateral,  dan condition. Principles of  the 5 C will be taken for credit giving consideration. While preference for the weighting of criteria using Analytical Hierarchy Process. Overall, the existing process on the decision support system  of  credit giving is manager assigned the criteria, sub criteria, sub-sub criteria and the score. Then manager give preference of criteria to generate criteria weights. The data has been inputted by manager is used by staff of credit department  to perform the assessment so that can be produced a credit decision is received or rejected.