The function of Big Data technology and Data Science in the contemporary digital economy is examined in this article. According to the author, large and medium-sized businesses in the retail and service sectors have a greater interest in utilizing them. Banks, mobile operators, and major manufacturing firms actively employ these technologies (Artificial Intelligence) to evaluate data on equipment failures and minimize downtime, which enables cost-reduction. Big Data technology serves as a liquid product and a prerequisite for boosting business profitability through individualized customer care and predictive analytics. It is crucial to ensure the formation of unique data exchanges and legalize a single definition of big data for the modern digital economy.

Traditional learning approaches in teaching English assume that learners have same background, ability, and requirements. In this study, we propose an adaptive learning systems-knowledge level (ALS-KL) is a learning system that can personalize materials due to proficiency level of English language learners. A pre-test is given to measure the proficiency level of learners. The level is divided into elementary, intermediate and advanced. The ALS-KL provides the most suitable materials according to the needs of the learners. We applied the proposed system on 90 learners with different English proficiency level. The effectiveness of the system was evaluated using post-test. The results of the post-test showed that the proposed system was able to improve learners with intermediate-level and advanced level 1.5 times and 4 times respectively while reducing the number of learners with elementary-level by more than 50%. These indicate the effectiveness of the proposed system.

Opinion mining is an automation technique of textual data from opinion sentence that produce sentiment information. It is also called sentiment analysis that involves the construction of a system for collecting and classifying opinions about a product review done by understanding, extracting and processing the text in an opinion sentence become positive, negative, and neutral. One of the techniques mostly used by data classification is Support Vector Machine (SVM). SVM is able to identify the separated hyper plane that maximizes the margin between two different classes. However, SVM has a weakness for parameter selection or suitable feature. In this research, the researchers made an improvement toward the previous research using combined method of feature selection in SVM through comparing three-feature selection; Genetic Algorithm, Particle Swarm Optimization, and Principal Component Analysis. It can be determined which one of the best feature selections that improve the classification accuracy of SVM. The dataset was cosmetic products review downloaded from www.amazon.com. Measurement is based on SVM accuracy by adding the feature selection method. While the evaluation used 10 Fold Cross Validation and the accuracy measurement used the Confusion Matrix and ROC Curve. The result of the measurement accuracy of SVM accuracy is obtained with average 82.00% and the average AUC 0.988. After the integration of SVM algorithm and feature selection, Genetic algorithm shows the best results with average accuracy 94.00% and the average AUC 0.984. Particle Swarm Optimization indicates the best results with average accuracy 97.00% and the average AUC 0.988. While Principal Component Analysis indicates the best results with average accuracy 83.00% and the average AUC 0.809. As conclusion, the research of SVM Algorithm showed the best accuracy improvement toward the feature selection of Particle Swarm Optimization integrated with the increased accuracy from 82.00% to 97.00%.

Di era digital, teknologi pembelajaran aktif merupakan kunci transformasi pendidikan, terutama di sekolah kejuruan menengah, namun implementasinya masih memerlukan penelitian lebih lanjut. Penelitian ini bertujuan untuk menilai efektivitas teknologi pembelajaran aktif dalam pendidikan kejuruan menengah, terutama dalam konteks pengembangan motivasi karir siswa. Dengan pendekatan mixed-methods, penelitian mengkombinasikan survei skala Likert dan wawancara mendalam, menilai pengalaman dan persepsi siswa serta guru terhadap teknologi. Survei mengukur keterlibatan, motivasi belajar, dan persepsi efektivitas pembelajaran, sementara wawancara mendalam menggali pengalaman dan tantangan dalam implementasi teknologi. Penelitian ini melibatkan 150 siswa dan 30 guru dari berbagai institusi kejuruan untuk memastikan representasi yang luas dari berbagai program studi, tahun ajaran, dan latar belakang guru. Hasil penelitian mengindikasikan bahwa teknologi pembelajaran aktif signifikan dalam meningkatkan keterlibatan dan motivasi siswa, dengan siswa menyoroti interaktivitas dan kolaborasi sebagai aspek positif. Guru juga mencatat peningkatan partisipasi dan interaksi di kelas. Namun, tantangan seperti kebutuhan pelatihan guru yang komprehensif dan peningkatan infrastruktur teknologi teridentifikasi. Penelitian ini menegaskan pentingnya mengintegrasikan teknologi pembelajaran aktif dalam pendidikan kejuruan menengah dan menekankan perlunya pendekatan holistik, melibatkan pelatihan guru yang efektif, kurikulum adaptif, dan infrastruktur yang memadai, untuk memaksimalkan potensi pendidikan dengan teknologi.

Hadith is the second source of Islamic law after Qur'an and an explanation of verses of the Qur'an. Today, there are many hadiths that appear and that are doubtful of its authenticity. The number of hadits that are doubtful of its authenticity or so-called dhaif and maudhu hadith can lead to errors in the determination of Islamic law for everyday life. The classification of hadith is required to know a hadith including dhaif (weak), maudhu (fabricated) or sahih (authentic) hadith. The sahih hadith identification is made to prevent the use of weak and fabricated hadiths in everyday life. This research classified and identified hadith using expert systems and simple algorithms with several stages of making a database of hadith, hadith level classification, implementation and test. Classification of hadith level used in this study consists of 19 hadith levels and 40 characteristics of hadith in terms of matn (content) and sanad (sequence of reporters) taken from the book of Hadith Science. Training data used for the classification consist of 274 hadiths, and data testing consist of 72 hadiths. The results of identification of the data testing resulted in 56 hadiths with the degree of Authentic Hadith, and 16 hadiths with the degree of unauthentic hadith, that are maudhu and dhaif. The result gave an error value of 0.00134%, ie less than 5%, which means the proposed classification model can be relied upon to identify degree of sahih hadith. This hadith degree identification can make it easier to determine sahih, dhaif and maudhu hadith.