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All Journal Sharia Economic and Management Business Journal (SEMBJ) Jurnal Intelek Dan Cendikiawan Nusantara
A. Nurul Hidayat
Universitas Muhammadiyah Palu
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Other

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Transformasi Evaluasi Digital Inovasi Klasifikasi Capaian Belajar Berbasis Blooket Menggunakan Sistem Cerdas A. Nurul Hidayat; Erniati Erniati
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 3 No. 02 (2026): APRIL - MEI 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Transformasi digital dalam pendidikan memerlukan metode evaluasi inovatif yang objektif dan adaptif. Penelitian ini bertujuan untuk mengklasifikasikan capaian belajar siswa menggunakan data yang diekstraksi dari platform gamifikasi Blooket melalui pendekatan komputasi sistem cerdas. Masalah utama yang diangkat adalah adanya celah dalam pengolahan data gamifikasi yang selama ini hanya menjadi angka statistik mentah tanpa analisis kategori kompetensi yang mendalam. Metodologi penelitian melibatkan alur sistematis mulai dari akuisisi data leaderboard, pra-pemrosesan dalam format CSV, hingga implementasi algoritma Naive Bayes di lingkungan Anaconda Jupyter Notebook. Hasil penelitian menunjukkan bahwa sistem mampu melakukan klasifikasi dengan akurasi mencapai 100%, memetakan siswa ke dalam kategori Mastery, Developing, dan Emerging secara tepat sesuai parameter akurasi. Studi ini menyimpulkan bahwa integrasi sistem cerdas pada data gamifikasi merupakan terobosan strategis bagi asesmen pedagogik modern di Universitas Muhammadiyah Palu guna meminimalkan subjektivitas penilaian.
User Satisfaction Classification of Tiktok Shop Skincare Products Using C4.5 and Random Forest for Recommendation Strategy Nursalim Nursalim; Muhamad Ziaul Haq; A. Nurul Hidayat; Budi Mulyono
Sharia Economic and Management Business Journal (SEMBJ) Vol. 7 No. 2 (2026): Sharia Economic and Management Business
Publisher : Yayasan Darussalam Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62159/sembj.v7i2.2234

Abstract

Background: TikTok Shop has become an important social commerce platform for skincare purchases; however, product recommendations are not always perceived as relevant by users. A data-driven satisfaction classification model is therefore needed to support more targeted recommendation strategies. Method: This study used a quantitative approach involving 150 TikTok Shop users who had purchased skincare products. Data were collected through an online questionnaire containing 14 Likert-scale items and three recommendation-preference items. Instrument quality was evaluated using corrected item-total correlation and Cronbach Alpha. The C4.5 decision tree and Random Forest models were evaluated using stratified 10-fold cross-validation. Results: All 14 items were valid, with item-total correlations ranging from 0.619 to 0.881, and the overall Cronbach Alpha was 0.969. The satisfaction classes were balanced, consisting of 75 satisfied and 75 unsatisfied respondents. Information gain analysis identified product delivery as the most influential attribute, with a gain value of 0.4551. C4.5 achieved 85.33% accuracy, while Random Forest achieved 83.33% accuracy. Conclusion: C4.5 provided competitive performance and stronger interpretability than Random Forest for this dataset. The resulting classification rules can be used to prioritize delivery reliability, application usability, and product quality in skincare recommendation strategies.
Co-Authors Budi Mulyono Erniati Erniati Muhamad Ziaul Haq Nursalim