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All Journal Jurnal Informatika dan Teknik Elektro Terapan JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknologi Informasi dan Komunikasi Jurnal Ekonomi Journal of Artificial Intelligence and Engineering Applications (JAIEA) BULLET : Jurnal Multidisiplin Ilmu JUKEMAS : Jurnal Pengabdian Kepada Masyarakat
Dikananda, Fatihanursari
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Analysis and Visualization of Sales Transaction Patterns using Decision Tree and Tableau Public Akbar, Miftahul; Rahaningsih, Nining; Ali, Irfan; Dikananda, Fatihanursari; Hayati, Umi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1849

Abstract

This study aims to analyze sales transaction patterns of rubber waste at PT Mandiri Enviro Technosio by integrating the Decision Tree algorithm with interactive visualization using Tableau Public. The dataset consists of 405 sales transactions recorded during the 2024–2025 period, comprising attributes such as transaction date, product type, quantity, unit price, total value, delivery region, and buyer category. The research methodology includes data acquisition, preprocessing to ensure data quality and consistency, construction of a classification model using the CART algorithm, evaluation of model performance through a confusion matrix, and development of interactive dashboards for enhanced interpretability. The Decision Tree model achieved an accuracy of 88.24% in classifying transaction values into low, medium, and high categories. Unit price and transaction period were identified as the most influential attributes in determining transaction value. Visualization using Tableau Public effectively presented the distribution of transaction values, sales trends, and geographical patterns, thereby strengthening analytical insights and supporting data-driven decision making. The integration of classification techniques and interactive visualization contributes to improving business intelligence capabilities and enables the formulation of more adaptive, evidence-based sales strategies.
Predicting Student Academic Performance Based on Learning Habits Using XGBoost and SHAP Latifah, Siti; Martanto; Dana, Raditya Danar; Dikananda, Fatihanursari; Hayati, Umi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1860

Abstract

This study developed a model for predicting student academic achievement based on learning habits using the XGBoost algorithm and SHAP interpretability techniques. The secondary dataset contains 1,000 entries and 16 variables (for example, hours of study per day, mental health, frequency of exercise, social media use, hours of sleep) pre-processed including cleaning, imputation, encoding, and normalization before being divided into train–test (80:20) and validated using 5-fold CV. Three models were tested: Linear Regression, Random Forest, and XGBoost. Evaluation using RMSE, MAE, and R² showed that XGBoost achieved RMSE = 0.335, MAE = 0.266, and R² = 0.882, while Linear Regression showed the best performance according to R² in certain configurations (R² = 0.888; RMSE = 0.326). SHAP analysis revealed that the most influential features were hours of study per day, mental health scores, exercise frequency, duration of social media use, and hours spent watching Netflix. The findings confirm that students' study habits and psychological conditions are the main determinants of academic achievement variation; the use of interpretable features strengthens the readability of the model for education stakeholders. Research recommendations include testing the model on longitudinal datasets, integrating socioeconomic factors, and implementing data privacy procedures before institutional-scale implementation.
PERBANDINGAN KINERJA SUPPORT VECTOR MACHINE ORIGINAL DAN SUPPORT VECTOR MACHINE BERBASIS SMOTE UNTUK ANALISIS SENTIMEN APLIKASI JKN MOBILE APIPAH, KUSNATUL; Martanto, Martanto; Dikananda, Fatihanursari; Bahtiar, Agus; Narasati, Riri
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 17 No. 1 (2026): Maret
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v17i1.1403

Abstract

Analisis sentimen terhadap ulasan pengguna aplikasi JKN Mobile digunakan untuk mengevaluasi kualitas layanan digital BPJS Kesehatan. Penelitian ini membandingkan kinerja algoritma Support Vector Machine (SVM) pada data asli dan SVM berbasis Synthetic Minority Oversampling Technique (SMOTE) dalam mengatasi permasalahan ketidakseimbangan kelas. Dataset diperoleh melalui proses scraping ulasan pengguna aplikasi JKN Mobile. Tahapan pra-pemrosesan meliputi case folding, tokenizing, stopword removal, dan stemming. Representasi fitur menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF). Evaluasi kinerja model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa penerapan SMOTE mampu meningkatkan kinerja klasifikasi, khususnya pada kelas minoritas, dibandingkan dengan SVM tanpa penyeimbangan data. Dengan demikian, SMOTE terbukti efektif untuk meningkatkan performa analisis sentimen pada data tidak seimbang.
Co-Authors Ahmad Faqih Akbar, Miftahul Antika, Rindi APIPAH, KUSNATUL Ash Shiddiqi, Jimly Gifthera Dwilestari Hayati, Umi Hoerunnisa, Anis Indriya Efendi, Dendy Irfan Ali, Irfan Khaerul Anam Khairul Akmal, Khairul khusnul khotimah Komalasari, Cahyaningrum Martanto Martanto . Muhammad Faisal Narasati, Riri Narasati Nining Rahaningsih Nurhalizah, Shafa Aristia Pratama, Denni Putri Ekawati Darma Raditya Danar Dana Rhamadanti, Anisa Rifa'i, Ahmad Rifa’I, Ahmad Rinaldi Dikananda, Arif Sadikin, Abdurrahman Saeful Anwar, Saeful Siti Latifah Sulaeman, Muhamad Sunana, Heliyanti Susana, Heliyanti Wahyudin, Edi