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Abstract. From the perspective of Micro, Small, and Medium Enterprises (MSMEs), fluctuations in raw material prices are highly concerning as they can significantly impact business stability. While MSMEs may tolerate price fluctuations to some extent, from an industrial engineering perspective, such a passive approach contradicts the principles of continuous improvement. This study seeks to predict the price of large red chili peppers using five regression models implemented through Orange Data Mining: Linear Regression, Support Vector Machine, Decision Tree, k-Nearest Neighbors (kNN), and Gradient Boosting. Due to the limited availability of daily data, particularly within a daily timeframe, the study utilized weekly data spanning three years. The results of the Test and Score evaluation shows Gradient Boosting as the best-performing model, achieving a Mean Absolute Percentage Error (MAPE) of 0.7%. However, the MAPE for predictions in January 2025 increased to 15.8%. This error is expected to decrease as more weekly data becomes available to mitigate the inaccuracies inherent in this model. Keywords: prediction, red chilli, regression, supervised learning , orange data mining. Abstrak. Dalam perspektif UMKM, fluktuasi harga bahan baku adalah suatu hal yang paling ditakuti karena berakibat pada ketahanan usaha yang menjadi tidak menentu. Pada suatu kondisi, fluktuasi harga dapat diterima para UMKM, namun dalam perspektif teknik industri, sikap UMKM tersebut tidak sesuai prinsip continuous improvement. Penelitian ini mencoba untuk memprediksi harga cabai merah besar dengan menggunakan 5 model regresi dibantu Orange Data Mining. Yaitu Linear Regression, Support Vector Machine, Tree, kNN, Gradient Boosting. Data yang diperlukan sebagian besar tidak tersedia, khususnya dalam kerangka waktu harian sehingga penelitian ini menggunakan data mingguan selama 3 tahun. Hasil Test and Score menunjukkan model Gradient Boost terpilih menjadi model terbaik dengan tingkat MAPE 0.7% namun MAPE pada tahap Prediction di bulan Januari 2025 menjadi 15.8%. Error tersebut akan berkurang ketika data mingguan sudah cukup banyak untuk menambal kesalahan yang dihasilkan model ini Kata kunci: prediksi, cabai merah, regression, supervised learning , orange data mining.

Diabetes is one of the diseases that poses a significant global health challenge, with a considerable impact on quality of life and mortality rates. This study examines the use of the Support Vector Machine (SVM) algorithm for diabetes classification through a literature review. SVM was chosen due to its ability to handle imbalanced and complex data. The aim of this study is to assess the effectiveness of SVM compared to other machine learning methods in detecting diabetes. The results of the literature review indicate that SVM achieves higher accuracy than other methods such as Naïve Bayes and Decision Tree, with some studies showing accuracy above 90%. This study is expected to provide deeper insights into the development of machine learning-based diagnostic systems for diabetes.