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All Journal Sistemasi: Jurnal Sistem Informasi JOINTER : Journal of Informatics Engineering
Menden, Lisa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Prediksi Tingkat Kemiskinan Kota Manado Menggunakan Algoritma Regresi Linier Berganda Menden, Lisa; Rantung, Vivi Pegie
JOINTER : Journal of Informatics Engineering Vol 6 No 02 (2025): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v6i02.428

Abstract

This study analyzes and predicts the poverty rate in Manado City using Multiple Linear Regression (MLR) based on annual data from Statistics Indonesia (BPS). The dependent variable is the percentage of poor people, while the predictor variables include total population, average years of education, and Human Development Index (HDI). An alternative specification incorporates time trend controls. The analysis includes multicollinearity testing, OLS estimation, model diagnostics, and Leave-one-Out Cross Validation (LOOCV). Model A showed moderate explanatory power (R² = 0.4178) and good prediction accuracy (MAPE = 4.17%). Model B improved the behavior of the residuals by reducing autocorrelation and increasing the overall stability of the model. Education shows a negative and significant relationship with poverty, while the HDI coefficient requires careful interpretation due to multicollinearity. These findings suggest that expanding educational attainment and strengthening human development can effectively reduce poverty. Future research can integrate additional socioeconomic variables or adopt time series-based models to improve long-term predictive performance.
Rice Plant Disease Detection System based on Leaf Image using Web-based CNN Algorithm Menden, Lisa; Santa, Kristofel; Kumajas, Sondy
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.6067

Abstract

Rice (Oryza sativa) plays a crucial role as a major staple food commodity. However, diseases such as Bacterial Blight, Brown Spot, and Leaf Blast can cause significant crop losses. Current manual identification methods have limitations due to high subjectivity and long diagnosis time. This study proposes a web-based automatic detection system using a Convolutional Neural Network (CNN). The dataset was obtained from Kaggle and consisted of 2,800 images evenly distributed across four classes (700 images per class). The data were split using an 80:20 ratio for training and validation sets, followed by preprocessing steps including resizing to 224×224 pixels and data augmentation. The CNN architecture was designed with four convolutional blocks and optimized using the Adam optimizer. Training for 50 epochs achieved an accuracy of 77.50%, precision of 82.98%, recall of 77.50%, and an F1-score of 72.84%. Based on the confusion matrix analysis, the model performed very well in detecting Bacterial Blight and Brown Spot but still faced difficulties in identifying the Leaf Blast class. Overall, the developed system has the potential to serve as a decision-support tool for farmers, although further performance improvements are required, particularly for detecting specific disease variants.
Co-Authors Kristofel Santa Sondy C. Kumajas Vivi Peggie Rantung