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All Journal Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Putu Satya Saputra
Politeknik Negeri Bali

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Analisis Prediktif Dropout Mahasiswa Berdasarkan Kinerja Akademik Semester Awal Menggunakan Machine Learning Putu Satya Saputra
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.791

Abstract

Student dropout is a critical issue in higher education. This study aims to develop a predictive model of dropout based on early academic performance using Random Forest and Gradient Boosting algorithms. The dataset, sourced from the UCI Repository, contains 4,424 student records. Key features analyzed include the number of enrolled courses, evaluations, average grades, and enrollment age. Results show that the Gradient Boosting algorithm achieved 70.05% accuracy, while Random Forest reached 70.16%, both performing best in classifying graduates. The model successfully identifies high-risk students, although challenges remain in predicting “enrolled” status. These findings highlight the potential of machine learning for early dropout detection and support more targeted academic interventions.
Aplikasi Klasifikasi Penyakit Kentang Menggunakan Algoritma Convolution Neural Network (CNN) I Putu Astya Prayudha; Gde Brahupadhya Subiksa; Putu Satya Saputra; I Nyoman Rai Widartha Kesuma Kesuma; Vianne Clarinta Putri Gurning
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.849

Abstract

Potatoes are an important horticultural commodity that is susceptible to various diseases such as black scurf, blackleg, common scab, dry rot, and pink rot. Early disease detection is crucial for reducing losses and increasing productivity. This study aims to develop an image-based potato disease classification model using the Convolutional Neural Network method. The dataset consists of 1,760 images divided into eight classes, with a data split of 70% training data, 20% validation data, and 10% test data. The data processing involves preprocessing and augmentation to improve the model’s generalisation. The convolutional neural network architecture used consists of four convolutional layers combined with batch normalisation, max pooling, dropout, and fully connected layers. The model was trained using the Adam optimiser with a learning rate of 0.0003 and a batch size of 32. Testing results show that the model achieved an accuracy of 86%, with an average precision of 0.87, a recall of 0.86, and an F1-score of 0.86. The evaluation results indicate that the model is capable of recognising most classes well, although errors still occur in those with high visual similarity. This study demonstrates that the CNN method is effective for classifying potato diseases based on digital images and has the potential for further development on larger datasets.  
Co-Authors Gde Brahupadhya Subiksa I Nyoman Rai Widartha Kesuma Kesuma I Putu Astya Prayudha I Putu Gede Abdi Sudiatmika Vianne Clarinta Putri Gurning