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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) International Journal Of Computer, Network Security and Information System (IJCONSIST)
Betty Dewi Puspasari
STT Atlas Nusantara Malang, Program Studi Teknik Informatika
Computer Science & IT Education Mathematics

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APLIKASI VISUALISASI DATA SEKOLAH DI KABUPATEN PASURUAN JAWA TIMUR BERBASIS ANDROID Pramono, Andy; Puspasari, Betty Dewi
MATICS MATICS (Vol 5, No 3
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (332.246 KB) | DOI: 10.18860/mat.v0i0.2421

Abstract

Pada saat ini kebutuhan akan ketersediaan informasi yang cukup akurat sangat dibutuhkan dalam  segala bentuk kegiatan atau usaha, baik itu dalam lingkup pemerintahan maupun usaha. Demikian pula dalam hal ketersediaan informasi data non akademik sekolah sangat diperlukan bagi Dinas Pendidikan dan kebudayaan Kabupaten Pasuruan, yang selama ini mengalami kesulitan dalam mengambil kebijakan pengembangan pendidikan di Kabupaten Pasuruan karena kurangnya informasi non akademik sekolah. Penelitian ini merupakan pengembangan dari penelitian sebelumnya yang berbasis web untuk propinsi Kalimantan Timur, penelitian ini akan menghasilkan suatu aplikasi visualisasi pemetaan sekolah Kabupaten Pasuruan yang akan menghasilkan suatu aplikasi yang mampu memberikan informasi mengenai data non akademik sekolah bagi Dinas Pendidikan Kabupaten Pasuruan yang divisualisasikan dalam bentuk pemetaan vektor 2D ini dilaksanakan dalam beberapa tahap melalui tahap pengumpulan data dan analisa, konsep perancangan aplikasi (konsep perancangan meliputi DFD, ER, Desain Interface) dan implementasi dan ujicoba. Hasil dari penelitian ini adalah memberikan alternatif solusi dalam memberikan informasi data non akademik sekolah di Kabupaten Pasuruan Jawa Timur.
Sentiment Analysis on Generation Z News Article using Support Vectore Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE) Kartini, Kartini; Hindrayani, Kartika Maulida; Puspasari, Betty Dewi
IJCONSIST JOURNALS Vol 5 No 2 (2024): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i2.141

Abstract

The development of digital media has increased the volume of news articles discussing various issues, including those involving Generation Z. Understanding public perception of these news items can be achieved by applying a crucial approach, namely sentiment analysis. This study aims to classify sentiment in news articles about Generation Z using the Support Vector Machine (SVM) algorithm. The main challenge in sentiment analysis is data class imbalance, where the amount of positive and negative sentiment data is often unbalanced. Therefore, the Synthetic Minority Over-sampling Technique (SMOTE) is used to address this problem by balancing the class distribution before model training. The datasets used were collected from various online news portals and analyzed through text preprocessing, feature extraction using Bag of Word, and SVM model training. The evaluation results show that the application of SMOTE significantly improves the model's performance in classifying sentiment, with improvements in accuracy, precision, recall, and F1-score compared to the model without data imbalance handling. This study demonstrates that the combination of SVM and SMOTE is effective in conducting sentiment analysis on Generation Z news articles. The accuracy shows 84% with 83% precision and 76% recall.
Comparative Study of Lightweight Deep Learning Architectures for Potato Plant Disease Detection Faisol, Ahmad; Rudhistiar, Deddy; Puspasari, Betty Dewi
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.90854

Abstract

Potato leaf diseases pose a significant threat to crop productivity and global food security, necessitating accurate and reliable diagnostic systems for early detection. Although deep learning based image classification has shown promising results in plant disease recognition, many existing studies rely on simple train test splits, insufficient handling of class imbalance, and limited statistical analysis. This study presents a comprehensive evaluation of multiple pretrained convolutional neural network architectures for multi class potato leaf condition classification, including disease categories and a healthy class. DenseNet121, EfficientNetV2 S, InceptionV3, MobileNetV3 Small, ResNet50, and Xception were evaluated using a stratified K fold cross validation framework. Class imbalance was addressed through class weighted loss functions, and model performance was assessed using accuracy, macro averaged F1 score, and weighted F1 score reported as mean values with 95% confidence intervals. The experimental results indicate that ResNet50 achieved the best overall performance with a mean accuracy of 99.07% ± 0.38% and a macro F1 score of 98.24% ± 0.80%, demonstrating strong and consistent classification across all classes. Lightweight architectures such as MobileNetV3 Small also delivered competitive results with an accuracy of 97.77% ± 0.59%, highlighting their suitability for deployment in resource constrained agricultural environments. These findings emphasize the importance of statistically robust evaluation and imbalance aware training strategies for developing reliable deep learning based systems in precision agriculture.
Co-Authors AHMAD FAISOL Andy Pramono Deddy Rudhistiar Kartika Maulida Hindrayani Kartini Kartini