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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) CARADDE: Jurnal Pengabdian Kepada Masyarakat International Journal Of Computer, Network Security and Information System (IJCONSIST) International Journal of Computer Science and Information Technology
Betty Dewi Puspasari
Computer Science And Information Engineering, National Dong Hwa University Taiwan
Computer Science & IT Education Mathematics Social Sciences

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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.
Pengembangan Wahana Game Pendidikan Berbasis Qr-Code Sebagai Upaya Peningkatan Kualitas Wahana Di Wana Wisata Bedengan Andy Pramono; Muhammad Nurwiseso Wibisono; Betty Dewi Puspasari; Emil Salamah
CARADDE: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2021): Agustus
Publisher : Ilin Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31960/caradde.v4i1.1022

Abstract

Kegiatan ini bertujuan untuk mengembangkan wahana wisata yang berada di lingkungan Wana Wisata Bedengan Desa Selorejo Kabupaten Malang, agar dapat meningkatkan jumlah pengunjung serta untuk meningkatkan kualitas wahana wisata di lingkungan Wana Wisata Bedengan Desa Selorejo Kabupaten Malang. Permasalahan yang muncul adalah kurangnya pengunjung dan kurang dikenalnya Wana Wisata Bedengan dibandingkan Wana Wisata lain di Kabupaten Malang. Berdasarkan analisa yang dilakukan hal ini salah satunya adalah kurangnya wahana yang terdapat di Wana Wisata Bedengan. Tujuan PKMtkan masyarakat desa selorejo sekitar Wana Wisata Bedengan. Tahapan metode pengembangan wahana wisata ini meliputi analisa kondisi, konsep dan perencanaan dasar, desain dan implementasi konten serta evaluasi desain. Hasil dari kegiatan ini adalah terbentuknya wahana wisata berbasis edukasi di lingkungan Wana Wisata Bedengan Kota Malang sebagai upaya peningkatan kualitas wahana.      
DEVELOPMENT OF INFORMATION SYSTEM FOR BOARDING HOUSES Betty Dewi Puspasari; Joseph Dedy Irawan; Ridho Arif Wicaksono; Dimas Rizky Pratama; Ahmad Bahrul Ilmi
International Journal of Computer Science and Information Technology Vol. 2 No. 1 (2025): IJCOMIT Vol 2 No 1
Publisher : Computer Science Department, Malang National Institute of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/ijcomit.v2i1.13659

Abstract

The management of boarding houses through manual bookkeeping still faces many challenges, such as recording errors, difficulty in monitoring room availability, and inefficiencies in communication between the manager and tenants. boarding houses face these issues, which can potentially lead to conflicts due to misinformation regarding billing and room status.To address these problems, this study aims to develop a web-based boarding house management information system integrated with WhatsApp as a billing notification tool. This system is designed to reduce recording errors, improve room management efficiency, and facilitate communication between tenants and the boarding house manager.With the implementation of this system, it is expected that boarding house managers can easily access tenant data, monitor room status in real-time, and automate the billing process for tenants. The expected outcome of this system development is improved efficiency in boarding house management, increased data recording accuracy, and ease of monitoring and communication
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 Bahrul Ilmi AHMAD FAISOL Andy Pramono Deddy Rudhistiar Dimas Rizky Pratama Emil Salamah Joseph Dedy Irawan Kartika Maulida Hindrayani Kartini Kartini Muhammad Nurwiseso Wibisono Ridho Arif Wicaksono