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All Journal JPM (Jurnal Pemberdayaan Masyarakat) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control International Journal of Artificial Intelligence in Medical Issues Linier: Literatur Informatika dan Komputer
Rahma Puspitasari
Universitas Negeri Malang
Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Implementasi K-Means Untuk Klasterisasi Kasus Penyalahgunaan Narkoba di Provinsi Sulawesi Selatan Ikmar Mawardi; Herdianti Darwis; Rahma Puspitasari
LINIER: Literatur Informatika dan Komputer Vol 2, No 4 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v2i4.3332

Abstract

Penyalahgunaan narkoba merupakan salah satu masalah paling mendesak dan kompleks di Indonesia, yang ditandai dengan meningkatnya jumlah pecandu narkoba, jumlah kasus kejahatan narkoba yang ditemukan, serta semakin beragamnya model dan jaringan distribusi. Penelitian ini bertujuan untuk melakukan klasterisasi penyalahgunaan narkoba di Provinsi Sulawesi Selatan dengan menerapkan metode K-Means. Klasterisasi dilakukan pada data pengguna dalam kasus penyalahgunaan narkoba di BNN Provinsi Sulawesi Selatan dengan mengelompokkan setiap sampel ke dalam klaster yang berbeda. Hasil penelitian ini memberikan informasi mengenai gambaran setiap variabel yang memiliki distribusi data berbeda berdasarkan jenis zat yang digunakan pada klaster tertentu. Pengujian metode menggunakan evaluasi Silhoutte Coefficient dan Elbow menunjukkan terhadap 4 variabel dengan K = 2 memiliki nilai terbaik sebesar 0,623
Federated Ensemble Learning with SHAP–LIME Interpretability for Smart Home Energy Prediction Rahma Puspitasari; Siti Sendari; Muhammad Arif Hermawan; Joshua Andrian; Ira Kumala Sari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2665

Abstract

The increased adoption of IoT-based Smart Home systems in Indonesia has resulted in a growing volume of device-level energy data, opening up opportunities for the development of predictive models to support efficient household electricity consumption. However, challenges related to accuracy, interpretability, and data privacy remain a major concern, especially when data is distributed across multiple devices. This study evaluates the performance of four tree-based ensemble models, namely Random Forest, Gradient Boosting, XGBoost, and LightGBM, in centralized learning and federated learning scenarios using the Indonesia Smart Home Dataset. After undergoing feature preprocessing and refinement, including the removal of Sofa Pressure and Bed Pressure due to high noise, each model was trained and evaluated using MAE, MSE, and RMSE metrics. Federated learning was implemented through the Federated Averaging (FedAvg) algorithm to maintain data privacy without the need to transfer raw data between devices. The results show that LightGBM consistently provides the best performance in both scenarios and demonstrates resilience to data fragmentation and heterogeneity. Although there was a slight increase in error in federated learning, the error values remained within an acceptable range. SHAP and LIME analyses revealed that high-power devices such as air conditioners, water pumps, rice cookers, lights, and refrigerators had the greatest contribution.
Implementasi Sistem Layanan Mandiri untuk Efisiensi Administrasi Desa Biji Nangka Kabupaten Sinjai Purnawansyah; Rahma Puspitasari; Abdul Rachman Manga'; Herdianti Darwis; Sitti Nurhalimah
Jurnal Pemberdayaan Masyarakat Vol 11 No 1 (2026): Mei
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jpm.v11i1.13200

Abstract

This community service program aims to improve administrative efficiency in Biji Nangka Village, which previously used manual processes and was prone to delays, inconsistencies, and the risk of archive loss. This activity implemented a website-based self-service system and provided training to village officials on the use of key features such as digital letter management, automatic numbering, and electronic archive storage. A total of 17 participants participated in the training and all successfully operated the system. Evaluation results showed that the time to create letters was reduced from 10–15 minutes to 3–5 minutes. Furthermore, the results of the pre-test and post-test comparison showed a 9.412% increase in participant understanding, indicating the effectiveness of the training in improving the digital competence of village officials. Overall, this program has had a positive impact on improving the quality of administrative services and supporting the realization of digital-based village governance.
Hybrid Feature Benchmark for Blood Cell Classification Using ResNet50 and EfficientNetV2 Features with SVM and ANN Classifiers via Unsupervised Segmentation Ahmad Kholish Fauzan Shobiry; Rahma Puspitasari
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.364

Abstract

Automated blood cell classification supports hematological diagnosis by providing objective and efficient analysis, but end-to-end deep learning models often require substantial computational resources that limit deployment on low-resource clinical devices. This study evaluates whether frozen deep features extracted from EfficientNetV2B0 or ResNet50 provide better separability for the eight BloodMNIST classes, and examines which classical classifier offers the most practical balance of accuracy, model size, and training time. The BloodMNIST dataset, consisting of 11,959 training images, 1,712 validation images, and 3,421 test images, is processed using data augmentation and Otsu-based unsupervised segmentation before the resulting masks are replicated into three channels and passed into pretrained ImageNet CNNs used strictly as frozen feature extractors. The extracted features are classified using Support Vector Machine with grid search, K-Nearest Neighbor, Artificial Neural Network, and Random Forest, with performance assessed through accuracy, precision, recall, and F1-score. EfficientNetV2 with Support Vector Machine achieves the highest performance, reaching 76.8% test accuracy, 75.3% precision, 72.6% recall, and a 73.6% F1-score, while EfficientNetV2 with Artificial Neural Network provides a comparable 76.2% accuracy and a 73.0% F1-score with a compact 2 MB model size. These findings highlight a clear trade-off between accuracy, model size, and computational cost, demonstrating that hybrid deep-feature pipelines offer lightweight and effective solutions for blood cell classification in resource-constrained clinical settings
Co-Authors Abdul Rachman Manga' Ahmad Kholish Fauzan Shobiry Herdianti Darwis Ikmar Mawardi Ira Kumala Sari Joshua Andrian Muhammad Arif Hermawan Purnawansyah Purnawansyah Siti Sendari Sitti Nurhalimah