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All Journal Jurnal Riset Informatika Technologia: Jurnal Ilmiah Jurnal Infortech Infotek : Jurnal Informatika dan Teknologi Jurnal Pengabdian Kreatif Cemerlang Indonesia Jurnal Aruna Mengabdi (ARMI) Jurnal Ilmu Komputer dan Sistem Informasi Prawara Jurnal Pengabdian Masyarakat
Ani Oktarini Sari
Universitas Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Pemanfaatan Aplikasi Word Wall untuk Meningkatkan Minat Belajar Anak Arfhan Prasetyo; Ishak Kholil; Sidik Sidik; Ani Oktarini Sari
PRAWARA Jurnal ABDIMAS Vol 3 No 4 (2024): PRAWARA JURNAL ABDIMAS
Publisher : CV. Manha Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63297/abdimas.v3i4.120

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan keterlibatan orang tua dalam proses pembelajaran anak-anak di Taman Pendidikan Al-Qur’an (TPA) melalui pemanfaatan aplikasi Wordwall. Wordwall merupakan aplikasi interaktif yang dapat digunakan untuk membuat berbagai permainan edukatif yang  mendukung proses pembelajaran menjadi lebih menarik dan menyenangkan. Dalam era digital saat ini, anak-anak lebih tertarik belajar melalui media interaktif yang merangsang minat dan motivasi mereka. Oleh karena itu, melibatkan orang tua dalam pemanfaatan teknologi ini diharapkan dapat meningkatkan kualitas interaksi dan efektivitas pembelajaran di rumah. Mitra kegiatan ini adalah orang tua murid TPA, yang telah mengikuti kegiatan PM dan didampingi oleh para sisten tutor mengenai cara menggunakan aplikasi Wordwall dalam membantu anak-anak mereka belajar banyak materi. Kegiatan ini dilakukan melalui serangkaian workshop, tutorial praktis, serta sesi diskusi dan evaluasi yang diharapkan mampu menciptakan lingkungan belajar yang kondusif di rumah.Hasil yang diharapkan dari kegiatan ini adalah peningkatan kemampuan orang tua dalam  menggunakan teknologi sebagai media pendukung pembelajaran dengan rata-rata nilai kepuasan peserta antara  4,00 hingga 4,53 yang tergolong kategori puas hingga sangat puas, serta peningkatan minat belajar anak-anak melalui pendekatan yang lebih menyenangkan. Selain itu, kegiatan ini juga diharapkan dapat mempererat hubungan antara orang tua dan anak melalui aktivitas belajar bersama yang kreatif dan interaktif.
ENHANCING SLEEP QUALITY PREDICTION THROUGH SMOTE-BASED DATA BALANCING AND HYBRID MACHINE LEARNING MODELS Ami Rahmawati; Ita Yulianti; Ani Oktarini Sari; Siti Nurajizah; Hikmatulloh
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.456

Abstract

Sleep is a vital aspect in maintaining a person's physical and psychological balance. Poor sleep quality can reduce physical and cognitive performance, increasing the risk of various health problems. This study aims to develop a predictive model for sleep quality based on factors such as lifestyle, stress, daily activities, and caffeine consumption, using XGBoost combined with Recursive Feature Elimination (RFE). XGBoost was chosen for its ability to handle imbalanced datasets and heterogeneous features, while RFE helps simplify the model without losing important information. In the data pre-processing stage, a class imbalance was found, so the Synthetic Minority Over-sampling Technique (SMOTE) process was carried out to balance the proportion of the minority class. The dataset in this study was divided into two parts, namely 80% as training data and 20% as testing data, and validated using cross-validation to ensure generalization. The results show very high model performance with an accuracy of 99.79% on training data, 99.63% on cross-validation, and 99.10% on testing data. This model was then developed into a web application for practical use in analyzing sleep quality prediction. This study emphasizes the methodological contribution of a SMOTE-based hybrid machine learning model and its ready-to-use application implementation, while also opening opportunities for further testing on more diverse datasets and evaluating biases caused by synthetic data.
Penerapan Arsitektur U-Net pada Segmentasi Cacat Biji Kopi untuk Optimalisasi Inspeksi Kualitas Ami Rahmawati; Ita Yulianti; Ani Oktarini Sari; Siti Nurajizah
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.365

Abstract

Manual visual inspection of coffee bean defects remains prone to subjectivity and inconsistency, necessitating a more accurate and efficient approach. This study proposes a deep learning-based coffee bean image segmentation method using the U-Net architecture to detect the presence of defects in coffee beans using a binary segmentation approach. The dataset consists of 300 coffee bean images evenly divided into 150 images of black coffee and 150 images of insect damage. Annotation was performed using a semi-automatic pseudo-labeling method based on Gaussian filtering, absolute difference, and thresholding to generate ground truth in binary mask format. Training data was enriched through augmentation techniques including horizontal flip, vertical flip, rotation, and brightness-contrast adjustment. The model was trained using a combined loss function of Dice Loss and Binary Cross-Entropy with the Adam optimizer over 15 epochs with an early stopping mechanism. Evaluation results demonstrate excellent performance with a Mean IoU of 0.9240, Precision of 0.9707, Recall of 0.9495, and F1 Score of 0.9600, with an overall correct prediction rate of 97.45% based on pixel-level confusion matrix analysis. These results indicate that the U-Net architecture is capable of segmenting defective coffee bean areas accurately and consistently, making it a promising foundation for the development of an automated coffee quality inspection system.
Co-Authors Ami Rahmawati Arfhan Prasetyo Hikmatulloh Ishak Kholil Ita Yulianti Laela Kurniawatih Kurniawatih M. Ifan Rifani Ihsan Muhammad Ifan Rifani Muhammad Ifan Rifani Ihsan Nicodias Palasara Ridan Nurfalah Setiaji Setiaji Sidik Sidik Siti Nurajizah Tati Mardiana Wati Duriat