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All Journal Jurnal Informatika Jurnal AbdiMas Nusa Mandiri Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer
Emiliana, Meutia Raissa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Other

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PENINGKATAN KAPASITAS KWT SERUNI MELALUI PARTICIPATORY ACTION RESEARCH DALAM URBAN FARMING DAN HIDROPONIK Solecha, Kusmayanti; Buani, Duwi Cahya Putri; Indriyani, Furi; Emiliana, Meutia Raissa; Apriliani, Resti Dhea Putri
Jurnal AbdiMas Nusa Mandiri Vol. 7 No. 2 (2025): Periode Oktober 2025
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v7i2.7265

Abstract

This community service program aims to enhance the capacity and self-reliance of the Seruni Women Farmer Group (KWT Seruni) in supporting food security through training on urban farming and hydroponic cultivation, implemented using a Participatory Action Research (PAR) approach. The program engaged lecturers, students, and group members in stages of socialization, training, technology implementation, mentoring, and sustainability planning. Evaluation results indicated a 12% improvement in participants’ understanding based on pre-test and post-test scores, demonstrating the program’s effectiveness in strengthening technical competence. Technological implementation, including the construction of a seed house and the use of pH and TDS meters, resulted in 1,500 high-quality seedlings and a hydroponic pakcoy harvest of 18.4 kg. Participants successfully applied practical skills independently, particularly in nutrient management and the cultivation of economically valuable crops. The program also fostered an internal training system for new members and generated socio-economic benefits, such as increased household income and strengthened women’s roles in agriculture. Overall, this activity aligns with SDGs 1, 2, 5, and 12.
Comparative Optimization of EfficientNetB3, MobileNetV2, and ResNet50 for Waste Classification Agustiani, Sarifah; Haryani, Haryani; Junaidi, Agus; Putri, Rizky Rachma; Emiliana, Meutia Raissa
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.27533

Abstract

Waste management has become a critical challenge in efforts to maintain environmental sustainability and public health. Poorly managed waste can cause environmental pollution, reduce quality of life, and complicate recycling processes. To address this issue, this study aims to classify waste based on images while optimizing several deep learning architectures, namely EfficientNetB3, MobileNetV2, and ResNet50, to identify the best model for waste classification. The research methodology includes data collection, preprocessing, data augmentation, model development, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The dataset, obtained from the Kaggle platform, consists of 4,650 images divided into six categories: battery, glass, metal, organic, paper, and plastic. The results show that EfficientNetB3 with the Adam optimizer achieved the best performance, with accuracy, precision, recall, and F1-score all at 93%, followed by ResNet50 at approximately 91%, and MobileNetV2 ranging from 70–73% depending on the optimizer. The use of different optimizers was found to influence model performance, and data augmentation helped improve generalization, especially for classes with limited samples. Limitations of this study include the relatively limited dataset coverage. Future research is recommended to expand the dataset and explore alternative or hybrid architectures. These findings demonstrate the potential of deep learning–based systems in supporting sustainable waste management.
Perbandingan Metode Naïve Bayes dan Support Vector Machine untuk Analisis Sentimen pada Ulasan Pengguna Aplikasi X Emiliana, Meutia Raissa; Fata, Muhammad Riza Indra; Adam, Muhammad Ghaly
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 2 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i2.1460

Abstract

Kemajuan teknologi digital telah mendorong meningkatnya penggunaan aplikasi mobile sebagai media untuk memperoleh informasi, melakukan komunikasi, serta mendukung berbagai aktivitas pengguna. Aplikasi X merupakan salah satu platform yang banyak dimanfaatkan, sehingga beragam ulasan yang diberikan oleh pengguna muncul sebagai representasi persepsi pengguna mengenai kualitas layanan yang diterima serta tingkat kenyamanan yang mereka rasakan saat memanfaatkannya. Informasi tersebut penting untuk dianalisis guna mengidentifikasi kecenderungan sentimen pengguna secara komprehensif. Penelitian ini mengkaji perbedaan performa dua algoritma machine learning, yakni Naïve Bayes dan Support Vector Machine, dalam mengklasifikasikan sentimen pada ulasan pengguna yang diperoleh melalui teknik web scraping dari Google Play Store. Dataset yang diambil selanjutnya diproses melalui serangkaian tahap text preprocessing, meliputi pembersihan data, normalisasi huruf, tokenisasi, eliminasi stopword, serta proses stemming. Berdasarkan hasil pengujian, Support Vector Machine menunjukkan kinerja yang lebih unggul dengan akurasi mencapai 0.984 dan F1-Score sebesar 0.840, sementara Naïve Bayes menghasilkan akurasi 0.849 dan F1-Score 0.550. Dengan demikian, Support Vector Machine dinilai lebih efektif dalam mengidentifikasi sentimen pengguna dan direkomendasikan sebagai metode yang lebih optimal untuk analisis ulasan pada aplikasi mobile yang memiliki karakteristik serupa.
Co-Authors Adam, Muhammad Ghaly Agus Junaidi Agustiani, Sarifah Apriliani, Resti Dhea Putri Duwi Cahya Putri Buani Fata, Muhammad Riza Indra Furi Indriyani Haryani Haryani Kusmayanti Solecha Putri, Rizky Rachma