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All Journal snimed Journal Research of Social Science, Economics, and Management
Paputungan, Irving Putra
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Environmental Science

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Perbandingan Implementasi Kartu Menuju Sehat Digital di Indonesia: Pelajaran dari Beberapa Aplikasi Lazuardy, Ainayya Ghassani; Setiaji, Hari; Afifah, Khairina; Paputungan, Irving Putra; Kusumawati, Amalia Citra
Seminar Nasional Informatika Medis (SNIMed) 2018
Publisher : Magister Teknik Informatika, Universitas Islam Indonesia

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Abstract

Kartu Menuju Sehat (KMS) adalah catatan yang berisi grafik tumbuh kembang anak, informasi imunisasi, dan informasi pemberian ASI eksklusif. Penggunaan KMS yang masih dalam bentuk kertas memiliki kekurangan yaitu mudah hilang atau rusak. KMS tersebut juga belum efektif jika petugas ingin mencari data perkembangan anak. Makalah ini mempresentasikan komparasi beberapa KMS digital yang sudah ada sebagai langkah awal pembuatan KMS yang lebih baik dengan cara mencari kelebihan dan kekurangan masing – masing.  Dari tujuh artikel penelitian dan dua implementasi tentang KMS digital di Indonesia, terdapat lima KMS digital yang layak dibandingkan berdasarkan fitur yang dimiliki. KMS yang dapat diakses secara online dan memiliki fitur penyimpanan riwayat tumbuh kembang anak adalah model KMS yang diperlukan di masa mendatang.
Classification of Roasting Maturity Levels of Coffee Beans Using CNN Method Based on Mobilenetv2 Rafidan Arsyan, Renalda Geriel; Kurniawardhani, Arrie; Paputungan, Irving Putra
Journal Research of Social Science, Economics, and Management Vol. 5 No. 6 (2026): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i6.1269

Abstract

Determining the roasting maturity level of coffee beans is an important process to maintain consistency in flavor quality. However, the assessment process, which is still largely manual, tends to be subjective and highly dependent on the experience of farmers. This research develops an automatic classification model for four categories of coffee bean roasting levels—green, light, medium, and dark—using a convolutional neural network (CNN) architecture based on MobileNetV2. The dataset was divided into training, validation, and testing sets with a ratio of 75:15:10. The model was trained in two stages: initial training with a frozen base model, followed by fine-tuning of the last quarter of the layers. The experimental results show that the model achieved an accuracy of 96% with stable performance, as indicated by the loss and accuracy curves. These findings demonstrate that MobileNetV2 can serve as an effective solution for classifying coffee bean roasting levels with efficient computational time and competitive accuracy.
Co-Authors Afifah, Khairina Arrie Kurniawardhani Kusumawati, Amalia Citra Lazuardy, Ainayya Ghassani Rafidan Arsyan, Renalda Geriel Setiaji, Hari