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All Journal Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Buana Informatika CommIT (Communication & Information Technology) Scientific Journal of Informatics Proceeding SENDI_U Jurnal Terapan Abdimas JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Algoritma Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Egia Rosi Subhiyakto, Egia Rosi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

 1 
Deteksi Dini Cacar Monyet menggunakan Convolutional Neural Network (CNN) dalam Aplikasi Mobile Triginandri, Rifqi; Subhiyakto, Egia Rosi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27625

Abstract

Monkeypox is a skin infection that has become a serious concern in Indonesia since the increase in cases in 2022. Diagnosis of monkeypox requires special expertise, laboratory tests, and clinical observations. Diagnosis generally uses PCR tests which are often not available in remote areas. This study aims to develop a deep learning-based mobile application for early detection of monkeypox through image classification of skin lesions. The CRISP-DM methodology is applied in developing this application, starting with collecting datasets from the Kaggle site consisting of 8,910 images and divided into 80% training groups, 10% validation, and 10% testing with augmentation techniques to improve model accuracy. The developed CNN model was implemented using Create ML on the iOS platform. The model evaluation uses several metrics such as accuracy, precision, recall, and F1 score, with the threshold being the highest probability of the model predicting model evaluation results show an accuracy of 81%, precision of 80.2%, recall of 76%, and F1 score of 0.78 for the test data. The resulting application allows rapid detection of monkeypox and is accessible to the wider community, thereby helping to reduce delays in diagnosis, especially in hard-to-reach areas. This study shows significant potential in supporting the health system in Indonesia through the application of artificial intelligence technology for infectious diseases.
Centing: Aplikasi Cegah Stunting Anak berbasis Android menggunakan TensorFlow Lite Abiyyi, Ryandhika Bintang; Subhiyakto, Egia Rosi; Sabilillah, Ferris Tita
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27875

Abstract

Stunting is a serious health problem that affects children's growth and development, especially in areas with limited access to early detection. This research aims to develop a TensorFlow Lite-based “CENTING” Android application to detect stunting risk quickly and accurately. The prototyping method is used with the stages of identifying user needs, making initial prototypes, testing, and refinement based on the feedback of health workers and parents, until the application is ready to be implemented optimally. The dataset contains 121,000 child growth data from public sources, with variables such as age, gender, height, and nutritional status to detect stunting traits early. The data was processed and split 80:20 for training and testing, resulting in a detection accuracy of 98%. The selection of TensorFlow Lite is based on its advantage in response speed on mobile devices. The results showed that the CENTING application functioned optimally with a user acceptance score of 89.5%. The app supports self-detection, prevention education, and offline access, relevant for network-limited areas. These findings accelerate stunting intervention efforts and support government programs in reducing stunting prevalence.
Analisis Value Proposition dan Persepsi Pengguna Terhadap Sistem Informasi Laboratorium (LIS) di Rumah Sakit Cahyati, Ade Puput; Subhiyakto, Egia Rosi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27997

Abstract

Patient data management information in the health sector is not only a technological consideration but also includes an evaluation to facilitate medical personnel to store patient data. Thus, the hospital, which is a forum for medical personnel, needs to consider the development of a new recording system. This study aims to analyze the value proportion and user perceptions of Laboratory Information System (LIS) for application development so that developers can offer features and designs that users need. Data collection techniques in this study used a questionnaire with a sample of 52 people. The data analysis technique used uses the UX Honeycomb method and the System Usability Scale to be able to analyze the LIS application. The results of this study indicate that the value proposition variable has a significant influence on the LIS application based on the UX Honeycomb indicator. The dominant indicators are useful and usable, while the indicators that need to be improved are findable and valuable. User perception has a significant influence on the LIS application; the average value of 85.14 means that it has a very usable influence and is easy to use. So that hospitals can switch to digital data and reduce physical documents.
Co-Authors Abiyyi, Ryandhika Bintang Abu Salam Adhitya Nugraha Andreas Wilson Setiawan Ardytha Luthfiarta Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Cahyati, Ade Puput Catur Supriyanto Dzaky, Azmi Abiyyu Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Faradeya, Muhammad Az-Zauqy Faudyta, Haniifa Aliila Fauzi Adi Rafrastara Guruh Fajar Shidik Hafidhoh, Nisa'ul Hapsari, Swita Amalia Hulaifah Al Abrori, Zahra Zul Ika Novita Dewi Junta Zeniarja Kurniawan, Defri Liya Umaroh, Liya Manurung, Ayub Michaelangelo Miyanthi, Sheryn Aulia Mutia Rahmi Pratiwi Ningrum, Novita Kurnia Ningrum, Novita Kurnia Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Paramita, Cinantya Prajanto Wahyu Adi, Prajanto Wahyu Pulung Nurtantio Andono Rahadian, Arief Sabilillah, Ferris Tita Safina, Nabila Safina, Nabila Santoso, Ilham Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Simbolon, Calvin Samuel Sinaga, Jesica Trivena Sindhu Rakasiwi Swanny Trikajanti Widyaatmadja T. Sutojo Triginandri, Rifqi Triono, Justin Matthew Utomo, Danang Wahyu Widi Utomo, Emanuel Pinesthi Yunianto, Amar Haris