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All Journal Sistemasi: Jurnal Sistem Informasi Gema Wiralodra Jurnal Informatika Teknologi dan Sains (Jinteks)
Sentoso, Thedjo
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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PERANCANGAN APLIKASI SISTEM PAKAR DIAGNOSA PENYAKIT PERUT DI PUSKESMAS PARUGA KOTA BIMA Wahyuningsih, Sri; Sentoso, Thedjo; Munandar, Muhammad Imam; Wardhani, Muhammad; Mawansyah, Julfikar
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 4 (2024): EDISI 22
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i4.5932

Abstract

Penyakit perut merupakan salah satu keluhan yang sering ditemui di fasilitas pelayanan kesehatan primer, termasuk di  Puskesmas Paruga Kota Bima. Namun, keterbatasan tenaga medis dan waktu konsultasi sering menjadi kendala dalam memberikan diagnosis yang cepat dan tepat. Penelitian ini bertujuan untuk merancang aplikasi sistem pakar berbasis forward chaining yang dapat membantu dalam mendiagnosis penyakit perut secara cepat dan akurat berdasarkan gejala-gejala yang dialami pasien. Metodologi yang digunakan dalam penelitian ini mencakup pengumpulan data melalui wawancara dengan dokter umum, studi literatur, serta perancangan dan implementasi sistem. Hasil dari penelitian ini adalah sebuah aplikasi sistem pakar yang mampu memberikan hasil diagnosa awal serta rekomendasi tindak lanjut medis. Sistem diuji menggunakan data uji kasus dan menunjukkan tingkat akurasi yang baik dalam mengenali pola gejala dan diagnosis. Dengan adanya aplikasi ini, diharapkan dapat meningkatkan efisiensi layanan kesehatan serta membantu petugas medis dalam proses diagnosa penyakit perut di lingkungan puskesmas.
The effectiveness of using RFID and IoT in digital transformation processes in garment companies using the UTAUT model2 Sentoso, Thedjo; Kusrini, Kusrini; Hanafi, Hanafi
Gema Wiralodra Vol. 14 No. 2 (2023): gema wiralodra
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/gw.v14i2.511

Abstract

This study aims to analyze the effectiveness of using RFID and IoT in the digital transformation process in a garment company using the UTAUT2 model. This research is necessary because it can influence the intentions and behavior of its users to increase production effectiveness. A quantitative approach uses the survey method used in this study to achieve the research objectives. The number of respondents in this study was 193 employees who worked in the preparation area. The data collected from the questionnaire results were analyzed using inferential statistics. The study results show that employee acceptance of using RFID and IoT in the digital transformation process gets a positive response. Each variable average value used is in the value range 3.79 – 4.44 (scale 1 to 5). In addition, it was found that Performance Expectancy, Effort Expectation, and Price Value positively influenced Behavioral Intention. In contrast, Habit and Behavioral Intention positively influenced Use Behavioral. As for the Social Influence and Hedonic Motivation variables on Behavioral Intentions and the Facilitating Conditions variable on Usage Behavior, no positive effect was found.
Identification of Lumpy Skin Disease in Cattle with Image Classification using the Convolutional Neural Network Method Sentoso, Thedjo; Ardiansyah, Fachri; Tamuntuan, Virginia; Wangsa, Sabda Sastra; Kusrini, Kusrini; Kusnawi, Kusnawi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.2569

Abstract

One of the problems often faced by cattle farmers is related to diseases in their cattle where one of the cattle diseases whose transmission rate is very fast is Lumpy Skin Disease (LSD). Currently, to identify the health of livestock, especially in cattle, is still very dependent on experts and of course this takes time, resulting in delays in the prevention and treatment of diseases in cattle, especially this LSD disease. The Convolutional Neural Network (CNN) algorithm is one of the algorithms can used for image classification of cows whether the cow is healthy or Lumpy. The stages of this research start from problem identification, literature study, data collection, algorithm implementation, testing, and performance evaluation results of the algorithm on cattle disease data. In this research, testing was conducted using three architectures for CNN: VGG16, VGG19, and ResNet50. The results of the experiment showed that VGG16 was the most effective architecture compared to VGG19 and ResNet50, with a training accuracy of 95.31% and a loss value of 0.1292, as well as a testing accuracy of 96.88% and a loss value of 0.102.
Co-Authors Ardiansyah, Fachri Hanafi Hanafi Kusnawi Kusnawi Kusrini, Kusrini Mawansyah, Julfikar Muhammad Wardhani Munandar, Muhammad Imam Raden Mohamad Herdian Bhakti Tamuntuan, Virginia Wangsa, Sabda Sastra