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All Journal Jurnal INKOM Bulletin of Electrical Engineering and Informatics Communication in Biomathematical Sciences
Riswantini, Dianadewi
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Computer Science & IT Electrical & Electronics Engineering Social Sciences

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Ubiquitous Health (U-Health) Untuk Pengobatan Herbal Nugraheni, Ekasari; Riswantini, Dianadewi; Khotimah, P. Husnul; Andriana, Dian
INKOM Journal Vol 4, No 1 (2010)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (350.665 KB) | DOI: 10.14203/j.inkom.53

Abstract

Teknologi informasi dan komunikasi telah mendorong lahirnya istilah Ubiquitous computing (U-comp), yang  merupakan suatu model pengembangan interaksi manusia dengan komputer. Pemrosesan informasi pada teknologi ini telah terintegrasi pada objek-objek aktivitas keseharian manusia. Ubiquitous networks  (U-net) didefinisikan sebagai suatu jejaring yang dapat diakses dimana saja, kapan saja,  dengan apa saja dan oleh siapa saja. Ubiquitous health (U-health) didefinisikan sebagai pemanfaatan ubiquitous computing dan ubiquitous network  di bidang kesehatan dalam rangka untuk menyediakan layanan informasi kesehatan jarak jauh dengan tujuan untuk peningkatan kualitas kesehatan manusia. U-health untuk pengobatan herbal menyediakan layanan informasi kesehatan dan pengobatan herbal yang dapat diakses secara on-line dan mobile. Sistem yang dibangun pada kegiatan penelitian ini menerapkan arsitektur 3-tier, yang terdiri dari komponen : server basisdata, aplikasi klien berbasis teknologi mobile dan aplikasi web server yang menghubungkan aplikasi klien dengan server basisdata. Keseluruhan kegiatan pengembangan ini menggunakan perangkat lunak yang bersifat open source. Pada sisi aplikasi server akan menggunakan bahasa pemrograman PHP dan web server Apache. Pada sisi server basisdata menggunakan basisdata MySQL yang berjalan dibawah sistem operasi Linux. Perangkat-lunak yang digunakan untuk pengembangan aplikasi-aplikasi mobile menggunakan Java Development Kit (JDK) 1.6 dengan platform Emulator Sun Java(TM) Wireless Toolkit 2.5.2 yang berisikan full konfigurasi CLDC 1.1 dan profile MIDP 2.0. Untuk lingkungan penulisan bahasa pemrograman Java menggunakan platform NetBeans  IDE (Integrated Development Environment) .Kata Kunci : Ubiquitous health, komunikasi bergerak, pengobatan herbal, layanan kesehatan jarak jauh, perangkat lunak open-source.
Chili leaf segmentation using meta-learning for improved model accuracy Suwarningsih, Wiwin; Kirana, Rinda; Husnul Khotimah, Purnomo; Riswantini, Dianadewi; Fachrur Rozie, Andri; Nugraheni, Ekasari; Munandar, Devi; Arisal, Andria; Roufiq Ahmadi, Noor
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.7929

Abstract

Recognizing chili plant varieties through chili leaf image samples automatically at low costs represents an intriguing area of study. While maintaining and protecting the quality of chili plants is a priority, classifying leaf images captured randomly requires considerable effort. The quality of the captured leaf images significantly impacts the development of the model. This study applies a meta-learning approach to chili leaf image data, creating a dataset and classifying leaf images captured using mobile devices with varying camera specifications. The images were organized into 14 experimental groups to assess accuracy. The approach included 2-way and 3-way classification tasks, with 3-shot, 5-shot, and 10-shot learning scenarios, to analyze the influence of various chili leaf image factors and optimize the classification and segmentation model's accuracy. The findings demonstrate that a minimum of 10 shots from the meta-test dataset is sufficient to achieve an accuracy of 84.87% using 2-way classification meta-learning combined with the mix-up augmentation technique.
Regresi Multiskala Tertimbang Geografis dan Temporal dengan LASSO dan Adaptif LASSO untuk Pemetaan Kejadian Tuberkulosis di Jawa Barat Habsy, Muhammad Yusuf Al; Rachmawati, Ro'fah Nur; Khotimah, Purnomo Husnul; Natari, Rifani Bhakti; Riswantini, Dianadewi; Munandar, Devi; Izzaturrahim, Muh. Hafizh
Communication in Biomathematical Sciences Vol. 8 No. 1 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.1.6

Abstract

Tuberculosis (TB) is a global health issue caused by Mycobacterium tuberculosis and can affect any organ of the body, especially the lungs. The trend of TB cases varies between regions, and analytic assessment is required to identify the predictor variables. The purpose of this research is to compare the Multiscale Geographically and Temporally Weighted Regression (MGTWR) and the Geographically and Temporally Weighted Regression (GTWR) method, which both use Gaussian, Exponential, Uniform, and Bi-Square kernel functions, to identify significant variables in each region annually. The MGTWR method has the advantage of using a flexible bandwidth for each observation, that results in more accurate coefficient estimates. The sample used was 27 districts and cities in West Java Province, involving 36 variables divided into 5 dimensions, namely global climate, health, demography, population, and government policy, with a time span of 2019–2022. To overcome the problem of multicollinearity, the approach was carried out using the Least Absolute Shrinkage Selection Operator (LASSO) and Adaptive LASSO methods. In determining the best model, the prioritized criteria are to achieve the highest R2, which indicates the optimal level of model fit, as well as the smallest AIC, which indicates the most efficient model goodness of fit. The best model is MGTWR with LASSO variable selection on the Bi-Square kernel. This model has an R2 of 91.25% and the smallest AIC of 139.868. From the best model, each region emerged with a cluster structure affected by various variables from 2019 to 2022, providing an in-depth understanding of TB mapping that can assist in formulating more effective intervention measures.
Co-Authors Andria Arisal, Andria Andriana, Dian Devi Munandar, Devi Fachrur Rozie, Andri Habsy, Muhammad Yusuf Al Husnul Khotimah, Purnomo Izzaturrahim, Muh. Hafizh Khotimah, P. Husnul Khotimah, Purnomo Husnul Natari, Rifani Bhakti Nugraheni, Ekasari Rachmawati, Ro'fah Nur Rinda Kirana Roufiq Ahmadi, Noor Wiwin Suwarningsih