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All Journal Jurnal Teknik dan Science (JTS) Prosiding SeNTIK STI&K
Dwi Widiastuti
Universitas Gunadarma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Metode Klasifikasi Untuk Deteksi Uniform Resource Locator (Url)Berdasarkan Jenis Serangan Menggunakan Algoritma Naive Bayes, C4.5 Dan K-Nearest Neighbor Moh Yunus; Dwi Widiastuti; Hasma Rasjid; Yulia Chalri
Prosiding Seminar SeNTIK Vol. 3 No. 1 (2019): Prosiding SeNTIK 2019
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Metode Klasifikasi Untuk Deteksi Uniform Resource Locator (Url)Berdasarkan Jenis Serangan Menggunakan Algoritma Naive Bayes, C4.5 Dan K-Nearest Neighbor
Prototype Design of InMed (Information Medicine) Application Using Goal-Directed Design (GDD) Method with Figma Tri Sulistyorini; Muhammad Achsan Isa AL Anshori; Nelly Sofi; Dwi Widiastuti
Jurnal Teknik dan Science Vol. 4 No. 2 (2025): Juni : Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v4i2.2144

Abstract

In the world of health, a platform is needed to make it easier for a person to find information, especially related to drugs practically. To realize this, this research was carried out to produce an application called InMed (Information Medicine). This research aims to analyze, design, and evaluate matters related to telemedicine and make it easier for users to find information about medicines. The method used in this study is Goal Directed Design (GDD) which consists of several steps, namely Research, Modeling, Requirements, Framework, Refinement, Support. The results of the Likert Scale test of the InMed application prototype received a score of 92%.
Penggunaan Kecerdasan Buatan untuk Menganalisis Faktor Risiko Diabetes dengan menggunakan Random Forest Classifier Tri Sulistyorini; Nelly Sofi; Dwi Widiastuti; Viliananda Tripita Claur
Jurnal Teknik dan Science Vol. 4 No. 3 (2025): Oktober: Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v4i3.2453

Abstract

Diabetes is a non-communicable disease that deserves attention and poses a significant public health challenge. Although not a contagious disease, preventive measures and early detection of diabetes risk are crucial. This study used machine learning-based artificial intelligence to identify diabetes risk factors. The model was created using the Random Forest Classifier (RFC) algorithm, which has 16 variables as parameters. The model was built using the Python programming language, with data collection spanning from 2015 to 2018. The research included needs analysis, data collection, data preprocessing, model training, predictive model creation, system design, implementation, and testing. The final results showed that, with an accuracy of 89%, the model could be used effectively to predict diabetes risk. Furthermore, the model identified more pre-diabetes classes than other classes.
Co-Authors Hasma Rasjid Moh Yunus Muhammad Achsan Isa AL Anshori Nelly Sofi Tri Sulistyorini Viliananda Tripita Claur Yulia Chalri