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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JURNAL MAHAJANA INFORMASI Journal of Computer Networks, Architecture and High Performance Computing Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) JURNAL MITRA PRIMA Digital Transformation Technology (Digitech) Data Sciences Indonesia (DSI)
Mawaddah Harahap
Universitas Prima Indonesia
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Veterinary

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan Convolutional Neural Network (CNN) Mawaddah Harahap; Em Manuel Laia; Lilis Suryani Sitanggang; Melda Sinaga; Daniel Franci Sihombing; Amir Mahmud Husein
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.778 KB) | DOI: 10.29207/resti.v6i1.3373

Abstract

The Coronavirus (COVID-19) pandemic has resulted in the worldwide death rate continuing to increase significantly, identification using medical imaging such as X-rays and computed tomography plays an important role in helping medical personnel diagnose positive negative COVID-19 patients, several works have proven the learning approach in-depth using a Convolutional Neural Network (CNN) produces good accuracy for COVID detection based on chest X-Ray images, in this study we propose different transfer learning architectures VGG19, MobileNetV2, InceptionResNetV2 and ResNet (ResNet101V2, ResNet152V2 and ResNet50V2) to analyze their performance, testing conducted in the Google Colab work environment as a platform for creating Python-based applications and all datasets are stored on the Google Drive application, the preprocessing stages are carried out before training and testing, the datasets are grouped into theNormal and COVID folders then combined m become a set of data by dividing them into training sets of 352 images, testing 110 images and validating 88 images, then the detection results are labeled with the number 1 means COVID and the number 0 for NORMAL. Based on the test results, the ResNet50V2 model has a better accuracy rate than other models with an accuracy level of about 0.95 (95%) Precision 0.96, Recall 0.973, F1-Score 0.966, and Support of 74, then InceptionResNetV2, VGG19, and MobileNetV2, so that ResNet50V2-based CNNs can be used as initial identification for the classification of a patientinfected with COVID or NORMAL.
K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region Mawaddah Harahap; Arief Wahyu Dwi Ramadhanu Zamili; Muhammad Arie Arvansyah; Erwin Fransiscus Saragih; Selwa Rajen; Amir Mahmud Husein
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4199

Abstract

Cocoa agricultural production in Indonesia is currently very low while demand continues to increase every year, so it is very important to build a model that can categorize cocoa farming data. The main objective of this research is to analyze agricultural data using data mining techniques that specifically use the K-Means Clustering algorithm, and Gaussian Mixture Models. In this research, we used quantitative research because it measure number-based data. The results of cocoa production so far still depend on land area, then the number of cocoa trees has a significant effect on the amount of production so it is very important for the government and researchers to develop technologies that can increase cocoa production yields where the demand for cocoa is currently very high in demand worldwide because it can classify the cocoa quality from good quality to poor quality. Based on testing the K-Means Clustering and Gaussian Mixture Model algorithms on data on cocoa production in four provinces, namely North Sumatra, West Sumatra, Lampung and Aceh which were optimized by the Silhouette method, it produced cluster values ​​of 2, 3 and 4. second with a value of 59.8%.
Co-Authors Ade Mutia Adinda Utari Putri Allwin M Simarmata Amir Mahmud Husein Amir Mahmud Husein, Mawaddah Harahap, Amir Aninda Muliani Arief Wahyu Dwi Ramadhanu Zamili Benyamin Sahputra Gurning Bryan Tawarikh Saragih Daniel Franci Sihombing David Benny Martulus Simatupang David Hadyanto Davin Winata Em Manuel Laia Ensunaria Ketaren Erwin Fransiscus Saragih Fachrul Rozi Ferdy Riza Indra Prianto Sinaga Jeckson Edition Sitorus Jefri Poltak Hutabarat Johannes Rianto Malau Kuandi Dharma Lilis Suryani Sitanggang Melda Sinaga Monalisa Br.Sirait Muhammad Arie Arvansyah Olga Angelo Fatarosa Laia Peter Fernandito Rosenty Damanik Selwa Rajen Siregar, Rosyidah Siregar, Saut Dohot Siti Aisyah Suci Yolanda Tentus Natoka Simangungsong Tommy Tommy Tonazisokhi Giawa Yennimar Yennimar Yusniar Lubis Yusniar Lubis Zakarias Situmorang