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All Journal Unri Conference Series: Community Engagement Jurnal Computer Science and Information Technology (CoSciTech) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Sistim Informasi dan Teknologi
Sy, Yandiko Saputra
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management

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Klasterisasi Pasien Rawat Inap Peserta BPJS Berdasarkan Jenis Penyakit Menggunakan Algoritma K-Means Yandiko Saputra Sy
Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (417.488 KB) | DOI: 10.37034/jsisfotek.v5i2.162

Abstract

Medical records of patients from the Health Insurance Administering Body (BPJS) consist of complete patient data along with a complex history of patient services stored in every health facility. Inpatient medical record data contains important data as well as contains useful information as new knowledge using data mining techniques. This study aims to assist and provide new information related to the clustering of BPJS inpatients at the Arifin Achmad Hospital, Riau Province, so as to obtain information related to the spread of the patient's disease. The data used are medical records of inpatients in 2021. The data obtained are then processed using the K-Means clustering algorithm with a total of 3 clusters. The study resulted in cluster K1 dominated by Malignant neoplasm, breast, unspecified (C50.9) and Non-Hodgkin's lymphoma, unspecified type (C85.9) disease. Cluster K2 is dominated by fracture of neck of femur, closed (S71.00) and Dengue haemorrhagic fever (A91).
Deep Learning untuk mendeteksi gangguan lambung melalui citra iris mata Mukhtar, Harun; Baidarus; Aryanto, Eggy; Saputra Sy, Yandiko
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6392

Abstract

The stomach is one of the essential organs of the human digestive system. If the stomach organ cannot work typically, it will cause problems. This is a disease that occurs in the stomach organs. Gastric disease also occurs due to a lack of knowledge about stomach disease, so people ignore the symptoms that arise. Gastric disease is a disease that is considered very serious. If left alone, it can cause other diseases to occur. Generally, finding out the presence of stomach disease is still done manually, and several tests are carried out when stomach disease has recurred. Gastric disorders were classified using 360 iris images taken manually via a digital camera and a web database of iris images. The author used the Radial Basis Function Neural Network (RBFNN) method to classify iris images of patients with gastric disorders in this study. The results obtained from this research can organize the iris images of people with gastric disturbances. Classification of iris images of patients with gastric disorders achieved a training accuracy rate of 65.00%.
Klasifikasi Kebakaran Hutan Dan Lahan Dengan Algoritma You Only Learn One Representation Rizki, Yoze; Yogi Alfinaldo; Soni; Sy, Yandiko Saputra; Rahmad Firdaus
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6434

Abstract

Forest areas have a function of storing carbon dioxide and producing oxygen from trees and plants. The function of forests is very important for life, so forests are highly protected. One solution that can be taken is to take preventive measures, namely monitoring fire hotspots in forest and land areas by air. This research was tested using the same dataset as the YOLO (You Only Look Once) algorithm against the You Only Learn One Representation (YOLOR) algorithm with a train data division model of 1188 image data and test data of 75 image data with mAP results of 66.36%. . So it can be confirmed that the YOLOR algorithm is better than the YOLO algorithm which gets an mAP value of 50.65%.
Implementasi Machine Learning Untuk Prediksi Penyakit Jantung Menggunakan Algoritma Support Vector Machine Hidayat, Rahmat; Sy, Yandiko Saputra; Sujana, Teguh; Husnah, Mirdatul; Saputra, Haris Tri; Okmayura, Finanta
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.152

Abstract

Heart disease is currently a disease that has taken over many human lives. Data shows that more than 17 million people have died from heart disease. The high number of deaths, therefore, requires special handling to treat and prevent heart disease. In the development of technology, diagnosis of heart disease can be done with the help of information technology, one of which is through machine learning. This study aims to implement machine learning through the SVM algorithm to predict heart disease. The model formed by SVM produces an evaluation value indicated by an accuracy value of 0.85, a precision of 0.93, a recall of 0.76, and an f-1 score of 0.83. This model is used as training data to predict heart disease which is then successfully used to create a system through the Streamlit library which can be easily accessed via the website.
Pelatihan Membuat Label Kemasan Produk Pelaku UMKM Kecamatan Bandar Laksamana Kabupaten Bengkalis Menggunakan Canva Salambue, Roni; Risanto, Joko; Fitriansyah, Aidil; Sukamto, Sukamto; Bahri, Zaiful; Mahdiyah, Evfi; Hidayat, Rahmat; Sujana, Teguh; Sy, Yandiko Saputra
Unri Conference Series: Community Engagement Vol 6 (2024): Seminar Nasional Pemberdayaan Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/unricsce.6.364-369

Abstract

Micro, Small and Medium Enterprises (UMKM) in Bandar Laksamana District, Bengkalis, Riau have been identified by FMIPA students from Riau University who participated in the Belajar Kampus Merdeka (MBKM) Community Service Program (Kukerta) in 2024. There are 66 UMKM in three villages with various businesses such as stalls, fried foods, beauty, culinary, craftsmen. The people of one village seemed apathetic in managing UMKM because they were considered as side businesses only, had no prospects for development and were not interested in participating in online marketing training. From observations, several UMKM products have the potential to be marketed online because they are unique as typical Malay foods. An approach was made to UMKM actors to be assisted in having the skills to design attractive and dynamic packaging labels, assisting online marketing until the market reaches various corners of the country. A good product packaging label will advance the business and introduce the diversity of typical Malay culinary delights to the public. Canva is a simple graphic design application that is easy for beginners to use. Canva provides paid and free services with various easy-to-adopt template features with various completeness such as image cropping, text insertion, color, and so on. The Canva application needs to be introduced to UMKM to attract their product market through unique and attractive banner, poster, and banner designs so that the product display remains fresh, updated according to needs.
Co-Authors Aidil Fitriansyah Aryanto, Eggy Baidarus Finanta Okmayura Harun Mukhtar Husnah, Mirdatul Mahdiyah, Evfi Rahmad Firdaus Rahmat Hidayat Risanto, Joko Rizki, Yoze Roni Salambue Saputra, Haris Tri Soni Sukamto Sukamto Teguh Sujana Yogi Alfinaldo Zaiful Bahri