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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurnal Informatika Kaputama (JIK) JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Pengabdian Masyarakat IPTEK Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Indonesian Journal of Education And Computer Science Nusantara Journal of Multidisciplinary Science
Marto Sihombing
STMIK KAPUTAMA
Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media

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Comparison Of K-Nearest Neighbor And CNN Classification Methods In Diabetic Data Sets Ajeng Arina Nisa R; A M H Pardede; Marto Sihombing
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.272

Abstract

The number of diabetics worldwide is projected to increase by 204 million (48%), from 425 million in 2017 to 629 million in 2045. Indonesia ranks sixth out of ten countries with the most number of diabetics in the world or 10 million people. The majority of people with diabetes are between 20 and 64 years old, or 327 million people, compared to 123 million people between 65 and 99 years old. The incidence of diabetes increases by about 4.8% at the age of 55-64 years, and women (1.7%) suffer from diabetes more than men (1.4%). Therefore, the authors will create a program to determine the patient's diabetes. One approach is to use machine learning as a data mining classification technique. The author will do a classification comparison with the two methods, namely the KNN and CNN methods to provide the best results of the two methods for testing. So that the accuracy of the data from the diagnosis and photo images of the disease can be known to provide early treatment before the severity of the disease.
Regional Data Mining Implementation Of Contraceptive Equipment Users In The City Of Binjai By Type Using Clustering method Nurul Edelwais; Magdalena Simanjuntak; Marto Sihombing
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.284

Abstract

The government of the Binjai City BKKBN Office is one of the institutions responsible for controlling population growth and family planning in Indonesia which has a long-term impact that will occur if the family planning program is not implemented properly, there will be a population explosion and will cause various problems, including declining degree of health, social welfare, economic and cultural issues. Therefore, it is important to understand about contraception which will be useful in assisting the community in regulating birth rates and improving the quality of life, how contraceptives are used in Binjai City and how the levels of their use vary by region. This study aims to identify areas that use contraceptives in Binjai City based on the type of contraceptive used and to provide useful information for the government and health organizations in making policies and programs that benefit the community. Based on the results of the research conducted using a sample of 20 data, the results obtained from the data group are 12 data with the area group of contraceptive users in Binjai City based on their type with Age (X) being 18-25 years, and for the Kelurahan group (Y) is Binjai , and the type of contraception (Z ) injection for family planning.
Classification Of Diseases In Patients Based On Factors Environment Using The K-Means Algorithm At Puskesmas Subdistrict Selesai Sisca Ramadhani; Marto Sihombing; Magdalena Simanjuntak
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.309

Abstract

Diseases caused by the environment are disease phenomena caused by the relationship between humans and environmental factors. Diseases that occur due to the environment that must be known by the public are such as ISPA, dermatitis, diarrhea, pulmonary TB, and so on. In the area of the Kecamatan Selesai, there are still many environmental conditions Not yet such as damaged roads and smoke from factories that cause air pollution, so with condition environment like This can affect public health. Puskesmas Selesai is Public health center Which located in region Kecamatan Selesai. The data of patients seeking treatment at this puskesmas are only used archives and to view the patient's medical history. The public should know about symptoms of the disease in order to get appropriate services. In data mining techniques for clustering patient disease data can be used as new information useful for puskesmas or related as material counseling to society. The purpose of this study is to analyze the results of the application of data mining using K-Means Clustering in grouping patient diseases based on the environment with age, village and disease diagnoses variables.
Co-Authors Ajeng Arina Nisa R Ambarita, Indah Aprillianda Pasaribu Buaton, Relita Budi Serasi Ginting Fatmaira, Zira Fauziah Ningsih Fuzy Yustika Manik Gultom, Imeldawaty Hari Sabana Kusmananda Lubis Kusuma Wardhani M Agung Hidayat M. Taufik Muktashim Billah Nurhayati Nurul Edelwais Pardede, Akim Manaor Hara Pasaribu, Tioria Perkasa Bangun Rahmadani Ratih Puspadini Rivalri Kristianto Hondro Rizki Gio Vani Rusmin Saragih, Rusmin Sihombing, Anton Simanjuntak, Magdalena Sisca Ramadhani Siswan Syahputra Sonadi Perangin Angin Sri Ayu Adhitya Widia Putri Dasopang Yani Maulita