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All Journal JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) JURNAL MAHAJANA INFORMASI JTIK (Jurnal Teknik Informatika Kaputama) MEANS (Media Informasi Analisa dan Sistem) Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Journal of Engineering, Technology and Computing (JETCom) Journal of Mathematics and Technology (MATECH) Jurnal Pengabdian Pada Masyarakat METHABDI Jurnal Ilmu Komputer dan Sistem Informasi Jurnal ABDIMAS MUTIARA Pascal: Journal of Computer Science and Informatics JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL
Simanjuntak, Magdalena
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Journal : Journal of Mathematics and Technology (MATECH)

 1 
PENGELOMPOKKAN MENGGUNAKAN METODE CLUSTERING UNTUK PEMBERIAN OBAT PADA PASIEN BPJS Ariska, Dedek; Simanjuntak, Magdalena; Lubis, Imran
Journal of Mathematics and Technology (MATECH) Vol. 2 No. 2 (2023): Journal MATECH (November 2023)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v2i2.153

Abstract

BPJS Kesehatan merupakan badan hukum yang menyelenggarakan program jaminan Kesehatan yang dibentuk untuk menyelenggarakan jaminan sosial dinamakan Badan Penyelenggara Jaminan Sosial (BPJS). Pada saat ini Pemberian resep obat di rumah sakit juga dilakukan oleh dokter sesuai dengan standar yang sudah ditetapkan oleh rumah sakit berdasarkan penyakit yang diderita oleh pasien yang menggunakan jasa dari jaminan pelayanan kesehatan. Karena banyaknya pasien yang menggunakan jasa BPJS menyebabkan menumpuknya data-data pemberian obat pada pasien, sehingga menyulitkan pihak instansi dalam mengolah data pemberian obat pada pasien, masalah ini sering terjadi karena data yang tersimpan masih tercatat secara terpisah antara laporan pemberian obat dan laporan data pasien BPJS sehingga sangat sulit dalam mengetahui jumlah pemberian obat yang ada saat ini. Untuk itu diperlukan suatu sistem tambahan yang akan digunakan dalam pengelompokkan pemberian obat pada pasien BPJS menggunakan variabel - variabel yang sudah ditentukan dengan menggunakan metode clustering, agar nantinya dapat mempermudah admin dalam mengolah data dan informasi yang ada. Dari 20 data yang digunakan sebagai sampel didapatlah hasil yang dibagi menjadi 3 yaitu grup 1 terdapat 12 data dan 2 grup terdapat 3 data dan grup 3 terdapat 5 data. Dengan penjelasan dengan titik Centroid pada grup 1 yaitu (3.50) (16.58) (13.17) dapat diketahui bahwasannya pada cluster 1 kelompok pemberian obat pada grup usia (X) adalah 36-45 Tahun, dan untuk kelompok jenis penyakit (Y) yang dialami oleh pasien adalah Hipertensi dengan penanganan pemberian Resep obat (Z) yaitu Clonidin 0,15 tab.
THE USE OF DEMPSTER SHAFER'S METHOD FOR DIAGNOSING ACUTE RENAL FAILURE IN CHILDREN Nopita, Siska; Simanjuntak, Magdalena; Lubis, Imran
Journal of Mathematics and Technology (MATECH) Vol. 2 No. 2 (2023): Journal MATECH (November 2023)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v2i2.156

Abstract

Acute kidney failure (acute kidney injury) is a condition in which the kidneys stop functioning suddenly. Acute kidney failure is known to attack children in the age range of 6 months-18 years, as of October 18, 2022, a total of 118 cases have been reported, most of which are dominated by ages 1-5 years. This condition can occur due to impaired blood flow to the kidneys, disorders in the kidneys, or blockages in the urinary tract, with initial symptoms in the form of gastrointestinal infections and symptoms of ARI, the typical symptom is the amount of urine that decreases even can not tub at all. In such conditions, it is already an advanced phase and must be immediately taken to health facilities such as hospitals. Acute renal failure in children is a significant health problem. Its prevalence has increased in recent years and can have serious repercussions on children's health. Acute kidney failure can lead to impaired kidney function, an increased risk of complications such as edema, electrolyte disturbances and impaired body fluid balance. The process of diagnosing acute kidney failure in children can be challenging because the symptoms and signs are often nonspecific. The Diagnosis is made based on clinical evaluation, laboratory tests such as urine and blood analysis, and imaging exams if necessary. Because the knowledge and information of the community / patient is still lacking, causing delays in handling the early symptoms of acute kidney failure in children. Therefore, it is necessary to have a system in addressing the problem of acute kidney failure in children by knowing the early symptoms of the disease and other information. The system that will be built will be able to detect acute renal failure disease with symptoms suffered by patients using the dempster shafer method at the output of the system. The purpose of this study is to design an expert system application using the Dempster Shaper method in diagnosing kidney failure in children.
APPLICATION OF NAIVE BAYES METHOD TO DIAGNOSE FMD DISEASE IN GOATS Sawitri, Sawitri; Simanjuntak, Magdalena; Pardede, Akim Manaor Hara
Journal of Mathematics and Technology (MATECH) Vol. 3 No. 2 (2024): Journal MATECH (November 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v3i2.171

Abstract

Hoof and mouth disease (FMD) is an infectious disease that affects cloven-hoofed farm animals such as cows, buffaloes, goats, sheep and pigs. The emergence of FMD is caused by a virus called foot and mouth diseases virus (FMVD). The Virus slowly eats away at the hooves and mouths of livestock, making the animals unable to eat and walk. Examination of disease in goats periodically is currently less attention so as to make goats susceptible to disease. This makes it difficult for farmers in the initial handling and do not know what to do in the absence of an expert. The process of disease diagnosis in goats can not be done by just anyone because between the types of diseases with symptoms have uncertainty. Based on these problems, the authors create an expert system that is able to diagnose diseases in goats as is commonly done by an expert using Naive Bayes method that will help livestock groups in diagnosing diseases in goats. The input is in the form of symptoms that occur in the field and the output is the result of diagnosis and treatment advice. From the test results obtained because the conclusion value (P / PMK type Oise (O)) is greater than the value (P|PMK type Asia 1) then the decision is “PMK type Oise (O)” with a value of 0.02.
THE USE OF BAYES METHOD TO DIAGNOSE GESTATIONAL DIABETES IN PREGNANT WOMEN (CASE STUDY: DR. EDWARD JOB,. SP.OG) Maulidina, Nadia; Simanjuntak, Magdalena; Maulita, Yani
Journal of Mathematics and Technology (MATECH) Vol. 3 No. 2 (2024): Journal MATECH (November 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v3i2.175

Abstract

 Gestational Diabetes is the cause of diabetes that occurs during pregnancy, generally, this pregnancy occurs in the second trimester, between Weeks 24 to 28. Gestational Diabetes is one of the causes of death of pregnant women due to lack of information or knowledge about the factors that cause gestational diabetes that occurs in mothers during pregnancy, causing health risks, and affect the decision of time, cost and others in carrying out direct medical consultations. Therefore, it is highly recommended that an expert system for treating gestational diabetes in pregnant women be able to find out early about what is happening and be able to overcome the causes and reduce mortality. With the existence of this system is expected to be an alternative for patients who experience complications of time, cost and others in consulting directly about the treatment of gestational diabetes in pregnant women, before coming directly to meet with a doctor/expert. From the process using the Bayes method above, it is explained that the diagnosis of gestational diabetes in pregnant women is diagnosed with gestational diabetes Diabetes Mellitus (P01) with a percentage of 90.73%.
EXPERT SYSTEM OF PREECLAMPSIA DIAGNOSIS USING CERTAINTY FACTOR METHOD Tarigan, Kiki Dea Ananda; Simanjuntak, Magdalena; Maulita, Yani
Journal of Mathematics and Technology (MATECH) Vol. 3 No. 2 (2024): Journal MATECH (November 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v3i2.176

Abstract

Preeclampsia is an increase in blood pressure and excess protein in the urine that occurs after more than 20 weeks of pregnancy. If not treated immediately, preeclampsia can cause complications that are dangerous for the mother and fetus. One factor that can increase the risk of preeclampsia is the age of pregnant women who are under 20 years or more than 40 years. This condition needs to be treated immediately to prevent complications or develop into preeclampsia that can threaten the lives of pregnant women and fetuses. The causes of preeclampsia are still not exactly known. However, this condition is thought to occur due to abnormalities in the development and function of the placenta, which is an organ that functions to deliver blood and nutrients to the fetus. The use of internet technology makes it easier for humans to access information without limited space and time and facilitates the design of expert systems to diagnose preeclampsia in mothers, and is expected to reduce or even eliminate existing problems, Therefore, an application is needed that can help to diagnose preeclampsia by using the certainty factor method which is easier and becomes an alternative in providing more knowledge about the results of preeclampsia diagnosis in pregnant women and can provide advice and consultation media about preeclampsia disease in patients and can reduce the cost of consulting an expert. Based on the calculation of CF, the highest value is the type of preeclampsia with a value of 0.9939 or 99.39%. From the results obtained, the system identifies that the patient has a type of preeclampsia.
Co-Authors Achmad Fauzi Adek Maulidya Agus Sapitri, Liana Ambarita, Indah Ana, Putri Annatasia, Kristina Apriandi Alfa Reza Saragih Ariska, Dedek Arliana, Lina Athaya, Fara Buaton, Relita Danisuwara, Ardiya Kansya Defita Br. Manurung, Sella Edelwais, Nurul Fauzan Akbar, Ahmad Fauzi Ahmad Muda Fauzi, Achmad Ginting, Budi Serasi Ginting, Darmawan Hasibuan, Layla Mutiara Imran Lubis Kadim, Lina Arliana Nur Kahfi Lanang Khadapi, Muammar Khair, Husnul Khairunisa, Salsabila MANURUNG, HOTLER Maulidina, Nadia Melda Pita Uli Sitompul Mirah, Alta Muammar Khadafi Nopita, Siska Novriyenni Novriyenni, Novriyenni Nurhayati Nurhayati Pardede, Akim Manaor Hara Pasaribu, Tioria Pramana, I Gusti Puspita Sari, Melani Putra, Andika Cahya Rahayu, Nur Aprilia Rahimah Faizah Rahmadilla, Semiati Ramadhani, Sisca Ratih Puspadini Roynaldi, Muhammad Rusmin Saragih, Rusmin Sari, Indah Kelana Satria R, Dandi Sawitri Sawitri Selfira, Selfira Sembiring, Arnes Sihombing, Anton Sihombing, Marto Sinaga, Ayu Puspita Sari Syahputra, Siswan Tarigan, Kiki Dea Ananda Yani Maulita Yulia Ningsih Yunika , Vica