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Imran Lubis
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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.
PENERAPAN DATA MINING UNTUK PREDIKSI PENJUALAN SPANDUK MENGGUNAKAN ALGORITMA C4.5 Triawan, Bagus; Lubis, Imran; Kadim, Lina Arliana Nur
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.v3i2.172

Abstract

Selling banners is an essential part of the advertising business, and having the ability to predict sales can assist companies in more effective production and marketing planning. In this research, we collected banner sales data from January to June 2023 and used the C4.5 algorithm to process the data. The decision tree method can help address issues occurring in the store. RapidMiner will aid in determining which products are more popular and less popular. Using the RapidMiner method will yield more accurate decision data and simplify product analysis. Based on the research findings, banners frequently ordered by consumers. The results of this research can serve as a guide for companies to optimize their banner sales strategies.
Co-Authors Abdul Jabbar Lubis, Abdul Jabbar Andi Marwan Elhanafi, Andi Marwan Annisah, Yessi Fitri Arief Budiman Ariska, Dedek Bagus Triawan, Bagus Batubara, Algazali Raihan Budi Budi Edy Rahman Syahputra, Edy Rahman Faisal, Ilham Fera Damayanti Ferdy Riza Handoko, Divi Harahap, Herlina Hutagalung, Hasan Basri IRWAN, DEDY Kadim, Lina Arliana Nur Khairani, Mufida Lubis, Husni lubis, ihsan Manik, Burju Manik Muhammad Hidayat Nopita, Siska Rachmat Aulia, Rachmat Ramadani, Suci Ria Pasaribu, Tio Rifqi, Luthfi Ahmad Sarifah Sarifah Simanjuntak, Magdalena Sirait, Ricoplyando Siregar , Diana Novita Siregar, Muhammad Ramulia Siregar, Rosyidah Syahputri, Nenna Irsa Syaiful Ikhram Lubis T.M Diansyah Tommy, Tommy Triwardana, Ravly Zul'Aini, Nurul Hasanah