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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURTEKSI Jurnal Minfo Polgan (JMP) Journal Of Artificial Intelligence And Software Engineering
Ahmadi Irmansyah Lubis
Politeknik Negeri Batam
Computer Science & IT Library & Information Science Mathematics Social Sciences

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Decision Tree for Predicting the Mortality in Hemodialysis Patient with Diabetes Noper Ardi; Ahmadi Irmansyah Lubis; Isnayanti
Jurnal Minfo Polgan Vol. 12 No. 1 (2023): Article Research March 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v12i1.12412

Abstract

Hemodialysis patients with diabetes face a significantly higher risk of mortality compared to those without diabetes. Accurate prediction of mortality in this patient population is crucial for guiding clinical decision-making, improving patient outcomes, and optimizing resource allocation. Hemodialysis is a procedure for cleaning the blood from the waste products of the body's metabolism. this is one of modality to treat end stage kidney disease. Diabetes mellitus is a significant contributor to the global burden of chronic kidney disease (CKD), and patients with diabetes undergoing hemodialysis are at a higher risk of mortality compared to those without diabetes. Identifying factors that influence mortality risk in this population can aid in clinical decision-making and improve patient outcomes. Dialysis is performed on patients with kidney failure, both acute kidney failure and chronic kidney failure. This study is aimed to predict the mortality risk of hemodialysis patients with diabetes. The Taiwanese hemodialysis center enrolled a total of 665 hemodialysis patients. The prediction is based on Decision Tree. Compared with K-Nearest Neighbor, linear discriminant, Logistic Regression, and Ensemble, Decission Tree performed better. As for related medical variables like parathyroid surgery, urea reduction ratio, etc., they play a much smaller role in mortality risk factors than diabetes and cardiovascular disease. Diabetes
Predicting Missing Value Data on IEC TC10 Datasets for Dissolved Gas Analysis using Tertius Algorithm Noper Ardi; Supardianto S; Ahmadi Irmansyah Lubis
Journal of Applied Informatics and Computing Vol 7 No 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5361

Abstract

IEC TC10 is the most widely used Dissolved Gas Analysis (DGA) measurement dataset nowadays. Many DGA-based studies have been carried out using conventional methods and methods based on Artificial Intelligence Techniques (AITs). DGA is a diagnostic test performed on power transformers to detect and diagnose potential faults. The test involves analyzing the gases that are dissolved in the transformer oil, which can provide important information about the condition of the transformer. DGA is a widely used technique for transformer monitoring and maintenance in the power industry. However, this dataset is not perfect. There are still many problems in this dataset, one of which is the problem of missing value data. This problem will be significant if not appropriately handled. More reliable data from DGA measurement results is an in-dispensable reference in diagnosing faults in power transformers. This study focuses on dealing with the problem of missing value data using the Tertius algorithm, then testing the results using the J48 and Random Forest algorithms. The results obtained are pretty significant. Of the total 56 missing data, 36 could be predicted perfectly. And received the results of measuring accuracy using the J48 method of 62.73% and the Random Forest method of 70.71%. This result shows that the approach we applied is relatively good for handling missing values in IEC TC10 datasets.
Klasifikasi Penyakit Diabetic Retinopathy Menggunakan Multilayer Perceptron Umri Erdiansyah; Ahmadi Irmansyah Lubis; Guntur Syahputra
Journal of Artificial Intelligence and Software Engineering (J-AISE) Vol 2, No 1 (2022)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v2i1.3084

Abstract

Diabetic Retinopathy merupakan salah satu komplikasi penyakit diabetes yang dapat menyebabkan kematian. Komplikasi ini berupa kerusakan pada retina mata. Kadar glukosa yang tinggi dalam darah dapat menyebabkan kapiler kecil pecah dan menyebabkan kebutaan. Penyakit ini dimulai dengan melemahnya atau rusaknya kapiler kecil di retina, memungkinkan darah mengalir dan kemudian menyebabkan penebalan jaringan, pembengkakan, dan pendarahan hebat. Penelitian ini bertujuan untuk menganalisis diagnosis retinopati diabetik berupa data rekam medis. Multilayer Perceptron merupakan salah satu algoritma jaringan syaraf tiruan yang sering digunakan untuk klasifikasi data dan digunakan dalam proses klasifikasi pada penelitian ini. Dataset yang digunakan dalam penelitian ini diperoleh dari UCI Machine Learning Repository, kumpulan data dari University of Debrecen, Hongaria, termasuk data pasien untuk retinopati diabetik. Evaluasi hasil klasifikasi yang digunakan adalah confusion matrix. Dari hasil perhitungan yang telah dilakukan, maka didapatkan hasil akurasi pada Multilayer Perceptron sebesar 71.80%, dengan nilai precision 72.50%, dan Recall 71.80%.
PROJECT-BASED LEARNING PERFORMANCE MEASUREMENT USING VIKOR METHOD AND RANK ORDER CENTROID Ahmadi Irmansyah Lubis; Supardianto Supardianto; Metta Santiputri; Noper Ardi; Alena Uperiati
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 3 (2024): Juni 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i3.2853

Abstract

Abstract: Project-Based Learning is a type of learning that is quite widely recommended today, especially in vocational type institutions where the learning is effective in the aim of involving students with direct learning content. The process of evaluating project-based learning on project teams in the performance assessment of each team to rank the order of best performance of all teams is still assessed based on subjective assessments. To overcome these problems, in this study the performance measurement of the project-based learning team by applying the VIKOR method and Rank Order Centroid in conducting assessments with test samples, namely in the Introduction to Database course. The test results obtained based on the calculation of VIKOR and Rank Order Centroid, namely PBL-TRPL01 Team 1 as the best alternative by obtaining based on variations of testing the VIKOR index value with values v=0.4, v=0.5, and v=0.6. Thus, it can be seen that the VIKOR and Rank Order Centroid methods can be applied to the calculation process of measuring team performance in project-based learning.            Keywords: decision Support System; project-based learning; rank order centroid; VIKOR Abstrak: Pembelajaran Berbasis Proyek merupakan jenis pembelajaran yang cukup banyak direkomendasikan di masa kini khususnya pada institusi berjenis vokasional. Pembelajaran tersebut efektif dalam tujuan melibatkan para peserta didik dengan konten pembelajaran secara langsung. Proses evaluasi pembelajaran berbasis proyek pada tim proyek dalam penilaian performa dari masing-masing tim untuk memeringkatkan urutan performa terbaik dari seluruh tim masih dinilai berdasarkan penilaian secara subyektif. Untuk mengatasi persoalan tersebut, pada penelitian ini pengukuran performa tim project-based learning dengan menerapkan metode VIKOR dan Rank Order Centroid dalam melakukan penilaian dengan sampel pengujian yaitu pada mata kuliah Pengantar Basis Data. Hasil pengujian yang diperoleh berdasarkan perhitungan VIKOR dan Rank Order Centroid yaitu bahwa alternatif PBL-TRPL01 Tim 1 sebagai alternatif terbaik dengan peroleh berdasarkan variasi pengujian nilai indeks VIKOR. Maka dengan demikian, dapat diketahui bahwa metode VIKOR dan Rank Order Centroid dapat diterapkan pada proses perhitungan pengukuran performa tim pada pembelajaran berbasis proyek. Kata kunci: pembelajaran berbasis proyek; rank order centroid; sistem pendukung keputusan; VIKOR
Co-Authors Alena Uperiati Guntur Syahputra Isnayanti Metta Santiputri Noper Ardi Supardianto Supardianto Umri Erdiansyah