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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Poltak Sihombing
Universitas Sumatera Utara, Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Penerapan Algoritma Adaboost Untuk Peningkatan Kinerja Klasifikasi Data Mining Pada Imbalance Dataset Diabetes Nia Novianti; Muhammad Zarlis; Poltak Sihombing
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.4017

Abstract

According to the World Health Organization (WHO), it has been recorded that up to now more than 150 million people have diabetes, whether they are elderly people, adults, teenagers, men or women. Early knowledge of diabetes can be seen based on data from patients who already have diabetes. The patient's disease data has previously been stored and arranged in a data warehouse or what is commonly referred to as a dataset. Therefore, it is necessary to process the data contained in the dataset. But the use of data mining techniques themselves must be assisted by using the techniques contained in the data mining, namely classification techniques. K-Nearest Neighbor (K-NN) is one of the methods used in the classification technique. In the results of the classification of the level of confidence obtained in the process, it is seen based on the amount of accuracy. However, there are important issues that need special attention. In the dataset used for the classification process, the data collected contains unbalanced class results (balance). The unbalanced data classification process becomes an important problem, this is because it can cause a decrease in performance. Adaboost is a technique in data mining that can be used to increase the level of accuracy in classification methods. The results showed that the adaboost algorithm can help improve classification performance. This can be seen from the increasing level of accuracy obtained from the process carried out before and after using the adaboost algorithm. The results obtained from the research show that the adaboost algorithm can be used properly to help the performance of the K-Nearest Neighbor algorithm for the classification process on diabetes datasets. It can be seen from 5 tests with values of K = 7, 13, 19, 25 and 31 there is an increase in the accuracy results obtained after using the adaboost algorithm.
Pemrosesan Query dan Pemeringkatan Hasil dalam Information Retrieval: Sebuah Kajian Literatur Roberto Kaban; Poltak Sihombing; Mahdianta Pandia; Purwanto Simamora
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.2867

Abstract

In this study, we reviewed the literature on information retrieval starting from the basics of Information Retrieval (IR), components and future challenges. The purpose of this study is to observe techniques that have been used by previous researchers in IR, especially query processing and ranking of search results. We used a literature review method by identifying, reviewing, and observing techniques in IR based on the results of several previous studies. We collected more literature sources from the ACM Digital Library, Researchgate and MDPI. From this research, we found several IR models for searching relevant documents (information) and ranking accurate results, including EXplanaTion RAnking (EXTRA), Deep-QPP, ColBERT-PRF and UQSCM-RFD.
Co-Authors Mahdianta Pandia Muhammad Zarlis Nia Novianti Purwanto Simamora Roberto Kaban