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All Journal Agroista: Jurnal Agroteknologi Jurnal Ilmiah Smart
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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SISTEM PENANAMAN LEGUME COVER CROP PADA LAHAN REPLANTING PERKEBUNAN KELAPA SAWIT Yohana Theresia Maria Astuti; Tri Nugraha Budi Santosa; Andi
AGROISTA : Jurnal Agroteknologi Vol. 2 No. 1 (2018): MEI
Publisher : Program Studi Agroteknologi INSTIPER

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (215.373 KB) | DOI: 10.55180/agi.v2i1.25

Abstract

The objectives of this study were to determine the effectiveness of LCC planting methods on replanting areal and to determine the best growth of LCC between lining, digging and broadcasting methods. This research was conducted in Ukui sub-district, Pelalawan district, Riau. This research was conducted by experimental method which was arranged in Randomized Complete Block Design (RCBD). The first factor is the method of planting which consists of lining system, digging system and broadcasting system. The second factor is land condition which consists of hilly area, flat area and low area. Each treatment combination with 3 replications. The data of the research were analyzed by analysis of variance at level of 5%. If there is a real difference, then tested by DMRT at 5% confidence level. The results showed that planting system gave the same effect to LCC growth. While the best condition of land for LCC growth is on flat area and low area than hilly area. Keywords : Lining, digging, broadcasting, hilly area, flat area, low area, LCC
IMPLEMENTASI ALGORITMA DECISION TREE C4.5 DALAM PERANCANGAN SISTEM INFORMASI DATA REKAM MEDIS PENYAKIT JANTUNG Andi
Jurnal Manajemen Akuntansi Dan Administrasi Bisnis Vol 5 No 1 (2021): Special Issue
Publisher : Sekolah Tinggi Manajemen Bisnis (STMB) MultiSmart

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62262/jmab.v5i1.228

Abstract

The heart is a muscle that is divided into four chambers, namely the right and left atria (atria) at the top, while two more chambers are located at the bottom, namely the right and left ventricles. In practice, in the world of medical education, medical record data for heart disease is often stored for the purpose of learning or processing it into knowledge. On the website https://www.kaggle.com/, there are many datasets that are stored and processed for learning purposes, including disease datasets, population datasets or other types of datasets. However, in reality, processing medical record data into knowledge is not easy, because the large number of recorded medical record data makes it impossible for humans to process it. In addition, conventional medical record data management has a poor level of accuracy so that the conclusions drawn will certainly be different and not so accurate. Because of these problems, it is necessary to apply data mining techniques in the medical field, especially medical record data management. In this study, the data mining algorithm used is Decision Tree C4.5. This algorithm was chosen because it is quite accurate and complex in the process of managing medical record data. The results showed that the application of the Decision Tree C4.5 algorithm was quite accurate in predicting heart disease medical record data where the test results obtained an accuracy of 96%.
Co-Authors Tri Nugraha Budi Santosa Yohana Theresia Maria Astuti