Penerapan Algoritme J48 Untuk Prediksi Penyakit Demam Berdarah

Astuti, Tri; Suciati, Suciati; Mujiati, Inke; Ayu, Dian; Ristianah, Vivit; Lestari, Widhi Asty

doi:10.35671/telematika.v9i2.459

Cited by 4 publications

(3 citation statements)

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“…Similar studies predicting the spread of dengue (Astuti et al, 2016) stated that the application of the J48 algorithm resulted in an accuracy value of 75.83% using cross validation fold 5 and an accuracy value of 80% using fold 10.…”

Section: Introductionmentioning

confidence: 74%

Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate

Suhendar,

Rohmawati,

Prasetyowati

2024

SinkrOn

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This study proposes utilizing the machine learning technique CART to classify the spread of dengue hemorrhagic fever (DHF). To expand the features used, the CART classification model was developed based on data collected over the previous 2 to 4 years. The data sources included the Bandung City Health Office for the cases of DHF, the Bandung Meteorology, Climatology and Geophysics Agency for the climate data, the Bandung City Central Statistics Agency for population and educational history data. The top-performing CART classification model over the past 2, 3, and 4 years achieved accuracies of 93%, 93%, and 90%, respectively. The models that exhibited the highest accuracy values and optimal number of feature extensions were chosen as the best ones. CART is among several machine learning techniques that can effectively measure the most impactful features during the classification process. The meteorological parameters were found to be irrelevant in the classification process. This study reveals that the population size, male population proportion, and educational attainment levels are the most impactful features in the classification of DHF spread in Bandung City. The research provides valuable insights into the classification of DHF spread in Bandung City through feature expansion.

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Section: Introductionmentioning

confidence: 74%

Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate

Suhendar,

Rohmawati,

Prasetyowati

2024

SinkrOn

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“…Anak perlu didamping dan diberikan penjelasan, bahwa sakit yang diderita adalah cobaan dari Tuhan (Superkertia et al, 2016). Sehingga ketika anak mulai tumbuh dan berkembang serta mulai mengidentifikasi dirinya tidak mengalami kebingungan harga diri akibat berbeda dengan orang lain (Cortes et al, 2014).…”

Section: Harga Diriunclassified

Faktor-faktor determinan penyebab loss to follow up pada orang dengan HIV dan AIDS (ODHA) yang menerima terapi Antiretroviral (ARV)

Kurniawan,

Pratiwi,

Suyono

2022

J.HolHealth

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Background: Based on data from the Tangerang District Hospital in October 2020, it was found that data on follow-up under antiretroviral therapy (ARV) among people living with HIV/AIDS (PLWHA) during the pandemic period for the last 3 months from February to April 2020 showed a decrease in the number but had a fluctuating graph in taking drugs every month, reaching 1012 patients, of which 20% were children. In addition, during the pandemic, there was an increase in LFU (lost to follow-up) cases of ARV treatment regimens.Purpose: To identify determinant factors of loss to follow-up under antiretroviral therapy (ARV) among people living with HIV/AIDS (PLWHA)Method: A quantitative research with a cross-sectional approach. The number of samples in this study was 45 respondents. The analysis used is univariate and bivariate. The statistical tests used are Chi-Square test and logistic regression.Results: Finding that the factors related to the cause of the withdrawal of ARV drugs were health insurance, side effects of ARVs, CD4 values, family support, hopelessness, stress levels, and self-esteem. Then a logistic regression test was carried out to find out which factor had the most influence and the results showed that the CD4 value was the most significant indicator with an OR (Odds Ratio) value of 26,812 812, meaning that people living with HIV who had CD4 values < 200 had a 26,812 times greater risk of dropping out of ARV drugs, compared to people living with HIV who have CD4 values 200.Conclusion: CD4 value is the most significant factor in the incidence of ARV drug withdrawal in living with HIV.Keywords: Loss to follow-up; Antiretroviral therapy (ARV); People living with HIV/AIDS (PLWHA)Pendahuluan: Berdasarkan data RSUD Kab.Tangerang Oktober 2020 didapatkan data follow up pada orang dengan HIV dan AIDS (ODHA) yang menerima terapi Antiretroviral (ARV selama masa pandemi 3 bulan terakhir Februari-April 2020 menunjukan penurunan angka follow up namum memiliki grafik fluktuatif dalam pengambilan obat tiap bulan mencapai 1012 pasien dimana 20% termasuk usia anak. Selain itu, selama pandemi terjadi peningkatan kasus LFU (lost to follow up) dari regiment pengobatan ARV.Tujuan: Mengidentifikasi faktor-faktor determinan penyebab loss to follow up pada orang dengan HIV dan AIDS (ODHA) yang menerima terapi Antiretroviral (ARV)Metode: Penelitian kuantitatif dengan pendekatan cross sectional. Jumlah sampel dalam penelitian ini 45 responden. Analisis yang digunakan adalah univariat dan bivariat. Uji statistik yang digunakan yakni, uji Chi Square dan regresi logistik.Hasil: Didapatkan bahwa faktor yang berhubungan dengan penyebab loss to follow up ARV adalah jaminan kesehatan, efek samping ARV, nilai CD4, dukungan keluarga, keputusasaan, tingkat stress, dan harga diri. Selanjutnya dilakukan uji regresi logistic untuk mendapatkan faktor mana yang paling berpengaruh dan didapatkan hasil bahwa nila CD4 merupakan indicator paling bermakna dengan nilai OR (Odds Rasio) sebesar 26.812 812 artinya ODHA yang memiliki kadar nilai CD4 < 200 memiliki risiko 26.812 kali lebih besar penyebab loss to follow up ARV dibandingkan dengan ODHA yang memiliki kadar nilai CD4 ≥ 200.Simpulan: Nilai CD4 merupakan faktor yang paling bermakna terhadap kejadian loss to follow up ARV pada ODHA.

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“…Dasar dari algoritma ini adalah untuk membagi data ke dalam beberapa bagian berdasarkan nilai atribut dari item yang ada pada training dataset. Algoritma J48 Decision Tree dapat melakukan klasifikasi baik melalui decision tree ataupun rules yang diperoleh dari pohon tersebut [3]. Metodologi penelitian yang digunakan pada penelitian ini adalah Knowledge Discovery in Databases (KDD) Process yang terdiri dari 5 tahapan [4].…”

Section: Masalah Yang Sering Dihadapi Oleh Para Investor Dalam Melakuunclassified

Analisis Kinerja Algoritma J48 Decision Tree untuk Pengambilan Keputusan Beli/Jual pada Saham PT Harum Energi Tbk. (HRUM)

Hayat

Prasetijo

Septiana

2019

jtim

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The problem that is often faced by investors in selling / buying stocks is the difficulty in analyzing a dataset of stock prices in large quantities.This analysis aims to predict the rise or fall of stock prices based on data obtained. To assist investors in determining buying / selling decisions on stock analysis based on technical and equipped with classification techniques in data mining. This study analyzes the performance of the J48 Decision Tree algorithm in the Waikato Environmental Software for Knowledge Analysis (WEKA) version 3.8.2 for PT. Harum Energi Tbk. (HRUM). The results showed in the testing data, the percentage of testing on data without normalization was higher by 87.3 (non-aggressive) and 88.8 (aggressive) compared to normalized data 84.2 (non-aggressive) and 85 (aggressive ). The biggest stock profit generated is in non-aggressive type data without normalized by 48.75 or Rp. 48,750.00.

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Penerapan Algoritme J48 Untuk Prediksi Penyakit Demam Berdarah

Cited by 4 publications

References 0 publications

Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate

Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate

Faktor-faktor determinan penyebab loss to follow up pada orang dengan HIV dan AIDS (ODHA) yang menerima terapi Antiretroviral (ARV)

Analisis Kinerja Algoritma J48 Decision Tree untuk Pengambilan Keputusan Beli/Jual pada Saham PT Harum Energi Tbk. (HRUM)

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