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All Journal Jurnal PIKOM (Penelitian Komunikasi dan Pembangunan) OKTAL : Jurnal Ilmu Komputer dan Sains
Abdul Rahman Harahap
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Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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EFEKTIVITAS PEMANFAATAN MOBILE PUSAT LAYANAN INTERNET KECAMATAN DI KABUPATEN LANGKAT, SUMATERA UTARA Abdul Rahman Harahap
Jurnal Penelitian Komunikasi dan Pembangunan Vol 17, No 1 (2016): Jurnal PIKOM (Penelitian Komunikasi dan Pembangunan)
Publisher : Institution: Ministry of Communication and Information Technology of Republic of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.957 KB) | DOI: 10.31346/jpikom.v17i1.1355

Abstract

EFEKTIVITAS PEMANFAATAN LAYANAN DESA BERDERING DI DESA BINTAN BUYU KABUPATEN BINTAN PROVINSI KEPULAUAN RIAU Abdul Rahman Harahap
Jurnal Penelitian Komunikasi dan Pembangunan Vol 16, No 2 (2015): Jurnal PIKOM (Penelitian Komunikasi dan Pembangunan)
Publisher : Institution: Ministry of Communication and Information Technology of Republic of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (680.576 KB) | DOI: 10.31346/jpikom.v16i2.1351

Abstract

PEMANFAATAN TEKNOLOGI INFORMASI DAN KOMUNIKASI DALAM PEMENUHAN INFORMASI BAGI RUMAH TANGGA USAHA PERTANIAN di KECAMATAN HALONGONAN KABUPATEN PADANG LAWAS UTARA Abdul Rahman Harahap
Jurnal Penelitian Komunikasi dan Pembangunan Vol 17, No 2 (2016): Jurnal PIKOM (Penelitian Komunikasi dan Pembangunan)
Publisher : Institution: Ministry of Communication and Information Technology of Republic of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (254.191 KB) | DOI: 10.31346/jpikom.v17i2.876

Abstract

Klasifikasi Penyakit Paru-paru Menggunakan Metode Decision Tree Rilo Pambudi; Abdul Rahman Harahap; Farhan Dwitama Saputra; Muhamad Jusub
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 09 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Lung disease is a health problem that greatly affects the quality of life and various types, such as pneumonia, bronchitis, tuberculosis, asthma and COPD require special attention. Accurate classification is essential to ensure effective treatment and prevent complications. The research used the C4.5 Decision Tree Algorithm method to classify lung cancer risk using a dataset that included 16 attributes, symptoms such as and risk factors including age, shortness of breath, and smoking habits, for a total of 309 data. The train_test_split method from Scikit-learn is used to split the data into 70% for training and 30% for testing. With 89% accuracy, 70% precision, and 74.5% recall on test data assessed using the Confusion Matrix, the C4.5 model demonstrated strong performance. These findings show that 83 of the 93 predictions in the test data were correct. This research concludes that the Decision Tree Algorithm has been proven to support the diagnosis of lung cancer. however, the model performance can be improved by comparing it with other algorithms to get more optimal results.
Co-Authors Farhan Dwitama Saputra Muhamad Jusub Rilo Pambudi