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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Sains Teknologi dan Sistem Informasi Data Sciences Indonesia (DSI)
Agung Prabowo
Faculty Of Science And Technology, Universitas Prima Indonesia, Medan, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Physics

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Komparasi Tingkat Akurasi Random Forest dan Decision Tree C4.5 Pada Klasifikasi Data Penyakit Infertilitas Agung Prabowo; Sumita Wardani; Rico Wijaya Dewantoro; Wilfredo Wesly; Leonardo
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1115

Abstract

Male fertility has declined over the past two decades. The decrease is due to environmental factors, such as lifestyle habits that can affect the quality of a man's sperm. Artificial intelligence technology is currently developing as a methodology for health decision support systems. In the process of predicting infertility can be done by applying Machine Learning technology. This study focuses on comparing the Random Forest classification method with Decision Tree C4.5 to see the level of accuracy in predicting the success of infertility data classification. Data for the Fertility Dataset was obtained from the UCI Machine Learning Repository with a total of 100 data records, 10 attributes and 2 attribute classes, namely Normal and Altered. The parameters used are age, childhood diseases, accidents or trauma, surgical operations, alcohol consumption and smoking habits. Then evaluate the testing of the two methods, namely by using 10fold Cross Validation. Based on the results of Random Forest and Decision Tree C4.5 testing, the average accuracy of Random Forest is 87.20% and Decision Tree C4.5 with an accuracy rate of 85.90%. From the results obtained, it can be concluded that Random Forest is a superior method by 1.3% when compared to Decision Tree C4.5 in predicting accuracy in the Fertility Dataset.
Diagnosis and Prediction of Chronic Kidney Disease Using a Stacked Generalization Approach Agung Prabowo; Sumita Wardani; Abdul Muis; Radiman Gea; Nathanael Atan Baskita Tarigan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3611

Abstract

Chronic Kidney Disease (CKD) is. In the past, several learners have been applied for prediction of CKD but there is still enough space to develop classi?ers with higher accuracy. The study utilizes chronic kidney disease dataset from UCI Machine Learning Repository. In this paper, individual approaches, viz., linear-SVM, kernel methods including polynomial, radial basis function, and sigmoid have been used while among ensembles majority voting and stacking strategies have been applied. Stacked Ensemble is based on various types of meta-learners such as C4.5, NB, k-NN, SMO, and logit-boost. The stacking approach with meta-learner Logit-Boost (ST-LB) achieves accuracy 98,50%, sensitivity 98,50%, false positive rate 20,00%, precision 98,50%, and F-measure 98,50% demonstrating that it is the best classi?er as compared to any of the individual and ensemble approaches
Analisis Metode WASPAS dalam Menentukan Pengangkatan Pegawai Kontrak Menjadi Pegawai Tetap Abdul Muis; Abdul Muis; Akbar Idaman; Handry Eldo; Agung Prabowo; Ryan Rinaldi Hadistio
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 4 No. 1 (2024): April 2024
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v4i1.3034

Abstract

Human Resources (HR) is a valuable asset for every company, and good management greatly affects operational success. PT XYZ faces challenges in the process of appointing contract employees to permanent employees which is currently done manually, causing a slow and less accurate process. This research aims to develop a method that simplifies and accelerates the process using Weight Aggregated Sum Product Assessment (WASPAS) to simplify and speed up the decision-making process in appointing contract employees to become permanent employees at PT. XYZ, so that decisions taken can be faster, more precise and accurate. This method was chosen for its ability to reduce errors and optimize the assessment with various criteria. The results showed that Alternative 9 was ranked first with a Qi value of 0.9685, showing the best performance among other candidates. The implementation of the WASPAS method is expected to help PT XYZ in making faster, more precise, and accurate decisions, thereby increasing efficiency and objectivity in employee hiring, as well as improving the performance and stability of the company's human resources.
Implementation of Complex Propotional Assesment (COPRAS) in Determining Air Conditioning System Traders Idaman, Akbar; Amrullah, Amrullah; Raja Gunung, Tar Muhammad; Eldo, Handry; Prabowo, Agung
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.19417

Abstract

Increased global warming and awareness of the need to reduce greenhouse gas emissions have strengthened the focus on energy efficiency in various sectors, including the HVAC (Heating, Ventilation, and Air Conditioning) industry. In this context, the selection of air conditioning (AC) systems becomes crucial in providing thermal comfort. However, decisions regarding air conditioning systems are often complex as they involve considerations of energy efficiency, operational costs, system reliability, and environmental impact. To address this complexity, Complex Proportional Assessment (COPRAS) emerged as an effective multi-criteria analysis method. However, the application of COPRAS in determining AC system traffickers is still limited. This research explores the possible application of COPRAS in this context and identifies key factors to consider. The evaluation results show that Medan Elektronik and Citra Inovasi Prima are the top choices in the selection of AC system traffickers. This research is expected to contribute to the development of more sophisticated analysis methods in the HVAC industry as well as assist decision makers in selecting more appropriate and sustainable air conditioning systems.
Perbandingan Kinerja Algoritma Random Florest Classifier Dan Lightgbm Classifier Untuk Prediksi Penyakit Jantung Duran, Filbert; Wijaya, Frederico; Hulu, Yakin Rianto; Harahap, Mawaddah; Prabowo, Agung
Data Sciences Indonesia (DSI) Vol. 3 No. 2 (2023): Article Research Volume 3 Issue 2, December 2023
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v3i2.3831

Abstract

Penyakit jantung merupakan masalah kesehatan serius yang dapat dicegah dan diobati. Dengan menjaga gaya hidup sehat, melakukan pemeriksaan kesehatan secara rutin, dan mengikuti anjuran dokter[1], risiko penyakit jantung dapat dikurangi. Random Forest Classifier (RFC) bagaikan hutan pohon keputusan yang bekerja sama untuk menghasilkan prediksi yang lebih jitu. Algoritma ini tergolong handal dan fleksibel, mampu menangani berbagai tugas klasifikasi dan regresi. Kelebihannya, RFC menawarkan akurasi tinggi, tahan terhadap overfitting, dan mudah diinterpretasikan[2]. RFC adalah algoritma machine learning yang kuat dengan banyak keunggulan, namun perlu dipertimbangkan pula keterbatasannya dalam hal komputasi dan fleksibilitas[3]. LightGBM merupakan algoritma machine learning yang kuat dan efisien untuk klasifikasi dan regresi. Kecepatan, akurasi, dan kemudahan penggunaannya menjadikannya pilihan yang menarik untuk berbagai aplikasi[4]. Dari hasil yang didapat dari penelitian ini adalah metode RFC dan LightGBM dapat disimpulkan bahwa metode RFC merupakan metode yang tergolong efektif dalam analisis penyakit jantung dengan akurasi prediksi dari model adalah 95,37%., dapat dikatakan bahwa metode Random Florest Classifier cocok untuk melakukan analisis penyakit jantung bedasarkan dataset yang ada.
Co-Authors ABDUL MUIS Abdul Muis Akbar Idaman Amrullah Duran, Filbert Eldo, Handry Handry Eldo Hulu, Yakin Rianto Leonardo Mawaddah Harahap, Mawaddah Nathanael Atan Baskita Tarigan Radiman Gea Rico Wijaya Dewantoro Ryan Rinaldi Hadistio Sumita Wardani Tar Muhammad Raja Gunung Wijaya, Frederico Wilfredo Wesly