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All Journal Jurnal Inotera Jurnal Janitra Informatika dan Sistem Informasi
Wanto, Donna Oktar Endras
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Penerapan Metode Algoritma C.48 untuk Klasisifikasi Penyakit Diabetes Wanto, Donna Oktar Endras; Harmon, Cendra; Jupron , Jupron
Jurnal Janitra Informatika dan Sistem Informasi Vol. 5 No. 2 (2025): Oktober - Jurnal Janitra Informatika dan Sistem Informasi
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/qyzpt451

Abstract

Penyakit diabetes merupakan salah satu gangguan metabolik kronis yang terus meningkat prevalensinya di berbagai negara, termasuk Indonesia. Deteksi dini diabetes sangat penting untuk mencegah komplikasi jangka panjang, dan salah satu pendekatan yang efektif adalah dengan memanfaatkan algoritma klasifikasi berbasis machine learning. Penelitian ini bertujuan untuk menerapkan algoritma C4.8 dalam mengklasifikasikan penyakit diabetes menggunakan aplikasi WEKA, serta mengevaluasi kinerjanya berdasarkan data medis yang tersedia. Dataset yang digunakan terdiri dari 768 instance dan 9 atribut, termasuk Glukosa, BMI, dan Usia. Hasil pelatihan model menunjukkan tingkat akurasi sebesar 84,11%, sementara pada pengujian menggunakan metode 10-fold cross-validation, akurasi menurun menjadi 73,70%, dengan nilai Kappa sebesar 0,414. Hasil visualisasi data mendukung temuan bahwa atribut Glukosa merupakan variabel paling dominan dalam membedakan kelas, diikuti oleh BMI dan Usia, sedangkan atribut seperti Tekanan Darah dan Ketebalan Kulit kurang memberikan kontribusi signifikan secara individual. Temuan ini menunjukkan bahwa algoritma C4.8 mampu digunakan sebagai dasar dalam sistem pendukung keputusan untuk diagnosis awal diabetes, meskipun masih terdapat ruang untuk peningkatan performa model melalui teknik lanjutan.  
Analysis and Prediction of Student Academic Performance Using the Random Forest Algorithm Wanto, Donna Oktar Endras; Dede Sahrul Bahri
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID657

Abstract

Student academic performance is one of the important indicators of success in the learning process. Therefore, a method is needed that can analyze and accurately predict students' academic performance. This study aims to analyze and predict students' academic performance using the Random Forest algorithm. The dataset used consists of 649 student records with 33 attributes covering student characteristics, family background, social activities, and academic grades. The students’ final grade (G3) is used as the target variable in the classification process.The research stages include data exploration, data preprocessing, splitting the data into training and testing sets, building the Random Forest model, and evaluating the model. The results show that the Random Forest model is able to achieve an accuracy rate of 92.31%. Testing using a confusion matrix indicates that the model has a relatively low prediction error rate. In addition, testing using the cross-validation method produces an average accuracy of 91.21%, indicating that the model has good stability. Feature importance analysis shows that previous academic grades, namely G2 and G1, are the most influential factors affecting students' academic performance. The results of this study indicate that the Random Forest algorithm can be effectively used to predict students' academic performance and can assist educational institutions in data-driven decision-making.
Co-Authors Dede Sahrul Bahri Harmon, Cendra Jupron Jupron