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Klasifikasi Penyakit Stroke Menggunakan Algoritma Decision Tree C.45 Randi Estian Pambudi; Sriyanto Sriyanto; Firmansyah Firmansyah
TEKNIKA Vol. 16 No. 2 (2022): Teknika Juli - Desember 2022
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stroke is a disorder of brain function, both local and comprehensive, which will cause the blood supply to the brain to be disrupted quickly and last more than 24 hours or end in death. Stroke is also one of the deadliest types of disease in Indonesia. The importance of knowing the symptoms of stroke early is an early prevention. Therefore, a study was conducted to analyze data related to the causes of stroke. The attributes involved in the cause of stroke are age, gender, glucose level, history of heart disease, hypertension, type of work, type of residence, smoking status, body mass index and marital status. A certain algorithm is needed to classify all these attributes. Decision Tree C4.5 is the most widely used algorithm, in this case the accuracy of the Decision Tree C4.5 algorithm is 99.07%.
teknika DATA MINING TO PREDICTE STUDENT ACHIEVEMENT BASED ON SOCIO-ECONOMIC, MOTIVATION, DISCIPLINE AND PAST ACHIEVEMENT AT VOCATIONAL SCHOOL 1 PENAWARTAMA TULBABAG USING THE C4.5 ALGORITHM: DATA MINING UNTUK MEMPREDIKSI PRESTASI PESERTA DIDIK BERDASARKAN SOSIAL EKONOMI, MOTIVASI, KEDISIPLINAN DAN PRESTASI MASA LALU DI SMKN 1 PENAWARTAMA TULANG BAWANG MENGGUNAKAN ALGORITMA C4.5 Suroto, Suroto; Purnomo, Hendri; Estian Pambudi, Randi
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 2 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.12743856

Abstract

SMK Negeri 1 Penawartama Tulang Bawang is a school whose students come from various villages in the district. Most of the students come from families with limited economic conditions and low levels of education. These factors often affect the students' learning motivation. The aim of this research is to predict students' academic performance based on parents' socioeconomic status, motivation level, discipline level, and previous academic achievements using data mining methods with the C4.5 algorithm. This research employs a quantitative approach involving 606 tenth-grade students at SMK Negeri 1 Penawartama Tulang Bawang. Data collection methods used include documentation and questionnaires. The research results show that the prediction analysis using decision trees has an accuracy rate of 98.02%, precision of 94.44%, and recall of 77.27%.   Keywords: data mining, C4.5 algorithm, accuracy, precision, and recall
Jurnal Pemanfaatan Data Mining untuk Prediksi Prestasi Akademik Siswa Berdasarkan Pola Kehadiran ,Aktivitas Belajar Mengguakan Naive Bayes Logistic Regression. Pmabudi, Randi Estian; Purnomo, Hendri; Irawan, Rendi
Jurnal Teknologi Informasi Mura Vol 16 No 2 (2024): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v16i2.2507

Abstract

Pendidikan memiliki peran penting dalam membentuk kualitas sumber daya manusia yang unggul. Untuk meningkatkan mutu pendidikan, teknologi data mining digunakan untuk mengungkap pola-pola tersembunyi dalam data akademik. Penelitian ini bertujuan untuk memprediksi prestasi akademik siswa berdasarkan pola kehadiran dan aktivitas belajar menggunakan metode Naive Bayes dan Logistic Regression. Data diperoleh dari institusi pendidikan dan diolah melalui tahap preprocessing, meliputi pembersihan, transformasi, dan seleksi fitur. Model prediktif divalidasi menggunakan teknik k-fold cross-validation dan dievaluasi menggunakan metrik akurasi, presisi, recall, serta F1-score.Hasil penelitian menunjukkan bahwa metode Naive Bayes mencapai akurasi sebesar 85,7%, presisi 83,2%, recall 84,5%, dan F1-score 83,8%. Sementara itu, metode Logistic Regression menghasilkan akurasi sebesar 88,9%, presisi 87,1%, recall 86,7%, dan F1-score 86,9%. Kombinasi kedua metode memberikan performa lebih baik, dengan akurasi mencapai 90,3%, presisi 89,5%, recall 88,8%, dan F1-score 89,1%. Temuan ini menunjukkan bahwa penggunaan teknik data mining secara terintegrasi dapat meningkatkan akurasi prediksi prestasi akademik siswa.Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam mendukung pengambilan keputusan berbasis data untuk meningkatkan kualitas pembelajaran di institusi pendidikan
Jurnal Pemanfaatan Data Mining untuk Prediksi Prestasi Akademik Siswa Berdasarkan Pola Kehadiran ,Aktivitas Belajar Mengguakan Naive Bayes Logistic Regression. Pmabudi, Randi Estian; Purnomo, Hendri; Irawan, Rendi
Jurnal Teknologi Informasi Mura Vol 16 No 2 (2024): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v16i2.2507

Abstract

Pendidikan memiliki peran penting dalam membentuk kualitas sumber daya manusia yang unggul. Untuk meningkatkan mutu pendidikan, teknologi data mining digunakan untuk mengungkap pola-pola tersembunyi dalam data akademik. Penelitian ini bertujuan untuk memprediksi prestasi akademik siswa berdasarkan pola kehadiran dan aktivitas belajar menggunakan metode Naive Bayes dan Logistic Regression. Data diperoleh dari institusi pendidikan dan diolah melalui tahap preprocessing, meliputi pembersihan, transformasi, dan seleksi fitur. Model prediktif divalidasi menggunakan teknik k-fold cross-validation dan dievaluasi menggunakan metrik akurasi, presisi, recall, serta F1-score.Hasil penelitian menunjukkan bahwa metode Naive Bayes mencapai akurasi sebesar 85,7%, presisi 83,2%, recall 84,5%, dan F1-score 83,8%. Sementara itu, metode Logistic Regression menghasilkan akurasi sebesar 88,9%, presisi 87,1%, recall 86,7%, dan F1-score 86,9%. Kombinasi kedua metode memberikan performa lebih baik, dengan akurasi mencapai 90,3%, presisi 89,5%, recall 88,8%, dan F1-score 89,1%. Temuan ini menunjukkan bahwa penggunaan teknik data mining secara terintegrasi dapat meningkatkan akurasi prediksi prestasi akademik siswa.Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam mendukung pengambilan keputusan berbasis data untuk meningkatkan kualitas pembelajaran di institusi pendidikan
Optimalisasi Proses Pemesanan Jasa Servis Komputer dan Laptop melalui Aplikasi Web Menggunakan Framework Codeigniter Faris; Hartono; Pambudi, Randi Estian
Sienna Vol 5 No 2 (2024): Sienna Volume 5 Nomor 2 Desember 2024
Publisher : LPPM Universitas Muhammadiyah Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47637/sienna.v5i2.1638

Abstract

The rapid growth of technology has made computers and laptops indispensable in various fields such as government, education, and commerce. Despite widespread ownership, users often lack the skills to handle technical issues, creating a demand for repair services. Many repair shops still rely on manual tools like Microsoft Word and Excel for operational tasks, which becomes inefficient as customer numbers grow. This research focuses on the development of a web-based service management system to address these challenges and enhance efficiency. Using the waterfall methodology, a systematic and sequential approach was employed to design and implement the system. The CodeIgniter framework, known for its PHP development efficiency and ease of implementation, was utilized. The system features include repair status tracking, customer account management, financial reporting, service order processing, and detailed payment tracking. These functionalities aim to improve the customer experience, increase operational efficiency, and simplify business processes.
Co-Authors Aminuyati Faris Firmansyah Firmansyah Hendri Purnomo Irawan, Rendi Sriyanto Sriyanto Suroto Suroto