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All Journal Syntax Jurnal Informatika Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks)
Mudzakir, Tohirin Al
Universitas Buana Perjuangan Karawang
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Implementasi Algoritma Support Vector Machine (SVM) dan Random Forest Untuk Klasifikasi Penyakit Hipertensi Berdasarkan Data Kesehatan Azhaar, Siti Alia; Mudzakir, Tohirin Al; Novita, Hilda Yulia; Faisal, Sutan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8744

Abstract

One of the most common non-communicable diseases causing death in Indonesia is hypertension. At one community health center, the prevalence of hypertension is quite high. Based on examination results, more than 1,000 patients are diagnosed with hypertension each year. The issue faced at this health center is the lack of structured data classification for hypertensive and normal patients. The objective of this study is to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in creating a hypertension classification model based on health examination data from the Anggadita Health Center. Data from 2,500 patients was collected and preprocessed, including handling missing values, removing duplicate data, transforming data using label encoding, and dividing the data into training and testing sets. The SVM method applied a Radial Basis Function (RBF) kernel, while the RF consisted of 100 decision trees. Evaluation was conducted using a confusion matrix to calculate accuracy, precision, recall, and F1-score. The results showed that the SVM method achieved an accuracy of 93%, precision of 0.96 (Normal) and 0.90 (Hypertension), and F1-scores of 0.94 and 0.92. Meanwhile, the RF model showed superior performance with an accuracy of 96%, precision of 0.97 (Normal) and 0.95 (Hypertension), and F1-scores of 0.97 and 0.95, respectively. Thus, the Random Forest algorithm performs better in classifying hypertension data and can be implemented as a tool to assist healthcare institutions in managing patient data.
Pengembangan Model Klasifikasi Jenis Pisang Menggunakan Convolutional Neural Network Dengan Arsitektur VGG16 Habibah, Nur Habibah; Mudzakir, Tohirin Al; Novita, Hilda Yulia; Fauzi, Ahmad
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8616

Abstract

Indonesia memiliki kekayaan varietas pisang yang melimpah, namun permasalahan utama yang dihadapi adalah kesulitan dalam mengidentifikasi dan mengklasifikasikan jenis-jenis pisang secara akurat, terutama karena kemiripan visual antar varietas. Proses identifikasi secara manual dinilai kurang efisien dan rawan kesalahan, terutama dalam skala besar. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi untuk lima jenis pisang, yaitu pisang ambon, pisang kapas, pisang nangka, pisang siam, dan pisang tanduk, menggunakan metode CNN berbasis arsitektur VGG16. Dataset yang digunakan terdiri dari 634 gambar pisang yang diperoleh melalui kamera smartphone dan telah melalui proses augmentasi serta normalisasi untuk meningkatkan keragaman data. Model dilatih dengan parameter learning rate 0,0001 batch size 32, dan epoch sebanyak 50. Hasil pelatihan akurasi mencapai 99,60% dan akurasi validasi sebesar 98,48%. Hasil evaluasi performa menggunakan confusion matrix dan matrix klasifikasi presisi, recall, dan F1-score menunjukan model memiliki kemampuan yang baik dalam menglasifikasikan jenis pisang dengan tingkat akurasi yang tinggi.
Co-Authors Abdul Gapur Abu Bakar Adi Rizky Pratama Agung Susilo Yuda Irawan Ahmad Farhan El Ramdhani Amril Mutoi Siregar Ayu Ratna Juwita Azhaar, Siti Alia Butar Butar, Naomi Nova Meylica Fadmadika, Fadilla Faisal, Sutan Farah Maulida, Oxana Farhan El Ramdhani, Ahmad Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Habibah, Nur Habibah Hanny Hikmayanti Handayani Hermawan, Slamet Heryana, Nono Iin Kurniawati Indra, Jamaludin Irawan, Agung Susilo Yuda Iskandar, Muhammad Irsyad Jordi, Muhammad Juwita, Ayu Ratna Maulana, Moch Sigit Rizky Muhammad Jordi Novita, Hilda Yulia Nugraha, Bagja Pratama, Adi Rizky Pratama, Irfan Nugraha Ramadhan, Rizky Agung Rizal Saepul Mahdi rizky pratama, adi Rohana, Tatang Saepul Mahdi, Rizal Septiany, Eva Senia Setiyo Eko Budiyono Sukmawati, Cici Emilia Tatang Rohana Wahiddin, Deden Yana Cahyana Yulia Novita, Hilda