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All Journal International Journal of Artificial Intelligence Research Jurnal Informatika Universitas Pamulang JURIKOM (Jurnal Riset Komputer) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Teknologi Terpadu Technologia: Jurnal Ilmiah INTI Nusa Mandiri Jurnal Sistem Komputer dan Informatika (JSON) Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Jurnal Media Informatika Jurnal Informatika Teknologi dan Sains (Jinteks)
Novita, Hilda Yulia
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Performance Evaluation of Popular Supervised Learning Algorithms Towards Cardiovascular Disease Masruriyah, Anis Fitri Nur; Novita, Hilda Yulia; Sukmawati, Cici Emilia
Jurnal Informatika Universitas Pamulang Vol 8 No 3 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i3.34103

Abstract

Many studies have discussed the advantages of supervised learning for dealing with extensive data on heart disease. However, only a few studies evaluate the performance of supervised learning algorithms. This research builds a classification model using supervised learning algorithms, including C4.5, Random Forest, Logistic Regression, and Support Vector Machine. The data processed is in the form of category data with character data types. The accuracy, precision, and performance evaluation results show that the Logistic Regression Algorithm has the most superior value compared to the others. On the other hand, it was found that the C4.5 and SVM algorithms had anomalous events. Although the accuracy and precision values of C4.5 were superior to SVM, SVM had better performance.
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.
Pemodelan Prediksi Ekspor Kopi Indonesia Berbasis Algoritma Machine learning Novita, Hilda Yulia; Rohana, Tatang; Nurlaelasari, Euis; Awal, Elsa Elvira
Jurnal Media Informatika Vol. 6 No. 6 (2025): Edisi Nopember - Desember 2025
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i6.7097

Abstract

Penelitian ini bertujuan untuk membangun model prediksi ekspor kopi di Indonesia dengan menggunakan tiga algoritma machine learning, yaitu regresi inier, neural networks, dan gradient boosting. Data yang digunakan berasal dari data historis ekspor kopi Indonesia. Penelitian dilakukan melalui tahapan pra-pemrosesan data, pemodelan, dan evaluasi kinerja masing-masing algoritma. Hasil penelitian menunjukkan bahwa ketiga algoritma mampu memprediksi ekspor kopi dengan performa yang cukup baik. Algoritma Linear Regression memberikan hasil terbaik dengan nilai mean squared error (MSE) sebesar 0.0000867, mean absolute error (MAE) sebesar 0.00766, dan skor R² sebesar 95%. neural networks menghasilkan MSE sebesar 0.000171, MAE sebesar 0.01196, dan skor R² sebesar 91%. Sementara itu, gradient boosting menunjukkan performa terendah dengan MSE sebesar 0.01918 dan skor R² sebesar 74%. Temuan ini menunjukkan bahwa pendekatan machine learning dapat digunakan sebagai alat bantu dalam memprediksi tren ekspor komoditas secara akurat.
Co-Authors ARIF, SITI NOVIANTI NURAINI Asep Maulana, Asep Awal, Elsa Elvira Azhaar, Siti Alia Butar Butar, Naomi Nova Meylica Dimas Sasongko Eko Pramono Faisal, Sutan Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Habibah, Nur Habibah Maharina, Maharina Maman Suherman Mudzakir, Tohirin Al Muhammad Khoiruddin Harahap Niendha Biell Binna Nurlaelasari, Euis RAMADHAN, ANGGA RAMDA Rohana, Tatang Sukmawati, Cici Emilia Wahiddin, Deden Zonyfar, Candra