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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 Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) 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 Decision Sciences, Operations Research & Management 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 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.
Implementation of Finite State Automata in Grocery Store Application Handayani, Hanny Hikmayanti; Novita, Hilda Yulia; Sukmawati, Cici; Jansen, Youbel; Putri, Jasmin
Techno Xplore: Journal of Computer Science and Information Technology Vol. 11 No. 1 (2026): Techno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi
Publisher : Informatics Engineering, Faculty of Engineering and Computer Science, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/fkhemv84

Abstract

Toko sembako merupakan bentuk ritel kebutuhan pokok yang sangat dekat dengan aktivitas masyarakat, namun proses transaksi pada toko tradisional masih kerap menimbulkan kendala seperti antrean, pemilihan barang yang memakan waktu, serta ketidakefisienan saat toko ramai. Artikel ini bertujuan merancang alur transaksi aplikasi toko sembako berbasis pemodelan Finite State Automata (FSA) untuk membantu pelanggan melakukan pemesanan lebih cepat dan membantu pemilik toko menyiapkan pesanan secara terstruktur. Metode yang digunakan adalah pemodelan FSA dengan pendekatan Non-Deterministic Finite Automata (NFA), dimulai dari identifikasi masalah, penyusunan himpunan state dan simbol input, perancangan fungsi transisi, pembuatan state diagram menggunakan Lucidchart, serta pembuatan konsep desain antarmuka (UI) menggunakan Figma. Hasil perancangan menghasilkan NFA dengan 12 state (q0–q11) dan 12 simbol input (a–l) yang merepresentasikan proses mulai transaksi, pemilihan barang (misalnya minyak, gula, telur, tepung, mie instan, beras), pengelolaan keranjang, hingga pemilihan metode pembayaran (tunai atau debit/QRIS) sampai transaksi selesai. Simulasi alur dan rancangan UI menunjukkan bahwa FSA/NFA dapat digunakan sebagai model formal untuk mendeskripsikan dan mengendalikan alur transaksi aplikasi toko sembako secara jelas, konsisten, dan mudah dikembangkan menjadi implementasi aplikasi nyata
Analisis Perbandingan Algortima Support Vector Machine, Random Forest dan Naive Bayes Untuk Prediksi Penyakit Kanker Paru-Paru Rizky, Rendy Alfa; Fauzi, Ahmad; Kusumaningrum, Dwi Sulistya; Novita, Hilda Yulia
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9611

Abstract

The lungs are one of the vital organs responsible for the processes of respiration and blood circulation, with smoking habits being the primary factor contributing to the development of lung cancer. In Indonesia, the prevalence of this disease continues to increase, placing it eighth in the Southeast Asian region. Globally, lung cancer accounts for approximately 11.6% of all cancer cases and 18% of total cancer-related deaths.This study aims to analyze and compare the performance of Support Vector Machine (SVM), Random Forest, and Naïve Bayes algorithms in predicting lung cancer, as well as to determine the best-performing algorithm based on accuracy, precision, and recall metrics. The study utilizes the Lung Cancer Prediction dataset obtained from Kaggle, consisting of 309 instances and 16 attributes. The approach involves the implementation of three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The research process includes data collection, preprocessing, data transformation, feature selection, model development, and evaluation using a confusion matrix. The experimental results show that both SVM and Naïve Bayes achieve the same accuracy of 91.07%, while Random Forest obtains an accuracy of 89.28%. In terms of evaluation metrics, SVM demonstrates more consistent performance with a precision of 95% and recall of 93%, whereas Naïve Bayes shows a higher recall of 95% with a precision of 93%. On the other hand, Random Forest exhibits limitations in identifying non-cancer cases. Based on the overall results, SVM is considered the most optimal method as it provides a better balance of performance. This study indicates that machine learning has significant potential as a supporting tool for early detection of lung cancer in a more accurate and efficient manner.
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 Hanny Hikmayanti Handayani Jansen, Youbel Kusumaningrum, Dwi Sulistya Maharina, Maharina Maman Suherman Mudzakir, Tohirin Al Muhammad Khoiruddin Harahap Niendha Biell Binna Nurlaelasari, Euis Putri, Jasmin RAMADHAN, ANGGA RAMDA Rizky, Rendy Alfa Rohana, Tatang Sukmawati, Cici Sukmawati, Cici Emilia Wahiddin, Deden Zonyfar, Candra