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All Journal JOIV : International Journal on Informatics Visualization Journal of Applied Data Sciences The Indonesian Journal of Computer Science Jurnal Pustaka Robot Sister
Rahmi, Nadya Alinda
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering

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The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification Rahmi, Nadya Alinda; Defit, Sarjon; Okfalisa, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3092

Abstract

This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification of academic papers into eight distinct fields: decision support systems, information technology, data science, technology education, artificial intelligence, expert systems, image processing, and information systems. The research dataset comprises reviews of scientific papers ranging from 150 to 500 words, collected from the repository of Universitas Putra Indonesia YPTK Padang. The classification process involved the application of the TF-IDF method for feature extraction, followed using various machine learning algorithms including SVM, MNB, KNN, and RF, with and without the integration of SMOTE for data balancing and Optuna for hyperparameter optimization. The results show that combining SMOTE with Optuna significantly improves the accuracy, precision, recall, and F1-score of the models, with the SVM algorithm achieving the highest accuracy at 90%. Additionally, the research explored the effectiveness of ensemble methods, revealing that hard voting combined with SMOTE and Optuna provided substantial improvements in classification performance. These findings underscore the importance of hyperparameter tuning and data balancing in optimizing machine learning models for text classification tasks. The implications of this research are broad, suggesting that the methodologies developed can be applied to various text classification tasks in different domains. Future research should consider exploring other hyperparameter tuning techniques and ensemble methods to further enhance model performance across diverse datasets.
SMART Method to Choose The Best Smartphone Purnama, Pradani Ayu Widya; Armonitha, Shary; Rahmi, Nadya Alinda; Pohan, Nurmaliana
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4101

Abstract

Smartphones are important devices for many people and are considered a necessity in today's industrial age 4.0. This is the beginning of the advancement of digital technology and the internet, where information can be easily accessed in various places. Therefore, people started buying smartphones. With this, people can utilise the Decision Support System. used to determine the data criteria needed to select a smartphone. With these criteria, users can accurately and quickly determine their choice. Where in the research in choosing a smartphone according to the needs of the community using 9 criteria that are often taken into consideration including Brand, Price, Processor Specifications, RAM Specifications, Model, Battery Capacity, Camera Resolution, Internal Capacity, Screen size which will be processed using the SMART method. Where the weight for each criterion has a value range of 0-100 depending on the priority given to each criterion. Based on calculations using the SMART method, the Iphone 11 Pro smartphone ranks highest with a final score of 8.3.
Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques Sovia, Rini; Anam, M. Khairul; Wisky, Irzal Arief; Permana, Randy; Rahmi, Nadya Alinda; Zain, Ruri Hartika
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1092

Abstract

This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.
Prediksi Status Kelulusan Mahasiswa pada Mata Kuliah Big Data Berdasarkan Kehadiran dan Nilai Akademik Menggunakan Algoritma Naïve Bayes Restiady, Adiddo; Purnama, Pradani Ayu Widya; Rahmi, Nadya Alinda
Jurnal Pustaka Robot Sister (Jurnal Pusat Akses Kajian Robotika, Sistem Tertanam, dan Sistem Terdistribusi) Vol 4 No 2 (2026): Jurnal Pustaka Robot Sister (Pusat Akses Kajian Robotika, Sistem Tertanam, dan Si
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakarobotsister.v4i2.1936

Abstract

Prediksi status kelulusan mahasiswa merupakan salah satu upaya yang dapat dilakukan untuk membantu proses evaluasi akademik dan pengambilan keputusan dalam lingkungan pendidikan. Penelitian ini bertujuan untuk menerapkan algoritma Naive Bayes dalam mengklasifikasikan status kelulusan mahasiswa berdasarkan data akademik yang meliputi Kehadiran, Nilai Tugas, Nilai Kuis, Nilai Ujian Tengah Semester (UTS), dan Nilai Ujian Akhir Semester (UAS). Dataset yang digunakan terdiri dari 15 data mahasiswa dengan dua kategori kelas, yaitu Lulus dan Tidak Lulus. Proses penelitian dilakukan melalui tahapan pengumpulan data, perhitungan probabilitas prior, perhitungan probabilitas kondisional setiap atribut, serta penentuan probabilitas posterior menggunakan metode Naive Bayes yang diimplementasikan pada Google Colab menggunakan bahasa pemrograman Python. Hasil perhitungan terhadap data mahasiswa yang diuji dengan atribut Kehadiran Tinggi, Nilai Tugas Sedang, Nilai Kuis Tinggi, Nilai UTS Tinggi, dan Nilai UAS Tinggi menunjukkan nilai probabilitas P(Lulus|X) sebesar 0,0428 dan P(Tidak Lulus|X) sebesar 0,0000. Berdasarkan hasil tersebut, mahasiswa diklasifikasikan ke dalam kategori Lulus karena memiliki probabilitas yang lebih tinggi dibandingkan kategori Tidak Lulus. Selain itu, hasil pengujian model menunjukkan tingkat akurasi sebesar 100%, yang mengindikasikan bahwa model mampu mengklasifikasikan data dengan sangat baik pada dataset yang digunakan. Dengan demikian, algoritma Naive Bayes dapat digunakan sebagai metode klasifikasi yang efektif untuk membantu memprediksi status kelulusan mahasiswa berdasarkan data akademik yang tersedia.
Co-Authors Anam, M Khairul Irzal Arif Wisky Lusinia, Shary Armonitha Okfalisa, - Permana, Randy Pohan, Nurmaliana Pradani Ayu Widya Purnama Restiady, Adiddo Ruri Hartika Zain Sarjon Defit Sovia, Rini