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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Nilovar Asyiah
Universitas Pamulang, Tangerang Selatan
Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi dan Evaluasi Performa Algoritma Naïve Bayes dalam Deteksi Dini Penyakit Diabetes Nurhasanah Nurhasanah; Nilovar Asyiah; Rahmawati Rahmawati
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1159

Abstract

Diabetes mellitus is one of the most prevalent chronic diseases worldwide and requires early detection to reduce the risk of severe complications through timely intervention. This study aims to implement and evaluate the performance of the Naïve Bayes algorithm in supporting the early detection of diabetes based on patients' health data. The study employed the Pima Indians Diabetes Dataset, consisting of 768 patient records with eight input attributes and one output attribute. During the preprocessing stage, zero values in physiological attributes were treated as missing values and replaced using the median of each respective attribute, followed by data consistency checking and dataset partitioning using the 80:20 split validation method. Model performance was evaluated using a confusion matrix with four performance metrics: accuracy, precision, recall, and F1-score. The experimental results showed that the Naïve Bayes algorithm achieved an accuracy of 88.31%, precision of 87.80%, recall of 90.00%, and an F1-score of 88.89%. These findings indicate that the proposed model performs well in classifying diabetes risk. The implementation of the model in a web-based application is expected to assist healthcare professionals and the general public as an early screening tool to support preliminary decision-making before comprehensive medical examination.
Implementasi dan Optimalisasi Metode Naive Bayes Dalam Sistem Deteksi Dini Penyakit Tiroid Nurhasanah Nurhasanah; Nilovar Asyiah; Okta Irawati
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7940

Abstract

This study aims to develop an early detection system for thyroid disease using the Naive Bayes algorithm. The dataset used is the Thyroid Disease Dataset from the UCI Machine Learning Repository, consisting of thousands of patient records. Prior to model training, the data undergoes preprocessing steps such as handling missing values, numerical normalization, and categorical encoding. The classification process involves calculating the prior probability, likelihood, and posterior probability for each class: normal, hypothyroid, and hyperthyroid. The system also presents the probability percentage for each class as an automated diagnosis result. Model accuracy is evaluated using a Confusion Matrix, achieving an accuracy score of 98.01% on the test data. These results indicate that the proposed approach can effectively and accurately classify thyroid conditions for early diagnosis purposes.
Penerapan BERTopic dan Analisis Sentimen Leksikal Pada Ulasan Relevan di Google Maps Mengenai Universitas Pamulang Nilovar Asyiah; Widodo Aktavia
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.8007

Abstract

The rapid advancement of information technology has encouraged the public to actively share reviews through digital platforms such as Google Maps. These reviews are not only informative but also reflect real user opinions and experiences regarding places or institutions, including higher education institutions. This study aims to analyze the main topics and sentiment classification contained in Google Maps reviews related to Universitas Pamulang. The approach used in this research combines two main methods. First, topic modeling is conducted using BERTopic, a modern technique based on transformer embeddings and HDBSCAN clustering algorithms, which can capture the semantic context of text more deeply. Second, sentiment analysis is performed using a lexicon-based approach, applying an Indonesian sentiment lexicon to efficiently identify the polarity of opinions without requiring model training.The data analyzed were collected through web scraping of relevant public reviews on Google Maps across four Universitas Pamulang locations: Central Campus, Viktor Campus, Witanaharja Campus, and Unpam Serang. The analysis revealed several dominant topics such as academic services, campus facilities, and bureaucracy. The majority of sentiments identified were neutral to positive, although negative opinions were also found in certain aspects. These findings are expected to serve as strategic input for the university to enhance service quality and strengthen its institutional image in the digital landscape.
Co-Authors Nurhasanah Nurhasanah Okta Irawati Rahmawati Rahmawati Widodo Aktavia