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All Journal Jurnal Masyarakat Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Indonesian Journal of Public Administration Review
Kurniawan, Defri
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Deteksi Dini Risiko Penyakit Jantung Koroner Menggunakan Algoritma Decision Tree dan Random Forest Nurrohman, Slamet Hudha; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7029

Abstract

Coronary heart disease is the leading cause of global mortality, accounting for 17.9 million deaths annually. Early detection is crucial in mitigating risks and preventing further complications. However, conventional diagnostic methods, such as traditional medical evaluations, often struggle to efficiently process large volumes of medical data, necessitating a more optimal approach. To enhance efficiency, this study employs machine learning to develop a classification model for coronary heart disease risk using Decision Tree and Random Forest algorithms. These models are then compared to determine the most optimal approach. The model is built using the Framingham Heart Study Dataset, consisting of 4,240 records with 15 relevant features. Due to class imbalance in the target variable, the Random Over-Sampling method is applied to improve classification performance. Model evaluation is conducted using a confusion matrix to compare the performance of both algorithms. The results indicate that Random Forest outperforms Decision Tree, achieving an accuracy of 97.64%, precision of 96.02%, recall of 99.29%, and F1-score of 97.63%. In contrast, Decision Tree yields an accuracy of 91.04%, precision of 84.76%, recall of 99.57%, and F1-score of 91.57%. This study suggests that Random Forest is more effective for early detection of coronary heart disease. Therefore, Random Forest-based models hold potential for clinical prediction systems, though further optimization is needed to enhance accuracy and reliability.
Optimasi Model Particle Swarm Optimization (PSO) Menggunakan SMOTE Untuk Menentukan Penyakit Diabetes Mellitus Putro Utomo, Satrio Allam; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7111

Abstract

Diabetes mellitus is a chronic disease that continues to increase globally and can affect various age groups. If not properly managed, this disease can lead to serious complications. In recent years, technological advancements, particularly in the field of machine learning, have significantly contributed to improving the accuracy of diabetes diagnosis and prediction. This study utilizes the Decision Tree algorithm, enhanced by two optimization methods: the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance and Particle Swarm Optimization (PSO) to optimize the model's hyperparameters, thereby improving classification accuracy. The dataset used in this study is the Diabetes Prediction Dataset available on Kaggle, consisting of 100,000 entries. Based on the analysis results, the implementation of data preprocessing and hyperparameter optimization has proven to increase the model's accuracy from 95.21% to 96.52%. Additionally, an evaluation using the confusion matrix shows an improvement in precision from 70.82% to 86.19% and an increase in the F1-score from 72.49% to 78.52%, although there is a slight decrease in recall from 74.24% to 72.11%. These findings demonstrate that a combination of data preprocessing, data balancing, and hyperparameter optimization can significantly enhance the performance of a classification model in detecting diabetes. For future development, it is recommended that the model be tested on other datasets to improve generalizability. Furthermore, exploring additional algorithms such as Random Forest or XGBoost could be beneficial in obtaining more optimal results.
Analisis Sentimen Ulasan Mobile JKN pada Playstore dengan Perbandingan Akurasi Algoritma Naïve Bayes dan SVM Pranata, Eka Arya; Budiman, Fikri; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7334

Abstract

The facilities provided by BPJS Health by releasing the Mobile JKN application, with this application the administrative process that previously had to be done directly can be done online and more flexibly. This research aims to see the sentiment of the community towards the JKN Mobile application review by comparing the SVM and Naïve Bayes algorithms. As well as optimizing the Naïve Bayes algorithm by using grid search. Reviews are taken from Google play with the help of Google Play Scraper API, the dataset taken amounted to 7,000 reviews. The results of using Naïve Bayes with an accuracy value of 86%, after tuning optimization using Grid Search significantly increases the accuracy value of the Naïve Bayes algorithm to 91% and for the SVM algorithm has an accuracy value of 92%. From the trial, it was found that the SVM algorithm is still better than the Naïve Bayes algorithm even though it has been optimized, but by optimizing the accuracy value Naïve Bayes is closer to SVM performance. This research can provide insight into the comparison of the two algorithms in identifying JKN Mobile reviews and the need for optimization to improve the performance of algorithms in sentiment analysis, besides that this research also contributes to the improvement and development of the JKN Mobile application so that it is useful for the community.
Analisis Sentimen Pengguna X terhadap Kasus Korupsi Gula Tom Lembong Menggunakan Naïve Bayes, SVM, dan Random Forest Kuncoro, Aneira Vicentiya; Budiman, Fikri; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8577

Abstract

The alleged sugar import corruption case involving Tom Lembong has become one of the most widely discussed public issues on social media, generating diverse reactions. This phenomenon illustrates how public opinion on legal issues is often influenced by perceptions of the public figures involved. This study aims to analyze public sentiment regarding the case on the social media platform X (formerly Twitter). The dataset consists of 1,802 tweets collected through a crawling process using the X API with the keyword “Tom Lembong.” The research stages include data cleaning, case folding, text normalization, tokenizing, stopword removal, stemming, sentiment labeling using a lexicon-based approach, and feature extraction with the Term Frequency–Inverse Document Frequency (TF-IDF) method. The prepared dataset was then tested using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The results show that the SVM algorithm achieved the highest accuracy (84%), followed by Random Forest (80%) and Naïve Bayes (76%). Based on the sentiment labeling results, positive sentiment dominated with 61%, while negative sentiment accounted for 39%. Although the analyzed issue concerns an alleged corruption case, the dominance of positive sentiment indicates that public opinion tends to focus on Tom Lembong’s personal image or public track record, which is viewed positively rather than on the substance of the legal allegations. These findings demonstrate the effectiveness of the SVM algorithm in analyzing high-dimensional text and provide insights into how public perception of legal issues can be influenced by image factors and the socio-political context on social media.
Model Klasifikasi Cerdas Gangguan Tidur Berbasis Machine Learning Random Forest pada Data Kesehatan dan Perilaku Harian Ni'mah, Laila Maulin; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8631

Abstract

Sleep disorders, such as insomnia and sleep apnea, have become a significant health issue in the modern era, driven by the demands of lifestyle changes. This condition highlights the urgent need for early detection tools that are not only accurate but also easily accessible to the general public. This research aims to design and implement an intelligent classification system to automatically identify the risk of sleep disorders based on health and daily behavior data. To achieve this goal, this study applies a machine learning method using the Random Forest algorithm, which was chosen for its reliable ability to handle complex and non-linear data relationships. The data used is the "Sleep Health and Lifestyle Dataset" sourced from the Kaggle platform, covering 374 respondents with 13 relevant features. The research process included data pre-processing steps to ensure input quality, model training, and rigorous performance evaluation. The evaluation results on the test data show that the developed Random Forest model exhibited very solid performance, successfully achieving an accuracy rate of 91% and a weighted average F1-Score of 0.90. This F1-Score metric, which balances precision and recall, confirms that the model is not only accurate but also has a balanced performance in detecting each class, which is crucial for health classification. Furthermore, the feature importance analysis confirmed that Stress Level, BMI Category, and Heart Rate are the three most dominant predictor factors. The culmination of this research is the successful implementation of this predictive model into an interactive web application developed using the Streamlit framework. This application allows users to independently input their health data and receive feedback in the form of a real-time risk prediction. With an intuitive interface and easy-to-understand results, this application serves as a practical and informative initial screening tool for personal sleep health analysis.
Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Status Gizi Balita Sabrina, Della; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8668

Abstract

Nutritional status in children under five years of age serves as a key indicator in assessing the overall health, growth, and development of children. Conventionally, nutritional status is determined through manual measurements and interpretation of anthropometric tables, which is time-consuming and prone to human error. With advances in technology, machine learning-based approaches can be used to help classify nutritional status more quickly, objectively, and accurately, thereby supporting decision-making in public health. This study focuses on analyzing and comparing the performance of three machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT) in classifying the nutritional status of toddlers using anthropometric data that includes variables such as age, gender, weight, and height. In this study, the nutritional status categories classified for the toddler weight dataset include: Severely Underweight, Underweight, Normal, and Overweight. The categories for the height dataset include Severely Stunted, Stunted, Normal, and Tall. The research stages included data preprocessing, data splitting into training and testing, and model performance assessment through accuracy, precision, recall, and F1-score matrices. Based on the evaluation results of the toddler height dataset, the K-Nearest Neighbors (KNN) algorithm proved to be the model with the best performance, with an accuracy of 99.91%. This value exceeded that of the Decision Tree, which achieved an accuracy of 99.89%, and the SVM (RBF) algorithm, which achieved 98.48%.
Co-Authors Abu Salam Ainul Yaqin Amartha, Putri Ananta, Devanda Radya Azizah, Hidayatul Dzaky, Azmi Abiyyu Dzaky, Fauzan Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fauziah, Mitha Fikri Budiman Haresta, Alif Agsakli Ida Ayu Putu Sri Widnyani Ika Novita Dewi Junta Zeniarja Kamarudin, Fatkhurridlo Pranoto Kuncoro, Aneira Vicentiya Muljono Muljono Ni'mah, Laila Maulin Ningrum, Novita Kurnia Nurrohman, Slamet Hudha Prajanto Wahyu Adi, Prajanto Wahyu Pranata, Eka Arya Purwanto Purwanto Putro Utomo, Satrio Allam Ramadhan Rakhmat Sani Rosi Subhiyakto, Egia Sabrina, Della Septian Enggar Sukmana Sindhu Rakasiwi Sri Winarno Utomo, Danang Wahyu