Abstract:Abstract: Heart disease is one of the leading causes of death worldwide, making early detection and accurate diagnosis crucial for reducing mortality rates and improving patient outcomes. This study aims to evaluate the…
effectiveness of four machine learning algorithms—Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—in predicting heart disease, with a focus on enhancing model performance using Linear Discriminant Analysis (LDA) for feature reduction. Among the models, SVM achieved the highest accuracy at 84.24%, followed by Logistic Regression at 83.70%. Although Random Forest and KNN showed lower accuracies, all models benefited from LDA's dimensionality reduction. This study suggests that SVM, combined with LDA, offers an optimal solution for early and accurate heart disease prediction in the healthcare industry.
Keywords: feature reduction; heart disease; linear discriminant analysis (LDA); machine learning; SVM
Abstrak: Penyakit jantung merupakan salah satu penyebab utama kematian di seluruh dunia, sehingga deteksi dini dan diagnosis yang akurat sangat penting untuk menurunkan angka kematian dan meningkatkan hasil pengobatan pasien. Penelitian ini bertujuan untuk mengevaluasi efektivitas empat algoritma pembelajaran mesin—Regresi Logistik, Random Forest, Support Vector Machine (SVM), dan K-Nearest Neighbors (KNN)—dalam memprediksi penyakit jantung, dengan fokus pada peningkatan kinerja model menggunakan Analisis Diskriminan Linear (LDA) untuk reduksi fitur. Di antara model yang diuji, SVM mencapai akurasi tertinggi sebesar 84,24%, diikuti oleh Regresi Logistik dengan 83,70%. Meskipun Random Forest dan KNN menunjukkan akurasi yang lebih rendah, semua model memperoleh manfaat dari reduksi dimensi yang diberikan oleh LDA. Studi ini menunjukkan bahwa SVM yang dikombinasikan dengan LDA merupakan solusi optimal untuk prediksi penyakit jantung secara dini dan akurat dalam industri kesehatan.
Kata kunci: linear discriminant analysis (LDA); machine learning; penyakit jantung; reduksi fitur; SVM.
Abstract:Abstract: Artificial Intelligence (AI) is currently gaining popularity across various industries, including healthcare, finance, and others. In this study, AI technology is employed to devise an optimal marketing strategy…
y for Code Verse Computer Accessories Store using the K-Means algorithm. As part of machine learning, the K-Means algorithm, categorized under unsupervised learning, is implemented to cluster sales data for computer accessory products over the last three months of 2023. The results of the K-Means analysis identify two main clusters. Cluster one (Cluster 1) comprises products such as Mouse, Keyboard, Monitor, Headset, and Speaker, indicating consistent purchasing patterns and high consumer interest. Recommendations are made to increase stock for Cluster 1. Meanwhile, Cluster two (Cluster 2) consists of Mic products with lower interest, and it is not advisable to increase stock. The implementation of K-Means provides insights into purchasing patterns, enabling Code Verse to develop more effective marketing and inventory management strategies.
Keywords: K-Means algorithm; artificial intelligence; clustering
Abstract: Kecerdasan Buatan (AI) kini meraih popularitas dalam berbagai industri, termasuk sektor kesehatan, keuangan, dan lainnya. Pada penelitian ini, teknologi AI digunakan untuk merancang strategi pemasaran optimal bagi Toko Aksesoris Komputer CodeVerse dengan menggunakan Algoritma K-Means. Sebagai bagian dari machine learning, Algoritma K-Means, yang termasuk dalam kategori unsupervised learning, diimplementasikan untuk mengelompokkan data penjualan produk selama tiga bulan terakhir tahun 2023. Hasil dari analisis K-Means mengidentifikasi dua cluster utama. Cluster pertama (Cluster 1) terdiri dari produk Mouse, Keyboard, Monitor, Headset, dan Speaker, menunjukkan pola pembelian yang konsisten dan tingginya minat konsumen. Rekomendasi untuk menambah stok diberikan. Sementara itu, Cluster kedua (Cluster 2) terdiri dari produk Mic dengan minat lebih rendah, dan tidak disarankan untuk menambah stok. Implementasi K-Means memberikan wawasan tentang pola pembelian, memungkinkan CodeVerse mengembangkan strategi pemasaran dan manajemen persediaan yang lebih efektif.
Keywords: Algoritma k-means; kecerdasan buatan; clustering
Abstract:Abstract: The JKN Mobile application is a mobile application created to facilitate healthcare administration in Indonesia since 2017. The application has been downloaded by over 10 million users and has received 484,000…
diverse reviews, including positive, negative, and neutral feedback. The average rating given by users is 4.5 out of 5 stars. This research aims to perform sentiment analysis on user reviews found in the Google Play Store review column. The methods used for sentiment analysis are Naive Bayes, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM). The test results show that with a 10% test data and 90% training data proportion, the SVM method achieves the highest accuracy of 95%. Naive Bayes follows with an accuracy of 87%, and K-NN with an accuracy of 75%.
Keywords: JKN mobile application, sentiment analysis, naive bayes, k-nearest neighbor (K-NN), support vector machine (SVM).
Abstrak: Aplikasi Mobile JKN adalah sebuah aplikasi yang dibuat untuk mempermudah administrasi kesehatan di Indonesia sejak tahun 2017. Aplikasi ini telah diunduh lebih dari 10 juta pengguna dengan 484 ribu ulasan beragam positif, negatif, dan netral. Rata-rata rating yang diberikan pengguna adalah 4,5 bintang dari 5 bintang. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan pengguna yang terdapat di kolom review Google Play Store. Metode yang digunakan untuk analisis sentimen adalah Naive Bayes, K-Nearest Neighbor (K-NN), dan Support Vector Machine (SVM). Hasil pengujian menunjukkan bahwa dengan menggunakan proporsi data uji sebesar 10% dan data training sebesar 90%, metode SVM mencapai akurasi tertinggi sebesar 95%. Diikuti oleh Naive Bayes dengan akurasi 87%, dan K-NN dengan akurasi 75%.
Kata kunci: JKN mobile, analisis sentimen, naïve bayes, k-nearest neighbor (K-NN), support vector machine (SVM).
Abstract:Abstract: Medical records are managed according to established standards, creating information that should be properly maintained and stored for easy access when needed. This study is motivated by the fact that the number…
r of immunization patients continues to increase daily, so authorities still need a long time to collect information in their registration systems. It is very long and has a significant impact on the performance of healthcare workers. This study aims to redesign the immunization information system to help manage medical records and maintain the quality of health care services. The design of the developed immunization system may include information on the processing and reporting of immunization data in hospitals to enable medical staff to report results to immunization services quickly and accurately. The research method used in this study is descriptive with a qualitative approach. Data collection was conducted through observations, interviews, and literature review from studies of researchers and practitioners. The system development method adopts the waterfall method. This study's results indicate that technology is an appropriate method to more efficiently support data processing and reporting when valid data are available.
Keywords: immunization; information system; medical record
Abstrak: Rekam medis dikelola sesuai dengan standar yang telah ditetapkan untuk menciptakan informasi yang harus dipelihara dan disimpan dengan baik sehingga informasi tersebut dapat dengan mudah diakses pada saat dibutuhkan. Penelitian ini dilatarbelakangi fakta bahwa petugas masih membutuhkan waktu yang lama untuk mengumpulkan informasi dalam kegiatan pelaporan, karena jumlah pasien yang imunisasi terus bertambah setiap harinya. Waktu yang dibutuhkan cukup lama berpengaruh secara signifikan terhadap kinerja tenaga kesehatan. Tujuan dari penelitian ini adalah mendesain ulang sistem informasi imunisasi untuk membantu pengelolaan pelaporan medis sebagai upaya menjaga kualitas pelayanan kesehatan. Rancangan sistem imunisasi yang dikembangkan dapat mencakup informasi pengolahan dan pelaporan data imunisasi di rumah sakit agar tenaga medis dapat melaporkan hasil secara cepat dan akurat kepada layanan imunisasi. Metode penelitian yang digunakan dalam penelitian ini adalah metode deskriptif dengan pendekatan kualitatif. Pengumpulan data dilakukan melalui observasi, wawancara dan studi literatur hasil penelitian peneliti dan praktisi. Metode pengembangan sistem menggunakan metode waterfall. Hasil penelitian ini menunjukkan bahwa penggunaan teknologi merupakan cara yang cukup untuk mendukung pekerjaan pengolahan data dan pelaporan secara lebih efisien ketika tersedia data yang valid.
Kata kunci: imunisasi; rekam medis; sistem informasi
Abstract:The integration of digitalization and Management Information Systems (MIS) has profoundly enhanced human resource efficiency, service quality, and operational effectiveness at Klinik Pratama Bunda Sarini. This study explores…
ores the role of MIS in optimizing workforce management and improving patient-centered care, highlighting both its advantages and the challenges encountered during implementation. Researchers employed a qualitative case study approach, utilizing semi-structured interviews and document analysis to examine HR professionals' experiences with digital transformation. The findings indicate that MIS has streamlined administrative workflows, reduced clerical inefficiencies, and improved real-time data accessibility, facilitating faster decision-making and higher patient satisfaction. However, workforce resistance, digital literacy limitations, and data security concerns created obstacles in the transition process. Structured HR-led training programs and phased implementation strategies effectively addressed these challenges. The study underscores the importance of sustained investment in workforce development, cybersecurity measures, and digital infrastructure to support long-term healthcare digitalization. Future research should evaluate the long-term impact of digital adoption in small-scale healthcare institutions.
Abstract:This research article aims to analyze factors associated to participation in the COVID-19 vaccine in the Noyontaan Primary Healthcare Center area, Pekalongan City. This research is a type of analytical research and the design…
esign used in this research is case control. This research uses primary data and secondary data. Samples were 46 cases and 46 controls using random sampling technique. The instrument used was a structured questionnaire. Data were analyzed with chi-square test. Results showed that knowledge (OR=4.136; 95% Cl=1.692-10.110), perception towards vaccine safety (OR=2.46; 95% Cl=1.055-5.736) were associated to COVID-19 vaccine participation. Meanwhile attitude (OR=1.312; 95% Cl=0.569-3.026), history of comorbid disease (OR=0.54; 95% Cl=0.236-1.237), perception towards AEFI (OR=0.825; 95% Cl=0.35-1.949), perception towards COVID-19 impact (OR=1.192; 95% Cl=0.524-2.709) were not were associated to COVID-19 vaccine participation.
Abstract:The rapid advancement of artificial intelligence (AI) has significantly impacted the healthcare sector, particularly in supporting the early detection of diabetes; however, many AI models still face challenges due to their…
ir black-box nature, where decision-making processes are not easily understood. This study aims to compare two Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive Explanations (SHAP), in interpreting the prediction results of an Artificial Neural Network (ANN) model using the Diabetes Health Indicator dataset. Prior to modeling, the data were preprocessed through cleaning and normalization to ensure quality and consistency. The trained ANN model was then analyzed using LIME and SHAP to evaluate the contribution of each feature to the prediction outcomes. The results show that both methods are capable of providing meaningful and interpretable explanations, although SHAP demonstrates more consistent and stable interpretations across the dataset. These findings highlight the importance of integrating XAI techniques to enhance model transparency, thereby increasing trust and supporting more reliable decision-making in clinical settings, particularly for diabetes diagnosis.