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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Scientia Computer Science (CO-SCIENCE) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Innovative: Journal Of Social Science Research Jurnal Multidisiplin Sahombu Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Desmulyati, Desmulyati
Universitas Bina Sarana Informatika
Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Public Health Transportation Other

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Analisis Kepuasan Pelanggan terhadap Beberapa Produk yang di Jual di E-Commerce Menggunakan Metode Naïve Bayes dan Logistic Regression: Penelitian Java Diovanka Alam; Musyaffa Ramdhan; Muhammad Yuzakki Raja Rafael; Muhammad Faiz Hamka; Desmulyati Desmulyati; Imam Budiawan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4970

Abstract

Customer satisfaction is a crucial element that plays a significant role in the sustainability of businesses in the e-commerce sector. Reviews provided by consumers serve as an important source of information to assess how satisfied they are with the products they purchased. This study aims to evaluate customer satisfaction levels using product review data through two classification methods: Multinomial Naive Bayes and Logistic Regression. The data used comes from a real Indonesian-language dataset that includes review texts and buyer ratings. The research process consists of several stages, starting from text preprocessing, feature extraction using the TF-IDF method, satisfaction label grouping, model training, and evaluation using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The findings of this study indicate that both methods can predict customer satisfaction with competitive accuracy. Logistic Regression demonstrates more consistent results compared to Naive Bayes in the context of Indonesian-language text. These results can be utilized by e-commerce companies to monitor product quality and continuously improve services for consumers.
Perbandingan Model Machine Learning dalam Prediksi Penyakit Jantung dengan Optimalisasi Fitur Gejala dan Faktor Risiko: Penelitian Ade Ikhsanudin Setiawan Wardhana; Galih Min Fadlil; Raihan Putra Wirahman; Deny Wahyu Fahrani; Imam Budiawan; Desmulyati Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4972

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection of its risk crucial to prevent severe complications. This study develops a heart disease risk prediction system using machine learning techniques, including Random Forest, Logistic Regression, and Support Vector Machine (SVM). The dataset is processed through several stages, including numerical feature selection, feature engineering with the addition of a total symptoms variable, and class imbalance handling using class-weight adjustments The model training process involves splitting the data into training and testing sets, followed by evaluation using accuracy, confusion matrix, and classification report metrics. The system also integrates an interactive interface that allows users to select symptoms and risk factors through widget-based checklists, enabling real-time prediction. The results show that the best-performing model achieves high accuracy and effectively identifies the most influential factors based on feature importance analysis. These findings indicate that machine learning provides a reliable and efficient tool to support early risk detection of heart disease.
Analisis Prediksi Nilai Akhir Mahasiswa Menggunakan Algoritma Regresi Linear Berbasis Machine Learning pada Program Studi Teknologi Informasi Universitas Bina Sarana Informatika: Penelitian Khalisa Salsabila; Nahya Faulya Maulidia; Shabrina Auliya Zahra Hafid; Aisyah Shinta Balqis; Imam Budiawan; Desmulyati Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4975

Abstract

The development of information technology in education demands a fast, objective, and data-driven academic evaluation system. Problems in higher education often involve lecturers' difficulty in monitoring and predicting student academic performance early, resulting in delayed response to declining performance. One solution that can be implemented is the use of Machine Learning. This study aims to analyze the prediction of students' final grades using a Machine Learning-based Linear Regression algorithm with attendance and assignment grades as variables. The case study was conducted on students of the Information Technology Study Program at Bina Sarana Informatika University using simulated data of 100 students, with the data divided into 80% training and 20% testing. Model evaluation used MSE, RMSE, and R². The results showed an R² value of 0.94, which means that 94% of the variation in students' final grades can be explained by attendance and assignment grades, while 6% is influenced by other factors. These findings indicate that the Linear Regression algorithm has excellent predictive performance in predicting students' final grades objectively and data-driven.
Perbandingan K-Means dan Agglomerative Hierarchical Clustering dalam Pengelompokan Provinsi di Indonesia Berdasarkan Tingkat Kepemilikan JKN Maulidina Cahaya Rani; Desmulyati Desmulyati
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7135

Abstract

The National Health Insurance (JKN) is a government program designed to achieve Universal Health Coverage; however, its ownership rate varies across provinces in Indonesia. This study aims to cluster the 38 provinces based on JKN ownership levels during 2023–2025 and compare the performance of the K-Means and Agglomerative Hierarchical Clustering (AHC) methods using the Ward Linkage approach. Data were obtained from Statistics Indonesia (BPS) and processed using Python in Google Colab through preprocessing, optimal cluster determination, clustering, and evaluation using the Silhouette Coefficient and Davies-Bouldin Index (DBI). The results indicate that the optimal number of clusters is three, representing low, medium, and high categories. K-Means achieved a Silhouette Coefficient of 0.595773 and a DBI of 0.453632, while AHC obtained a Silhouette Coefficient of 0.587456 and a DBI of 0.433335. Based on the higher Silhouette Coefficient, K-Means is recommended as the more effective method to support policy evaluation and the equitable distribution of JKN participation across Indonesia.
Co-Authors Abdul Khalik Walya Ade Ikhsanudin Setiawan Wardhana Agus Irawan Aisyah Shinta Balqis Ali Mukhtar Amar Khadafi Amriandry Maulana Ananda Lutfi Setiabudi Andi Taufik Aryo Satrio W Budiman, Ade Surya Chaerullah Fadli Dedy Ariyanto Deny Wahyu Fahrani Devin Nurman Wijaya Dzattho Key Fani Fahrizal Fahrizal Fajar Dwi Prasetyo Faris Ramadhan Febriyanto Febriyanto Feri Andrianto Fitria Purwaningsih Galih Min Fadlil Hafdiarsya Saiyar Hafdiarsya Saiyar Hafdiarsya Saiyar, Hafdiarsya Hasbi Rizki Sulistyo Ibnu Agustian Pratama Imam Budiawan Imam Budiawan Imam Sujarwo Java Diovanka Alam Khalisa Salsabila Levina Cecilia P M. Iqbal GF Martua Hami Siregar Maulidina Cahaya Rani Mohammad Noviansyah Muhammad Faiz Hamka Muhammad Haikal Nugroho Muhammad Ibnu Raihan Muhammad Ihsan Muhammad Jadetz Mulyono Muhammad Maulana Muhammad Yuzakki Raja Rafael Musyaffa Ramdhan Nahya Faulya Maulidia Nico Hadipratama Perdana Putra, Muhammad Rizki Prasetyo Bintang S.N Raihan Putra Wirahman Ramadhina Fajriani, Rieka Reafael Andrian Canavaro Ridwan Sholeh Sumitra Rizky Apriansyah Hadi Rizky Fauzi Shabrina Auliya Zahra Hafid Sifatul Akmal Sofiyan Aris Saputra Syahrul Suci Romadhon Try Budi Septiandoko Wahyudi Wahyudi