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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 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
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Perbandingan Model Machine Learning dalam Prediksi Penyakit Jantung dengan Optimalisasi Fitur Gejala dan Faktor Risiko: Penelitian Wardhana, Ade Ikhsanudin Setiawan; Fadlil, Galih Min; Wirahman, Raihan Putra; Fahrani, Deny Wahyu; Budiawan, Imam; 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 Salsabila, Khalisa; Maulidia, Nahya Faulya; Hafid, Shabrina Auliya Zahra; Balqis, Aisyah Shinta; Budiawan, Imam; 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.
Penerapan Algoritma Machine Learning Untuk Deteksi Akses Tidak Sah Pada SIAKAD IAI Al-Ghurabaa Wahyudi, Wahyudi; Noviansyah, Mohammad; Saiyar, Hafdiarsya; Siregar, Martua Hami; Desmulyati, Desmulyati
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.10021

Abstract

Abstrak: Sistem Informasi Akademik (SIAKAD) merupakan komponen vital dalam pengelolaan data akademik di perguruan tinggi, termasuk Institut Agama Islam Al-Ghurabaa. Akses tidak sah terhadap sistem ini dapat menyebabkan kebocoran data, perubahan nilai, dan gangguan integritas informasi akademik. Penelitian ini bertujuan untuk mengembangkan model deteksi dini terhadap aktivitas akses tidak sah menggunakan algoritma machine learning.Metode penelitian meliputi pengumpulan dan pra-pemrosesan data log akses SIAKAD, ekstraksi fitur perilaku pengguna (frekuensi login, waktu akses, IP address, dan pola aktivitas), serta pelatihan model klasifikasi menggunakan algoritma Random Forest dan Support Vector Machine (SVM). Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score.Hasil pengujian menunjukkan bahwa algoritma Random Forest menghasilkan tingkat akurasi tertinggi sebesar 97,3%, dengan kemampuan deteksi anomali akses yang lebih baik dibanding SVM (93,8%). Model yang diusulkan mampu mendeteksi aktivitas login mencurigakan secara real-time, sehingga dapat menjadi lapisan keamanan tambahan untuk SIAKAD IAI Al-Ghurabaa. Penerapan machine learning dalam keamanan aplikasi akademik terbukti efektif dalam meningkatkan ketahanan sistem terhadap serangan berbasis autentikasi dan penyalahgunaan akun penggunaKata kunci: SIAKAD; keamanan data; deteksi anomali; machine learning; Random Forest; SVM; Abstract: The Academic Information System (SIAKAD) is a vital component of academic data management in higher education institutions, including Institut Agama Islam Al-Ghurabaa. Unauthorized access to this system can lead to data breaches, grade manipulation, and loss of information integrity. This research aims to develop an early detection model for unauthorized access using machine learning algorithms. The methodology includes collecting and preprocessing SIAKAD access log data, extracting behavioral features (login frequency, access time, IP address, and activity patterns), and training classification models using Random Forest and Support Vector Machine (SVM) algorithms. Evaluation metrics used are accuracy, precision, recall, and F1-score. Experimental results show that the Random Forest algorithm achieved the highest accuracy of 97.3%, outperforming SVM (93.8%) in detecting anomalous access attempts. The proposed model can identify suspicious login activities in real-time, providing an additional security layer for SIAKAD IAI Al-Ghurabaa. The study demonstrates that machine learning-based intrusion detection is effective in enhancing system resilience against authentication-based attacks and user account misuse.Keywords: SIAKAD; data security; anomaly detection; machine learning; Random Forest; SVM;
Co-Authors Agus Irawan Akmal, Sifatul Alam, Java Diovanka Andi Taufik ARIYANTO, DEDY Balqis, Aisyah Shinta Bintang S.N, Prasetyo Budiman, Ade Surya Canavaro, Reafael Andrian Cecilia P, Levina Chaerullah Fadli Fadlil, Galih Min Fahrani, Deny Wahyu Fahrizal Fahrizal Fani, Dzattho Key Febriyanto Febriyanto Feri Andrianto Fitria Purwaningsih GF, M. Iqbal Hafdiarsya Saiyar Hafdiarsya Saiyar Hafdiarsya Saiyar, Hafdiarsya Hafid, Shabrina Auliya Zahra Hamka, Muhammad Faiz Imam Budiawan Imam Sujarwo Khadafi, Amar Martua Hami Siregar Maulana, Amriandry Maulidia, Nahya Faulya Mohammad Noviansyah Muhammad Ihsan Muhammad Maulana Mukhtar, Ali Mulyono, Muhammad Jadetz Nico Hadipratama Nugroho, Muhammad Haikal Perdana Putra, Muhammad Rizki Prasetyo, Fajar Dwi Pratama, Ibnu Agustian Rafael, Muhammad Yuzakki Raja Raihan, Muhammad Ibnu Ramadhan, Faris Ramadhina Fajriani, Rieka Ramdhan, Musyaffa Rizky Apriansyah Hadi Rizky Fauzi Salsabila, Khalisa Saputra, Sofiyan Aris Satrio W, Aryo Setiabudi, Ananda Lutfi Sulistyo, Hasbi Rizki Sumitra, Ridwan Sholeh Syahrul Suci Romadhon Try Budi Septiandoko Wahyudi Wahyudi Walya, Abdul Khalik Wardhana, Ade Ikhsanudin Setiawan Wijaya, Devin Nurman Wirahman, Raihan Putra