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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL SISTEM INFORMASI BISNIS Teknika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) INOVTEK Polbeng - Seri Informatika JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya Jurnal Teknoinfo Mathvision : Jurnal Matematika Jurnal Abdimas Bina Bangsa JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pepadun Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Publika Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bangsa Jurnal Informatika: Jurnal Pengembangan IT Jurnal Informatika Medis (J-INFORMED) IJENSET Journal of Data Science Methods and Applications
Neni Purwati
Universitas Muhammadiyah Lamongan
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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A Data-Driven Machine Learning for Predicting Student Stress Levels Purwati, Neni; Priyono, Agus; Rizky Al'insani, Adam; Darmawan
Indonesian Journal of Engineering, Science and Technology Vol. 2 No. 2 (2025): VOL. 02 NO. 02 (DECEMBER 2025)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v2i2.1380

Abstract

Academic stress significantly impacts students' psychological well-being and academic performance. This study focuses on predicting students' stress levels using a data-driven machine learning framework. The dataset was obtained from a questionnaire comprising 25 indicators encompassing emotional, psychological, academic, and environmental aspects of students. The research procedure involved data preprocessing, checking for missing values and redundancy, normalization, descriptive statistical analysis, model development, and performance evaluation using metrics such as recall, precision, sensitivity, specificity, F-measure, and accuracy. The implemented algorithm achieved excellent results, with an overall accuracy of 0.98. The model demonstrated high effectiveness in classifying Eustress and Distress, while its performance in detecting the No Stress category was limited, although precision and specificity indicate a strong capacity to differentiate between classes. These findings confirm that a machine learning approach can effectively capture patterns of student stress based on questionnaire responses and offers valuable guidance for developing early warning systems and targeted psychological intervention strategies. The study highlights the potential of data-driven predictive methods in supporting students' mental health through empirical data analysis. Keywords - LibSVM; Machine Learning; Predicting; Stress Levels; Tree Ensemble.
Prediction of new student admissions to higher education using support vector machines Neni Purwati; Windya Harieska Pramujati; A. Aviv Mahmudi; Mira Febriana Sesunan; Yahya Yahya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2484-2493

Abstract

Higher education institutions across various regions operate using systems that generate large amounts of data. This data is stored and utilized for strategic decision-making, providing significant business value to these institutions. Support vector machine (SVM) has become popular due to its strong generalization capability, high prediction accuracy, and faster training speed. SVM employs kernels as tuning parameters. This study aims to enhance the accuracy of student admissions prediction in higher education institutions using the SVM classification model. The SVM model was applied to a dataset comprising 5,936 records with four attributes and was evaluated using the use training set, 10-fold cross-validation, and percentage splits of 70%–30% and 80%–20%. Initially, the SVM-kernel model achieved high accuracy but failed to identify any true positive instances, indicating its inability to detect the minority “not accepted” class due to severe class imbalance. After applying class balancing techniques, the model’s performance improved significantly in terms of area under the curve (AUC), F-measure, and Matthews correlation coefficient (MCC), reflecting a more balanced classification between majority and minority classes. The SVM with Pearson VII function-based universal kernel (PUK) and classifier version 4.5 (C4.5) models achieved the best performance, indicating that class balancing effectively enhances both sensitivity and fairness in predictive classification.
Alzheimer’s Disease Classification using Lightweight Network MobileNet-V3 Muhammad Sadewa Wicaksana Wibowo; M Cahyo Kriswantoro; Neni Purwati
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 14 No 1 (2026): TEKNOIF APRIL 2026 (In Progress)
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2026.V14.1.47-58

Abstract

Alzheimer’s disease is a major public health concern characterized by progressive cognitive decline due to irreversible neuronal damage. Alzheimer’s disease represents a major global health concern in the twenty-first century. Although magnetic resonance imaging (MRI) is widely used for early diagnosis, manual interpretation is time-consuming and subject to variability. This study proposes an automated classification system based on the lightweight MobileNetV3 architecture to improve diagnostic efficiency. The model leverages depthwise separable convolutions to reduce computational complexity while maintaining high performance. MobileNetV3 models is then evaluated using appropriate metrics to assess the effectiveness of the proposed classification approach. Data augmentation techniques, including random rotation and flipping, are applied to enhance model generalization.  Experimental results demonstrate that the MobileNetV3 Small model achieves superior performance, with an accuracy and F1-score of approximately 0.94, compared to 0.90 for the MobileNetV3 Large model. These findings indicate that the compact architecture provides better efficiency and reliability for Alzheimer’s disease classification. The proposed approach is suitable for deployment in resource-constrained medical environments.
STUDI PERBANDINGAN ALGORITMA MACHINE LEARNING : SUPPORT VECTOR MACHINE, DECISION TREE DAN RANDOM FOREST DALAM KLASIFIKASI PENYAKIT DIABETES Muhammad Shodiq; Agus Priyono; Neni Purwati
Jurnal Informatika Medis (J-INFORMED) Vol. 4 No. 1 (2026): Jurnal Informatika Medis (J-INFORMED)
Publisher : LPPM Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v4i1.4221

Abstract

Hyperglycemia, or elevated blood glucose levels, is a primary indicator of diabetes mellitus, a chronic metabolic disorder whose prevalence continues to rise globally according to reports from the World Health Organization (WHO). Early detection of diabetes risk is crucial for preventing severe long-term complications. This study aims to evaluate and compare the performance of three machine learning algorithms Support Vector Machine (SVM), Decision Tree, and Random Forest in classifying diabetes based on health indicators and lifestyle patterns. The dataset used was obtained from Kaggle, with preprocessing stages including handling missing values and normalization. Model performance was assessed using accuracy, precision, recall, and F1-score. The experimental results show that the SVM algorithm achieved the highest accuracy at 74.89%, followed by Decision Tree with 73.39%, and Random Forest with 72.41%. This research is expected to serve as a reference for developing early medical screening systems to intelligently and accurately detect diabetes risk.
MENINGKATKAN PEMBELAJARAN SISWA DENGAN PENGENALAN BERBASIS DATA DAN MACHINE LEARNING Egi Safitri; Sri Karnila; Neni Purwati; Hendra Kurniawan; Nurjoko Nurjoko; Ruki Rizalnul Fikri
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i2.22096

Abstract

Abstrak: Data merupakan aset penting yang memiliki potensi besar untuk menjadi sumber informasi yang berharga dalam proses pengambilan keputusan. Namun, pada kenyataannya, masih banyak data yang belum dimanfaatkan secara optimal karena keterbatasan pengetahuan dalam memprosesnya. Contohnya adalah data kasus COVID-19. Kegiatan ini dilakukan di SMKN 7 Bandar Lampung dengan melibatkan 31 siswa dan 2 guru pendamping kelas. Tujuan utamanya adalah meningkatkan kualitas pembelajaran siswa dalam memahami berbagai jenis data, analisis data, dan dasar-dasar machine learning. Metode pelaksanaan yang digunakan adalah workshop, yang berfokus pada pemahaman siswa terhadap konsep data. Kegiatan tersebut dimulai dengan sosialisasi, pengenalan data di sekitar kita, penekanan pada data COVID-19 sebagai topik yang sedang tren, cara mendapatkan data, teknik analisis data, dan pengantar tentang machine learning. Teknologi juga diterapkan melalui penggunaan modul sederhana guna meningkatkan efektivitas pembelajaran dalam Program Kreativitas Mahasiswa ini. Hasil dari kegiatan ini termasuk perbaikan hasil akademis siswa serta peningkatan kesadaran mereka terhadap literasi data, dan membuktikan bahwa pendekatan inovatif ini memberikan kontribusi positif terhadap literasi data siswa dan meningkatkan pembelajaran berbasis data di era kemiskinan informasi, hal itu dapat dilihat dari hasil kuesioner yang telah diberikan dengan nilai tertinggi 77% mengatakan bahwa pelaksanaan pengabdian telah dilakukan sesuai dengan kebutuhan siswa, dan sebesar 71% kegiatan PkM berhasil meningkatkan kesejahteraan/kecerdasan siswa.Abstract: Data is an important asset that has great potential as a valuable source of information in decision-making processes. However, in reality, there is still much data that needs to be optimally utilized due to limitations in knowledge to process it. An example is COVID-19 case data. This activity was conducted at SMKN 7 Bandar Lampung, involving 31 students and 2 accompanying teachers. The main objective is to improve students' learning quality in understanding various types of data, data analysis, and the basics of machine learning. The implementation method used is a workshop focusing on students' understanding of data concepts. The activity begins with socialization, introducing data around us, emphasizing COVID-19 data as a trending topic, ways to obtain data, data analysis techniques, and an introduction to machine learning. Technology is also applied through the use of simple modules to enhance learning effectiveness in this Student Creativity Program. The results of this activity include improvements in students' academic performance and increased awareness of data literacy. It proves that this innovative approach positively contributes to students' data literacy and enhances data-based learning in the information poverty era. It can be seen from the questionnaire results that the highest score of 77% stated that the service implementation had been done according to the student's needs, and 71% of the PKM activities successfully improved students' welfare/intelligence.
Prediksi Penyakit Paru-Paru Dengan Algoritma Naïve Bayes Audria, Selvida Widi; Farikhah, Izzah; Saputra, Reza Maulana; Purwati, Neni
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Paru-paru memiliki peran penting dalam tubuh manusia, yaitu sebagai organ utama dalam sistem pernapasan, berfungsi mengolah karbon dioksida yang dibawa oleh darah menjadi oksigen dari udara yang dihirup, yang kemudian disebarkan ke seluruh tubuh untuk memenuhi kebutuhan oksigen. Gangguan paru-paru juga berisiko terhadap Kesehatan hingga kematian. Diperlukan metode yang akurat untuk mendiagnosis penyakit paru-paru agar penanganan dapat dilakukan dengan tepat. Dataset yang digunakan sebanyak 10000 dengan 10 attribut yakni usia, jenis kelamin, kebiasaan merokok, status pekerjaan, kondisi rumah tangga, aktivitas begadang, aktivitas rumah tangga, kepemilikan asuransi, riwayat penyakit bawaan, dan label hasil. Tujuan penelitian ini adalah untuk memprediksi penyakit paru-paru menggunakan model naïve bayes dengan hasil validasi yang robust dan reliable dengan penerapan dual-validation framework yakni Split validation dan k-Fold Cross Validation. Metode yang digunakan adalah pengumpulan data, pengelolaan data (seleksi dan pembersihan data), penerapan metode, pengujian metode dan kesimpulan. Hasil dari penerapan model naïve bayes dari data testing sebanyak 2000 menunjukkan nilai accuracy tertinggi sebesar 86.90%, precision tertinggi sebesar 87.65% diperoleh dari iterasi sebanyak 10 Fold Cross Validation, sedangkan nilai recall tertinggi diperoleh dari penerapan Split Validation sebesar 87.75%, sehingga hasil tersebut termasuk klasifikasi yang sangat baik diterapkan untuk melakukan prediksi penyakit paru-paru yang di derita masyarakat.
Co-Authors - Nurjoko -, Mutia A. Aviv Mahmudi Achmad Nuzul Mariyus Agnes Dwi Januanti Agus Priyono Agus Priyono Agus Rahardi Agus Rahardi, Agus AGUS WIDODO Aisyifa, Dinda Fega Audria, Selvida Widi Aziz, RZ. Abdul Dani Rofianto Darmawan Denny Andreas Egi Safitri Farikhah, Izzah Felisita Klaralia Halimah Halimah Hariyanto Wibowo Hariyanto Wibowo Hendra Kurniawan Hendra Kurniawan Herwanto, Riko Herwanto, Riko Hetty Muniroh Ihwan Nurdin Inas Yuni Tsamarah Indera Julius, Felix K, Hendra Khoirinnisa, Sinta Kintan Imanita M. Cahyo Kriswantoro M. Zaky Fanany Zaky Mira Febriana Sesunan Muhammad Sadewa Wicaksana Wibowo Muhammad Shodiq Muji Lestari Naila, Azira Nuriya Novi Herawadi Sudibyo Nurjoko Nurjoko Nurjoko Nurjoko Nurlistiani, Rini Nursiyanto Ochi Marshella Febriani Oscar Devinsen Pedliyansah, Yogi Raden Abdurrahman Rini Nur Listiani Rini Nurlistiani Rizal, Ruki Rizky Al'insani, Adam Ruki Rizal Ruki Rizal Ruki Rizalnul Fikri Safitri, Egi Saputra, Reza Maulana Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Karnila Sri Rahayu Supri Yanto Sushanty Saleh Syakur Syakur Windya Harieska Pramujati Windya Harieska Pramujati Yahya Yahya Yogi Pedliyansah Yurpan Hani