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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL SISTEM INFORMASI BISNIS 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 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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Prediksi Pasien Pusat Kesehatan Masyarakat Menggunakan Machine Learning Purwati, Neni; Pramujati, Windya Harieska; Syakur, Syakur; Safitri, Egi
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 3 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i3.80135

Abstract

The fluctuating nature of patient visits makes it difficult for hospital management to plan, so it is important to predict patient visits by community health centers (PusKesMas) based on gender. The purpose of this study is to predict whether patients who come for treatment at the community health center can be served immediately, the supply/stock of drugs can meet the needs of patients and the availability of sufficient medical equipment, so that community health center services improve for the better. Based on good performance in solving the problems that have been formulated, the methods used are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The programming language used is Python using Google Colab. The stage of separating tain and test data using the scikit-learn train_test_split module with a percentage of 70% for train data and 30% for test data produces an accuracy in RF of 0.69 while in XGBoost it is 0.93. The results of the confusion matrix from XGBoost are true positive (TP), namely data that is predicted correctly and precisely as much as 53, false negative (FN) worth 3, false positive (FP) worth 2 and 1, true negative (TN) worth 40, 4, 1, 46. Meanwhile, the results of the XGBoost classification report model from the weighted Average precision value of 0.93, the recall value of 0.93 and the F1-Score value is also 0.93. These results indicate that the model used has good quality performance, so it is worthy of use. The application carried out is with the XGBoost data classification to assess patient visits in the next 5 years, with a prediction of achieving 93% accuracy.
Diabetes Mellitus Disease Prediction using Machine Learning Algorithms Safitri, Egi; Rofianto, Dani; Purwati, Neni; Kurniawan, Hendra; Karnila, Sri
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 4 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i4.84620

Abstract

Diabetes mellitus is a chronic disease with a rapidly increasing global prevalence, affecting around 422 million people, predominantly in low- and middle-income countries. Effective management of diabetes requires early detection and timely intervention. This study aims to develop an accurate predictive model for diabetes mellitus using three machine learning algorithms: Random Forest, Logistic Regression, and Decision Tree. The Pima Indians Diabetes dataset, comprising 768 patient records with various health indicators, was utilized for model training and evaluation. Exploratory data analysis revealed significant correlations between glucose levels, BMI, age, and diabetes risk. The dataset was split into 80% training and 20% testing sets. Models were validated using cross-validation and evaluated based on accuracy, precision, recall, and F1-score. Results indicated that Logistic Regression achieved the highest accuracy (75%) and balanced performance in identifying both positive and negative cases. Decision Tree excelled in recall, while Random Forest showed a slightly lower balance between precision and recall. The ROC curve analysis demonstrated that Random Forest had the highest AUC (0.82), followed by Logistic Regression (0.81) and Decision Tree (0.73). This study confirms that machine learning algorithms can effectively predict diabetes, providing valuable tools for early detection and intervention, ultimately reducing the global burden of diabetes mellitus.
Peningkatan Literasi Keuangan Koperasi Simpan Pinjam PKK Desa Turusgede Kecamatan Rembang Kabupaten Rembang Muniroh, Hetty; Purwati, Neni; Widodo, Agus
Jurnal Publika Pengabdian Masyarakat Vol 6, No 2 (2024): Jurnal Publika Pengabdian Masyarakat
Publisher : Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jppm.v6i2.4054

Abstract

Kegiatan koperasi simpan pinjam yang dikelola PKK Desa Turusgede kabupaten Rembang tergolong cukup bagus, namun masih terkendala dengan keterbatasan pengelola tentang literasi keuangan dan literasi tentang koperasi simpan pinjam. Pencatatan masih sangat sederhana, bahkan bisa dikatakan kurang akuntabel. Selain itu pembagian SHU juga belum pernah dilakukan sebagaimana lazimnya koperasi. Keterbatasan pengetahuan tentang akuntansi keuangan menjadikan kelompok PKK Desa Turusgede belum mampu membuat pembukuan dengan baik. Program pengabdian kepada masyarakat ini bertujuan untuk memberikan literasi keuangan dan literasi koperasi kepada koperasi simpan pinjam PKK Desa Turusgede. Metode yang digunakan yaitu survei, sosialisasi dan pelatihan, praktek penyusunan laporan keuangan sederhana, pelatihan pembagian SHU, literasi tentang pentingnya pemisahan fungsi dan tugas dalam pengelolaan koperasi, evaluasi. Program pengabdian masyarakat dilakukan selama 2 bulan, dengan memberikan pelatihan dan pendampingan. Pelaksanaan kegiatan Pengabdian kepada Masyarakat ini telah membantu pengelola Koperasi Simpan Pinjam PKK Turusgede melakukan pencatatan simpanan dan pinjaman anggota PKK dengan baik, mampu membuat pembukuan sederhana berupa laporan perubahan posisi keuangan, laporan laba/rugi dan laporan perubahan modal, dibuktikan dengan telah terisinya buku yang disediakan oleh tim pengabdi yang telah dilakukan pendampingan proses pengisiannya, dan hasilnya telah sesuai ilmu pembukuan yang telah diberikan, serta telah menghasilkan laporan pembukuan dengan benar.
Optimization of Genetic Algorithm from Comparison of Machine Learning for Heart Disease Prediction Purwati, Neni; Nurlistiani, Rini
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 2 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i2.87898

Abstract

Ischemic Heart Disease (IHD) is the leading cause of death worldwide, accounting for 13% of global fatalities. The number of deaths caused by IHD rose from 2.7 million in 2000 to 9.1 million in 2021, an increase of 6.4 million. IHD can be diagnosed through medical examinations or various health tests, as well as by leveraging technological advancements in artificial intelligence to enable early disease detection. This early detection is crucial for preventing heart disease, as there is currently no cure for the condition. This study aims to compare machine learning algorithms based on decision tree methods (Decision Tree, Random Forest, and Gradient Boosted Tree) with optimization using genetic algorithms to predict heart disease. The dataset used includes information from 8,625 patients who have experienced heart attacks, featuring attributes such as Sex, General Health, Age Category, Height (in meters), Weight (in kilograms), BMI, and "Had Heart Attack" as the label attribute. The initial modeling phase involved splitting the data into 80% for training (6,900 samples) and 20% for testing (1,725 samples). The results showed that the Random Forest model achieved the highest accuracy at 95.26%, narrowly surpassing the Decision Tree model, which attained 95.22%, by 0.04%. Meanwhile, the Gradient Boosted Tree model demonstrated the lowest accuracy at 90.99%. Subsequently, the application of the Genetic Algorithm significantly improved the accuracy, precision, and recall metrics across all three models, although the recall value for the Gradient Boosted Tree model decreased by 5.17%.
Pemberdayaan Masyarakat dengan Kreativitas, Digitalisasi dan Sosial melalui Program KKN Tematik Naila, Azira Nuriya; Khoirinnisa, Sinta; Aisyifa, Dinda Fega; Purwati, Neni
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 6 (2025): Agustus
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i6.2817

Abstract

Program KKN Tematik di Desa Ngotet dirancang sebagai bentuk pengabdian masyarakat yang mengintegrasikan kreativitas, digitalisasi, dan penguatan nilai social religious berbasis pendekatan Asset-Based Community Development (ABCD). Tujuan program ini adalah untuk memberdayakan masyarakat melalui empat focus utama: pelatihan keterampilan ekonomi kreatif berupa pembuatan Chunky Bag, pelatihan pemasaran digital, pengembangan kebun produktif untuk ketahanan pangan, serta pendampingan pembacaan Al Barzanji bagi anak dan remaja. Metode yang digunakan dalam program ini adalah action research dengan tahapan pemetaan asset lokal, perencanaan intervensi, pelaksanaan pelatihan, dan evaluasi reflektif. Hasil kegiatan menunjukkan peningkatan keterampilan teknis dan kepercayaan diri warga, terbentuknya kanal pemasaran digital, tumbuhnya kesadaran ketahanan pangan rumah tangga, serta munculnya antusiasme generasi muda dalam menghidupkan kembali tradisi religious. Keempat program ini saling terintegrasi dan menunjukkan bahwa pemberdayaan masyarakat yang holistik dapat membentuk sistem sosial yang tangguh, mandiri dan berkelanjutan.
Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan Purwati, Neni; Pedliyansah, Yogi; Kurniawan, Hendra; Karnila, Sri; Herwanto, Riko
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4876

Abstract

 Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used.
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.
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 Egi Safitri Farikhah, Izzah Felisita Klaralia Halimah Halimah Hariyanto Wibowo Hendra Kurniawan Hendra Kurniawan Herwanto, Riko Herwanto, Riko Hetty Muniroh Ihwan Nurdin Inas Yuni Tsamarah Indera Khoirinnisa, Sinta Kintan Imanita M. Cahyo Kriswantoro Mira Febriana Sesunan Muhammad Sadewa Wicaksana Wibowo Muhammad Shodiq Muji Lestari Naila, Azira Nuriya Novi Herawadi Sudibyo Nurjoko Nurjoko Nurjoko Nurlistiani, Rini Nursiyanto Ochi Marshella Febriani Oscar Devinsen Pedliyansah, Yogi Raden Abdurrahman Rini Nur Listiani Rini Nurlistiani 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 Rahayu Supri Yanto Sushanty Saleh Syakur Syakur Windya Harieska Pramujati Windya Harieska Pramujati Yahya Yahya Yogi Pedliyansah Yurpan Hani