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All Journal 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 Publika Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bangsa Jurnal Informatika: Jurnal Pengembangan IT Journal of Data Science Methods and Applications
Neni Purwati
Institut Informatika Dan Bisnis Darmajaya
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 Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Sistem Informasi Monitoring Dan Evaluasi Vaksinasi Wilayah Provinsi Lampung Hendra Kurniawan; Denny Andreas; Neni Purwati; Sri Karnila; Nurjoko; Egi Safitri; Ruki Rizal
TEKNIKA Vol. 17 No. 2 (2023): Teknika Juli - Desember 2023
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10205750

Abstract

Coronavirus Disease 2019 (COVID-19) is a new type of disease that has never been previously identified in humans. Residents in the Lampung Province Region, especially the East Lampung Regency area. The community has difficulty seeing the vaccination distribution map, they still use table data containing only the names and types of vaccines made by the East Lampung Health Service. The data collection is computerized but the data is presented in tabular form, using Google Forms, converted using Google Sheets and then reported to the district to be submitted to the East Lampung Health Office. Meanwhile, system development uses the Agile Development method. This research produces a web-based geographic information system that can display data in the form of a map of the distribution of covid-19 vaccination locations for the Lampung Province, especially East Lampung Regency. Keywords: Coronavirus Disease, Vaccination, Geographic Information System
Pemodelan Matematika dan Analisis Penyebaran Demam Berdarah Egi Safitri; Hendra Kurniawan; Neni Purwati; Sri Karnila; Nurjoko; Ruki Rizal
MathVisioN Vol 6 No 1 (2024): Maret 2024
Publisher : Prodi Matematika FMIPA Unirow Tuban

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55719/mv.v6i1.1094

Abstract

Demam berdarah merupakan permasalahan kesehatan yang serius di seluruh dunia dengan jumlah kematian yang signifikan sebagai akibat penyakit ini. Penelitian ini mengusulkan sebuah model SIR untuk populasi manusia dan model UV untuk populasi vektor dengan tingkat saturasi insiden, bertujuan untuk menggambarkan transmisi demam berdarah. Dilakukan perhitungan titik keseimbangan dan basic reproduction number, serta identifikasi kondisi yang mempengaruhi keseimbangan bebas penyakit dan keseimbangan endemik. Stabilitas lokal titik keseimbangan dianalisis menggunakan nilai eigen dari matriks Jacobi. Hasil penelitian menunjukkan bahwa stabilitas titik keseimbangan bebas penyakit (DFE) dipengaruhi oleh nilai , sedangkan  menunjukkan stabilitas keseimbangan endemik. Analisis elastisitas dan sensitivitas terhadap parameter model dilakukan terhadap . Hasil akhir mengidentifikasi parameter bv  sebagai parameter yang paling sensitif, dengan pengaruh tertinggi terhadap . Demam berdarah merupakan permasalahan kesehatan yang serius di seluruh dunia dengan jumlah kematian yang signifikan sebagai akibat penyakit ini. Penelitian ini mengusulkan sebuah model SIR untuk populasi manusia dan model UV untuk populasi vektor dengan tingkat saturasi insiden, bertujuan untuk menggambarkan transmisi demam berdarah. Dilakukan perhitungan titik keseimbangan dan basic reproduction number, serta identifikasi kondisi yang mempengaruhi keseimbangan bebas penyakit dan keseimbangan endemik. Stabilitas lokal titik keseimbangan dianalisis menggunakan nilai eigen dari matriks Jacobi. Hasil penelitian menunjukkan bahwa stabilitas titik keseimbangan bebas penyakit (DFE) dipengaruhi oleh nilai , sedangkan  menunjukkan stabilitas keseimbangan endemik. Analisis elastisitas dan sensitivitas terhadap parameter model dilakukan terhadap . Hasil akhir mengidentifikasi parameter bv sebagai parameter yang paling sensitif, denganpengaruh tertinggi terhadap .
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.
Pelatihan Mengoperasikan Microsoft Office Pada Siswa-Siswi SMPN Satu Atap Pesawaran Hendra Kurniawan; Neni Purwati; Sri Karnila; Nurjoko Nurjoko; Egi Safitri; Sushanty Saleh; Ruki Rizal
Jurnal Publika Pengabdian Masyarakat Vol 5, No 2 (2023): Jurnal Publika Pengabdian Masyarakat
Publisher : Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jppm.v2i01.3101

Abstract

This community service aims to provide insight and knowledge to students on SMPN Satu Atap how to operate Microsoft Office so that students' ability to use Microsoft Office can be used to assist or support academic activities at school by providing training. The implementation of this service is completed in three stages of activity, namely preparation, implementation, reporting. Preparation is done by conducting a preliminary survey to find out what students need to support teaching activities in class. The training was carried out using the lecture method, namely the presentation technique, followed by direct practice assisted by the team, accompanied by questions and answers and exercises carried out by the training participants. This activity was attended by approximately 30 students, from 08.00-16.00 WIB for one day, taking place in classes of the SMPN Satu Atap. The final stage of this service is to report the results of the activities. The results of the activities that have been carried out by the students look very enthusiastic in following every material presented by the service team as evidenced by the activity between students and the service team.Keywords — Training, Microsoft Office, Students
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.
Co-Authors - Nurjoko -, Mutia Achmad Nuzul Mariyus Agnes Dwi Januanti Agus Rahardi Agus Rahardi, Agus AGUS WIDODO Aisyifa, Dinda Fega Aziz, RZ. Abdul Dani Rofianto Denny Andreas Egi Safitri Felisita Klaralia Halimah Halimah Hariyanto Wibowo Hariyanto Wibowo Hendra Kurniawan Hendra Kurniawan Hendra Kurniawan Hendra Kurniawan Herwanto, Riko Herwanto, Riko Hetty Muniroh Ihwan Nurdin Inas Yuni Tsamarah Indera Izzah Farikhah Julius, Felix K, Hendra Khoirinnisa, Sinta Kintan Imanita M. Zaky Fanany Zaky Muji Lestari Naila, Azira Nuriya Novi Herawadi Sudibyo Nurjoko Nurjoko Nurjoko Nurjoko Nurlistiani, Rini Nursiyanto Ochi Marshella Febriani Oscar Devinsen Pedliyansah, Yogi Raden Abdurrahman Reza Maulana Saputra Rini Nur Listiani Rini Nurlistiani Rizal, Ruki Ruki Rizal Ruki Rizal Ruki Rizalnul Fikri Safitri, Egi Selvida Widi Audria 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 Yogi Pedliyansah Yurpan Hani