Article Per Year (5 Year)
p-Index From 2020 - 2025
3.96
P-Index
This Author published in this journals
All Journal Scientific Journal of Informatics Jurnal Teknologi Informasi MURA Jurnal Teknologi Jurnal Mantik Journal of Intelligent Decision Support System (IDSS) Pena : Jurnal Ilmu Pengetahuan dan Teknologi Jurnal Cahaya Mandalika Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Mandiri IT Jurnal Minfo Polgan (JMP) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Masyarakat Madani Indonesia Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia Vertex Jurnal Teknologi Informasi Mura IKRAM: Jurnal Ilmu Komputer Al Muslim ALMUISY: Journal of Al Muslim Information System Jurnal Pengabdian Masyarakat Terapan
Andriani, Wresti
Unknown Affiliation
Aerospace Engineering Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 29 Documents
Search

 1   2   3 
Integrasi Sistem Cerdas Berbasis AI untuk Penyaluran Bantuan Langsung Tunai yang Tepat Sasaran Andriani, Wresti; Wahyuning Naja, Naella Nabila Putri
ALMUISY: Journal of Al Muslim Information System Vol. 4 No. 1 (2025): ALMUISY: Journal of Al Muslim Information System
Publisher : STMIK Al Muslim

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

Abstract

Penelitian ini mengembangkan sistem cerdas berbasis AI untuk penyaluran Bantuan Langsung Tunai (BLT) yang tepat sasaran menggunakan algoritma Decision Tree, K-Nearest Neighbors (KNN), dan Naive Bayes. Evaluasi awal menunjukkan akurasi rata-rata model berada di bawah 50%, dengan AUC terbaik sebesar 0.47 pada Naive Bayes. Setelah optimasi menggunakan Particle Swarm Optimization (PSO), algoritma KNN menunjukkan peningkatan terbaik dengan AUC sebesar 0.51, sementara Decision Tree mencapai AUC sebesar 0.49. Sistem ini memanfaatkan data seperti penghasilan, kondisi kesehatan, dan status tempat tinggal untuk menentukan kelayakan penerima BLT. Penelitian ini membuktikan bahwa penggunaan metode AI dengan optimasi mampu meningkatkan efisiensi dan akurasi dalam mendistribusikan BLT secara lebih tepat sasaran, memberikan kontribusi signifikan pada perbaikan sistem bantuan sosial.
Anomaly Detection of Parasitic Plankton in Brebes Eco-Waters Using Vision-Based Autoencoder AI Gunawan, Gunawan; Andriani, Wresti; Maryanto, Sesilia Putri; Mustaqiim, Restu Abi
Vertex Vol. 14 No. 2 (2025): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/75mwxm55

Abstract

The escalating impact of environmental stress on coastal ecosystems necessitates reliable, scalable tools for monitoring marine biodiversity. This study proposes an unsupervised anomaly detection framework to identify parasitic and morphologically abnormal plankton in the waters of Brebes, Indonesia. The primary aim is to develop an interpretable, vision-based system capable of detecting visual anomalies without relying on labeled anomaly data. The research integrates convolutional autoencoders for reconstructing normal plankton images, Principal Component Analysis (PCA) for feature extraction, and One-Class Support Vector Machines (OC-SVM) for classification. Monthly microscopic images were obtained from selected mangrove and aquaculture pond sites in Brebes, Central Java, using portable digital microscopy under standardized field conditions. Images that exceeded a dynamic reconstruction threshold were flagged as anomalous and validated by marine biology experts. The system achieved an F1-score of 86.1%, a precision of 85.3%, and an AUC of 0.94, demonstrating high effectiveness in distinguishing between normal and anomalous plankton. With an average inference time of 0.37 seconds per image, the system supports near real-time monitoring. These results confirm the potential of the proposed method as a low-latency, field-deployable solution for aquatic ecosystem surveillance. By integrating AI-based detection with ecological expert validation, this research offers a scalable approach for marine biodiversity assessment and establishes a foundation for future adaptive environmental monitoring systems.
Obesity risk estimation using ensemble learning and synthetic data augmentation techniques Ujianto, Nur Tulus; Gunawan, Gunawan; Andriani, Wresti; Ramadhani, Ivan Rizky; Nasichatun, Nasichatun
Vertex Vol. 14 No. 2 (2025): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/1bg4ws75

Abstract

Obesity has become a primary global health concern due to its strong association with various chronic diseases such as diabetes, cardiovascular disorders, and certain types of cancer. Accurate and early risk prediction of obesity is essential for effective prevention and intervention strategies. However, predictive modeling in this domain often encounters two critical challenges: the presence of imbalanced datasets and the complex, nonlinear nature of behavioral and anthropometric features. This study aims to address these challenges by developing a robust classification model that integrates ensemble learning with synthetic data augmentation techniques. The research utilizes the Obesity Dataset from Kaggle, which comprises 2,111 records labeled into seven obesity levels, reflecting a realistic class distribution imbalance. Preprocessing steps included data cleaning, encoding, and stratified splitting. To enhance class representation, two augmentation methods were applied: SMOTE for synthetic oversampling and Generative Adversarial Networks (GANs) for generating realistic minority samples. A stacking ensemble model was constructed using Random Forest and XGBoost as base learners, with Logistic Regression serving as the meta-learner. Hyperparameter optimization was conducted using both grid and randomized search methods. Evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess performance. The proposed model achieved a 91% accuracy and an F1-score of 0.89, significantly outperforming models from previous studies. These findings suggest that combining ensemble learning with hybrid augmentation strategies effectively addresses class imbalance and improves predictive reliability in obesity risk estimation. The developed model holds practical value as a decision-support tool for early screening and targeted intervention in obesity prevention programs.
Sosialisasi Dan Pelatihan Penerapan Aplikasi E-Posyandu Bagi Kader Posyandu Desa Bandasari Di Kabupaten Tegal Syefudin, Syefudin; Nugroho, Bangkit Indarmawan; Murtopo, Aang Alim; Surorejo, Sarif; Santoso, Nugroho Adh; Arif, Zaenul; Gunawan, Gunawan; Andriani, Wresti
Jurnal Masyarakat Madani Indonesia Vol. 2 No. 4 (2023): November
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/js.v2i4.161

Abstract

Pengabdian masyarakat ini bertujuan untuk menyediakan sosialisasi dan pelatihan intensif kepada kader Posyandu di Desa Bandasari, Kabupaten Tegal, dalam rangka menerapkan aplikasi E-Posyandu. Tujuan utama adalah untuk meningkatkan pemahaman dan keterampilan kader Posyandu dalam penggunaan aplikasi E-Posyandu sebagai alat efisien untuk mengumpulkan, merekam, dan menganalisis data kesehatan masyarakat. Metode pelaksanaan melibatkan sesi sosialisasi konsep aplikasi dan pelatihan praktis dalam pengoperasian aplikasi tersebut. Hasil dari kegiatan ini diharapkan dapat mengoptimalkan peran Posyandu dalam perawatan kesehatan masyarakat, dengan pemantauan data yang lebih akurat dan real-time. Keberhasilan dalam menghadirkan teknologi ini diharapkan mampu menjadi contoh positif untuk program serupa di daerah lain yang memerlukan peningkatan efisiensi dalam pemantauan kesehatan masyarakat
Evaluasi Algoritma Kruskal dan Prim dalam Optimalisasi Pembangunan Infrastruktur Jaringan Internet Di Daerah Terpencil Andriani, Wresti; Almujahidah, Lutfiatun Rahma; Ferdyansyah, Muhammad Rio
IKRAM: Jurnal Ilmu Komputer Al Muslim Vol. 4 No. 1 (2025): IKRAM: Jurnal Ilmu Komputer Al Muslim
Publisher : IKRAM: Jurnal Ilmu Komputer Al Muslim

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

Abstract

Penelitian ini membandingkan kinerja algoritma Kruskal dan Prim dalam membangun Minimum Spanning Tree (MST) pada sebuah graf berbobot. Hasil menunjukkan bahwa kedua algoritma menghasilkan MST dengan bobot total yang sama, yaitu 7.0, meskipun menggunakan pendekatan yang berbeda. Algoritma Kruskal bekerja dengan memilih edge terkecil secara global, sedangkan algoritma Prim memulai dari satu node dan menambahkan edge dengan bobot terkecil yang terhubung. Dari segi efisiensi, algoritma Kruskal lebih cocok untuk graf jarang (sparse), sementara algoritma Prim lebih optimal untuk graf padat (dense). Kompleksitas algoritma Kruskal adalah O(E log E), sedangkan algoritma Prim bergantung pada representasi graf, dengan kompleksitas O(V²) atau O(E + V log V) menggunakan heap. Penelitian ini menyimpulkan bahwa pemilihan algoritma yang tepat bergantung pada karakteristik graf, di mana Kruskal ideal untuk jaringan dengan koneksi minim, sementara Prim lebih sesuai untuk jaringan dengan konektivitas tinggi, seperti pada pengembangan infrastruktur jaringan internet di daerah terpencil.
Tinjauan Pustaka Sistematis: Pemanfaatan Big Data Dalam Konsep Smart City Andriani, Wresti; Arianti, Tezya Sekar; Gunawan, Gunawan
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 1 No. 1 (2022): Jurnal Ekonomi, Teknologi dan Bisnis
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v1i1.3

Abstract

Era digital yang saat ini telah menyeluruh ke seluruh dunia dan terus mengalami perkembangan menjadi suatu upaya perubahan di seluruh aspek kehidupan, salah satunya dalam pembangunan berkelanjutan Smart City. Pembangunan berkelanjutan Smart City memungkinkan pemerintah untuk membuat sebuah aplikasi berbasis internet atau digital untuk mengelola sebuah pemerintahan baik dalam lingkup di sisi pemerintahan atau pada masyarakat. Oleh karena itu, Big Data sangat memiliki peranan penting dalam upaya pembangunan berkelanjutan agar dapat terlaksana secara efisien dan efektif. Penggunaan big data diharapkan mampu untuk memberikan kemudahan, memperlancar terlaksananya pembangunan berkelanjutan Smart City. Pada penelitian ini Big Data memiliki fungsi sebagai sebuah bagian untuk melakukan pemrosesan dalam aplikasi pemerintah dengan mengelola data yang sangat banyak yang dikarenakan jumlah masyarakat yang sangat banyak.
Transformasi Literasi Digital dalam Pendidikan Vokasi untuk Perlindungan Privasi dan Keamanan Siber di SMK Astrindo Kota Tegal, Jawa Tengah Gunawan, Gunawan; Ujianto, Nur Tulus; Andriani, Wresti; Firmansyah, Hasbi; Dari, Mayang Melan; Harefa, Reyvan Sinatria; Limaknun, Lulu
Jurnal Pengabdian Masyarakat Terapan Vol 2 No 2 (2025): JUPITER Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jupiter.2.2.69

Abstract

Perkembangan teknologi digital menuntut pelajar vokasi memiliki literasi digital yang memadai, terutama dalam perlindungan data pribadi dan keamanan siber. Kegiatan pengabdian ini dilaksanakan di SMK Astrindo Kota Tegal, Jawa Tengah, dengan tujuan meningkatkan pemahaman siswa terhadap literasi digital melalui pendekatan edukatif berbasis praktik. Metode yang digunakan adalah pre-experimental design model one group pretest-posttest terhadap 80 siswa kelas X, dengan instrumen kuesioner skala Likert lima poin yang telah divalidasi. Kegiatan mencakup pretest, penyuluhan interaktif, simulasi praktik, dan posttest. Hasil menunjukkan peningkatan rata-rata skor dari 58,2 menjadi 83,6, dengan uji paired sample t-test menunjukkan signifikansi < 0,05. Peserta menunjukkan perubahan perilaku, seperti peningkatan kesadaran privasi digital dan penggunaan sandi yang lebih aman. Partisipasi aktif selama simulasi dan refleksi menunjukkan keberhasilan metode edukatif yang diterapkan. Kegiatan ini memberikan dampak jangka pendek berupa peningkatan kognitif, serta mendorong pembentukan kebiasaan digital yang lebih aman di lingkungan sekolah vokasi.
Decision Support System to assess customer satisfaction using Analytical Hierarchy Process Andriani, Wresti; Gunawan, Gunawan; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 4 (2023): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i4.163

Abstract

Transportation is an important aspect of mobility or global movement and activities. As public transportation that can be accessed online by the public, Gojek and Grab types of transportation provide transportation services and are growing rapidly. At the time of Covid 19 around 2020, online transportation was very important and much sought after. More and more online transportation companies are appearing, especially in Tegal City, so that there are more service offerings that consumers can use. User or consumer satisfaction measurements were carried out using Fuzzy Logic Method Analytical Hierarchy Process (AHP) on 200 consumers who used Gojek or Grab or other online transportation for 3 to 4 months in 2022 in Tegal City. The results obtained by customers or consumers were satisfied with Gojek transportation at 45%, with male consumers at 67%, and Grab at 37%, with male consumers at 65%, followed by other online transportation (X and Y). These results can be used as an option for consumers who expect the best service.
Machine learning algorithm-based decision support system for prime bank stock trend prediction Gunawan, Gunawan; Budiono, Wahyu; Andriani, Wresti; Naja, Naella Nabila Putri Wahyuning
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.207

Abstract

In the complex landscape of financial markets, predicting bank stock trends is a critical aspect that supports more accurate investment decision-making. This study aims to develop and evaluate machine learning algorithms—Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN)—for predicting the trends of major bank stocks in Indonesia using the IDX-PEFINDO dataset from January 1, 2020, to December 31, 2023. The adopted methodology includes collecting historical data, initial processing, feature selection, and training and validating models using evaluation metrics such as Accuracy, Precision, Recall, F1-Score, MAE, and RMSE. Results indicate that although no single algorithm is dominant, SVM and ANN perform better within the given data context. This research underscores the importance of a tailored approach to maximize the potential of machine learning algorithms in stock prediction, providing new insights into developing decision support systems for bank stock investments. This study implies that it recommends the integration of broader economic indicators and the exploration of advanced machine-learning techniques to enhance stock prediction accuracy in the future.
Identification of vacant land in Tegal Regency using cnn algorithm based on goolge earth imagery Andriani, Wresti; Fatkhurrohman, Fatkhurrohman; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.243

Abstract

This research developed a Convolutional Neural Network (CNN) algorithm to identify vacant land in Tegal Regency using imagery from Google Earth. By utilizing labeled imagery datasets, CNN models are optimized to recognize texture characteristics, colors, and distribution patterns of vacant land. Preprocessing and image sharing techniques are applied to improve model quality. The results of this study offer a new methodology in visual data processing for accurate and efficient identification of vacant land, providing a solid basis for more sustainable and efficient land use policies. This research contributes significantly to the scientific literature and field practice, particularly in natural resource management and regional planning
Co-Authors Aang Alim Murtopo Aimar Akbar, Aminnur Al Fattah, Muhammad Raikhan Almujahidah, Lutfiatun Rahma Anandianskha, Sawaviyya Arianti, Tezya Sekar Arif, Zaenul Arrohman, Zidni Dlia Aslam, Muhammad Nur Bangkit Indarmawan Nugroho Budiono, Wahyu Dari, Mayang Melan Fatkhurrohman Fatkhurrohman, Fatkhurrohman Ferdyansyah, Muhammad Rio Firmansyah, Hasbi Gunawan Gunawan Gunawan Gunawan Gunawan, Gunawam Harefa, Reyvan Sinatria Hidayatullah, Fikri Zain Limaknun, Lulu Maryanto, Sesilia Putri Maulana, M Taufik Fajar Mustaqiim, Restu Abi Naja, Naella Nabila Putri Wahyuning Nasichatun, Nasichatun Putra, Alif Sya’Bani Ramadhani, Ivan Rizky Santoso, Nugroho Adh Sarif Surorejo Septian Ari Wibowo Setiawati, Windi Syefudin, Syefudin Ujianto, Nur Tulus W.N, Naella Nabila Putri Wahyuning Naja, Naella Nabila Putri Yan Kurniawan, Yan