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All Journal Scientific Journal of Informatics Jurnal Teknologi Informasi MURA Jurnal Teknologi INFOMATEK: Jurnal Informatika, Manajemen dan 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 Teknoif Teknik Informatika Institut Teknologi Padang Abdi Teknoyasa Jurnal Masyarakat Madani Indonesia Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia Vertex Jurnal Teknologi Informasi Mura ALMUISY: Journal of Al Muslim Information System Jurnal Pengabdian Masyarakat Terapan
Andriani, Wresti
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Aerospace Engineering Humanities 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 Environmental Science 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

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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
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
Application of deep neural network with stacked denoising autoencoder for ECG signal classification Gunawan, Gunawan; Aimar Akbar, Aminnur; Andriani, Wresti
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.v7i2.247

Abstract

Applying deep neural networks with stacked denoising autoencoders (SDAEs) for ECG signal classification presents a promising approach for improving the accuracy of arrhythmia diagnosis. This study aims to develop a robust model that enhances the classification of ECG signals by effectively denoising the input data and extracting rich feature representations. The research employs a method involving data preprocessing, feature extraction using SDAEs, and classification with a deep neural network (DNN) validated on the MIT-BIH Arrhythmia Database. The results demonstrate that the proposed model achieves an impressive accuracy of 98.91%, significantly outperforming traditional machine learning methods. The implications of this research are substantial, offering a reliable and automated tool for arrhythmia diagnosis that can be utilized in clinical settings to improve patient care. The study highlights the model's potential for real-time clinical application, although further validation on more extensive and diverse datasets is necessary to confirm its generalizability and robustness. This research contributes to the field by integrating advanced SDAEs with deep learning, paving the way for more accurate and efficient ECG signal classification systems
Penerapan Metode Dobel Exponential dan Smoothing Analytical Hierarchy Process untuk Prediksi Tingkat Kerawanan Tanah Longsor Di Kabupaten Brebes Putra, Alif Sya’Bani; Surorejo, Sarif; Andriani, Wresti; Gunawan, Gunawan
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i3.10505

Abstract

Pengembangan metode prediksi tingkat kerawanan tanah longsor di Kabupaten Brebes menggunakan kombinasi double exponential smoothing dan analytical hierarchy process (AHP). Tujuan penelitian ini adalah meningkatkan pemahaman dan prediksi terhadap fenomena tanah longsor dan memanfaatkan data historis dan analisis kriteria multi-faktor. Metodologi penelitian ini melibatkan analisis seri waktu menggunakan double exponential smoothing untuk memprediksi variabel-variabel penting seperti curah hujan, dan pergerakan tanah. Sementara AHP digunakan untuk menilai dan mengintegrasikan berbagai faktor risiko tanah longsor, termasuk kondisi geologi, kemiringan lereng, dan penggunaan lahan. Hasil penelitian ini adalah model yang diusulkan mampu memprediksi tingkat kerawanan tanah longsor dengan akurasi yang lebih tinggi dibandingkan metode yang ada. Penelitian ini memberikan kontribusi penting dalam upaya mitigasi bencana tanah longsor di Kabupaten Brebes, serta membuka peluang untuk aplikasi metode serupa di wilayah lain yang memiliki risiko tanah longsor.
ANALISIS PENERAPAN SMART LIVING DALAM PEMBANGUNAN SMART CITY DI KOTA TEGAL Arrohman, Zidni Dlia; Andriani, Wresti; Gunawan, Gunawan
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 2 (2023)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i2.1448

Abstract

Smart City is a city that can monitor and combine the situation of all infrastructure, both physical, social and business fields. The purpose of making a Smart City design is to make the city more efficient, prolonged, balanced and appropriate to live in. This design can also be applied to programming the rules of space, area or city, not only to the handling of cases in large cities. Each programming that uses the Smart City design intends to make the city sustainable. Regarding this is synergy with the programming of the landscape of prolonged natural tourism in Tegal City. The programming of the landscape to be raised is the realization of integrity and production power as well as the base of natural energy and multifunctional creation. To create programming purposes, Smart City designs that advance the use of IT can be applied to landscape programming zones and activities to be raised. IT systems used include intelligent inspection equipment installed in landscapes, features or equipment that can associate one network with another, computerization, social tools and GIS. The markers of success are measured by smart city design applications, including better management and organization, more advanced technology, the creation of good government, stakeholders understand and can use the technology applied, the economy increases, infrastructure development is better and the presence of areas is prolonged.
Co-Authors Aang Alim Murtopo Aimar Akbar, Aminnur Al Fattah, Muhammad Raikhan Anandianskha, Sawaviyya Arif, Zaenul Arrohman, Zidni Dlia Aslam, Muhammad Nur Bangkit Indarmawan Nugroho Budiono, Wahyu Dari, Mayang Melan Fatkhurrohman Fatkhurrohman, Fatkhurrohman Firmansyah, Hasbi Gunawan Gunawan Gunawan Gunawan Gunawan, Gunawam Harefa, Reyvan Sinatria Hidayatullah, Fikri Zain Jannah, Reva Khulatul 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 Rosyid , Mohamad Raychan Abdul Santoso, Nugroho Adh Sarif Surorejo Septian Ari Wibowo Sya'bani , Mohammad Syefudin, Syefudin Ujianto, Nur Tulus W.N, Naella Nabila Putri Wahyuning Naja, Naella Nabila Putri Yan Kurniawan, Yan