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Application of sma method and ahp to predict the level of tidal flood vulnerability in Tegal City Nugroho, Bangkit Indarmawan; Farkhan, Muhammad; Anandianskha, Sawaviyya; 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.v7i2.235

Abstract

This study examines the application of the Simple Moving Average (SMA) and Analytic Hierarchy Process (AHP) methods to predict tidal flood vulnerability in Tegal City. The objective is to develop a more accurate prediction method for tidal flood vulnerability. The methods used are a combination of SMA and AHP. The results indicate that this combination is effective in producing more accurate predictions compared to conventional methods. Villages such as Muarareja, Tegalsari, Mintaragen, and Panggung have been identified as highly vulnerable and require more intensive mitigation. The implications highlight the importance of a multi-method approach to understanding complex phenomena like flood vulnerability. For future research, it is recommended to integrate real-time weather data and consider socio-economic factors to enhance accuracy and relevance in disaster mitigation. The findings are expected to assist in better urban planning and resource allocation, as well as improve community resilience against tidal flood disasters.
Customer segmentation in sales transaction data using k-means clustering algorithm Nugroho, Bangkit Indarmawan; Rafhina, Ana; Ananda, Pingky Septiana; 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.v7i2.236

Abstract

Customer segmentation against sales transaction data using K-Means clustering algorithm. The purpose of this research is to develop and validate a customer segmentation model using an optimized K-Means clustering algorithm to enable more accurate customer grouping based on sales transaction data. The methodology used includes quantitative design combined with experimental techniques, quantitative data analysis, and model validation, where rice sales transaction data from Tegal city traditional market is processed to identify customer segments. The results showed the effectiveness of the optimized K-Means algorithm in grouping customers into three clusters based on purchase characteristics, and C4-SUPER rice proved to be the best-selling among consumers. These insights enable the development of more targeted and personalized marketing strategies, enrich the academic literature on customer data analysis, and move towards the practical application of more effective customer segmentation through the use of advanced analytical technologies
Anomaly detection in network security systems using machine learning Santoso, Nughroho Adhi; Lutfayza, Rezi; Nughroho, Bangkit Indarmawan; 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.v7i2.238

Abstract

Anomaly Detection in Network Security Systems Using Machine Learning highlights the importance of developing effective models for data security. This research aims to develop an adaptive and automated anomaly detection model using the Naive Bayes algorithm and cross-validation. The methodology applied includes security log data collection, data preprocessing, implementation of Naive Bayes algorithms, and model evaluation using metrics such as accuracy, precision, recall, and F1-score. The results showed that the developed model was able to achieve high accuracy in detecting anomalies, with significant performance in identifying real threats without negative errors. The implication of this research is the improvement of network security through the application of machine learning, providing practical solutions for practitioners to deal with increasingly complex cybersecurity challenges
Application of artificial neural network method for early detection of dengue fever Surorejo, Sarif; Ningrum, Isna Lidia; Ananda, Pingky Septiana; 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.v7i2.240

Abstract

Dengue fever is a tropical disease whose diagnosis is often delayed due to limitations of conventional diagnostic methodologies, which have an impact on the effectiveness of medical interventions. This research is designed to develop an Artificial Neural Network (ANN) model aimed at improving accuracy and speed in dengue diagnosis. Through quantitative methods, clinical data from 50 patients during the period 2020-2021 were analyzed using machine learning techniques to train the ANN model, including the process of data normalization and selection of relevant features. The test results of the model showed excellent diagnostic performance with accuracy reaching 87%, precision 92%, and F1-Score 92%, indicating its effective ability to identify positive and negative cases. The conclusion of this study is that the developed ANN model is able to overcome the limitations of conventional diagnostics and shows significant potential in improving medical responses to dengue outbreaks. Further research is recommended to expand the datasets used in order to improve the validation and generalization of the model in the context of broader clinical applications
Application of the nearest neigbour interpolation method and naives bayes classifier for the identification of bespectacled faces Murtopo, Aang Alim; Januarto, Sigit; Anandianskha, Sawaviyya; 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.v7i2.242

Abstract

Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology
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
Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction Surorejo, Sarif; Ubaidillah, Muhamad Rizal; Syefudin, Syefudin; 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.v7i2.245

Abstract

The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence
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.
Perbandingan Metode Fuzzy Mamdani dan Fuzzy Tsukamoto untuk Identifikasi Tingkat Serangan Penyakit pada Tanaman Bawang Merah Hidayatullah, Bryan Adam; Nugroho, Bangkit Indarmawan; Santoso, Nugroho Adhi; 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.v4i3.10506

Abstract

Penelitian ini membandingkan metode fuzzy Mamdani dan fuzzy Tsukamoto dalam mengidentifikasi tingkat serangan penyakit pada tanaman bawang merah untuk meningkatkan deteksi dini penyakit dan produktivitas pertanian. Menggunakan dataset parameter kesehatan tanaman, termasuk gejala penyakit dan kondisi lingkungan, penelitian mengaplikasikan kedua metode fuzzy tersebut untuk memperkirakan kerentanan tanaman terhadap penyakit. Hasil menunjukkan bahwa fuzzy Tsukamoto lebih akurat dan efisien, terutama dalam data kompleks. Penelitian ini memberikan pemahaman baru dalam aplikasi fuzzy logic pada penyakit tanaman bawang merah dan pengembangan model serupa di pertanian. Temuan ini penting untuk pengembangan sistem pendukung keputusan yang lebih efisien dalam pertanian, mengintegrasikan teknologi informasi dalam manajemen kesehatan tanaman.
Co-Authors Aang Alim Murtopo Aditdya, Maulana Ahmad Zulfikri Aimar Akbar, Aminnur Aisyach Aminarti Santoso Al Fattah, Muhammad Raikhan Alan Eka Prayoga Albana, Muhammad Syifa Ali Murtopo, Aang Amalani, Mukhamad Zulfa Bakhtiar Ananda, Pingky Septiana Anandaianskha, Sawaviyya Anandianshka, Sawaviyya Anandianska, Sawaviyya Anandianskha, Sawaviyya Andriani, Wresti Andriani, Wresty Anshori, Abu Hasan Al Arianti, Tezya Sekar Arif, Zaenul Arifiyah, Nur Latifatul Arrohman, Zidni Dlia Aslam, Muhammad Nur Aziz, Taufiq Azmi, Isni Azmi, Muchamad Nauval Bangkit Indarmawan Nugroho Budiono, Wahyu Cahyo, Septian Dwi Catur Supriyanto Dari, Mayang Melan Dewi, Errika Mutiara Didiek Trisatya Dodi Setiawan Dodi Setiawan Dwi Fina Fahirah Dwi Kurniawan, Rifki Fadila, Nurul Fahirah, Dwi Fina Fanti, Azizah Permata Farkhan, Muhammad Fatkhurrohman Fatkhurrohman, Fatkhurrohman Firmansyah, Akhmad Lutfi Firmansyah, Hasbi Firmansyah, Muchamad Aries Gunawan Gunawan Hafid Subechi, Fadlan Handayani, Sri Harefa, Reyvan Sinatria Haris Fadillah Hassan, Muhamad Nur Hidayatullah, Bryan Adam Intan Mayla Faiza Intan Mayla Faiza Januarto, Sigit Khadziqul Humam Munfi Khasanah, Apriliani Maulidya Khusni, Muhammad Wazid Kurniawan, Rifki Dwi Lestari, Nindy Putri Limaknun, Lulu Lutfayza, Rezi Marzuqi, Maezun Nafis Maulana, M Taufik Fajar Miftakhuddin, Ahmad Miftakhudin, Muhammad Milkhatunisya, Milkhatunisya Moonap, Dinar Auranisa Muchamad Nauval Azmi Muh Ridwan Muhammad Sulthon Mutaqin, Ahadan Fauzan Muttaqin, Anik Naja, Naella Nabila Putri Wahyuning Ningrum, Isna Lidia Nughroho, Bangkit Indarmawan Nugroho Adhi Santoso Nugroho, Bangkit Indramawan Nur Aisyah Nur Tulus Ujianto Nurokhman, Akhmad Nursahid, Wahyu Nursidik, Maulia Nurul Fadhilah Nurul Fadilah, Nurul Prayoga, Alan Eka Priyo Haryoko Purwanto Purwanto Putra, Alif Sya’Bani Qurrotu Aini, Atikah Rafhina, Ana Ramadhan, Ilham Gema Rifki Dwi Kurniawan Rivaldiansyah, Rafik Riyadi, Fajar Sugeng Santoso, Aisyach Aminarti Santoso, Bayu Aji Santoso, Nughroho Adhi Santoso, Nugroho Adh Santoso, Nugroho Adhi Santoso, Nugroho Adi Saputra, Aryan Dandi Sarif Surorejo Sawaviyya Anandianskha Sawaviyya Anandianskha Sawaviyya Anandianskha Sawavyya Anandianskha Septian Ari Wibowo Septiana Ananda, Pingky Septiana, Pingky Setiawati, Windi Surur, Misbahu Sya’bani, Adhita Zulfa Syefudin, Syefudin Triwinanto, Mohammad Amin Triwinanto Ubaidillah, Muhamad Rizal Ujianto, Nur Tulus W.N, Naella Nabila Putri Wahyu Pratama, Raka Wahyuning Naja, Naella Nabila Putri Wilda Shabrina Wresti Andriani Wresti Andriani Wresti Andriani Yan Kurniawan Yan Kurniawan, Yan Yulison Herry Chrisnanto Zaenul Arif Zain Hidayatullah, Fikri Zain, Ahmad Muzakky