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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
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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

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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.
Comparison of dijkstra and genetic algorithms for shortest path guci Surorejo, Sarif; Al Fattah, Muhammad Raikhan; Andriani, Wresti; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.298

Abstract

This study aims to compare the performance of the Dijkstra algorithm and the Genetics algorithm in determining the shortest path to the Guci tourist destination. The research design combines experimental methods, quantitative analysis, and model validation. The data used is the distance between points on two alternative routes to Guci. Data pre-processing is done to ensure quality and consistency. The relevant variables are selected, and model optimization is performed to obtain the best parameter configuration for both algorithms. Dijkstra and Genetics algorithms are implemented using Python, taking into account computational efficiency and ease of integration. Model evaluation is done through a series of tests with time execution and convergence metrics. The results showed that Dijkstra's algorithm was superior in finding the shortest path with a distance of 43.0 km and an execution time of 0.0017 seconds, compared to the Genetics algorithm which found a path with a distance of 44.7 km and an execution time of 0.0048 seconds. It can be concluded that Dijkstra's algorithm is more effective and efficient in this case, but Genetics algorithms have the potential for more complex optimization problems.
Application of fuzzy tsukamoto method in forecasting weather Murtopo, Aang Alim; Aslam, Muhammad Nur; Andriani, Wresti; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.305

Abstract

In today's information age, accurate weather prediction is essential given its far-reaching impact on various aspects of life and economic activity. This study aimed to test the effectiveness of Fuzzy Tsukamoto's method in predicting important weather variables such as temperature, humidity, and precipitation. This research method uses a combination design that includes experimental methods for model development, quantitative analysis of historical weather data, and model validation using separate data. The results showed that the Fuzzy Tsukamoto method was able to increase the accuracy of weather predictions compared to conventional methods, with a significant decrease in the value of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In conclusion, this study successfully demonstrates that Fuzzy Tsukamoto's method can be a more accurate alternative in weather prediction, making a significant contribution to the field of meteorology and its practical application in decision-making in various sectors that depend on weather prediction.
Application of the latent dirichlet allocation method to determine news text topics Surorejo, Sarif; Maulana, M Taufik Fajar; Andriani, Wresti; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.306

Abstract

This research discusses the application of the Latent Dirichlet Allocation (LDA) method to determine news text topics, providing new insights into media content analysis. This research aims to develop a model that can increase the accuracy and efficiency of topic identification in Indonesian news texts. The research uses a quantitative approach with experimental methods, quantitative analysis, and model validation, where news text data is processed and analyzed using LDA. The results show that the developed model can accurately identify news topics, showing significant improvements compared to existing methods. The implications are substantial for practitioners and researchers in journalism and media analysis, offering more efficient and effective strategies for managing and understanding large flows of information and opening new directions for advanced research in news text analysis.
Penerapan Metode Association Rule Dan Algoritma Apriori Untuk Analisis Pola Frekuensi Tinggi Prediksi Curah Hujan Di Kota Tegal Gunawan; Andriani, Wresti; Hidayatullah, Fikri Zain
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 11 No 2 (2023): TEKNOIF OKTOBER 2023
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2023.V11.2.45-53

Abstract

Rainfall is a very important factor in daily life, especially in agriculture and water resources management. Accurate rainfall forecasts are essential to mitigate the impact of floods, droughts, and water shortages. This study aimed to predict rainfall in Tegal City using data on rainfall, temperature, humidity, and barometric pressure. Explore association rules to define relationships between elements to predict weather. Then, the data is processed using a priori algorithms to find patterns of relationships between variables in the data. The results showed that a priori algorithms can be used to find ways of association that can be used to predict rainfall in Tegal City. Based on the research results and discussions that have been carried out, it can be concluded that the Association Rule method using a priori algorithm can be applied quite well in rainfall forecasting simulations in Tegal City. Based on the analysis, it was found that some association rules have a lift ratio value greater than 1, thus indicating that these rules have a significant level of strength and can be relied upon as a guideline in forecasting rainfall in Tegal City. This method can help predict weather conditions and provide useful information for the public and authorities to decide on outdoor activities.
Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies Gunawan, Gunawan; Andriani, Wresti; Anandianskha, Sawaviyya; Murtopo, Aang Alim; Nugroho, Bangkit Indarmawan; Naja, Naella Nabila Putri Wahyuning
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47935

Abstract

Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.
Implementasi Algoritma Greedy untuk Optimasi Rute Layanan Logistik UMKM di Kota Tegal Andriani, Wresti; Gunawan, Gunawan; W.N, Naella Nabila Putri
Jurnal Teknologi Vol. 13 No. 1 (2025): Jurnal Teknologi
Publisher : Universitas Jayabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31479/jtek.v13i1.415

Abstract

Permasalahan efisiensi rute pengiriman masih menjadi hambatan utama layanan logistik UMKM di Kota Tegal. Penelitian ini bertujuan mengimplementasikan algoritma Greedy nearest neighbor untuk menyusun rute pengiriman yang lebih efisien dibandingkan rute manual kurir. Metode yang digunakan adalah eksperimen kuantitatif berbasis distance matrix dari Google Distance Matrix API pada studi kasus 10 titik dan skenario perluasan hingga 30–50 titik. Algoritma diimplementasikan dengan Python dan dievaluasi menggunakan metrik jarak tempuh, waktu tempuh, persentase penghematan, serta simpangan baku dan interval kepercayaan 95%. Hasil pengujian menunjukkan bahwa pada 10 titik, rute manual menempuh sekitar 46,05 km (±92,10 menit), sedangkan rute Greedy hanya 25,91 km (±51,82 menit) dengan penghematan jarak dan waktu sekitar 43,74%. Pada skenario 30 dan 50 titik, jarak berkurang sekitar 35–36% dengan waktu komputasi di bawah 1 detik. Temuan ini mengindikasikan algoritma Greedy nearest neighbor layak dijadikan fondasi sistem optimasi rute logistik UMKM berbasis data. Keywords: Delivery route planning, Google Distance Matrix API, Greedy nearest neighbor, MSME logistics, Route optimization.
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