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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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Evaluasi Model Deep Learning pada Pola Dataset Biomedis Gunawan, Gunawan; Wibowo, Septian Ari; Andriani, Wresti
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i2.738

Abstract

This study aims to evaluate the effectiveness and efficiency of various deep learning models in recognizing patterns within diverse biomedical datasets. The methods involved the collection of biomedical data from various public and synthetic sources, including chest radiographs, MRI, CT scans, as well as electrocardiogram (ECG) and electromyography (EMG) signals. The data underwent preprocessing steps such as normalization, noise removal, and data augmentation to improve quality and variability. The deep learning models evaluated included Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which were trained to identify patterns within the data. The performance evaluation was conducted using metrics like accuracy, sensitivity, specificity, and AUC to ensure the models' generalization capabilities on test datasets. The results revealed that CNNs excelled in medical image analysis, particularly in terms of accuracy and interpretability, while RNNs were more effective in handling sequential data such as medical signals. The primary conclusion of this study is that the selection of deep learning models should be tailored to the type of data and specific application requirements, emphasizing the importance of improving model interpretability and generalization for broader applications in clinical settings.
Impact of Palestine-Israel conflict on multinational stock prices use neural network and support vector machine comparison Andriani, Wresti; Gunawan, Gunawan; Wahyuning Naja, Naella Nabila Putri
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5196

Abstract

One form of prolonged geopolitical event is the conflict between Palestine and Israel, which has complex historical, political, and religious roots in the Middle East. This research aims to determine whether this conflict influences the share prices of the companies Unilever, McDonald's, and KFC. These three large companies are known as allies of one of the disputing countries. The method used by the Neural Network is compared with Support Vector Machine to find the best accuracy using RMSE and MAE. The greater the error value, the more affected the company is by this geopolitical factor. As a result, the accuracy of the SVM method is better than NN; the company most affected is KFC, with the RMSE value of 0.111, MAE of 0.020, followed by Unilever with RMSE 0.034, MAE 0.025 then McDonald's with RMSE 0.026 and MAE 0.116, is expected to help investors choose to invest in the company McDonald’s then Unilever.
Application of machine learning for short-term climate prediction in Indonesia Gunawan, Gunawan; Andriani, Wresti; Aimar Akbar, Aminnur
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5215

Abstract

This study explores the Application of Machine Learning for Short-Term Climate Prediction in Indonesia, focusing on enhancing forecast accuracy through advanced computational models. The primary objective was to develop and validate Random Forest and Support Vector Machine (SVM) models to predict short-term climate conditions accurately across ten major Indonesian cities. Employing a quantitative approach, the study utilized experimental design, rigorous data analysis, and model validation using historical weather data from April 2024 provided by the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The results indicate that both Random Forest and SVM significantly outperform traditional climate prediction models, with Random Forest achieving an average accuracy of 87.5% and SVM 85.2%. These findings underscore the potential of machine learning to revolutionize short-term climate predictions in regions with complex meteorological dynamics like Indonesia, offering substantial implications for disaster preparedness, agricultural planning, and urban management. Future research can expand upon these models by incorporating real-time data and exploring deep learning techniques to enhance predictive reliability further
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.
Analisis Perbandingan Model Jaringan Saraf Tiruan dan Support Vector Machine dalam Memprediksi Indeks Harga Saham Gabungan Gunawan, Gunawan; Andriani, Wresti; Wibowo, Septian Ari
Pena: Jurnal Ilmu Pengetahuan dan Teknologi Vol. 38 No. 2 (2024): PENA SEPTEMBER 2024
Publisher : LPPM Universitas Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31941/jurnalpena.v38i2.4942

Abstract

The Jakarta Composite Index (IHSG) is a key indicator that reflects the performance of the stock market in Indonesia. It is often used by investors, analysts, and decision-makers to assess economic conditions and make investment decisions. However, the fluctuating and dynamic nature of the stock market makes predicting the IHSG a significant challenge. This study compares the effectiveness of Neural Network (NN) and Support Vector Machine (SVM) with optimization methods such as Particle Swarm Optimization (PSO) and Evolutionary Algorithm (EVO) in predicting stock prices. The results show that the combination of SVM with EVO provides the best prediction accuracy with the lowest error values (RMSE: 0.07, MAE: 0.09, MSE: 0.004). In contrast, NN with PSO and EVO showed higher prediction errors, indicating lower accuracy levels. These findings highlight the potential of optimization methods in enhancing the performance of stock prediction models, with SVM+EVO being the most effective combination.
Application of machine learning for election data classification in Tegal city based on political party support Andriani, Wresti; Gunawan, Gunawan; Naja, Naella Nabila Putri Wahyuning; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

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

Abstract

Elections are a critical aspect of democracy, where voter sentiment and political party support significantly influence outcomes. This study aims to predict election results in Tegal City using machine learning models, specifically Neural Networks, Random Forest, and Naive Bayes. Each algorithm was applied to a dataset containing demographic, polling, and Sentiment data to analyze political party support. The research revealed that Neural Networks outperformed other models in terms of accuracy (92%) and F1 scores for both positive (91%) and negative sentiments (92%). Random Forest and Naive Bayes, while effective, displayed lower overall performance. The findings highlight the value of utilizing advanced algorithms for local election sentiment analysis to help candidates adjust campaign strategies. This approach enhances understanding of voter behavior and supports more informed decision-making processes for the public and policymakers
Review Penerapan Smart City dalam Sistem Informasi Desa Gunawan, Gunawan; Kurniawan, Yan; Andriani, Wresti
Jurnal Teknik Indonesia Vol. 1 No. 1 (2022): Jurnal Teknik Indonesia
Publisher : Publica Scientific Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (514.283 KB) | DOI: 10.58860/jti.v1i1.5

Abstract

Introduction: The industrial era 4.0 and civil society 5.0 have changed the paradigm of culture, especially in rural communities thatnot only want innovation in sustainable rural development but in the form of administrative and non-administrative services carried outby the Village Government wanting excellent service. The community paradigm that wants everything to be on time and to fulfill the need for good service is the desire and hope of the community. So that a solution is needed from the Village Government, oneof which is the application of the intelligent village concept,which is based on Total Quality Service (TQS) and remains focused on service satisfaction for customers (village communities). Purpose: to analyze the review of the implementation of smart cities in village information systems. Methods: The method used is a qualitative research method with the research locus in several villages in the Pemalang Regency area. Results: The intelligent village concept has been appliedin several towns in the Pemalang Regency area,which is oriented to deliveringvillage information and assisting in providing services to the community. The results achieved are prettygood but require improvements in the applied management information system. Conclusion: The PemalangRegency Government utilizes rural sites/websites facilitated by PUSPINDES to develop the Pemalang area. Conduct village website management training. It usesICT for public information disclosure. Utilize ICT as a forum for village information and village promotion.
ANALISIS SENTIMEN PERAN PEREMPUAN DALAM PEMBANGUNAN KOTA TEGAL MELALUI TWITTER Andriani, Wresti; Gunawan, Gunawam; W.N, Naella Nabila Putri
Jurnal Teknologi Informasi Mura Vol 17 No 1 (2025): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i1.2612

Abstract

Penelitian ini bertujuan mengevaluasi persepsi publik terhadap peran perempuan dalam pembangunan di Kota Tegal melalui analisis data Twitter. Sebanyak 500 tweet dari tahun 2020 hingga 2025 digunakan sebagai data utama. Klasifikasi sentimen dilakukan menggunakan algoritma Naïve Bayes dan metode pembobotan TF-IDF. Data dianalisis setelah melalui tahap preprocessing seperti normalisasi, pembersihan simbol, stopword removal, dan stemming. Hasil analisis menunjukkan 60% opini positif, 25% negatif, dan 15% netral. Kata-kata seperti “UMKM”, “Aktif”, dan “Perempuan Tegal” mendominasi sentimen positif, sementara sentimen negatif mencakup frasa “Kurang Dilibatkan” dan “Minim Dukungan”. Model Naïve Bayes menunjukkan performa baik dengan akurasi 87%, presisi 90%, recall 78%, dan F1-score 84%. Temuan ini menyarankan perlunya peningkatan peran perempuan melalui dukungan UMKM dan pelibatan dalam kebijakan pembangunan
ANALISIS SENTIMEN PERAN PEREMPUAN DALAM PEMBANGUNAN KOTA TEGAL MELALUI TWITTER Andriani, Wresti; Gunawan, Gunawam; W.N, Naella Nabila Putri
Jurnal Teknologi Informasi Mura Vol 17 No 1 (2025): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i1.2612

Abstract

Penelitian ini bertujuan mengevaluasi persepsi publik terhadap peran perempuan dalam pembangunan di Kota Tegal melalui analisis data Twitter. Sebanyak 500 tweet dari tahun 2020 hingga 2025 digunakan sebagai data utama. Klasifikasi sentimen dilakukan menggunakan algoritma Naïve Bayes dan metode pembobotan TF-IDF. Data dianalisis setelah melalui tahap preprocessing seperti normalisasi, pembersihan simbol, stopword removal, dan stemming. Hasil analisis menunjukkan 60% opini positif, 25% negatif, dan 15% netral. Kata-kata seperti “UMKM”, “Aktif”, dan “Perempuan Tegal” mendominasi sentimen positif, sementara sentimen negatif mencakup frasa “Kurang Dilibatkan” dan “Minim Dukungan”. Model Naïve Bayes menunjukkan performa baik dengan akurasi 87%, presisi 90%, recall 78%, dan F1-score 84%. Temuan ini menyarankan perlunya peningkatan peran perempuan melalui dukungan UMKM dan pelibatan dalam kebijakan pembangunan
Penerapan Metode Self Organizing Map dan Simple Additive Weighting untuk memilih Tempat Wisata di Tegal Setiawati, Windi; Surorejo, Sarif; Andriani, Wresti; Gunawan, Gunawan
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13667

Abstract

Penelitian ini mengusulkan penggunaan kombinasi metode Self Organizing Map (SOM) dan Simple Additive Weighting (SAW) untuk optimasi pemilihan dan pengembangan tempat wisata di Tegal. Metode SOM, sebagai alat pembelajaran mesin, digunakan untuk menganalisis pola historis kunjungan dan mengidentifikasi preferensi pengunjung, sementara metode SAW digunakan untuk menilai dan memberikan peringkat pada kriteria yang dianggap penting oleh pengelola pariwisata. Integrasi kedua metode ini bertujuan untuk meningkatkan keakuratan dalam pemilihan dan pengelompokan atraksi wisata berdasarkan variabel yang relevan, serta membantu dalam alokasi sumber daya yang lebih efisien. Hasil dari penelitian ini menunjukkan bahwa penggunaan kombinasi SOM dan SAW secara efektif mendukung pengambilan keputusan dalam pengembangan pariwisata yang berkelanjutan di Tegal, dengan memberikan rekomendasi yang berbasis pada analisis data canggih. Implementasi metode ini memberikan wawasan baru dalam pengelolaan pariwisata dan dapat diadopsi oleh daerah lain dengan kondisi serupa untuk meningkatkan pengelolaan destinasi wisata mereka. Penelitian ini berkontribusi pada literatur dengan menunjukkan bagaimana teknologi analisis data dan kecerdasan buatan dapat diintegrasikan dalam pengelolaan pariwisata untuk meningkatkan pengalaman pengunjung dan pertumbuhan ekonomi lokal.
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