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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JMM (Jurnal Masyarakat Mandiri) Jurnal Pendidikan Matematika (Kudus) Madani : Indonesian Journal of Civil Society International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Sri Hidayati
Department Of Information System, School Of Industrial Engineering, Telkom University Surabaya, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Classification of Drought Impact by Drought Vulnerability Indicators in Probolinggo Regrency Using Naive Bayes Sri Hidayati
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 1 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i1.31

Abstract

Drought in Probolinggo is a big problem because most of the people in this work as farmers. Drought is a natural phenomenon, difficult to define due to differences in hydrometeorological variables and socio economic factors along with the stochastic nature of water demand in various regions. Resident vulnerability to drought hazard is varie. Vulnerability can be measured using vulnerability indicators such as economic factors, social factors, and ecological factors. This research used several vulnerability indicators to classified the impact of drought in three villages in Probolinggo Regency (Sumberkare, Tandonsentul, and Tegalsono). The classification method used in this research is Naïve Bayes. The 10-fold cross validation method was used to train the developed predictive model and the performance of the models evaluated. The accuracy of drought impact by the naive bayes is 85,90 %. Naïve Bayes classifier classify indicators of the impact of drought accurately.
Comparison of K-Means, Fuzzy C-Means, Fuzzy Gustafson Kessel, and DBSCAN for Village Grouping in Surabaya Based on Poverty Indicators Sri Hidayati; Aviolla Terza Darmaliana; Raulia Riski
Jurnal Pendidikan Matematika (Kudus) Vol 5, No 2 (2022): Jurnal Pendidikan Matematika (Kudus)
Publisher : Institut Agama Islam Negeri (IAIN) Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jpmk.v5i2.16552

Abstract

The population growth rate in various countries in the world is increasing, including Indonesia. The population explosion as a result of rapid population growth has a negative impact on the socio-economic life of the community, such as increasing unemployment rates, food shortages, and high poverty rates. Therefore, local governments in each country try to overcome the poverty problem using various policies, including in Surabaya, East Java, Indonesia. This study aims to classify villages in Surabaya using non-hierarchical clustering, such as K-Means, Fuzzy C-Means, Fuzzy Gustafson Kessel, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise), based on poverty indicators. Before analysis, the villages in Surabaya, East Java, Indonesia were classified using non-hierarchical clustering, and the results of cluster analysis were compared from various methods using the value of within clusters sum of squares and average silhouette width. Comparison between village grouping methods results in K-Means being the best method for village grouping in Surabaya, East Java, Indonesia based on the values of the within clusters sum of squares. While based on the average silhouette width value, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method is found to be the best method because its value was close to 1 compared to the other methods. Thus, it can be concluded that K-Means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is the best method for village grouping in Surabaya, East Java, Indonesia in relation to poverty problems. Laju pertumbuhan penduduk di berbagai negara di dunia semakin meningkat, termasuk Indonesia. Ledakan penduduk akibat pertumbuhan penduduk yang pesat berdampak negatif terhadap kehidupan sosial ekonomi masyarakat, seperti meningkatnya angka pengangguran, kekurangan pangan, dan tingginya angka kemiskinan. Oleh karena itu, pemerintah daerah di setiap negara berusaha mengatasi masalah kemiskinan dengan berbagai kebijakan, termasuk di Surabaya, Jawa Timur, Indonesia. Penelitian ini bertujuan untuk mengklasifikasikan desa-desa di Surabaya, Jawa Timur, Indonesia menggunakan non-hierarchial clusterings, seperti K-Means, Fuzzy C-Means, Fuzzy Gustafson Kessel, dan DBSCAN (Density-Based Spatial Clustering of Applications with Noise), berdasarkan indikator kemiskinan. Sebelum dilakukan analisis, desa-desa di Surabaya, Jawa Timur, Indonesia diklasifikasikan menggunakan non-hierarchical clustering, dan hasil analisis cluster dibandingkan dari berbagai metode dengan menggunakan nilai cluster sum of squares dan rata-rata lebar siluet. Perbandingan antar metode pengelompokan desa menghasilkan K-Means menjadi metode terbaik untuk pengelompokan desa di Surabaya berdasarkan nilai cluster sum of squares. Sedangkan berdasarkan nilai rata-rata lebar siluet, metode DBSCAN (Density-Based Spatial Clustering of Applications with Noise) merupakan metode yang paling baik karena nilainya mendekati 1 dibandingkan dengan metode lainnya. Dengan demikian, dapat disimpulkan bahwa K-Means dan DBSCAN (Density-Based Spatial Clustering of Applications with Noise) merupakan metode terbaik untuk pengelompokan desa di Surabaya, Jawa Timur, Indonesia dalam kaitannya dengan masalah kemiskinan.
PENINGKATAN KEMAMPUAN DESAIN MEDIA PEMBELAJARAN BAGI GURU DI SEKOLAH MENENGAH ATAS Aris Kusumawati; Raulia Riski; Sri Hidayati; Farid Duta Hadyanto; Anandito Satria Pradana; Fitri Rayani Siahaan
JMM (Jurnal Masyarakat Mandiri) Vol 7, No 2 (2023): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v7i2.13383

Abstract

Abstrak: Pembelajaran daring mendorong kebutuhan guru akan media pembelajaran yang menarik untuk memaksimalkan efektivitas pembelajaran dan menghadirkan minat belajar siswa. Namun, kebanyakan guru belum memiliki kemampuan desain media pembelajaran yang memadai sesuai dengan perkembangan teknologi. Pengabdian masyarakat ini bertujuan untuk meningkatkan kemampuan desain media pembelajaran guru-guru di SMA Negeri 1 Surabaya melalui pelatihan desain menggunakan platform desain Canva. Sebanyak 25 guru dengan latar belakang pengajaran yang berbeda-beda menerima materi cara mendesain media pembelajaran menggunakan Canva serta melakukan praktik mandiri didampingi tim pengabdi. Evaluasi dilakukan dengan cara obeservasi, wawancara, dan pemberian kuesioner. Hasil evaluasi menunjukkan bahwa pelatihan ini berhasil meningkatkan kemampuan desain peserta dengan persentase kepuasan sebesar 88%, serta potensi keberlanjutan pemanfaatan Canva untuk mendesain media pembelajaran sebesar 90%. Abstract: Online learning encourages teachers' need for interesting learning media to maximize learning effectiveness and generate student learning interest. However, most teachers do not have adequate learning media design skills in accordance with technological developments. This community service aims to improve the learning media design skills of teachers at SMA Negeri 1 Surabaya through design training using the Canva design platform. As many as 25 teachers with different teaching backgrounds received material on how to design learning media using Canva and carried out independent practice accompanied by the service team. Evaluation was carried out by observing, interviewing, and giving questionnaires. The evaluation results show that this training succeeded in increasing the participants' design skills with a satisfaction percentage of 88%, as well as the potential for sustainable use of Canva to design learning media by 90%.  
Pelatihan Desain Poster Menggunakan Canva Bagi Siswa SMA Negeri 1 Surabaya Aris Kusumawati; Sri Hidayati; Raulia Riski; Muhammad Zhahnur Arif; Ridho Rahmatullah; Annisa Nur Salsabilla
Madani : Indonesian Journal of Civil Society Vol. 5 No. 2 (2023): Madani, Agustus 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v5i2.1708

Abstract

The rapid development of science and technology has resulted in various applications that can help various human needs, including in terms of design. Currently, there are various online-based design applications that provide a variety of attractive designs that can be accessed for free or for a fee, including Canva. This community service aims to increase the design creativity of SMAN 1 Surabaya students, especially in making poster designs with the Canva application. The results of the training showed that SMA Negeri 1 Surabaya students agreed that the training could increase students' knowledge and skills in making poster designs using Canva, as seen from the results of the questionnaire where 81.33% of students gave very good predicates to the training they had attended. The knowledge gained during the training is expected to be useful for students in the short and long term, namely in the wider scope of student life in the future.
RAINFALL FORECASTING WITH AN INTERMITTENT APPROACH USING HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK Regita Putri Permata; Amri Muhaimin; Sri Hidayati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0457-0466

Abstract

Rainfall forecasting is crucial in agriculture, water resource management, urban planning, and disaster preparation. Traditional approaches fail to capture complicated and intermittent rainfall patterns. The “Hybrid Exponential Smoothing Neural Network” is introduced in this study to handle intermittent rainfall forecasting issues. Exponential Smoothing, an established approach for discovering underlying patterns and seasonal fluctuations in time series data, is combined with Neural Networks, which are good at capturing complex linkages and nonlinearities. Using these two methods, this model hopes to deliver a complete rainfall forecasting solution that accounts for short-term changes and long-term patterns. This research uses residuals from the exponential smoothing model and is modeled using a Neural Network. The residual input is transformed using rolling mean. The results show that the hybrid model is able to capture patterns well, but there are still patterns that experience time lag. Experimental results obtained reveal that the hybrid methodology performs better than the model exponential smoothing, implying that the proposed model hybrid synergy approach can be used as an alternative solution to the rainfall time series forecasting. The results show that the Hybrid method can form patterns better than individual exponential smoothing models or neural networks. The RMSSE values for all areas are 1.0185, 1.55092, 1.0872.
Co-Authors Amri Muhaimin Anandito Satria Pradana Annisa Nur Salsabilla Aviolla Terza Darmaliana Farid Duta Hadyanto Fitri Rayani Siahaan Kusumawati, Aris Muhammad Zhahnur Arif Regita Putri Permata Ridho Rahmatullah Riski, Raulia