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The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang Santika, Gayatri Dwi; mahmudy, wayan f
MATICS Vol 9, No 1 (2017): MATICS
Publisher : Department of Informatics Engineering

This study applied Fuzzy Inference System Sugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference System Sugeno using historical data with Fuzzy Inference System Sugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE) of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy Inference System Sugeno using only historical data.

Optimization Improved K-Means on Centroid Initialization process using Particle Swarm Optimization for Tsunami Prone Area Groupings Santika, Gayatri Dwi; Sari, Nadia Roosmalita; S, M Zaki; Mahmudy, Wayan Firdaus
MATICS Vol 10, No 1 (2018): MATICS
Publisher : Department of Informatics Engineering

Tsunami is a high wave caused by tectonic earthquakes, volcanic eruption or landslides in the ocean.Â Indonesia is one of the countries that has thousands of islands. Lots of towns is a city on the banks or waterfront city. Indonesia becomes Tsunami prone areas. Tsunami can affect damage in various sectors, namely land degradation and infrastructure, environmental damage, fatalities, even the psychological impact on the victims themselves. Therefore, it takes a clustering of tsunami-prone areas. The result of clustering can give information to the public to remain alert to the danger of the tsunami. Also, clustering of the tsunami can be used by a government to prepare policies in overcoming the danger of the tsunami. Improved K-Means is an approach that proposed in this study to clustering the tsunami prone areas. In selecting the initial centroid must be done properly to produce a high accuracy. We proposed a method to determine the initial centroid appropriately, so that can increase the accuracy. The proposed method is Particle Swam Optimization (PSO). This study also uses comparison methods, such as K-Means, K-Means Improved, and K-Means Improved PSO. This study uses silhouette coefficient to test the accuracy of the system. The result showed that the proposed method has higher accuracy than the comparison method. Silhouette coefficient generated at 0.99924223 with smaller computing time

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting Gayatri Dwi Santika; Wayan Firdaus Mahmudy; Agus Naba
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 4: August 2017
Publisher : Institute of Advanced Engineering and Science

The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature.

Implementasi Metode Hybrid AHP dan TOPSIS pada Sistem Pendukung Keputusan Pemilihan Lokasi Tempat Pembuangan Sampah Sementara Bayhaqqi Bayhaqqi; Saiful Bukhori; Gayatri Dwi Santika
INFORMAL: Informatics Journal Vol 6 No 2 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i2.25648

Temporary Waste Disposal Site (TPSS) is a place to collect waste from various community activities which will later be transported to the final disposal site by garbage trucks. There are many considerations in choosing a TPSS location, so the selection of a TPSS location is very important in supporting the collection of waste that will be transported to final disposal. The Jember Regency Environmental Service is an agency in charge of waste management, including the selection of TPSS locations. Choosing the location of TPSS so far is still manual, where manual selection cannot be separated from human error, so that choosing the location of TPSS is not accurate can cause new problems in the community. In addition, there is no standardized assessment system in the TPSS selection process, so a decision support system is needed that can be used to assist the process of selecting the best TPSS location recommendations. In making this research system, we implemented the hybrid method of AHP and TOPSIS. Where the AHP method is used to determine the weight of the criteria while the TOPSIS method is used for the selection process for TPSS candidates.

Segmentasi Citra Tanda Tangan Menggunakan Fitur Titik SURF (Speeded Up Robust Features) dan Klasifikasi Jaringan Syaraf Tiruan hidayat, muhamad arief; retnani, windy eka yulia; Firmansyah, Diksy Media; Santika, Gayatri Dwi; Furqon, Muhammad ‘Ariful
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.53514

Signature image classification is an important field of image processing. One of the stages of signature classification is segmentation. The segmentation process aims to detect image pixels that are part of the signature and separate them from text or logo pixels in a document image. There is a signature segmentation technique using interest points extracted using the SURF (Speeded Up Robust Features) algorithm [1] In this technique, a connected component pixel will be considered part of the signature if it has more SURF points in common with the database connected component pixel signature. Compared to the similarity with the database connected component non-signature pixels. This method is able to provide good accuracy results for signature pixel segmentation. However, the recall value is relatively low, namely 56%. This is because many connected component logos are considered as connected component signatures. In this study, signature segmentation was carried out using SURF points by adding two things: 1) using internal connected component characteristics as additional classification atributs: extent, solidity, ratio, and circularity 2) using an Artificial Neural Network classification algorithm to assist the segmentation process. The test results show that the proposed method improves segmentation quality by an average of 20.7% for an increase in accuracy, an average of 22.4% for an increase in precision, and an average of 18.6% for an increase in recall. When compared with the results reported in (Ahmed et al., 2012), the recall has increased by 38.3% - 42.8%

Efektivitas Edukasi Daun Kelor terhadap Pengetahuan Gizi dan Pencegahan Stunting di Desa Klatakan Santika, Gayatri Dwi; Syavira, Anintya Alisya; Allysa, Pupud Deanira; Farchanulhady, Reefadhinta Ubaidillah; Satrio Thoriq Shenny
Room of Civil Society Development Vol. 4 No. 2 (2025): Room of Civil Society Development
Publisher : Lembaga Riset dan Inovasi Masyarakat Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59110/rcsd.453

Stunting merupakan masalah gizi kronis yang berdampak jangka panjang terhadap kualitas hidup dan produktivitas anak. Program KKN Tematik Universitas Jember melalui kegiatan edukasi gizi berbasis pangan lokal bertujuan untuk meningkatkan pengetahuan ibu balita dalam mencegah stunting di Desa Klatakan, Kecamatan Kendit, Kabupaten Situbondo. Kegiatan ini mengusung pendekatan edukatif-partisipatif melalui penyuluhan, demonstrasi pembuatan makanan tambahan berupa nugget daun kelor, serta diskusi interaktif. Evaluasi dilakukan menggunakan instrumen pre-test dan post-test sebanyak 15 soal untuk mengukur peningkatan pengetahuan peserta. Hasil menunjukkan peningkatan signifikan, dari 24,7% peserta dalam kategori “baik” sebelum pelatihan menjadi 60% setelah pelatihan. Temuan ini menunjukkan bahwa edukasi berbasis praktik dan pemanfaatan pangan lokal secara aktif dapat meningkatkan literasi gizi dan mendukung upaya pencegahan stunting di masyarakat. Program ini juga mendorong partisipasi aktif serta membentuk keterampilan baru yang aplikatif dan berkelanjutan di tingkat rumah tangga.

Stroke Disease Prediction Using Support Vector Machine Method Gayatri Dwi Santika; Valiant Shabri Rabbani
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.274

Stroke is one of the leading causes of death globally and is particularly prevalent in Indonesia. Early prediction of stroke is critical to reducing the risk of long-term disability and mortality. This study aims to build a stroke prediction model using the Support Vector Machine (SVM) classification method. The dataset used is sourced from Kaggle, containing 5,110 records with class imbalance. To address the imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. The study evaluates model performance across multiple data splits (70:30, 80:20, 90:10) and k-fold cross-validation values (k=5, 7, 10). The SVM was tested with various kernel types—linear, polynomial, and radial basis function (RBF)—along with parameter tuning for C, gamma, and degree. The results show that the polynomial kernel yielded the highest prediction accuracy of 92%. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics.

Edu-Game Sebagai Media Pembelajaran Pendidikan Anak Usia Dini Di Taman Kanak-Kanak Dharma Wanita 56 Santika, Gayatri Dwi
TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Vol. 1 No. 2 (2021): TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat
Publisher : Fakultas Teknik, Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/tekiba.v1i2.1620

Educational games are very interesting to develop because of the visualization of real problems. Based on the pattern possessed by the game, players are required to learn so that they can solve existing problems. Game status, instructions, and tools provided by the game will guide players actively to explore information so that they can enrich their knowledge and strategies while playing. Development of mobile game applications as an alternative learning media to recognize symbols, count, match pictures and arrange random words as an alternative medium for early childhood education for teacher learning in changing conventional learning methods to learning simulation games, so as to develop children's creativity, because in educational games have elements of challenge, accuracy, reasoning and ethics.

Efektivitas Edukasi Daun Kelor terhadap Pengetahuan Gizi dan Pencegahan Stunting di Desa Klatakan Santika, Gayatri Dwi; Syavira, Anintya Alisya; Allysa, Pupud Deanira; Farchanulhady, Reefadhinta Ubaidillah; Satrio Thoriq Shenny
Room of Civil Society Development Vol. 4 No. 2 (2025): Room of Civil Society Development
Publisher : Lembaga Riset dan Inovasi Masyarakat Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59110/rcsd.453

Stunting merupakan masalah gizi kronis yang berdampak jangka panjang terhadap kualitas hidup dan produktivitas anak. Program KKN Tematik Universitas Jember melalui kegiatan edukasi gizi berbasis pangan lokal bertujuan untuk meningkatkan pengetahuan ibu balita dalam mencegah stunting di Desa Klatakan, Kecamatan Kendit, Kabupaten Situbondo. Kegiatan ini mengusung pendekatan edukatif-partisipatif melalui penyuluhan, demonstrasi pembuatan makanan tambahan berupa nugget daun kelor, serta diskusi interaktif. Evaluasi dilakukan menggunakan instrumen pre-test dan post-test sebanyak 15 soal untuk mengukur peningkatan pengetahuan peserta. Hasil menunjukkan peningkatan signifikan, dari 24,7% peserta dalam kategori “baik” sebelum pelatihan menjadi 60% setelah pelatihan. Temuan ini menunjukkan bahwa edukasi berbasis praktik dan pemanfaatan pangan lokal secara aktif dapat meningkatkan literasi gizi dan mendukung upaya pencegahan stunting di masyarakat. Program ini juga mendorong partisipasi aktif serta membentuk keterampilan baru yang aplikatif dan berkelanjutan di tingkat rumah tangga.

Stroke Disease Prediction Using Support Vector Machine Method Gayatri Dwi Santika; Valiant Shabri Rabbani
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.274

Stroke is one of the leading causes of death globally and is particularly prevalent in Indonesia. Early prediction of stroke is critical to reducing the risk of long-term disability and mortality. This study aims to build a stroke prediction model using the Support Vector Machine (SVM) classification method. The dataset used is sourced from Kaggle, containing 5,110 records with class imbalance. To address the imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. The study evaluates model performance across multiple data splits (70:30, 80:20, 90:10) and k-fold cross-validation values (k=5, 7, 10). The SVM was tested with various kernel types—linear, polynomial, and radial basis function (RBF)—along with parameter tuning for C, gamma, and degree. The results show that the polynomial kernel yielded the highest prediction accuracy of 92%. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics.

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