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All Journal Jurnal Teknik Informatika C.I.T. Medicom Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Bakti Sekawan : Jurnal Pengabdian Masyarakat
Siti Agrippina Alodia Yusuf
Universitas Muhammadiyah Mataram
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Health Professions Social Sciences Other

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Application of Gray Level Co-Occurrence Matrix and Histogram Feature Extraction Methods for Batik Image Classification Nani Sulistianingsih; Siti Agrippina Alodia Yusuf; Muhamad Irwan
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Batik is the art of integrating cultural elements like symbols and techniques into cotton and silk clothes. The Indonesian population has traditionally used batik in their daily lives. Each location has distinctive designs, patterns, and colors that reflect its meanings and philosophical perspectives. There are many different motifs on batik fabric, including geometric, geometrical, animal, and other designs. Batik motifs are frequently employed to convey social rank. The variety of batik designs and motifs is challenging for machine learning-based pattern detection and classification. This research applies the Gray Level Co-occurrence Matrix (GLCM) feature extraction method and Histogram feature extraction on batik images and the K-Nearest Neighbor (KNN) classifier. This study focuses on 4 batik patterns (motifs), namely Lereng, Nitik, Kawung, and Tambal. Dissimilarity, Correlation, Contrast, Homogeneity, and Energy from various angles and distances are the GLCM features employed, and their sum equals 1. Mean, standard deviation, smoothness, skewness, energy, and entropy are the histogram features employed. This work uses 120 batik image data—90 training data and 30 test data—. The findings indicate that at k=15 and k=17, accuracy attained using GLCM feature extraction is 77%, while Precision and Recall are 77%. Comparatively, the histogram feature extraction accuracy, Precision, and Recall are 53%, 54%, and 53%, respectively, with a value of k=27. This outcome demonstrates how feature extraction using GLCM can more accurately portray batik.
Pelatihan Penggunaan Aplikasi LMS Kelas Online untuk Staf Admin LKP Zahra Computer Siti Agrippina Alodia Yusuf; Nani Sulistianingsih
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol 2 No 1 (2022): Juni
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (343.713 KB) | DOI: 10.35746/bakwan.v2i1.221

Abstract

Participating in online training will help to continue the present capacity-building process. To meet these demands, training service providers such as LKP Zahra Computer have developed a Learning Management System (LMS) that allows for more flexible training. LMS administration for Admin Staff is one of the reasons that becomes a barrier for LKP, especially when it comes to publishing new classes, adding questions to the system, and upgrading classes if there are modifications that are needed. The initial stage of this training is an interview with the administrative staff to identify potential impediments. Following that, the second level, which included online class LMS management training, was completed. And at last, the Admin Staff constructed a mock class to evaluate their understanding of the Online Class LMS management.
Pembuatan dan Pelatihan Pengelolaan Website di SMPN 6 Kota Bima sebagai Media Informasi dan Promosi Sekolah Nani Sulistianingsih; Siti Agrippina Alodia Yusuf
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 3 No. 2.2 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara
Publisher : Sistem Informasi dan Teknologi (Sisfokomtek)

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Abstract

Pemanfaatan teknologi informasi berupa website saat ini menjadi kebutuhan prioritas dalam berbagai bidang. Seiring berjalannya waktu pemanfaatan website tidak terbatas pada perusahaan namun sudah merambah ke berbagai bidang, termasuk dunia pendidikan. Pada dunia pendidikan salah satu pemanfaatan website adalah sebagai media penyebaran informasi dan promosi sekolah. Selain untuk penyebaran informasi terkait sekolah, keberadaan website dimanfaatkan sebagai media atau sarana dalam melakukan branding digital. Hal ini dilakukan guna meningkatkan citra sekolah di masyarakat luas. Website sekolah ini mendapatkan respon yang baik dari berbagai pihak, baik dari tenaga pendidik, siswa dan juga masyarakat. Demi meningkatkan mutu dan pelayanan pendidikan, SMPN 6 Kota Bima sebagai salah satu instansi pendidikan membutuhkan sarana website sekolah, yang dapat dimanfaatkan sebagai sarana menyiarkan informasi-informasi, kegiatan dan prestasi yang diraih. Berdasarkan hal tersebut, maka kegiatan ini berfokus pada pembuatan website SMPN 6 Kota Bima serta memberikan pelatihan pengelolaan website bagi operator sekolah. Tahapan dalam kegiatan ini adalah studi lapangan, identifikasi masalah, pembuatan dan pelatihan pengelolaan website. Hasil dari kegiatan menunjukkan bahwa operator dapat mengoperasikan dan mengelola website dengan baik. Selain itu dengan adanya website sekolah ini, SMPN 6 Kota Bima sudah dapat memanfaatkan website sebagai sarana penyebaran informasi dan promosi sekolah.
EKSTRASI FITUR SINYAL EKG MYOCARDIAL INFARCTIN MENGGUNAKAN DISCRETE WAVELET TRANSFORMATION Siti Agrippina Alodia Yusuf; Nani Sulistianingsih; Helmi Imaduddin
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.96

Abstract

One important step in the process of identifying EKG signals is feature extraction, where the obtained features characterize the condition of the heart. The condition of the heart can be observed based on the waves produced in the EKG signal, which are generated by the electrical activity of the heart. In this study, two types of mother wavelets will be compared to determine which type is most suitable for extracting features from EKG signals. The types of mother wavelets to be compared are Daubechies and Symlet with orders of 5, 6, and 7 for Daubechies, and 6, 7, and 8 for Symlet. EKG signals with MI and normal heart conditions that have been improved in quality and have undergone signal segmentation are extracted using Discrete Wavelet Transformation (DWT) with Daubechies and Symlet mother wavelets at the two-level decomposition, and statistical features such as mean, median, standard deviation, kurtosis, and skewness are taken. Features are extracted from the D2 and D1 sub-bands, resulting in a total of 10 features obtained. The EKG signals are then classified using the KNN method, and to obtain generalized results, K-fold cross-validation is also applied. Based on the experiments conducted, the highest accuracy obtained was 94% with sensitivity and specificity of 82% and 91% by applying the Daubechies mother wavelet of order 7.
Co-Authors Helmi Imaduddin Muhamad Irwan Nani Sulistianingsih Nani Sulistianingsih