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All Journal Jurnal Kesehatan Lingkungan indonesia International Journal of Advances in Intelligent Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Bakti Saintek: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Shofwatul 'Uyun
Magister Teknik Informatika, Fakultas Sains Dan Teknologi, UIN Sunan Kalijaga, Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering Environmental Science Public Health Other

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Pelatihan Reference Manager, Management Cite on Your Research, Tools: Mendeley, Zotero, End Note Endra Yuliawan; Wildan Nadiyal Ahsan; Sophan Sofian; Bambang Sugiantoro; Shofwatul 'Uyun; Maria Ulfa Siregar
Jurnal Bakti Saintek: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 6 No. 2 (2022)
Publisher : Fakultas Sains dan Teknologi, UIN Sunan Kalijaga Yogyakarta

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

Abstract

Penulisan karya ilmiah yang yang memerlukan tata cara penulisan yang sesuai dengan kaedah-kaidah penulisan yang benar sesuai dengan sitasi sehingga tidak menimbulkan plagiasi merupakan hal yang wajib dipahami oleh semua peneliti yang melakukan karya ilmiah. Tools atau alat yang tepat dalam mengorganisir kebutuhan ini sangat membantu peneliti agar sesuai dengan ketentuan yang distandarkan oleh masing-masing jurnal, karya tulis ilmiah dan prosiding. Kegiatan Webinar dan Sharing Seassion ini merupakan proses pendampingan yang berorientasikan pada tata laksana secara impelementasi sehigga mahasiswa khusus nya jenjang sarjana dapat melakukan penulisan karya ilmiah nya dengan benar sesuai kaedah-kaedah penulisan karya ilmiah dengan mensitasi karya peneliti lain tanpa melakukan plagiasi atau mengutip tanpa menyertakan sumber referensinya. Pada pelaksaan kegiatan ini dilakukan secara online atau daring dikarenakan kondisi yang masih belum memungkinkan dilakukan secara tatap muka dan sebagai kepatuhan dalam menjalankan protokol kesehatan dari virus Corona (Covid-19). Dari hasil pemaparan materi, diskusi dan hasil implementasi secara langsung dapat disimpulkan sesuai dengan yang diharapkan dengan peserta 71.6% adalah jenjang mahasiswa 28.4% jenjang pasca sarjana dengan demikian mahasiswa jenjang sarjana dapat mengambil manfaat untuk menyusun karya ilmiah skripsi nya dan pasca sarjana dapat lebih mendalam memahami dalam lakukan sitasi pada tesis dan jurnal yang dituju untuk publikasi. [Writing scientific papers that require writing procedures that are in accordance with the correct writing rules in accordance with citations so as not to cause plagiarism is something that must be understood by all researchers who do scientific work. The right tools or tools in organizing these needs really help researchers to comply with the provisions standardized by each journal, scientific paper, and proceedings. This Webinar and Sharing Session activity is an implementation-oriented mentoring process so that undergraduate students in particular can write their scientific papers correctly according to the rules of scientific writing by citing the work of other researchers without plagiarizing or quoting without including sources. the reference. The implementation of this activity is carried out online or online due to conditions that are still not possible to do face-to-face and as compliance in carrying out health protocols from the Coronavirus (Covid-19). From the results of the material presentation, discussion, and direct implementation results, it can be concluded as expected with 71.6% of participants being 28.4% postgraduate level students so that undergraduate students can take advantage to compose their scientific thesis and postgraduate students can understand more deeply in doing citations on theses and journals intended for publication.]
COMPARISON OF K-NEAREST NEIGHBOR (KNN) AND LINEAR DISCRIMINANT ANALYSIS (LDA) ALGORITHMS FOR MATURE AJWA DATE FRUIT CLASSIFICATION Risna Risna; Fadila Amanda; Shofwatul Uyun
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.26

Abstract

Currently, many applications of artificial intelligence in various fields of life, especially in image data, require digital image processing. One example of the use of digital images often encountered is image processing of fruit ripeness. Dates are a fruit in great demand by the people of Indonesia, and one of the most popular dates is the Ajwa date. The author is interested in developing previous research regarding identifying the ripeness of Ajwa Dates, where previous research used RGB color image processing with the HIS method. Therefore, the authors want to apply a different method, namely the K-Nearest Neighbor (K-NN) method and Linear Discriminant Analysis (LDA), in classifying the ripeness of the Ajwa Dates by applying a statistical feature algorithm. This research aims to develop a classification model for the maturity level of Ajwa Dates. Furthermore, it is expected to provide better classification results than the previous algorithm. The test results using the KNN method can produce higher accuracy than the LDA, where the KNN method is obtained from the calculation of the Euclidean distance k = 1 100% and Manhattan with a value of k = 1 and k = 2 worth 100%, but the minimum accuracy value is 53.33 % is found at k = 9 in the Euclidean distance calculation, while the LDA accuracy value can reach 93.33%
Penilaian Kesehatan Sungai Menggunakan Metode Biotik dan Fisik di Sungai Boyong, Sleman, Yogyakarta Eka Sulistiyowati; Dien F. Awaliyah; Shofwatul Uyun
Jurnal Kesehatan Lingkungan Indonesia Vol 23, No 2 (2024): Juni 2024
Publisher : Master Program of Environmental Health, Faculty of Public Health, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jkli.23.2.162-169

Abstract

Latar belakang: Penilaian kesehatan sungai merupakan upaya penting dalam membantu memonitor keseimbangan ekosistem sungai.  Penelitian dilakukan untuk mendapatkan penilaian kualitas Sungai Boyong sebagai hulu dari Sungai Code yang memasok air ke wilayah perkotaan Yogyakarta dengan menggunakan kombinasi metode biotik dan fisik untuk mendapatkan hasil yang lebih komprehensif.Metode: Penilaian kesehatan sungai dilakukan secara kuantitatif dan kualitatif dengan dua metode yakni metode biotik yang terdiri atas metode biotilik dan metode RHA, serta metode pengamatan fisik. Metode biotilik merupakan metode pengukuran kuantitif terhadap komunitas makroinvertebrata akuatik. Analisis data biotik dengan menghitung biotik indeks menggunakan panduan biotilik oleh Ecoton. Sampel yang diambil berupa makroinvertebrata akuatik dengan menggunakan jaring. Sampel tumbuhan diambil pada transek yang berada pada verge, bank, dan in-stream. Analisis data riparian vegetation menggunakan indeks Riparian Health Assessment. Sedangkan metode pengamatan fisik menggunakan analisis dari panduan biotilik oleh Ecoton.Hasil: Hasil penelitian menunjukkan bahwa dengan menggunakan metode biotilik diketahui bahwa stasiun 1, 2, dan 3 berada pada kondisi sedang, meskipun nilai indeks biotilik stasiun 2 lebih rendah daripada yang lain. Penggunaan metode RHA menunjukkan bahwa kondisi ekosistem riparian di stasiun  1 dan 3 lebih baik daripada stasiun 2. Sedangkan dengan metode pengamatan fisik diketahui bahwa kondisi stasiun 1 dan 3 berada pada kategori sehat, dan stasiun 2 masuk dalam kategori kurang sehat.Simpulan: Penelitian menyimpulkan bahwa kondisi ekosistem Sungai Boyong yang paling baik di Stasiun 2 dan yang kurang baik pada stasiun 1 dan 3. ABSTRACTTitle: River Health Assessment using Biotic and Physical Methods in Boyong River, Sleman, YogyakartaBackground: River health assessment is important to monitor the balance of river ecosystem.. The study was conducted to assess  the quality of Boyong River as the upstream of the Code River which supplies water to the urban area of Yogyakarta using a combination of biotic and physical methods for a comprehensive result.  Method: River health assessment was carried out by two methods,  first was the biotic methods which consisted the biotilik method and the RHA method, and  second was the physical observation method . The biotilik method is a quantitative measurement method of aquatic macroinvertebrate communities. Analysis of biotic data by calculating biotic index using biotilik guidance by Ecoton. The samples taken were aquatic macroinvertebrates using nets. Plant samples were taken on transects located on verge, bank, and in-stream. Analysis of riparian vegetation data using the Riparian Health Assessment index. While the physical observation method uses analysis by Ecoton. Result: The results showed that using the biotilik method it was known that stations 1, 2, and 3 were in moderate condition, although the biotilic index value of station 2 was lower than the others. The use of the RHA method shows that the riparian ecosystem conditions at stations 1 and 3 are better than at station 2. Meanwhile, with the physical observation method, it is known that the condition of stations 1 and 3 is in the healthy category, and station 2 is included in the unhealthy category.Conclusion:  It was concluded that the ecosystem condition of Boyong River was best at Station 2 and not good at Stations 1 and 3.
Improvement of CNN model using integration of multimodal MRI sequences and multilevel fusion to enhance performance for brain tumor classification Ade Umar Ramadhan; Shofwatul Uyun
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.1975

Abstract

The prevalence of brain tumors has been increasing annually, and headaches, a common initial symptom, represent the most common manifestation. However, there is a paucity of research on effective methods of assessing brain tumors. This study proposes a novel approach by introducing various modality fusion techniques based on their fusion levels, which are then categorized into four groups: single-modal, data-level fusion, feature-level fusion, and multilevel fusion. A total of 51 combinations are designed to evaluate the efficacy of these fusion techniques and modality configurations. The experiments used a BraTS2021, which comprises four magnetic resonance imaging (MRI) sequences (flair, t1, t1ce and t2). Initially, the image was pre-processed, encompassing data selection, conversion, and normalization. Subsequently, it was input into a 13-layer CNN architecture for feature extraction. Classification was facilitated by a soft voting method in ensemble learning, incorporating support vector machine (SVM), k-nearest neighbor (KNN), logistic regression, random forest, and decision tree algorithms. The predictive efficacy of the model was rigorously assessed through a comprehensive suite of metrics, prominently featuring accuracy, AUCROC, AUCPR, Cohen's Kappa, and MCC. The results indicate that multilevel fusion exhibits optimal performance, with an average accuracy of 95.84%, followed by feature-level fusion and data-level fusion, at 95.12% and 94.77%, respectively. The optimal fusion technique was identified as the combination with the FF configuration (1,2),3,4), producing an accuracy of 96.62%. The best-model combination proposed exhibited an accuracy difference of nearly 6% from the baseline model, underscoring the efficacy of the proposed approach. These empirical results establish a robust baseline for future investigations into sophisticated fusion architectures across hierarchical integration levels.
Comparison of CNN Performance in Brain Tumor Classification using an Explainable AI Approach Amin Darussalam; Shofwatul Uyun
SISTEMASI Vol 15, No 9 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i9.6917

Abstract

Brain tumors are life-threatening conditions that require accurate and timely diagnosis. In clinical practice, diagnosis is commonly performed through Magnetic Resonance Imaging (MRI) analysis by medical experts, which is time-consuming and subject to variability. Recent advances in Convolutional Neural Networks (CNN) have shown promising performance in medical image classification; however, their black-box nature limits clinical trust and interpretability. This study proposes a comparative analysis of CNN-based models integrated with Explainable Artificial Intelligence (XAI) to improve both classification performance and model transparency. A transfer learning approach is employed using two architectures, ResNet50 and MobileNetV2, on a multi-class brain tumor MRI dataset. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize discriminative regions influencing model predictions. Experimental results demonstrate that ResNet50 achieves an Accuracy of 93,7%, outperforming MobileNetV2 with 92.93%. Furthermore, Grad-CAM visualizations consistently highlight tumor-relevant regions, indicating that the models learn meaningful spatial features for classification. These findings confirm that integrating CNN with XAI not only provides high classification Accuracy but also improves model interpretability, thereby increasing its potential applicability in supporting reliable and transparent medical diagnosis.
Co-Authors Ade Umar Ramadhan Amin Darussalam Bambang Sugiantoro Dien F. Awaliyah Eka Sulistiyowati Endra Yuliawan Fadila Amanda Maria Ulfa Siregar Risna Risna Sophan Sofian Wildan Nadiyal Ahsan