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All Journal Jurnal Ilmu Komputer dan Informasi Journal of Information Systems Engineering and Business Intelligence Bantenese Jurnal Pengabdian Masyarakat Jurnal ABDINUS : Jurnal Pengabdian Nusantara Journal of Applied Computer Science and Technology (JACOST) Journal of Software Engineering, Information and Communication Technology Industrial Research Workshop and National Seminar
Syakrani, Nurjannah
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Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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Analisis Data Eksplorasi Klasifikasi Aktivitas Otak yang Berbahaya Putriadhinia, Salma Syawalan; Mulia, Syelvie Ira Ratna; Awaludin, Iwan; Sholahuddin, Muhammad Rizqi; Syakrani, Nurjannah; Hayati, Hashri
Prosiding Industrial Research Workshop and National Seminar Vol. 15 No. 1 (2024): Prosiding 15th Industrial Research Workshop and National Seminar (IRWNS)
Publisher : Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/irwns.v15i1.6234

Abstract

Elektroensefalografi (EEG) merupakan alat yang vital dalam rekaman dan analisis aktivitas listrik otak, sering digunakan dalam penelitian dan perawatan medis. Peletakan elektroda EEG mengikuti sistem internasional 10-20, dengan huruf dan angka tertentu untuk menandakan lokasi spesifik di otak. Kualitas pengukuran EEG sangat penting, dengan upaya mengeliminasi artifact yang bisa berasal dari sumber biologis maupun nonbiologis. Monitoring EEG di ICU telah meningkat, terutama untuk mendeteksi pola IIIC yang berbahaya. Pola tersebut sulit dibedakan dari kejang biasa dan dapat menyebabkan kerusakan otak. Penelitian ini bertujuan untuk melakukan analisis terhadap dataset EEG yang memiliki pola IIIC sehingga harapannya dapat berguna untuk peneliti yang hendak menggunakan data tersebut. Penelitian ini menggunakan dataset dari platform Kaggle, tepatnya HMS – Harmful Brain Activity Classification. Dataset tersebut memiliki data mentah EEG dan spektogram yang sudah dianotasi oleh ahli. Analisis data menunjukkan bahwa dataset tersebut memiliki keseimbangan jumlah data yang dianotasi untuk masing-masing kategori IIIC. Dalam dataset tersebut, terdapat data rekaman EEG dan data spektogram yang memiliki nilai kosong (null value) sehingga perlu dilakukan penangan terlebih dahulu sebelum diolah lebih lanjut.
Rancang Bangun Standar Operasional Prosedur (SOP) Aplikasi Layanan Syariah Berbasis Web Pada Koperasi Warga Polban Di Bandung Purwihartuti, Koernia; Karnawati, Hennidah; Syakrani, Nurjannah; Kristianingsih, Kristianingsih; Angestiwi, Tiafahmi; Firmansyah, Yayan; Wisnuadhi, Bambang; Mauludi , Hasbi Assidiki
BANTENESE : JURNAL PENGABDIAN MASYARAKAT Vol. 6 No. 2 (2024): Bantenese: Jurnal Pengabdian Masyarakat
Publisher : Pusat Studi Sosial dan Pengabdian Masyarakat Fisipkum Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/ps2pm.v6i2.9381

Abstract

Currently Koperasi Warga POLBAN (kwp) has carried out increased preparation activities for the transition from conventional USP to Sharia. This community service activity is a continuation of community service 2023. The reviewer's suggestion for community service 2024 is that there is continuity in creating web-based Sharia service applications. Making Sharia service applications must follow the Sharia service SOP, while KWP does not yet have a Sharia service SOP. The purpose of this community service is to produce the design and creation of Sharia service applications using digital web applications in accordance with the Syariah-based Service Operational Procedure Standard (SOP). The design of the SOP digital application is aligned with the aspects contained in the AD and annual members meeting which are applied to the KWP. The method used is an appropriate technology program in collaboration with KWP. In this program, KWP provides the data needed for preparing SOPs. In order to collect the necessary data, observations and interviews were carried out to observe the routine activities of service units in KWP, interviews with administrators and managers regarding the real conditions faced, surveys with KWP members to find out members' opinions regarding the content of Sharia service SOPs before they were input into the digital application. The result is a digital application web for sharia services based on SOPs that have been tested on representatives of members, administrators, supervisors and KWP managers. The trial showed that the sharia service web can be operated well and easily according to their expectations.
Morphological Grayscale Pre-processing to SAR Images for Reducing Noise in Ship Detection Based on YOLOv8 Pratidina, Caturiani; Safira, Decia; Gelar, Trisna; Permana, Heru; Suprihanto, Suprihanto; Syakrani, Nurjannah; Fauzi, Cholid
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 5, No 2: December 2024
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v5i2.75970

Abstract

The development of a ship detection system using SAR pictures loaded with noise poses issues for pictures Intelligence (IMINT). The YOLOv8 model is utilized for ship identification. The preprocessing approaches entail employing a fusion of grayscale morphology techniques and image restoration using a harmonic mean filter and a bandpass. This technique is designed to assess the effect of noise reduction to enhance the accuracy of detecting objects in SAR images. The preprocessing technique is categorized into two methods: basic grayscale morphology (GM1-GM6) and a fusion of image restoration with grayscale morphology (GHB1-GHB6). The model's performance is assessed using mAP and IoU criteria. This research discovered that ship objects were not detected successfully in the presence of several types of noise. These failures were attributed to factors such as tiny ship size, low picture quality, and inadequate preprocessing techniques for noise handling. The findings indicate a substantial enhancement in ship detection, specifically in synthetic aperture radar (SAR) images affected by sidelobe noise. There were noticeable enhancements in the accuracy of images that underwent preprocessing using GHB5. GHB5 employs a combination of image restoration, closure, and erosion techniques.
Preprocessing Impact on SAR Oil Spill Image Segmentation Using YOLOv8 Syakrani, Nurjannah; Kurniawan, Dimas; Nugraha, Wili Akbar; Hidayatullah, Priyanto; Firdaus, Lukmannul Hakim; Sholahuddin, Muhammad Rizqi
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i1.1380

Abstract

Synthetic Aperature Radar (SAR) is a sensory equipment used in marine remote sensing that emits radio waves to capture a representation of the target scene. SAR images have poor quality, one of which is due to speckle noise. This research uses SAR images containing oil spills as objects that are detected using machine learning with the YOLOv8 model. The dataset was obtained from MKLab by preprocessing to improve the quality of SAR images before processing. Preprocessing involves annotating the dataset, augmenting it with flip augmentation, and filtering it using threshold and median filters in addition to a sharpen kernel that finds the optimal midway value. The default value of the YOLOv8 hyperparameter is used with addition of delta as well as subtraction of the same delta. The implementation of preprocessing and combination of hyperparameters is examined to optimize the YOLOv8 model in detecting oil spills in SAR images. Based on 10 experimental scenarios, initial results with the original MKLab image provide an mAP50 of 49.7%. Implementing Flip augmentation alone on the data set increases the mAP50 value by 18.8%. Followed by the sharpen 1.2 kernel filter increasing the mAP50 value to 68.89%, while the median and thresholding filters tend to reduce the mAP50 value. The combination of experiments with the best results was preprocessing with flip augmentation and sharpen 1.2 kernel filter with hyperparameters: epoch 200, warmup 4.0, momentum 0.9, warmup bias lr 0.01, weight decay 0.005, and learning rate 0.000714, resulting in an mAP50 value of 68.89%. In addition, it was found that the sharpening kernel with a real number midpoint of 1.2 and combination with flipping augmentation had the greatest impact on increasing the MAP50 value in SAR oil spill image segmentation by YOLOv8.
Konsistensi Model Regresi Empat Variabel Pada Populasi dan Sampel untuk Prediksi Temperatur Syakrani, Nurjannah; Naufal Athaya S. R
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.971

Abstract

The ability to predict future events or trends has become very important today. One method that can be used to predict the future is to use linear regression. Accurate regression modeling requires sampling representative data, especially when working with large datasets. This research takes a relatively large volume as a data set by looking at the accuracy and consistency of the coefficients of a multi-variable linear regression model for temperature prediction which is built based on all the data, and looks at the differences in the regression model built from the sample data. The number of sample data (n) is determined based on the Slovin formula which depends on the number of population data (N) and the level of confidence (ơ), so that as many as (N/n) new regression models can be built. Each group of sample data is divided into 75% for modeling and 25% testing data. The dataset used is weather information in the Szeged area which was measured in 2006 - 2016. So the regression model is in the form of Y (temperature value) which is influenced by Xi (weather factors), namely humidity, wind speed, wind direction and visibility. Using 96,453 data records and a 1% significance level in Slovin's formula, 10 samples were generated. Nine out of ten sample regression models agree with the population model, with positive coefficients for visibility and wind direction and negative values for humidity and wind speed. There is an abnormality in sample #4. While the other nine sample regression models are consistent with positive R2 values, Sample #1 displays an oddity with negative values. The RMSE interval values for each regression model in this study fall between 4.334 and 9.582.
Pelatihan Peningkatan Kompetensi Guru melalui Media Board Game sebagai Inovasi Pembelajaran Computational Thinking di Pondok Pesantren Darul Fithrah Kabupaten Bandung Fitriani, Sofy; Rachmat, Setiadi; Sari, Aprianti Nanda; Syakrani, Nurjannah; Hidayatullah, Priyanto; Soewono, Eddy Bambang; Widhiyasana, Yudi; Abdillah, Trisna Gelar; Setiarini, Siti Dwi; Sholahuddin, Muhammad Rizqi
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 10 No 1 (2026): Volume 10 Nomor 1 Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v10i1.27180

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) ini dilatarbelakangi oleh keterbatasan guru pesantren dalam memahami dan mengimplementasikan keterampilan computational thinking dalam pembelajaran. Di Pondok Pesantren Darul Fithrah, media pembelajaran inovatif berupa board game berbasis Unplugged Computational Thinking dirancang untuk menjembatani kebutuhan tersebut sekaligus memberikan alternatif metode pembelajaran yang lebih interaktif. Metode pelaksanaan meliputi perancangan board game, penyusunan instrumen pelatihan, pelaksanaan pre-test, pemberian materi dan simulasi board game, serta post-test dan observasi implementasi di kelas. Hasil kegiatan menunjukkan adanya peningkatan signifikan pada pemahaman guru mengenai computational thinking, terlihat dari kenaikan skor rata-rata pre-test dan post-test, khususnya pada aspek pengenalan pola, penyusunan langkah pemecahan masalah, dan pemahaman istilah computational thinking. Respon kuesioner dan feedback guru juga menunjukkan bahwa board game dipandang interaktif, mudah digunakan, serta potensial untuk diterapkan dalam pembelajaran di pesantren. Dengan demikian, kegiatan PKM ini berhasil mendorong guru lebih siap mengintegrasikan keterampilan abad 21 ke dalam praktik pendidikan
Optimization of 3D U-Net Using Attention Mechanism for Accurate Protein Object Identification in Cryo-Electron Tomography Theniana, Ghessa; Setiawan, Sendi; Syakrani, Nurjannah; Yudi Widhiyasana
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1622

Abstract

Object segmentation in 3D tomograms is a key problem in Cryo-Electron Tomography (Cryo-ET) analysis. In this work, performance for the 3D U-Net architecture, and its variants with three attention mechanisms (Attention Gate (AG), Squeeze-and-excitation (SE), and Convolutional Block Attention Module (CBAM)) was evaluated. Experiments were conducted on a publicly available Cryo-ET dataset comprising two tomogram samples using a combination of (16,32,64,128) and (32,64,128,256) channel configurations, and with patch sizes of (32,64,64), (32,96,96), and (32,128,128) respectively. Model performance was evaluated with the F-Beta Score metric. The results of the analysis show that larger patch sizes significantly improve performance, and deep channel configurations do not always lead to better performance. Compared to the baseline 3D U-Net, which achieved a best score of 0.670, 3D UNet + SE led to the best model performance with the highest F-Beta Scores at 0.718, representing an improvement of 0.048. 3D U-Net + CBAM was second with F-Beta Scores at 0.707, improving by 0.037 over the baseline, while 3D U-Net + AG exhibited prediction inconsistency, with its accuracy falling below the baseline in multiple settings. Overall, these results show that incorporating either SE or CBAM, is a better approach to improve segmentation accuracy for 3D tomogram analysis.
Improving SAM-Road Model for Occlusion Handling in Road Networks Extraction from Satellite Images with Gamma Correction and Modified A* Algorithm Fathul'ibad, Mohammad; Firmansyah, Maolana; Syakrani, Nurjannah; Fauzi, Cholid
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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

Abstract

Background: Occlusion in satellite imagery often leads to disconnected road networks, reducing the quality and reliability of geospatial data, which in turn hampers infrastructure and transportation planning. Although models like SAM-Road show promising results, they still struggle in handling occluded areas, especially at intersections and curved roads. Some existing methods, such as the extended-line approach, have been proposed to address occlusion; however, they are typically limited to handling linear road segments and are less effective in complex structures. To overcome these limitations, this study enhances the SAM-Road framework by incorporating gamma correction and a modified A algorithm in the post-processing stage. This combined approach improves the visibility of partially hidden roads and successfully reconnects fragmented segments, even in non-linear and occlusion-heavy areas. Objective: This study aims to improve the accuracy of road network extraction by enhancing the SAM-Road model with gamma correction and a modified A* algorithm, specifically to address the problem of occlusion in satellite imagery. Methods: This research adopts a quantitative experimental approach. Gamma correction is applied to enhance the visual contrast of roads in occluded satellite images, while the A pathfinding algorithm is modified to reconnect disjointed road segments. The integrated method is then evaluated using accuracy metrics, specifically the TOPO and APLS (Average Path Length Similarity) scores. Results: The experimental findings indicate that each method—SAM-Road baseline, gamma correction, the modified A* algorithm, and their combination—delivers distinct performance improvements. Gamma correction alone achieves the best results at gamma 1.5 (TOPO 80.61%) and 1.25 (APLS 70.91%) on SpaceNet, and gamma 2.0 (TOPO 78.56%) and 1.25 (APLS 68.73%) on City-scale. The modified A* algorithm performs best at 16/8 (TOPO 80.22%) and 32/16 (APLS 70.71%) on SpaceNet, and 16/8 (TOPO 77.29%) and 64/32 (APLS 70.94%) on City-scale. When combined, the method yields results within the range of TOPO 75.41–80.59% and APLS 67.59–71.17% on SpaceNet, and TOPO 76.52–78.56% and APLS 66.39–71.19% on City-scale. Conclusion: This study concludes that the integration of gamma correction and a modified A* algorithm effectively addresses occlusion-related challenges in satellite imagery. While each technique contributes unique improvements, their combination significantly enhances the accuracy and continuity of extracted road networks—not only in straight road segments such as extended-line method, but also in more complex or occluded areas. The results confirm that this hybrid approach yields road extraction outputs that more closely align with ground truth in terms of topology and structure. Future research could explore integrating gamma correction and the modified A* algorithm directly into the training process, aiming to enhance model performance while maintaining high accuracy.   Keywords: SAM-Road, modified A*, gamma correction, occlusion, road network extraction, satellite imagery, topology, geometry
Co-Authors Cholid Fauzi Dimas Kurniawan Eddy Bambang Soewono Fathul'ibad, Mohammad Firdaus, Lukmannul Hakim Firmansyah, Maolana Gelar, Trisna Hayati, Hashri Iwan Awaludin Karnawati, Hennidah Kristianingsih, Kristianingsih Mauludi , Hasbi Assidiki Muhammad Rizqi Sholahuddin Mulia, Syelvie Ira Ratna Naufal Athaya S. R Nugraha, Wili Akbar Permana, Heru Pratidina, Caturiani Priyanto Hidayatullah, Priyanto Purwihartuti, Koernia Putriadhinia, Salma Syawalan Rachmat, Setiadi Safira, Decia Sari, Aprianti Nanda Setiawan, Sendi Siti Dwi Setiarini Sofy Fitriani Suprihanto Suprihanto Theniana, Ghessa Tiafahmi Angestiwi Widhiyasana, Yudi Wisnuadhi, Bambang YAYAN FIRMANSYAH, YAYAN Yudi Widhiyasana