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All Journal Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika JOIV : International Journal on Informatics Visualization Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Scine and Information Technology Jurnal Riset dan Pengabdian Masyarakat
Silfia Andini, Silfia
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Other

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Perancangan Dan Implementasi Supply Chain Management (SCM) Pada CV Hayati Padang Andini, Silfia
Jurnal EDik Informatika Vol 1, No 1 (2014)
Publisher : STKIP PGRI Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (825.918 KB) | DOI: 10.22202/ei.2014.v1i1.1479

Abstract

Manajemen rantai pasok  adalah suatu sistem pada suatu organisasi itu menyalurkan barang produksi dan jasanya kepada para pelanggannya. Dari sudut struktural, sebuah supply chain management merujuk kepada jaringan yang rumit dari hubungan dimana organisasi mempertahankan dengan partner bisnis untuk memperoleh bahan baku, produksi dan menyampaikannya kepada konsumen. Manajemen rantai pasok bagi CV HAYATI yang mengelola distribusi dan penjualan, sangat membantu dalam pemenuhan kebutuhan  konsumen, meningkatkan volume penjualan produk sehingga memberikan nilai positif bagi perusahaan.Berdasarkan penelitian yang dilakukan secara langsung ke lapangan dengan metode observasi, serta mempelajari literature yang berhubungan dengan masalah yang dibahas, diharapkan sistem yang baru ini dapat meningkatkan kualitas informasi sehingga bermanfaat bagi instansi yang bersangkutan.                       Kata Kunci : Supply Chain Manajemen (SCM), CV.HAYATI, Website, Distribusi.
Pemanfaatan Algoritma Fletcher-Reeves untuk Penentuan Model Prediksi Harga Nilai Ekspor Menurut Golongan SITC Ginantra, Ni Luh Wiwik Sri Rahayu; GS, Achmad Daengs; Andini, Silfia; Wanto, Anjar
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.887 KB) | DOI: 10.47065/bits.v3i4.1449

Abstract

Conjugate gradient Fletcher-Reeves algorithm, according to some literature, is an optimization method that is suitable when juxtaposed with the backpropagation method because this method can speed up the training time to achieve a minimum convergence value. Therefore, this study aims to prove whether the algorithm has good performance and can provide efficient convergence results when used to solve prediction problems in the case of export values ​​according to the Standard International Trade Classification (SITC) class. The results of this study are a predictive model that can be used and developed to make predictions in seeing the development of the export value of the SITC class based on the US Dollar currency. The research data was taken from the website of the Central Statistics Agency for 2010-2020. Prediction models that will be analyzed using the Fletcher-Reeves algorithm include 5-20-1, 5-25-1, and 5-30-1, with the activation functions of tansig and logsig. Based on the analysis carried out through excel calculations from the training and testing process using the Matlab-2011b application, the results obtained that the 5-25-1 network model is the best model with a performance value or Mean Square Error 0.00287273 compared to the other four models. So it can be concluded that the Fletcher-Reeves algorithm is proven to produce faster convergence; it can be seen from the epoch generated from each model that it is not too large and the time required is relatively short
Menggagas Dunia Digital di Pedesaan: Evaluasi Efektivitas Pendampingan Sistem Informasi di Gampong Balee Kecamatan Meureubo Aceh Barat Zia Farhan Afra; Lukman Ibrahim; Wahyuni Sutari Hsb; Andini, Silfia; Rizkia, Eva
Jurnal Riset dan Pengabdian Masyarakat Vol. 4 No. 1 (2024): Jurnal Riset dan Pengabdian Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/jrpm.v4i1.3967

Abstract

Dalam era globalisasi dan kemajuan teknologi informasi, peran teknologi digital menjadi krusial dalam membentuk gaya hidup dan perkembangan masyarakat. Penelitian ini menyoroti pentingnya pendampingan sistem informasi desa dalam mendukung program pemerintah gampong khusunya Gampong Balee, Kecamatan Meureubo, Kabupaten Aceh Barat. Meskipun perkembangan teknologi digital dapat diimplementasikan di berbagai lapisan masyarakat, desa-desa seperti Gampong Balee menghadapi tantangan khusus, termasuk keterbatasan infrastruktur dan literasi teknologi. Evaluasi efektivitas pendampingan sistem informasi Gampong Balee menunjukkan dampak positif dalam meningkatkan transparansi dan efisiensi penyusunan program gampong. Meskipun Geuchik dan Ketua PKK Gampong Balee mengakui manfaatnya, namun pandangan masyarakat terbagi yaitu dengan beberapa dari mereka menyambut baik transparansi sistem, sementara yang lain mengatakan bahwa ketersediaan data masih sangat kurang bahkan sebagian yang lain belum bisa mengaksesnya. Disimpulkan bahwa diperlukan perhatian dan upaya yang lebih intensif untuk meningkatkan pemahaman dan aksesibilitas sistem informasi desa agar manfaatnya dapat dinikmati secara merata oleh seluruh masyarakat Gampong Balee melalui pendampingan dari Pemerintah Daerah, lembaga-lembaga mitra Pemerintah dan perguruan tinggi. Evaluasi ini memberikan dasar untuk perbaikan dan peningkatan penerapan teknologi informasi desa guna mendukung pembangunan yang lebih efektif dan inklusif.
Analysis of Information Technology Governance in the Transportation Agency using the Cobit 4.1 Framework Septiawan, Edo; Andini, Silfia; Rahmawati, Sri
Journal of Computer Scine and Information Technology Volume 10 Issue 4 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i4.112

Abstract

Information technology plays a role in supporting the goals of companies and government agencies by providing a fast, easy and accurate information and communication platform, increasing the effectiveness and efficiency of the planning process, and supporting innovation in companies and government agencies to develop. The existence of Information Technology has experienced development and the relationship between its operational roles in running an organization, where it is not only as a support for operations. Research was conducted at the Transportation Agency which concluded that the old system used was ineffective because the process carried out took time and space, making employee performance less efficient. After searching for data at the Transportation Agency by collecting data, it can be concluded that the information system used is still not optimal. Therefore, a system design was carried out. The results of the analysis were applied to a cobit system application program. After the research was conducted, the results of the calculation of the maturity model level obtained an average index of 3.16 ( Level 3). This means that the system. The new information system designed is expected to improve the quality of information and performance in the future
Improved feature extraction method and K-means clustering for soil fertility identification based on soil image Ramadhanu, Agung; Hendri, Halifia; Enggari, Sofika; Andini, Silfia; Devita, Retno; Rianti, Eva
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2001-2011

Abstract

This research is conducting analysis of digital land images using digital image processing techniques. The main purpose of the research is to classify soil fertility based on two-dimensional RGB colored digital soil images. The research is done by extracting features and shapes from the soil image. The research uses methods of segmentation, extraction, and identification against digital soil images. This research is carried out in three stages. The first phase of this research is image pre-processing which begins with the conversion of RGB color image to Grayscale then color conversion to binary which subsequently performs noise reduction with the method Three-layer median filter. The second stage is a process that is divided into the first two stages, namely the process of segmentation by grouping RGB color images into L*a*b which is continued by clustering using the K-means clustering method. The second is the extraction of characteristics of the soil image which is characteristic of shape and texture. The final stage is the identification of soil images that are clustered into two types: fertile soils and unfertile soil. The study achieved an accuracy of 85% which could accurately identify 20 images while inaccurately classifying 5 images out of a total of 25 input images.
Development of ResNet-18 architecture to lesion identification in breast ultrasound images Andini, Silfia; Sumijan, Sumijan; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1236-1248

Abstract

Breast ultrasound (USG) is widely used for early breast cancer detection, but challenges such as noise, low contrast, and resolution limitations hinder accurate lesion identification. This study proposes a modified residual network-18 (ResNet-18) architecture for breast lesion segmentation, aimed at improving detection accuracy. The methodology involves preprocessing steps including red green blue (RGB) to Grayscale conversion, contrast stretching, and median filtering to enhance image quality. The modified ResNet-18 model introduces additional convolutional layers to refine feature extraction. The proposed model was trained and validated on 30 breast ultrasound images, with evaluation metrics including accuracy, sensitivity, and specificity. Experimental results indicate that the modified architecture outperforms the baseline model, achieving an average accuracy of 0.97093, sensitivity of 0.90056, and specificity of 0.97705. Validation by a radiology specialist confirms the model’s clinical relevance. These findings suggest that the enhanced ResNet-18 model has the potential to assist radiologists in more accurately identifying breast lesions. Future research should focus on expanding the dataset, integrating multi-modal imaging, and optimizing model generalizability for real-time clinical applications. The study contributes to advancing artificial intelligence (AI)-driven breast cancer diagnostics, supporting early detection, and improving patient outcomes.
Optimization of Shape, Texture, and Color Extraction Methods in Concrete Strength Detection Ramadhanu, Agung; Hendri, Hallifia; Majid, Mazlina Abdul; Enggari, Sofika; Andini, Silfia; Hidayat, Rahmad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4164

Abstract

The growing demand for an accurate and rapid method to assess concrete strength has driven the development of non-destructive and cost-effective techniques. This paper aims to enhance the process of extracting shape, texture, and color features from concrete surface images to improve the accuracy of strength classification through digital image processing and artificial intelligence (AI). The study uses a dataset of 300 high-resolution photographs of concrete samples, categorized by their compressive strength levels: weak, moderate, and strong. These images were taken under controlled background and lighting conditions to ensure consistency. The methodology involves three stages: image preprocessing, feature extraction, and classification. During preprocessing, RGB images are converted to the Lab color space, and a three-layer median filter is applied to reduce noise. The K-Means clustering algorithm segments the images, and relevant features such as Metric, Eccentricity, Contrast, Correlation, Energy, Homogeneity, Hue, and Saturation are extracted. Among these, Correlation and Energy are the most influential in classification accuracy. The experimental results show that the proposed approach can reach up to 90 percent accuracy in classifying concrete strength into three categories. This suggests that visual features have strong potential to replace traditional destructive testing methods. The findings also point to the possibility of enhancing prediction accuracy with deep learning models and developing real-time, field-based evaluation tools to aid quality control in the construction industry.
Co-Authors Devita, Retno Enggari, Sofika GS , Achmad Daengs Halifia Hendri Hendri, Hallifia Iskandar Fitri, Iskandar Lukman Ibrahim Majid, Mazlina Abdul Ni Luh Wiwik Sri Rahayu Ginantra Raden Mohamad Herdian Bhakti Ramadhanu, Agung Rianti, Eva Rizkia, Eva Septiawan, Edo Sumijan Sumijan Wahyuni Sutari Hsb Wanto, Anjar Zia Farhan Afra