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All Journal Abdi Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pengabdian Kepada Masyarakat Jurnal Algoritma Scientific Journal of Informatics Jurnal ilmiah teknologi informasi Asia
Safitri, Pima Hani
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Creating E-commerce for MSMES in Panjunan Village: Steps towards a Smart Village Rahmawati, Dyah Putri; Amri, Arni Muarifah; Rahmawati, Dewi; Safitri, Pima Hani
Abdi Masyarakat Vol 6, No 2 (2024): Abdi Masyarakat
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58258/abdi.v6i2.7300

Abstract

Micro, small, and medium enterprises (MSMEs) are crucial sectors that support economic growth in Indonesia. In the massive development of the digital economy, there has been a shift from offline to online transactions. MSME actors are now required to adopt technology to follow consumer shopping trends. One way to do this is by implementing e-commerce. The development of MSMEs through e-commerce is a smart step to increase visibility and market access. By leveraging e-commerce platforms, MSMEs can reach a wider audience, boost sales, and expand their business footprint without geographical limitations. In addition to digitalization, MSMEs need guidance in online sales, inventory management, digital marketing, and financial management. This guidance helps MSMEs maximize their potential in e-commerce. Desa Panjunan in Duduksampeyan District, Gresik Regency, is one village with MSME potential that needs to be developed through e-commerce. This community service activity will include creating an MSME e-commerce website for the village as a step towards becoming a Smart Village. The developed e-commerce has been tailored to the characteristics and needs of MSME actors in Desa Panjunan. This e-commerce initiative in Desa Panjunan can open up broader market access for local products, improve competitiveness, product quality, and MSME business management, as well as increase the income and welfare of the local community. 
Analisa Kematangan Manajemen Layanan Teknologi Informasi Dengan Standar Information Technology Infrastructure Library (ITIL V3): Studi Kasus Pengadilan Negeri Bondowoso Alhari, Muhammad Ilham; Safitri, Pima Hani; Amri, Arni Muarifah; Ramadan, Arip
Jurnal Ilmiah Teknologi Informasi Asia Vol 18 No 2 (2024): Volume 18 nomor 2 2024 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v18i2.974

Abstract

This study aims to analyze the maturity level of Information Technology (IT) service management in Bondowoso District Court using the Information Technology Infrastructure Library (ITIL) version 3 (V3) framework. The use of IT in the state court has become crucial to support daily operations, efficiency, and quality of service to the community. The research method used is a case study, with data collection through interviews, observation, and document analysis. The results showed that Bondowoso District Court has a level of IT service management maturity that still needs to be improved. ITIL V3 is used as a guideline to evaluate IT service management processes in such courts. The results of this research can also be an important contribution in the context of developing IT service management in similar institutions, focusing on the application of the ITIL V3 framework. It is hoped that with corresponding improvements, the court can reach a higher level of IT service management maturity and provide better service to society. Keywords: Information Technology Infrastructure Library (ITIL); Pengadilan Negri Bondowoso, Manajemen layanan
A Deep Learning Model Comparation for Diabetic Retinopathy Image Classification Mustaqim, Tanzilal; Safitri, Pima Hani; Muhajir, Daud
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20939

Abstract

Purpose: This study compares the performance of various deep learning models for diabetic retinopathy (DR) classification, emphasizing the impact of different optimization functions. Early detection of DR is vital for preventing blindness, and the research investigates how optimization functions influence the classification accuracy and efficiency of several convolutional neural networks (CNNs). This study fills a gap in the existing literature by examining how optimization functions affect model performance in conjunction with architectural considerations. Methods: This paper uses the APTOS 2019 dataset, which comprises 3,663 retinal fundus images classified into five classes of diabetic retinopathy severity. Four CNN-based models, including CNN, ResNet50, DenseNet121, and EfficientNet B0, were trained using five optimization techniques: Adam, SGD, RMSProp, AdamW, and NAdam. The performance of the experimental scenarios was evaluated through accuracy, precision, recall, F1-score, training duration, and model size. Result: EfficientNet B0 demonstrated superior computational efficiency with a minimal model size of 16.16 MB. Subsequently, DenseNet121 with the SGD optimizer achieved the highest test accuracy of 96.86%. The experimental results indicate that the optimizer significantly influences model performance. AdamW and NAdam yield superior outcomes for deeper architectures such as ResNet50 and DenseNet121. Novelty: This paper offers an analytical examination of deep learning models and optimization techniques for DR classification, helping to clarify the trade-offs between computational efficiency and classification performance. The findings contribute to the development of more accurate and efficient DR detection systems, which could be utilized in real-world, resource-limited settings.
Implementasi Digitalisasi Pembuatan Rapor untuk TPQ Al-Mubaarok Surabaya dalam Mendukung Evaluasi Santri Vessa Rizky Oktavia; Rausanfita, Alqis; Safitri, Pima Hani; Satria Bahari Johan, Ahmad Wali
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 5 No 1 (2025): Mei 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v5i1.4102

Abstract

Taman Pendidikan Al-Quran (TPQ) is a non-formal educational institution. Often, TPQ prioritizes the quality of educational materials so that it slightly ignores the administrative aspects. A TPQ ​​usually contains asatidz (ustadz and ustadzah) who have expertise in the field of religion, but do not have staff who are experts in administrative matters. Not a few TPQs still use manual recording such as using paper to make report cards. As a result, there are still often errors in recording grades on report cards and asatidz who find it difficult to manage report cards. The problems faced by asatidz become more complicated when the number of students increases. Digitalization is a solution that can solve the problems faced by TPQ. With the digitalization of report cards, asatidz will easily manage student grades and print report cards massively in a short time. Application creation is carried out by analyzing needs, designing interfaces, implementing, and training. This activity was carried out by a community service team from Telkom University and partners of TPQ Al-Mubaarok Surabaya. The result is an application that has reliability in managing student grade data. This application can be accessed from anywhere by TPQ, making it easier for TPQ to manage student report cards. Positive impacts felt include reducing errors when entering data and ease in storing digital data.
Rancang Bangun Prototipe Sistem Deteksi Dini Retinopathic Diabetic Berbasis Website Muhajir, Daud; Mustaqim, Tanzilal; Safitri, Pima Hani; Oktavia, Vessa Rizky
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2255

Abstract

Diabetic Retinopathic (DR) is one of the retinal disorders caused by high blood sugar levels. There are fewer ophthalmologists available, and treating DR patients manually is a time-consuming process. Therefore, there is a need for an automatic DR early detection method using Deep Learning. The purpose of this research is to build a web-based DR early detection prototype with retinal image classification using the DenseNet121 Deep Learning model and the Stochastic Gradient Descent (SGD) optimizer to improve the accessibility and efficiency of screening. The software development method used in this research is waterfall which consists of analysis phase, design phase, implementation phase, and testing phase. To ensure the prototype runs as planned, black-box testing is carried out on each of its features to ensure system functionality in accordance with predetermined specifications. This research produces a RD early detection prototype that has been tested with all 16 test cases and has a suitable status. Future research can be carried out further system development by involving real users such as ophthalmologists and can be applied in hospitals.
Implementasi Sistem Perpustakaan Digital Berbasis Website untuk Peningkatan Efisiensi dan Kualitas Layanan Satria Bahari Johan, Ahmad Wali; Safitri, Pima Hani; Rachmaniar, Desita Nur; Wicaksono, Ardian Yusuf; Sebastian, Gerrard; Firdaus, Raihan; Saputra, Ananda Bintang; Putra, Muhammad Alifian; Krishna Lokajaya, Gabriel Azarya
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 3 (2025): Edisi Juli - September
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i3.6455

Abstract

Perpustakaan sebagai fasilitas penunjang yang menyediakan sumber bacaan mengelola bahan pustaka, baik berupa buku maupun bukan berupa buku untuk mendukung proses pembelajaran di dunia Pendidikan. Layanan perpustakaan yang masih bersifat konvensional ataupun manual banyak sekali menimbulkan berbagai kendala dalam pemberian dan pemenuhan layanan. Solusi permasalahantersebut dengan menghadirkan terobosan terbaru dibidang perpustakaan yaitu terciptanya inovasi produk perpustakaan digital yaitu berupa website. Metode yang digunakan dalam kegiatan pengabdian kepada masyarakat ini adalah sosialisasi hasil pengembangan website perpustakaan. Hasil kegiatan pengabdian kepada masyarakat ini berupa website perpustakaan yang terdiri atas informasi-informasi seperti koleksi buku favorit, fitur-fitur utama yang dapat dinikmati, layanan yang dapat diakses, lokasi perpustakaan serta informasi mengenai sosial media dan contact person maupun hotcall. Hal ini menunjukkan bahwa peran serta teknologi informasi menjadi solusi yang efektif bagi permasalahan perpustakaan konvensional
Freshwater Filling Optimization Based on Price Using XGBoost and Particle Swarm Optimization on Cargo Ship Voyage Yulianto, Ilham; Fauzi, Muhammad Dzulfikar; Safitri, Pima Hani
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24988

Abstract

Purpose: Efficient freshwater management is critical in cargo ship operations, yet current practices often involve fixed refilling strategies that ignore price differences across ports and fail to predict actual consumption accurately. These inefficiencies lead to unnecessary operational costs. To address this, the study introduces a combined approach using XGBoost for predict freshwater usage and Particle Swarm Optimization (PSO) to minimize refilling costs through optimal port selection. Methods: Freshwater demand was predicted using an XGBoost regression model trained on real operational data from 2024, which included historical voyage distances and freshwater consumption records from cargo ships. Based on these predictions, Particle Swarm Optimization (PSO) was applied to identify cost-efficient refilling locations along each ship’s route, minimizing total water procurement cost while satisfying operational constraints. The proposed framework was validated through simulated voyage scenarios to evaluate its impact on cost efficiency and planning effectiveness. Result: The integration of XGBoost and PSO effectively optimized freshwater refilling strategies, achieving a relative prediction error of 9.48% in freshwater consumption prediction and cost savings from 9 to 40% from across 3 ships sample through strategic port selection based on consumption patterns and price variability. Novelty: Unlike prior works focused on fuel or generic logistics optimization, aim of this study is to combine XGBoost and PSO for optimizing freshwater refilling on cargo ship voyages using actual operational data. The results demonstrate practical, scalable improvements in cost efficiency, making a novel contribution to maritime resource planning.
Analisis Perbandingan Metode Preprocessing untuk Citra Retinopati Diabetik Menggunakan Deep learning Safitri, Pima Hani; Mustaqim, Tanzilal; Muhajir, Daud
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2324

Abstract

Retinopati diabetik adalah gejala yang disebabkan oleh komplikasi diabetes yang menyerang mata penderitanya. Bercak-bercak pada retina penderita menjadi ciri gejalanya. Semakin banyak bercak, maka semakin parah retinopati diabetik yang diderita. Upaya peneliti untuk mendeteksi retinopati diabetik dengan citra retina sudah mulai dikembangkan dengan teknologi kecerdasan buatan, salah satunya berbasis deep learning. Kesulitan selanjutnya adalah kualitas citra retina yang kurang baik, sehingga mengakibatkan hasil deteksi yang kurang baik. Oleh karena itu, penelitian ini mengusulkan analisis perbandingan teknik untuk meningkatkan akurasi pengolahan citra deteksi retinopati diabetik berbasis deep learning. Data yang digunakan adalah data APTOS2019, yang terdiri dari 5 kelas berdasarkan tingkat keparahan penyakit. Ada tiga teknik yang digunakan: CLAHE, gamma correction, dan Retinex. Arsitektur deep learning yang digunakan adalah DenseNet121 dan EfficientNetB0 karena telah banyak digunakan pada data citra medis. Hasilnya, kombinasi gamma correction dan DenseNet121 menghasilkan akurasi tertinggi yaitu 81,4%. Sedangkan akurasi terendah diperoleh dari kombinasi menggunakan Retinex. Arsitektur terbaik secara keseluruhan adalah EfficientNetB0, dengan rata-rata akurasi sebesar 81,9%. Selanjutnya, penelitian ini dapat digunakan untuk memperbaiki citra retinopati diabetik sehingga deteksi dapat dilakukan sedini mungkin.
Peningkatan Sensitivitas Deteksi Diabetic Retinopathy melalui Mekanisme Hierarchical Self-Attention pada Swin Transformer Mustaqim, Tanzilal; Safitri, Pima Hani; Oktavia, Vessa Rizky
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2986

Abstract

Diabetic Retinopathy (DR) is a complication of diabetes that can cause blindness if not detected early. CNN has limitations in capturing scattered lesions due to its narrow receptive field, while Vision Transformers are generally less computationally efficient. The objective of this study is to develop an approach that can capture long-range spatial dependencies while maintaining computational efficiency for resource-limited clinical applications. The Swin Transformer-Tiny was implemented with a shifted window-based hierarchical self-attention mechanism on the APTOS 2019 dataset (3,663 retinal images), with pre-processing (CLAHE, gamma correction, Gaussian filtering) and data augmentation. The model was trained using SGD with CosineAnnealingLR and evaluated based on accuracy, precision, recall, and F1-score with a focus on minimizing false negatives. Swin Transformer-Tiny achieved an accuracy of 84.99%, precision of 84.89%, and recall of 84.99%, surpassing EfficientNet-B0 by 1.32% in F1-score and outperforming ResNet50 by 5.60%. The attention mechanism reduces false negatives by 1.28% compared to conventional CNNs while maintaining linear computational complexity. This research contributes to showing that hierarchical self-attention in Swin Transformer effectively improves DR detection sensitivity by overcoming the limitations of CNN receptive fields, while maintaining computational efficiency for clinical implementation.
Co-Authors Ahmad Wali Satria Bahari Johan Alqis Rausanfita Ardian Yusuf Wicaksono Arip Ramadan Desita Nur Rachmaniar DEWI RAHMAWATI Fauzi, Muhammad Dzulfikar Firdaus, Raihan Krishna Lokajaya, Gabriel Azarya Muhajir, Daud Muhammad Ilham Alhari Putra, Muhammad Alifian Rahmawati, Dyah Putri Saputra, Ananda Bintang Sebastian, Gerrard Tanzilal Mustaqim Vessa Rizky Oktavia Yulianto, Ilham