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All Journal INOVTEK Polbeng - Seri Informatika Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Restikom : Riset Teknik Informatika dan Komputer West Science Social and Humanities Studies Jurnal Pengabdian Kepada Masyarakat Indonesia
Ata Amrullah
Universitas Islam Darul Ulum Lamongan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Implementasi dan Analisis Protokol Komunikasi IoT untuk Crowdsensing pada Bidang Kesehatan Ata Amrullah; M. Udin Harun Al Rasyid; Idris Winarno
Jurnal Inovtek Polbeng Seri Informatika Vol 7, No 1 (2022)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v7i1.2365

Abstract

Perkembangan teknologi informasi dan komunikasi telah menandai berlangsungnya era revolusi industri 4.0. Kemudahan pertukaran data antar perangkat yang bergerak menjadikan paradigma baru pada pengumpulan data terpusat yang disebut crowdsensing. Pada bidang kesehatan, crowdsensing tidak lagi mengandalkan telepon bergerak sebagai perangkat pengumpul informasi karena keterbatasan sensor tertanam pada telepon. Berbagai penelitian menggunakan crowdsensing telah mengandalkan kemampuan dari perangkat Internet of Things (IoT). Crowdsensing pada sektor kesehatan dapat membantu mengumpulkan sumber data yang substansial tentang kondisi kesehatan masyarakat secara umum. Namun, kebanyakan teknik crowdsensing hanya mengandalkan satu protokol komunikasi. Metode ini dapat menyebabkan masalah jika perangkat IoT menggunakan protokol komunikasi yang beragam. Oleh sebab itu, kami mengusulkan arsitektur gateway protokol multi-komunikasi untuk crowdsensing. Ketiga protokol komunikasi yang dijalankan pada gateway adalah MQTT, HTTP dan CoAP. Gateway ini berfungsi untuk menangkap data dari crowdsensor dan mengubah ketiga protokol ke dalam protokol yang sama dengan back-end server di cloud. Hasil pengujian menunjukkan bahwa gateway mampu menerima data dengan baik meskipun ketiga protokol dijalankan secara bersamaan. Protokol CoAP memiliki kinerja yang lebih baik daripada kedua protokol dalam pengujian throughput. Protokol MQTT memiliki performa terbaik pada pengukuran delay.
Analysis of IoT Technology Utilization and Inventory Management System on Operational Efficiency and Visitor Experience at Tourism Destinations in Bandung Loso Judijanto; Bambang Suharto; Ata Amrullah
West Science Social and Humanities Studies Vol. 2 No. 12 (2024): West Science Social and Humanities Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsshs.v2i12.1493

Abstract

This study examines the impact of IoT technology utilization and inventory management systems on operational efficiency and visitor experience at tourism destinations in Bandung City. Employing a quantitative research approach, data were collected from 220 respondents using a structured questionnaire with a Likert scale (1–5). The relationships among variables were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings reveal that both IoT technology and inventory management systems significantly enhance operational efficiency, which in turn positively impacts visitor experience. IoT technology showed a stronger direct effect on visitor experience, while inventory management systems had a greater influence on operational efficiency. Operational efficiency also played a mediating role, linking technological and management practices to improved visitor satisfaction. These results highlight the importance of integrating advanced technologies and efficient management systems to enhance the competitiveness and service quality of tourism destinations. The study provides actionable insights for tourism stakeholders to prioritize technology investments and strategic planning for sustainable growth.
Penerapan Dimensi Reduksi Pada Machine Learning Dalam Klasifikasi Kanker Payudara Berdasarkan Parameter Medis Jamilatul Badriyah; Nilam Ramadhani; Agung Muliawan; Khanun Roisatul Ummah; Ata Amrullah
Jurnal RESTIKOM : Riset Teknik Informatika dan Komputer Vol 6 No 3 (2024): Desember
Publisher : Program Studi Teknik Informatika Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/restikom.v6i3.379

Abstract

Kanker payudara adalah salah satu penyakit mematikan yang menyerang wanita karena jaringan payudara tumbuh tidak terkendali. Penelitian ini bertujuan untuk menerapkan teknik machine learning dalam klasifikasi kanker payudara untuk mendukung diagnosis yang lebih cepat dan akurat. Data yang digunakan dalam penelitian ini mencakup berbagai parameter medis seperti panjang radius, cekungan, jumlah titik cekung, posisi simetri, dimensi fraktal, luas, kehalusan dan lainnya. Metode yang digunakan dalam penelitian ini meliputi teknik klasifikasi seperti Deep learning dan Neural Network dengan kombinasi dimensi reduksi menggunakan Principal Component Analysis (PCA). Penggunaan dimensi reduksi dalam mengurangi kompleksitas data dan meningkatkan kinerja model. Hasil dari penelitian ini menunjukkan bahwa dimensi reduksi menggunakan Principal Component Analysis pada machine learning dapat meningkatkan akurasi kinerja model dengan akurasi tertinggi 96,84 pada Deep Learning
Strategi Pengembangan Destinasi Wisata Embung Popoan melalui Destination Branding di Desa Kepohagung, Tuban Ata Amrullah; Siti Alfiatur Rohmaniah; Miko Tri Afandi
Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Vol. 5 No. 2 (2025): Agustus: Jurnal Pengabdian Kepada Masyarakat Indonesia (JPKMI)
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpkmi.v5i2.8229

Abstract

This community service project aimed to empower the Kepohagung Village community by optimizing Embung Popoan's tourism potential through a comprehensive destination branding strategy. Despite its significant natural appeal, the destination was underdeveloped due to a lack of directed image management, resulting in low visitor numbers and limited local economic impact. Employing a participatory action research method, the team collaborated with local stakeholders to design and implement a new visual identity, including a distinct logo, interactive tourism map, and a compelling tagline. Furthermore, a robust digital promotional ecosystem was developed, featuring a user-friendly interactive website and physical merchandise. Preliminary post-implementation assessment revealed a significant increase in public and visitor awareness; early data indicated an estimated 20% rise in visitor inquiries and a 15% increase in social media engagement within the first two months. Visitor feedback highlighted improved information clarity and enhanced destination attractiveness. This initiative not only diversified local economic activities through merchandise sales but also fostered a stronger sense of local ownership, directly contributing to sustainable tourism development and community welfare in Kepohagung Village.
Analisis Sentimen Publik terkait Migrasi Tenaga Kerja Indonesia di Platform X menggunakan SVM-IndoBERT Ata Amrullah
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 11 No 1 (2025): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v11i1.9891

Abstract

Diverse public opinions on social and economic issues related to labor migration are often expressed on the social media platform X (Twitter). This research aims to classify public sentiment toward this phenomenon by analyzing tweets containing the hashtag "#KaburAjaDulu". Sentiment classification is performed by comparing two Support Vector Machine (SVM) approaches that utilize indoBERT embeddings, a language model designed to capture the nuances of the Indonesian language. Both SVM models are trained using web crawling data from the X platform, with the main difference lying in the application of hyperparameter tuning on one of the models. The data collected through web crawling from the X platform then undergoes a pre-processing stage that includes text normalization and stopword removal. The results show that the SVM model optimized through hyperparameter tuning achieved an accuracy of 90.5%, higher than the SVM model without tuning which achieved only 77.7%. This finding underscores the importance of hyperparameter tuning in improving the performance of sentiment classification models, especially when utilizing rich feature representations such as indoBERT embeddings to understand deeper language context.
Co-Authors Agung Muliawan Bambang Suharto Idris Winarno Jamilatul Badriyah Khanun Roisatul Ummah Loso Judijanto M Udin Harun Al Rasyid, M Udin Harun Miko Tri Afandi Ramadhani, Nilam Siti Alfiatur Rohmaniah