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All Journal Indonesian Journal of Electronics and Instrumentation Systems Elkom: Jurnal Elektronika dan Komputer Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal CoreIT Jurnal SOLMA Jurnal TAM (Technology Acceptance Model) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Informasi dan Komputer IJISCS (International Journal Of Information System and Computer Science) Jurnal Cendikia CCIT (Creative Communication and Innovative Technology) Journal JUSIM (Jurnal Sistem Informasi Musirawas) Applied Technology and Computing Science Journal Journal Software, Hardware and Information Technology Jurnal Algoritma Universal Raharja Community (URNITY Journal) RISTEC : Research in Information Systems and Technology Jurnal Teknologi dan Informatika (JEDA) Jurnal Alih Teknologi Komputer (ALTEK) Abdi Akommedia : Jurnal Pengabdian Masyarakat Journal of Information Systems Management and Digital Business Seminar Nasional Teknologi dan Multidisiplin Ilmu Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal TAM (Technology Acceptance Model) IJISCS (International Journal of Information System and Computer Science) Journal of Moeslim Research Technik Journal of Social Science Utilizing Technology
Hilda Dwi Yunita
Universitas Mitra Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Neuroscience Physics Social Sciences Transportation Other

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Evaluasi Keterlibatan Mahasiswa Dalam Lingkungan Pembelajaran Daring Menggunakan Natural Language Processing (NLP) dan Analisis Sentimen Hartantom, Budi; Yunita, Hilda Dwi; Fahurian, Fatimah; Dirayati, Fadhilah; Winarko, Triyugo; Marliana, Iin
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.22-1.2154

Abstract

This research aims to evaluate student engagement in online learning environments using Natural Language Processing (NLP) and sentiment analysis. The research method involves text analysis of student interactions on a Learning Management System (LMS) platform, including discussion forums, comments, and messages. NLP techniques were used to identify patterns of student engagement, while sentiment analysis assessed the emotions contained in the interactions, including positive, negative, or neutral sentiments. The results show that student engagement can be effectively measured through this analysis, as well as providing an overview of engagement patterns and the factors that influence them. The findings are expected to be used to improve the quality of online learning.
Optimization of Student Grade Data Management Using RESTful API and Microservices Architecture : Case Study at Universitas Mitra Indonesia hartanto, M. Budi; Fawaati, Teuku Muhammad; Fahurian, Fatimah; Yunita, Hilda Dwi; Zuhri, Khozainuz
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 2 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i2.3603

Abstract

The management of student grade data is a critical component of academic information systems that require high efficiency and reliability. At Universitas Mitra Indonesia, the old monolithic system faced challenges in scalability, security, and accessibility. This study proposes the implementation of RESTful API and microservices architecture to optimize the management of student grade data. RESTful API serves as the primary interface for inter-service communication, while microservices allow for independent management of modules such as user authentication, grade data processing, and academic service provision. Implementation results demonstrate a 35% increase in data access speed, a 25% reduction in server load, and enhanced security through token-based authentication. This study significantly contributes to modernizing academic information systems, especially in improving the performance and scalability of digital academic services.
Big Data Processing with Neural Networks on RESTful API for Product Recommendation Using Python hartanto, budi; Fahurian, Fatimah; Dwi Yunita, Hilda; Winarko, Triyugo
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.34704

Abstract

The exponential growth of e-commerce data has created an urgent need for efficient and scalable systems that provide personalized product recommendations. This study addresses that challenge by integrating big data processing with neural networks and delivering recommendations via RESTful APIs. The primary objective is to develop a system capable of handling large datasets and providing real-time recommendations to enhance user engagement. The methodology involves using Apache Spark for distributed big data processing and feature engineering, followed by the implementation of neural networks in Python using TensorFlow to generate recommendations. The system integrates the model with a RESTful API to support seamless interaction with external applications. Extensive testing was conducted on a dataset containing over a million user-item interactions to evaluate performance and scalability. The results show that the proposed system achieves better recommendation accuracy compared to traditional machine learning approaches. It processes high-dimensional data efficiently and maintains latency below 200 milliseconds per API request, making it suitable for real-time applications. The novelty of this research lies in the end-to-end design that combines a big data framework with neural networks and RESTful APIs for practical implementation. This research provides a scalable and adaptive solution for e-commerce platforms and serves as a foundation for the advancement of real-time recommendation systems in the future.
Pemberdayaan Masyarakat Desa Lampung Selatan Melalui Transfer Teknologi Python untuk Ekonomi Kreatif Hartanto, M. Budi; Yunita, Hilda Dwi; Fahurian, Fatimah; Winarko, Triyugo
Jurnal SOLMA Vol. 14 No. 2 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i2.18412

Abstract

Pendahuluan: Pemberdayaan masyarakat desa merupakan komponen penting dalam meningkatkan kesejahteraan sosial dan ekonomi, khususnya di wilayah Lampung Selatan. Namun, keterbatasan akses terhadap teknologi dan pengetahuan menjadi hambatan utama dalam pengembangan ekonomi kreatif yang berbasis digital. Studi ini bertujuan untuk mentransfer teknologi berbasis Python guna meningkatkan kompetensi pelaku usaha kecil dan menengah (UKM) di Kecamatan Kalianda, Kabupaten Lampung Selatan. Metode: Pelatihan berbasis praktik, lokakarya interaktif, pendampingan teknis, serta konsultasi intensif. Hasil: Adanya peningkatan dalam pemahaman dan penerapan teknologi digital, khususnya dalam manajemen usaha, analisis pasar, dan optimasi produksi. Sebanyak 65% peserta berhasil mengadopsi teknologi Python secara mandiri dalam usahanya, dengan peningkatan efisiensi operasional dan kesiapan digital. Kesimpulan: Transfer pengetahuan dan teknologi Python terbukti efektif dalam mendukung pengembangan ekonomi kreatif berbasis digital di desa, selama diiringi strategi pembelajaran yang tepat dan berkelanjutan.
PELATIHAN DASAR PYTHON UNTUK MENDUKUNG LITERASI PEMROGRAMAN DI SEKOLAH MENENGAH KEJURUAN PELITA PESAWARAN hartanto, budi; Fawaati, Teuku Muhammad; Fahurian, Fatimah; Yunita, Hilda Dwi; Zuhri, Khozainuz
Universal Raharja Community (URNITY Journal) Vol. 5 No. 2 (2025): URNITY (Universal Raharja Community)
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/urnity.v5i2.3613

Abstract

Pelatihan dasar Python menjadi langkah strategis untuk meningkatkan literasi pemrograman di kalangan pelajar Sekolah Menengah Kejuruan (SMK) sebagai persiapan menghadapi tantangan Revolusi Industri 4.0. Bahasa pemrograman Python dipilih karena sintaksisnya sederhana, fleksibel, dan banyak digunakan di berbagai bidang seperti data science, kecerdasan buatan (AI), dan Internet of Things (IoT). Program ini dirancang untuk memperkenalkan konsep pemrograman dasar kepada siswa SMK Pelita Pesawaran melalui pendekatan berbasis proyek. Materi pelatihan meliputi pengenalan sintaks dasar Python, implementasi logika pemrograman sederhana, hingga pembuatan aplikasi dasar berbasis data.Metode pelaksanaan terdiri atas pembelajaran teori secara daring dan praktik langsung melalui lokakarya tatap muka. Pelatihan ini bertujuan tidak hanya untuk meningkatkan pemahaman siswa tentang pemrograman, tetapi juga untuk memotivasi mereka agar dapat menerapkan Python dalam proyek inovatif di sekolah maupun dunia kerja. Hasil kegiatan menunjukkan peningkatan signifikan pada pemahaman siswa tentang pemrograman dan kemampuannya mengimplementasikan Python untuk menyelesaikan masalah nyata. Dengan dukungan dari pihak sekolah dan komunitas lokal, pelatihan ini diharapkan menjadi program berkelanjutan untuk mendukung pengembangan SDM yang siap bersaing di era digital. Basic Python training serves as a strategic step to enhance programming literacy among vocational high school (SMK) students, preparing them to face the challenges of the Fourth Industrial Revolution. Python was chosen due to its simple syntax, flexibility, and extensive applications in fields such as data science, artificial intelligence (AI), and the Internet of Things (IoT). This program is designed to introduce fundamental programming concepts to students of SMK Pelita Pesawaran through a project-based approach. The training materials include an introduction to Python syntax, implementation of basic programming logic, and the development of simple data-driven applications.The implementation method involves theoretical online learning and hands-on practice through in-person workshops. This training aims not only to enhance students' understanding of programming but also to motivate them to apply Python in innovative projects at school and in their future careers. Results from the activity demonstrated a significant improvement in students' programming comprehension and their ability to implement Python in solving real-world problems. With support from the school and the local community, this program is expected to become a sustainable initiative to foster the development of human resources ready to compete in the digital era.
Analisis Performansi Pendekatan Machine Learning Pada Deteksi Penyakit Daun Tanaman Kopi Purnomo, Rosyana Fitria; Yodhi Yuniarthe; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3302

Abstract

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.
SOFTWARE ENGINEERING FOR ZAKAT MANAGEMENT PLATFORMS: A STUDY ON TRANSPARENCY, SECURITY, AND USER TRUST Zuraida Zuraida; Rina Farah; Hilda Dwi Yunita
Journal of Moeslim Research Technik Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i6.2499

Abstract

The management of zakat is crucial in Islamic finance, and digital platforms have increasingly been adopted to enhance transparency, security, and trust among users. This study examines the software engineering aspects of zakat management platforms, focusing on these critical dimensions. The research aims to identify key software design considerations that can improve transparency, ensure data security, and foster user trust within digital zakat platforms. A mixed-method approach is used, involving both qualitative interviews with zakat management professionals and quantitative analysis of platform users' perceptions. The findings suggest that clear communication regarding financial transactions, robust data protection measures, and user-friendly interfaces are essential for building trust. Furthermore, implementing blockchain technology was found to significantly enhance transparency and security. The study concludes that for zakat platforms to be successful, they must not only comply with Shariah principles but also integrate advanced technology solutions that align with user expectations for security and transparency. This research provides a comprehensive framework for the development of zakat management platforms that can be adopted by stakeholders in the Islamic finance sector.
PELATIHAN PEMROGRAMAN PYTHON UNTUK MENINGKATKAN KETERAMPILAN TEKNOLOGI GURU DI ERA DIGITAL : PYTHON PROGRAMMING TRAINING TO IMPROVE TEACHER TECHNOLOGY SKILLS IN THE DIGITAL ERA Budi Hartanto; Hilda Dwi Yunita; Fatimah Fahurian; Triyugo Winarko; Iin Marliana; Yodhi Yuniarthe
Laporan Upaya Nyata Inovasi Ilmu Komputer JPKM Lunik - Vol 03, No. 01, April 2025
Publisher : FMIPA Unila

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

Abstract

Pelatihan pemrograman Python untuk meningkatkan keterampilan teknologi guru di era digital merupakan suatu upaya untuk menjawab tantangan perkembangan teknologi yang semakin pesat, khususnya dalam dunia pendidikan. Guru sebagai agen perubahan diharapkan dapat memanfaatkan teknologi untuk mendukung proses pembelajaran yang lebih efektif dan efisien. Tujuan dari pengabdian ini adalah untuk memberikan pelatihan kepada guru dalam menguasai pemrograman Python sebagai salah satu alat bantu dalam pembelajaran berbasis teknologi. Mitra dalam kegiatan ini adalah sejumlah guru dari beberapa sekolah yang memiliki latar belakang pendidikan yang beragam. Implementasi pengabdian dilakukan melalui serangkaian pelatihan praktis dengan pendekatan langsung pada penggunaan Python dalam pembelajaran. Hasil dari kegiatan ini menunjukkan adanya peningkatan pemahaman guru terhadap teknologi informasi, khususnya dalam penerapan Python untuk berbagai keperluan pengajaran, serta antusiasme yang tinggi dari peserta dalam mengikuti setiap sesi pelatihan. Kesimpulan dari kegiatan ini adalah pelatihan pemrograman Python efektif dalam meningkatkan keterampilan teknologi guru, yang dapat berkontribusi pada pembelajaran yang lebih inovatif di era digital.
PENINGKATAN LITERASI DIGITAL DAN KEAMANAN SIBER BAGI PELAKU UMKM DI BANDAR LAMPUNG MELALUI PELATIHAN BERBASIS TEKNOLOGI INFORMASI budi hartanto; Yudhinanto Cahyo Nugroho; Fatimah Fahurian; Hilda Dwi Yunita
Laporan Upaya Nyata Inovasi Ilmu Komputer JPKM Lunik - Vol 04, No. 01, April 2026
Publisher : FMIPA Unila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/lunik.v4i01.52

Abstract

Perkembangan teknologi digital yang pesat menuntut para pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) untuk memiliki kemampuan literasi digital dan kesadaran terhadap keamanan siber yang memadai. Di Kota Bandar Lampung, sebagian besar pelaku UMKM masih menghadapi tantangan dalam pemanfaatan teknologi informasi secara aman dan produktif. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan literasi digital dan pemahaman tentang keamanan siber melalui pelatihan berbasis teknologi informasi yang interaktif dan aplikatif. Mitra dalam kegiatan ini adalah kelompok UMKM di Kecamatan Sukarame, Bandar Lampung. Pelatihan dilakukan dengan metode workshop, pendampingan, dan simulasi langsung menggunakan perangkat digital yang dimiliki peserta. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan peserta memahami konsep keamanan digital, pengelolaan data, dan praktik aman dalam transaksi online. Selain itu, peserta mampu mengoptimalkan penggunaan media digital untuk promosi produk secara efektif dan aman. Kegiatan ini disimpulkan berhasil meningkatkan kesadaran dan keterampilan digital pelaku UMKM sebagai langkah menuju transformasi digital yang berkelanjutan di era ekonomi digital.
Analisis Performansi Pendekatan Machine Learning pada Deteksi Penyakit Daun Tanaman Kopi Yodhi Yuniarthe; Rosyana Fitria Purnomo; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/p2t2nm71

Abstract

Abstract. Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.   Keywords: Coffee Classification, Image Processing, Machine Learning, Plant Disease Detection.  
Co-Authors Ahmad ikhwan ahmad ikhwan Aldesion Denagi Zenda Amelia Hayati Anna, Eva Itma Ardiansyah Farid Aryatama, Apriyan Aulia, Pitri budi hartanto Budi Hartanto Desi Febriyani Devi Cantika Dirayati, Fadhilah Fatimah Fahurian Fawaati, Teuku Muhammad Hamdan Sukri Hamdan Sukri Hartanto, M Budi Hartanto, M.Budi Hartantom, Budi Helen Aggraini Hendro Pramono, Doni Eko iin marliana ikhwan, Ahmad Khozainuz Zuhri M. Budi Hartanto, M. Budi Mahon Nitin marliana, iin Nitin, Mahon Pangestu, Yoga Putra, Billy Sistian Putri Deli Suciati Rahmadi, Putra Rina Farah Rio Idha Rio Okida Romadhona, Rio Rosyana Fitria Purnomo Rudi Setiawan Satrianah Satrianah Sri Ipnuwati Tarnando, Kiki Triyugo Winarko Triyugo Winarko Triyugo Winarko, Triyugo Winariyani . Winarko , Triyugo Yodhi Yuniarthe Yudhinanto Cahyo Nugroho Zuraida Zuraida