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All Journal Indonesian Journal of Electronics and Instrumentation Systems TEKNIK INFORMATIKA 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 Cendikia CCIT (Creative Communication and Innovative Technology) Journal Istech Journal (International Science And Technology Journal) Jurnal Algoritma Universal Raharja Community (URNITY Journal) Jurnal Ilmu Komputer, Sistem Informasi, Teknik Informatika (JILKOMSITI) Jurnal Teknologi dan Informatika (JEDA) Abdi Akommedia : Jurnal Pengabdian Masyarakat Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Pengabdian Masyarakat Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal TAM (Technology Acceptance Model) Journal of Computer Science Advancements Journal of Loomingulisus ja Innovatsioon Journal of Social Science Utilizing Technology
Fatimah Fahurian
Program Studi S-1 Sistem Informasi, Fakultas Komputer, Universitas Mitra Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Meningkatkan Kinerja IoT dengan Reinforcement Learning: Integrasi Data Science dan GenAI untuk Industri 5.0 Winarko, Triyugo; Fahurian, Fatimah; Yunita, Hilda Dwi; Hartanto, M Budi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 9, No 1 (2025): SEMNAS RISTEK 2025
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v9i1.7948

Abstract

Perkembangan Internet of Things (IoT) dalam era Industri 5.0 memerlukan pendekatan yang lebih cerdas dan adaptif untuk meningkatkan kinerjanya. Salah satu metode yang dapat digunakan adalah Reinforcement Learning (RL), yang memungkinkan perangkat IoT belajar dan beradaptasi dengan lingkungan secara mandiri untuk mencapai tujuan tertentu. Penelitian ini bertujuan untuk mengkaji integrasi antara Data Science dan Generative AI (GenAI) dalam meningkatkan kinerja sistem IoT melalui pendekatan RL. Metode yang digunakan dalam penelitian ini adalah studi literatur yang mengidentifikasi penerapan RL dalam berbagai kasus IoT, serta pemanfaatan Data Science untuk analisis data besar dan GenAI untuk pengembangan model prediktif. Hasil penelitian menunjukkan bahwa penerapan RL pada sistem IoT dapat meningkatkan efisiensi operasional dengan meminimalkan latensi, optimasi penggunaan energi, dan peningkatan interaksi perangkat. Selain itu, integrasi Data Science dan GenAI memungkinkan pembuatan model yang lebih akurat dan responsif terhadap perubahan kondisi. Penelitian ini memberikan kontribusi dalam memahami potensi teknologi canggih untuk memajukan sektor industri menuju era 5.0, dengan tantangan dan peluang yang perlu diperhatikan untuk implementasi lebih lanjut.
ONLINE THESIS GUIDANCE MANAGEMENT INFORMATION SYSTEM AT MITRA INDONESIA UNIVERSITY Fatimah Fahurian; Khozainuz Zuhri
Jurnal TAM (Technology Acceptance Model) Vol 11, No 2 (2020): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v11i2.911

Abstract

The process of guiding the final project at the University of Mitra Indonesia has several obstacles when the process of working on student skirpsi is hampered, the difficulty of students and supervisors to conduct the guidance process. To overcome the existing problems, one effort is to build a thesis guidance information system. Therefore, the author proposes a thesis guidance information system that can be accessed anywhere and anytime. The online thesis guidance system that has been built can be applied to facilitate lecturers in examining and revising documents sent by students so that the design of an online thesis guidance system can help the final project guidance process at theUniversity of Mitra Indonesia, starting from the student registration process, sending files and interacting with the guidance process between lecturers and students. In addition, with the existence of an online thesis guidance system, it is expected that the process of implementing thesis guidance for students and lecturers will become clearer and more targeted.
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.
Penerapan Metode Simple Additive Weighting (SAW) Pada Sistem Pendukung Keputusan Kelayakan Sertifikasi Guru Sutriana, Mrs; Inuz, Khozainuz; Fahurian, Fatimah
Jurnal Teknologi dan Informatika (JEDA) Vol 6, No 1 (2025): APRIL 2025
Publisher : Universitas Mitra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57084/jeda.v6i1.1991

Abstract

SMK Persada Bandar Lampung merupakan salah satu lembaga pendidikan yang memiliki permasalahan seperti pada proses penilaian kelayakan sertifikasi guru yang belum tersistem dimana sering terjadinya kekeliruan dalam proses penilaian. Proses penilaian kelayakan sertifikasi guru masih bersifat subjektif dan membutuhkan waktu untuk mengolah hasil dan dalam proses penilaian kelayakan sertifikasi guru kinerja guru dimungkinkan terjadi kesalahan. Berdasarkan uraian tersebut SMK Persada Bandar Lampung membutuhkan suatu sistem pendukung keputusan yang diharapkan dapat menemukan solusi dalam proses penentuan guru terbaik. Sistem keputusan penilaian kelayakan sertifikasi guru yang dapat mempermudah proses pengolahan data hasil penilaian penilaian kelayakan sertifikasi guru di SMK Persada Bandar Lampung. Oleh karena itu peneliti mengusulkan suatu rekayasa perangkat lunak sistem pendukung keputusan dengan menggunakan Simple Additive Weighting (SAW). Sistem ini dapat digunakan untuk memudahkan dalam proses penilaian kelayakan sertifikasi guru di SMK Persada Bandar Lampung untuk membantu sekolah khususnya kepala sekolah dalam mengevaluasi kekurangan dan kelebihan kinerja guru di SMK Persada Bandar Lampung.
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.
PKM Dosen Umitra : Terapkan Hasil Inovasi Pertanian Berbasis IoT Untuk Pengusiran Hama Burung Sawah Zuhri, Khozainuz; Fahurian, Fatimah; Ananta, Regi Pramudia
ABDI AKOMMEDIA : JURNAL PENGABDIAN MASYARAKAT Vol. 3 No. 2 (2025)
Publisher : ABDI AKOMMEDIA : JURNAL PENGABDIAN MASYARAKAT

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

Abstract

Salah satu hama padi adalah burung pipit, karena biasanya burung pipit ini akan mulai makan padi ketika padi berbuah sampai panen. Burung pipit selalu berkelompok ketika terbang dan makan padi, sehingga jika jumlahnya banyak tentu bulir padi yang di makan akan semakin banyak dan tidak hanya satu hari saja, melainkan setiap hari. Untuk mengatasi masalah tersebut maka tim PKM Fakultas Komputer Universitas Mitra Indonesia dengan memanfaatkan teknologi melakukan demonstrasi penggunaan teknologi Hasil Inovasi Pertanian berbasis IoT untuk Pengusiran Hama Burung Sawah. Selain itu kegiatan sosialisasi dan demontrasi dilakukan melalui beberapa sesi, sehingga peserta memiliki waktu yang cukup untuk memahami teknologi tersebut dan mengembangkan keterampilan dalam menggunakan produk hasil teknologi inovasi. Penerapan hasil Inovasi Pertanian berbasis IoT untuk Pengusiran Hama Burung Sawah dapat berjalan dengan baik. Kegiatan penerapan teknologi IOT melalui demontrasi alih teknologi secara langsung pendampingan (transfer ilmu) melalui kegiatan sosialisasi dan demontrasi alih teknologi IoT. Hasil pelaksanaan kegiatan pengabdian ini diharapkan dapat mengoptimalisasi produktivitas dan kapasitas sekaligus menjawab upaya-upaya yang telah dilakukan sebelumnya dan dapat meningkatkan hasil panen sekaligus akan berdampak positif bagi perekonomian mitra desa Tanjung Harapan Kecamatan Seputih Banyak Lampung Tengah.
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.
Sentiment Analysis of Twitter Discussions About Lampung Robusta Coffee: A Comparative Study of Machine Learning Algorithms with SVM as The Optimal Model Yuniarthe, Yodhi; Syarif, Admi; Shofi, Imam Marzuki; Fatimah Fahurian
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.41316

Abstract

Lampung Robusta coffee is an important commodity in Indonesia, particularly in terms of local economic potential and global recognition. However, public perception of this product on social media, particularly Twitter, remains underexplored. This study addresses the need for a deeper understanding of consumer sentiment towards Lampung Robusta coffee, which could inform branding and marketing strategies. To approach this issue, we used five supervised machine learning algorithms-KNN, Naive Bayes, SVM, Decision Tree, and Logistic Regression-to perform sentiment classification on a dataset of tweets containing relevant keywords. The dataset was pre-processed using standard natural language processing techniques, including tokenization, stopword removal, and TF-IDF feature extraction. The SVM achieved the best performance on the unbalanced dataset for all metrics, with high and consistent accuracy and F1 scores. Logistic regression followed closely with similarly strong and stable results. Therefore, SVM is recommended as the final model. These results suggest that machine learning approaches can effectively classify sentiment in social media discussions about regional agricultural products and that random forest may provide the most robust performance in this context  
Co-Authors Adit Kusnandar Admi Syarif Ahmad Ihkwan Ahmad ikhwan ahmad ikhwan Amelia Hayati Ananta, Regi Pramudia Arie Setya Putra Arief Rahman Wahid Budi Hartanto budi hartanto Deni Hardiannata Dirayati, Fadhilah Fathurrahman Kurniawan Ikhsan Fawaati, Teuku Muhammad Febi Andika Putra Ferry Susanto Hafiz, Aliy Hamdan Sukri Hartanto, M Budi Hartanto, M.Budi Hartantom, Budi Hilda Dwi Yunita Icha Nuari iin marliana ikhwan, Ahmad Inuz, Khozainuz Khozainuz Zuhri Kusnadi Kusnadi M. Budi Hartanto, M. Budi Mahon Nitin marliana, iin Maulidun Nur Ming Pong Nitin, Mahon Pramana, Ahmad Abilah Putra, Billy Sistian Romi Hendri Rosyana Fitria Purnomo Ryan Teo Shofi, Imam Marzuki Sri Ipnuwati Sutriana, Mrs Teddy Oswari Triyugo Winarko Triyugo Winarko, Triyugo Wati, Deva Imelda Setia Wijayanti, Mira Winarko , Triyugo Yodhi Yuniarthe Yudhinanto Cahyo Nugroho