Article Per Year (5 Year)
p-Index From 2021 - 2026
1.745
P-Index
This Author published in this journals
All Journal Prosiding SNATIF International Journal of Artificial Intelligence Research Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal RESISTOR (Rekayasa Sistem Komputer) JURNAL SURYA ENERGY Building of Informatics, Technology and Science Jurnal Digital Teknologi Informasi Informanika Aptekmas : Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science
Emilia Hesti
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 17 Documents
Search

 1   2 
Penerapan Internet Of Things Dalam Rancang Bangun Telemedis Kadar Glukosa Suruso, Suruso; Simanjuntak, Luckyta; Hesti, Emilia
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 6 No. 2 (2023): Jurnal RESISTOR Edisi Agustus 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v6i2.1417

Abstract

Diabetes mellitus (DM) adalah kondisi di mana kadar glukosa dalam darah melebihi batas normal. Penelitian ini dilakukan untuk memantau kadar glukosa dan meminimalkan risiko diabetes. Salah satu solusi yang digunakan adalah implementasi Internet of Things (IoT). Tujuan dari penelitian ini adalah mengaplikasikan IoT dalam bidang medis untuk memantau kadar gula darah, di mana data dipantau melalui aplikasi Telegram dan ditampilkan pada layar LCD 16×2. Perangkat keras yang digunakan meliputi sensor max30100, Arduino Nano, ESP8266, LCD 16×2, dan sumber daya listrik 5V. Penelitian ini telah diuji pada 15 responden yang berbeda dan melibatkan dua tahap pengukuran, menggunakan alat penelitian yang dikembangkan dan perangkat medis glukosa standar. Hasil penelitian menunjukkan bahwa prototipe berhasil mendeteksi kadar gula darah dan memantau hasil deteksi yang ditampilkan melalui aplikasi Telegram. Percobaan ini menunjukkan bahwa perangkat yang dibuat oleh para peneliti menggunakan IoT dan sensor max30100 mencapai akurasi sebesar 94,73% dengan tingkat kesalahan 5,27% dibandingkan dengan perangkat medis standar sehingga penggunaaan IOT pada penelitian ini dapat mempermudah pekerjaan dan lebih hemat biaya dari alat standar medis.
Pengamanan Data Nilai Mahasiswa Menggunakan Algoritma Caesar Chiper dan RSA Berbasis Web Putri, Nurhaliza Aulia; Hesti, Emilia; Aryanti, Aryanti
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 7 No. 2 (2024): Jurnal RESISTOR Edisi Agustus 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v7i2.1645

Abstract

The development of technology and information in the current digital era has profoundly influenced human activities, particularly in the field of education. In this highly sophisticated age, all academic activities utilize technology with appropriate security measures to safeguard confidential data. Various security techniques, including cryptographic methods, have been developed to ensure data confidentiality. The methods employed for data security utilize the Caesar Cipher and RSA algorithms. In applying these algorithms, several stages are involved: the original data (plaintext) is encrypted using the Caesar Cipher algorithm by determining a shift value K  to displace each character. Subsequently, the data is encrypted using the RSA algorithm by determining two distinct keys p and  q. The RSA algorithm involves two keys, namely the public and private keys. The public key is used for encryption, while the private key is used for decryption processes. This research focuses on securing student grade data from unauthorized access and falsification using the Caesar Cipher and RSA methods. Test results indicate that both the Caesar Cipher and RSA encryption processes successfully transform plaintext into ciphertext that cannot be directly read without appropriate decryption processes.
PENYULUHAN ALAT PEMANGGANG LEMANG BAMBU PAGAR ALAM BERBASIS IOT UMKM PALEMBANG Emilia Hesti; M.Zakuan Agung; Ibnu Ziad
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 4 (2024): Aptekmas Volume 7 Nomor 4 2024
Publisher : Politeknik Negeri Sriwijaya

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

Abstract

Proses pemanggangan berperan penting untuk membuat makanan lebih tahan lama dan awet. Pada pembuatan lemang, tahap pemanggangan membutuhkan waktu sekitar dua hingga tiga jam, yang durasinya dipengaruhi oleh besar atau kecilnya bara api yang digunakan. Berdasarkan hasil survei lapangan oleh tim peneliti, ditemukan sejumlah kendala dalam proses pemasakan lemang, mulai dari lamanya waktu pemanggangan hingga risiko kesehatan akibat asap yang dihasilkan dari pembakaran bambu, yang dapat menimbulkan iritasi, kekurangan oksigen, dan sesak napas. Alat pemanggang lemang dirancang dengan menempatkan bahan bakar di bagian dasar agar panas dapat merata dan lemang matang sempurna. Proses pemanggangan memanfaatkan kayu sebagai bahan bakar untuk menghasilkan panas yang optimal. Alat ini mampu memanggang 7–10 lemang sekaligus dalam satu siklus. Dengan penerapan teknologi berbasis Internet of Things, pengaturan durasi pemanggangan dapat dilakukan lebih cepat dan efisien, sehingga meningkatkan produktivitas dan hasil produksi lemang bagi pelaku usaha.
Implementasi ESP32-CAM pada Sistem Identifikasi Pengunjung Perpustakaan Menggunakan QR Code Berbasis Internet of Things Salamah, Irma; Hesti, Emilia; Oktavia, Nadia
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 2 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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

Abstract

Perpustakaan yang berfungsi sebagai pusat sumber informasi, menjadi pondasi kemajuan institusi, terutama di bidang pendidikan. Sebagian besar perpustakaan yang ada di perguruan tinggi, sekolah, dan tempat umum masih menggunakan buku pengunjung manual untuk akses masuk dan keluar perpustakaan. Hal ini dianggap kurang efektif karena tidak menjamin keamanan data pengunjung. Penelitian ini bertujuan untuk membangun suatu sistem identifikasi menggunakan QR Code dengan mengimplementasikan fungsi dari modul kamera ESP32-CAM sebagai teknologi pemindaiannya, serta diintegrasikan dengan database menggunakan bahasa pemrograman berupa HTML dan PHP. Library quirc.h diimplementasikan untuk melakukan proses decoding pada QR Code, sehingga ESP32-CAM dapat difungsikan sebagai pemindainya. Pengujian dilakukan dengan cara melakukan pemindaian gambar QR Code ke modul ESP32-CAM, lalu sistem akan mendeteksi serta memproses gambar tersebut, jika gambar yang dipindai dinyatakan valid maka data pengunjung akan masuk ke database. Hasil pengujian yang telah dilakukan didapatkan tingkat keberhasilan sebesar 90% dengan rata-rata waktu scan sebesar 8.1 detik. Berdasarkan hasil uji yang didapatkan, sistem ini layak untuk diimplementasikan pada sistem identifikasi pengunjung perpustakaan.
Deteksi URL Phishing Menggunakan Natural Language Processing Dan Support Vector Machine Berbasis Machine Learning Nabila, Nabila; Hesti, Emilia; Aryanti, Aryanti
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7443

Abstract

Phishing represents a significant danger in cybersecurity, using malicious URLs to mislead users into revealing critical information. This research seeks to create a phishing URL detection model using machine learning via the integration of structural URL feature extraction, Natural Language Processing (NLP) methodologies, and the Support Vector Machine (SVM) classification algorithm. Indicators of phishing trends are derived from features such as URL length, the quantity of dots, and slashes, while URL content is quantified as numerical vectors using Term Frequency-Inverse Document Frequency (TF-IDF). All characteristics are subsequently integrated as input into a support vector machine model with a linear kernel for classification. The evaluation results from the classification report indicate that the integration of TF-IDF and linear kernel SVM achieves optimal performance, with 90% accuracy, 92% precision, 89% recall, and 90% F1-score. Conversely, the confusion matrix reveals 90.29% accuracy, 91.66% precision, 88.62% recall, and 90.12% F1-score. This study primarily contributes by integrating NLP and SVM into a unified adaptive phishing detection model via the amalgamation of structural and textual aspects of URLs. This strategy facilitates enhanced phishing detection relative to techniques reliant only on manual characteristics. This model, unlike other research that concentrated on particular instances or excluded NLP, is engineered to identify many categories of phishing URLs broadly, hence enhancing its relevance in tackling the dynamic nature of assaults.
Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
Traffic Density Classification Based on V2X Communication Using XGBoost Algorithm Harianja, Yusuf Alfredo; Suroso, Suroso; Hesti, Emilia
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 4 (2026): MALCOM October 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i4.2980

Abstract

This study aims to develop a traffic density classification model based on V2X communication simulation using the Extreme Gradient Boosting (XGBoost) algorithm. A simulation-based quantitative approach was employed, utilizing Simulation of Urban Mobility (SUMO) to generate vehicle mobility data and Network Simulator 3 (NS-3) to simulate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. Mobility and communication data were integrated using the simulation and step attributes, then aggregated to obtain average Quality of Service (QoS) parameters at each timestep. The features included average delay, average packet loss, average throughput, and average jitter. Traffic density levels were determined using the quantile method and categorized into three classes: Low, Medium, and High. The results indicate that the V2I model outperformed the V2V model, achieving accuracies of 78.79% and 59.42%, respectively. Feature importance analysis identified average delay as the most influential feature in the classification process for both communication scenarios. These findings demonstrate that integrating SUMO, NS-3, and XGBoost can effectively classify traffic density based on V2X communication QoS parameters. This approach has the potential to support the development of intelligent transportation systems by using vehicle communication data as a basis for predictive solutions for traffic monitoring and management. Furthermore, it offers opportunities for adaptive
Co-Authors Ade Silvia Handayani Adewasti, Adewasti Afifah, Luthfia Agung, Muhammad Zakuan Ahmad Taqwa ana, Sarj Apriliza, Anggia Nur Apriliza, Anggia Nur Aryanti Aryanti Asriyadi Asriyadi Fatima, Siti Harianja, Yusuf Alfredo Ibnu Ziad, Ibnu Irawan Hadi Irma Salamah Julianti, Amelia Kaila, Afifah Syifah M. Ardiansyah M.Zakuan Agung Nabila, Nabila Oktavia, Nadia Putri, Nurhaliza Aulia Rosita, Ella Sarjana Sarjana Sholihin Sholihin Simanjuntak, Luckyta Suroso Suroso Suruso, Suruso suzan zefi