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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Prosiding SNATIF Jurnal Informatika dan Teknik Elektro Terapan Proceeding SENDI_U Jurnal Terapan Abdimas PROtek : Jurnal Ilmiah Teknik Elektro International Journal of Artificial Intelligence Research Jurnal RESISTOR (Rekayasa Sistem Komputer) Informanika Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Masyarakat Madani Indonesia Innovative: Journal Of Social Science Research Jurnal Nasional Teknik Industri, Sistem Informasi dan Teknik Informatika CSRID
suzan zefi
Program Studi Teknik Telekomunikasi, Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya
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RANCANG BANGUN MODEL MONITORING UNDERGROUND TANK SPBU DENGAN MENGGUNAKAN SENSOR ULTRASONIK BERBASIS INTERNET OF THINGS (IOT) Sari, Sabrina Rianda; Halimatussa'diyah, RA; Zefi, Suzan; Duri, Rapiko
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5372

Abstract

The management of underground tanks at gas stations is a crucial aspect to ensure the availability and safety of fuel. However, conventional methods for monitoring fuel levels in tanks are often ineffective and prone to human error. To address this issue, this research designs and develops a model for an underground tank monitoring system using ultrasonic sensors Internet of Things (IoT) integrated technology. The ultrasonic sensors are used to measure the fuel level in the tank in real-time, and this data is then transmitted to an IoT platform for analysis and monitoring through a user interface that can be accessed remotely. This system is expected to enhance operational efficiency and safety in the management of underground tanks at gas stations and provide early warnings in the event of abnormal conditions, such as leaks or excessively low fuel levels. The implementation results demonstrate that this system can provide accurate and reliable information, enabling better decision-making in fuel stock management
Sistem Deteksi URL Phishing Menggunakan Random Forest dan Gradient Boosting untuk Pencegahan Kejahatan Dunia Maya Khairunnisya, Aqilla; Lindawati, Lindawati; Zefi, Suzan
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.296-310

Abstract

Phishing attacks through malicious URLs have become a critical cybersecurity threat, resulting in substantial financial losses and data exposures on a global scale. Conventional approaches like blacklisting and rule-based detection often fall behind as phishing methods become more advanced, including zero-day phishing URLs. In this research, machine learning models based on Random Forest and Gradient Boosting are designed and tested to accurately identify phishing URLs. The dataset, obtained from Kaggle, consists of 11,430 URLs with extracted features representing URL characteristics such as length, subdomain count, HTTPS status, and domain age. The two models underwent training and validation with the help of stratified train-test splits and cross-validation techniques. To evaluate the models, several performance indicators—such as accuracy, precision, recall, F1-score, and ROC AUC—were applied. Results from the experiments reveal that Gradient Boosting slightly exceeds the performance of Random Forest, achieving an accuracy of 98.0%, precision of 98.1%, and an F1-score of 98.0%. The best-performing model was integrated into a web application built with Streamlit, providing real-time phishing detection for end-users. This research contributes to developing adaptive and efficient phishing URL detection systems, enhancing cybersecurity defenses against evolving phishing threats. The implementation demonstrates practical applicability and ease of use for non-expert users.
Sosialisasi Penerapan Digitalisasi Dalam Penataan Administrasi Laboratorium Teknik Telekomunikasi Nurhaliza, Rindu; Zefi, Suzan; Anugraha, Nurhajar; Agung, M. Zakuan; Duri, Rapiko; Annisa, Annisa; Ramadhona, Yuris
Jurnal Masyarakat Madani Indonesia Vol. 4 No. 4 (2025): November
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/2xqh8b88

Abstract

Digitalisasi peminjaman alat dan ruangan di Laboratorium Teknik Telekomunikasi Politeknik Negeri Sriwijaya bertujuan meningkatkan efisiensi, mempercepat administrasi, dan mendukung sistem paperless. Sistem berbasis web ini dikembangkan dengan PHP dan MySQL serta diuji menggunakan XAMPP. Hasilnya, proses peminjaman menjadi lebih cepat, akurat, dan terdokumentasi dengan baik melalui fitur login, form peminjaman, validasi data, pemantauan status, dan laporan. Digitalisasi ini berhasil mengurangi kesalahan, meningkatkan transparansi, dan mempercepat pengambilan keputusan. Selain itu, kegiatan ini dilakukan dengan metode sosialisasi kemudian dilanjutkan dengan uji coba website oleh mahasiswa, Tenaga Kependidikan dan Dosen selaku user. Hasil uji coba website menunjukkan bahwa sistem yang dibuat layak digunakan dan diharapkan dapat membantu dalam pengelolaan alat dan peminjaman ruangan pada Laboratorium Teknik Telekomunikasi agar lebih efisien. Berdasarkan hasil kuesioner nilai rata-rata uji kelayakan sistem sebesar 86,1% sangat layak.
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.
Co-Authors Ade Silvia Handayani Ade Wasti, Ade Afifah, Luthfia Agung, M. Zakuan Agung, Muhammad Zakuan Ali Nurdin Annisa Annisa aryanti aryanti Asriyadi Asriyadi Asyraf, Muhammad Irfan Mishbahuddin Duri, Rapiko Emilia Hesti Febrianti Eka Yunita4 Halimatussa'diyah, RA Halimatussadiyah, RA Hj. Lindawati Indah Nurjanah Indri Sulistiana Irma Salamah Irma Salamah Kaila, Afifah Syifah Khairunnisya, Aqilla Kusmanto, RD. Lindawati Lindawati LINDAWATI, HJ. M. Ardiansyah Maharani, Luftya Marlianita, Marlianita Martinus Mujur Rose Muhammad Filly Primulia Nadia Putri D.L Ningrum, Shinta Sulistiya Nurhajar Anugraha Nurhaliza, Rindu Ovanta, Riris Juniarni Picara, Deya tasya Primulia, Muhammad Filly Ramadhona, Yuris Ranti Kartila Rapiko Duri Rodicky, Nadio Rosita, Ella Sari, Sabrina Rianda Sarjana Sarjana Shol ihin Sholihin Sholihin Sholihin Siswandi Sopian Soim, Sopian Tamaulina Br Sembiring UTAMI, FUTRI Utami, Futri UTAMI, FUTRI