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All Journal Jurnas Nasional Teknologi dan Sistem Informasi UNEJ e-Proceeding JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Informatika Global Jurnal Teknologi Terpadu Progresif: Jurnal Ilmiah Komputer Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Jurnal Generic Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Paradigma SENTRI: Jurnal Riset Ilmiah MDP Student Conference Jurnal Rekayasa Sistem Informasi dan Teknologi Applied Information Technology and Computer Science (AICOMS) JISCOMP (Journal of Information System and Computer) INOVTEK Polbeng - Seri Informatika
Abdul Rahman
Universitas Multi Data Palembang
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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Pengembangan Sistem Pemantauan Kebakaran Real-Time dan Peringatan Dini Menggunakan Teknologi LoRa pada Kawasan Perkotaan Abdul Rahman; Eka Puji Widiyanto
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 12 (2025): SENTRI : Jurnal Riset Ilmiah, Desember 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i12.5196

Abstract

Fires in urban areas require reliable monitoring and early warning systems to reduce potential losses. This study aims to evaluate the performance of components that build an urban fire monitoring and early warning system, consisting of the DHT22 temperature and humidity sensor, MQ-2 smoke sensor, 5-channel IR flame detector, and LoRa E220-900T22D communication module as the main components of the IoT-based system. The testing methods include measuring the accuracy of temperature and humidity using the DHT22, smoke detection tests with MQ-2, fire detection distance tests with the IR flame detector, and communication range tests of LoRa in urban environments. The results show that the DHT22 provides high accuracy with an average of 98.67% for humidity and 97.82% for temperature. The MQ-2 consistently detects varying smoke concentrations, while the IR flame detector perfectly detects fire at a distance of 90 cm on all channels and only on certain channels at 300 cm. The LoRa module demonstrates an effective range of 1–2 km with relatively high reliability, though performance decreases beyond 3 km due to physical obstacles and signal interference. Overall, the system can be effectively implemented for urban fire monitoring and early warning, although additional strategies such as denser gateway placement or mesh architecture are required to improve communication stability.
Keyword Extraction Abstrak Jurnal Ilmiah Menggunakan Metode TF-IDF dan KeyBERT Rayvin Suhartoyo; Valen Julyo Armando Davincy Lin; Hafiz Irsyad; Abdul Rahman
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1814

Abstract

Keyword extraction is a significant technique in natural language processing (NLP) that serves to summarize the essence of a document, such as a scientific journal summary. This study aims to analyze the effectiveness of two keyword extraction methods, namely Term Frequency-Inverse Document Frequency (TF-IDF) and KeyBERT, in finding significant keywords from a collection of scientific journal abstracts. The dataset used consists of several scientific journal abstracts accompanied by manual keywords as a basis for assessment. The TF-IDF method relies on the frequency of words in the document, while KeyBERT utilizes a cosine similarity approach based on the BERT transformer model to determine the most meaningful keywords. The research findings show that the KeyBERT method and the TF-IDF method have a moderate level of similarity with semantic similarity values ​​of 0.578 for the KeyBERT method and 0.469 for the TF-IDF method, respectively. These results show significant potential for the use of machine learning and deep learning-based models with both methods for topic classification systems, especially in the fields of information retrieval and text mining.
PELATIHAN PENGGUNAAN SISTEM MONITORING KUALITAS AIR KOLAM DI DINAS PERIKANAN OGAN KOMERING ILIR SUMATERA SELATAN Abdul Rahman; Dedy Hermanto; Mulyati Mulyati; Inayatulah Inayatulah
FORDICATE Vol 5 No 2 (2026): April 2026
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v5i2.15744

Abstract

Inefficient water monitoring remains a challenge for aquaculture in Ogan Komering Ilir. This community service initiative aims to equip local Fishery Service staff with the skills to operate digital water quality monitoring systems. Using socialization, tool demonstrations, and hands-on practice, participants were trained to accurately monitor crucial parameters such as pH, temperature, and oxygen levels. The results indicate a strengthening of technical capacity in adopting sensory systems for pond oversight. By transitioning from manual to digital methods, staff can now detect water quality degradation early to prevent farmer losses. This program is expected to drive the modernization of the fishery sector in South Sumatra by optimizing the supervisory functions of the relevant agency.
Classification of Dog Emotion Using Transfer Learning on Convolutional Neural Network Algorithm Steven Tribethran; Nicolas Jacky Pratama Hasan; Abdul Rahman
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.5295

Abstract

Recognizing your pet's emotions are very important to improve health, welfare and to detect certain diseases in the animal. The emotions in question are categorized into four categories, namely anger, happiness, calmness, and sadness. The model is designed by utilizing transfer learning techniques using the VGG16 architecture to perform image feature extraction for dog emotion classification based on the image of the animal's facial expression. The research produced an accuracy value of 96.72% on the training set and 88.05% on the validation set, as well as an average F1-Score value of 84.30% on the test set. This research shows the great potential of utilizing transfer learning in dog emotions classification and contributes to more advanced emotion recognition techniques to improve pet’s welfare.
Optimization of Household Energy use Prediction Using Random Forest with Genetic Algorithm Feature Selection Nyimas Nisrinaa Kamilah; Abdul Rahman
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/zedpkg51

Abstract

Electrical energy consumption continues to increase every year, so accurate prediction models are needed to support household electrical energy efficiency. This study analyzed high-resolution household electricity consumption data using the Random Forest (RF) algorithm and evaluated the influence of feature selection based on Genetic Algorithms (GA) in improving the performance of RF predictions. The base RF model achieves an RMSE of 0.6148, a MAE of 0.3478, and an R² of 0.5047. After implementing GA-based feature selection, the RF model with GA yields an RMSE of 0.6125 and an R² of 0.5084, indicating a marginal performance improvement. However, the MAE value increased slightly to 0.3503, which suggests that the increase was not uniform across the evaluation metrics. Overall, the RF approach with GA offers a modest improvement in prediction stability but with very limited accuracy gains, which highlights its potential and limitations in optimizing household energy consumption prediction.
Co-Authors Achmad Rizky Zulkarnain Akbar, Naufal Arifin Arifin Aurelia, Reni Axel Natanael Salim Billy Fernandes Cindy Meilani Daniel Udjulawa Dedy Hermanto Dewa Dewa Eka Puji Widiyanto Hafiz Irsyad Iis Pradesan Inayatulah Inayatulah Jacky Pratama Hasan, Nicolas Jasuandi Wijaya Jeason Lie Johan Halim Kamilah, Nyimas Nisrinaa Kevin Sanjaya Krispinus Pierre Dimas P Molavi Arman Muhammad Sya’ban Nugroho mulyati mulyati Nicolas Jacky Pratama Hasan Nyimas Nisrinaa Kamilah Prawinnetou, Wassy Rayvin Suhartoyo Rivaldo Rivaldo Rizky, Azzar Sachin Sabloak Saputra, Joneten Shandy Shinta Puspasari Steven Tribethran Tasya Angelya Tasya Angelya Tribethran, Steven Valen Julyo Armando Davincy Lin Vicky Salim Wahyu Sudrajat Wayan Dana Widhiarso, Wijang