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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Teliska Jurnal technoscientia ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika PROtek : Jurnal Ilmiah Teknik Elektro ITEj (Information Technology Engineering Journals) Sistemasi: Jurnal Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining Jurnal RESISTOR (Rekayasa Sistem Komputer) Patria Artha Technological Journal Building of Informatics, Technology and Science Jurnal Teknik Industri Terintegrasi (JUTIN) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Aptekmas : Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika
Fadhli, Mohammad
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Perancangan Emergency Buttton Untuk Pendaki Gunung Dengan Sistem Komunikasi Multihop Berbasis LoRa Affrylia, Gita; Fadhli, Mohammad; Lindawati, Lindawati
PROtek : Jurnal Ilmiah Teknik Elektro Vol 8, No 2 (2021): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v8i2.3330

Abstract

Mountain is one of the tourist destination that are currently in great demand by various circles of society, sepecially for young people. This is evidenced by the increase in mountaineering tourist visiotr data that increase every years. The increase in public interest in mountaineering also leads to many cases of accidents. That occur duirng mountaineering, such as the loss climbers in mountainousareas, the loss of climbers on muntain tracks, and other accidents that must immediately require emergency assistance. The difficult to find by guards and others search teams. Therefore, in this study wa designed an Emergency Button tool for mountaineers designed to be able to perform the emergency call process when a person has an accident while in an inaccessible terrain such as mountains. It is integrated in the LoRa-based Multihop Communications System an Global Positioning System (GPS). Signals or data sently by climbers through the emergency button in the form of coordinate points of the location of climbers. The coordinates sent by the climbers will later be sent to the mountain guard post server which was previously via Relay, the coordinate data has been received will automatically be uploaded to thinkspeak so that it can be acces using the internet and converted into a google maps display from. It is hoped that this will make it easier to find the location of climbers accurately and quackly.
Rancang Bangun Sistem Pemantau Penerima Sinyal Automatic Dependent Surveillance-Broadcast (ADS-B) Berbasis Raspberry Pi dan Antena Ground Plane Sebagai Antena Penerima Maharani, Fistania Ade Putri; Soim, Sopian; Fadhli, Mohammad
PROtek : Jurnal Ilmiah Teknik Elektro Vol 9, No 2 (2022): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v9i2.4690

Abstract

Currently, the technology used for aircraft monitoring is Radar technology. Radar's accuracy is hampered, however, by the fact that while an aircraft is airborne or in flight, it is frequently obscured by clouds, making it difficult to detect the aircraft. Due to constraints in radar technology, Automatic Dependent Surveillance-Broadcast technology was developed to improve the performance of air navigation security. Raspberry Pi, RTL-SDR, and LNA were used as the research method in designing the ADS-B signal receiver monitoring system. The 1090 MHz Ground Plane Antenna is preferable because it has a higher gain and a radiation pattern influenced by radial elements, allowing it to collect a considerably broader signal when combined with Raspberry Pi RTL-SDR and LNA. The monitoring results based on the test data are impressive, generating six aircraft simultaneously, indicating that dump 1090 is very effective at translating the aircraft's ADS-B signal data emitted every 0.5 seconds. ICAO, Altitude and Longitude information are clear Based on tests, the aircraft's ADS-B signal was identified between 4,000 and 38,000 feet over the maximum value, causing the signal to disappear and fail to decode the information. The maximum distance measured during the test was 49.7 kilometers from the elevated location where the ADS-B signal monitoring device was installed. The results of the first test were placed 10 meters above the ground. The result is significantly better than the short antenna position. This significantly improves the accuracy of the ADS-B signal monitoring system and the synchronization of information exchange through real-time monitoring.
Link Budget Prediction at 28 GHz Frequency Based on Rain Attenuation Model in Palembang City Tarnita Rizky Prihandhita; Soim, Sopian; Fadhli, Mohammad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apvf0b18

Abstract

5G communication technology brings a major leap in the development of wireless networks, especially through the utilization of millimeter wave frequencies. However, mmWave signals are very susceptible to rain attenuation, which can significantly reduce the quality of 5G services. This study aims to estimate how much signal loss and reception power there will be for 5G at a frequency of 28 GHz in Palembang City and to examine how rain affects the mmWave signals. MATLAB simulations use Palembang rainfall data for the March 2025 period and use the ITU-R P.618-5 rain attenuation model, Simple Attenuation Model, and ITU-R Tropical. Using the Urban Macro propagation scenario in line-of-sight and non-line-of-sight conditions. The results show that distance and rainfall affect signal attenuation, with NLOS conditions producing worse attenuation than LOS. At high rainfall of 84.5 mm/hour in NLOS conditions, ITU-R P.618-5 predicts the highest total path loss of about 317 dB, ITU-R Tropical about 247 dB, and SAM about 227 dB. With the received power of ITU-R P.618-5 model of -260 dBm, ITU-R Tropical of -190 dBm, and SAM of -170 dBm.
A Analysis of Machine Maintenance System Using Preventive Maintenance Method with Always Better Control (ABC) Classification and Modular Design Fadhli, Mohammad; Saifuddin Z.S, Joumil Aidil; Winursito, Yekti Condro
ITEJ (Information Technology Engineering Journals) Vol 10 No 1 (2025): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i1.204

Abstract

Companies must pay special attention to the machines used to make goods because if the machine is damaged, the resulting product will be damaged or the product will take a long time in the production process. By performing regular machine maintenance, companies can maintain and extend the service life of the machine. PT XYZ is a company in Indonesia that has an installed production capacity of 29 million tons of cement per year. The company uses a continuous production system, namely ensuring that all machines are in good condition so that the production process does not experience delays or losses. The results of observations show that the packer operation is the production process with the longest waiting time, with the Roto packer 638PM1 being the machine with the longest waiting time. Corrective and preventive maintenance are two types of maintenance currently used, and the current preventive maintenance strategy is currently suboptimal. This study aims to develop an efficient preventive maintenance system by providing preventive maintenance recommendations using the design modularity method and the Always Better Control (ABC) classification. By combining the classification of machines between critical levels and the utility value of each machine component, operational efficiency will increase, the risk of production disruptions caused by critical component shortages and unnecessary storage costs will be reduced. By applying this method, the total maintenance cost incurred is Rp. 771,782,456, this result has a difference of Rp. 406,113,204 smaller than the total maintenance cost currently used by the company, which is Rp. 1,177,895,660. The results demonstrate that the proposed maintenance method is effective and feasible, achieving a 34.47% cost efficiency improvement over the company’s current maintenance system.
PENERAPAN TEKNOLOGI KOMUNIKASI MULTIHOP UNTUK MONITORING KONDISI LAMPU PENERANGAN JALAN UMUM BERBASIS LORA: Teknik Telekomunikasi Ade Rosta Paramitha, Nadila; Fadhli, Mohammad; Mujur Rose, Martinus
JURNAL TELISKA Vol 16 No II Juli (2023): TELISKA Juli 2023
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.8183298

Abstract

Street lighting is one of the public facilities that plays a vital role in providing safety and comfort for road users. Therefore, any problems that occur with street lighting must be handled quickly and precisely. To achieve this goal, this study proposes a system for monitoring the condition of street lighting using LoRa-based multihop communication technology. In this study, each street light unit is equipped with an ACS172 current sensor and a BH1750 light sensor to determine the damage that occurs to the street lights automatically. Data from these two sensors is sent using LoRa Ra-02 to the relay and then forwarded to the gateway. The gateway forwards the data to the Firebase server so that the results of monitoring public street lighting can be observed via the internet network. Based on the results of system testing, the street light units (sensor nodes) can communicate with relays up to a distance of 200 m and with gateways up to a distance of 400 m. The gateway can also properly send sensor data to the Firebase server, so monitoring results can be viewed online via a smartphone.
K-Means Algorithm Implementation for IoT-Based Early Fire Detection in Oil Palm Plantations Utomo, Tri Binarko; Suroso; Fadhli, Mohammad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9xjpmv81

Abstract

Oil palm plantation fires continue to be a significant problem, significantly impacting the environment, public health, and economic activity. By combining the K-Means algorithm, processed directly on an ESP32 microcontroller, with an Internet of Things (IoT)-based early detection system, this research has produced an innovation that does not require an external server. To monitor hazardous gases, smoke, and temperature, the system uses thermocouples and MQ-2 and MQ-135 sensors. Conditions are then categorized into Safe, Alert, and Fire. Using 15 test data samples, the evaluation was conducted in the field, specifically in the oil palm plantation area in Banyuasin, South Sumatra. The test results showed that the classification had 100% accuracy. However, the limited amount of data was one of the obstacles to this study, so additional testing is needed to ensure the accuracy of the large-scale study. This system is suitable for remote and limited infrastructure, helping to develop effective and responsive early fire detection technology.
Development of a CNN-Based Mental Health Consultation Application Integrating Facial Expressions and DASS-42 Questionnaire Salsabila, Meidita; Lindawati, Lindawati; Fadhli, Mohammad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37525

Abstract

Early detection of psychological disorders such as Depression, stress, and anxiety is still limited due to a lack of awareness and inadequate access to mental health consultation services. This study aims to develop a mental health consultation application that utilizes facial expressions and the Depression, Anxiety, and Stress Scale (DASS-42) questionnaire, employing a Convolutional Neural Network (CNN) algorithm. The CNN algorithm is used to detect and classify facial expressions into emotional categories, such as anger, sadness, disgust, and fear,  as early indicators of mental conditions. In addition, the DASS-42 questionnaire provides a structured psychological assessment to determine the severity of Depression, anxiety, and stress. This combination offers a more comprehensive and accurate evaluation, thus bridging the gap in early detection methods for mental health. Based on the development and testing results, a mental health consultation app utilizing facial expressions and the DASS-42 questionnaire was successfully created by using the CNN algorithm as a facial expression detector. The system can identify facial expressions such as sadness, anger, disgust, and fear with an accuracy of 81%, showing excellent performance in detecting early signs of mental disorders.
Evaluating Entropy-Based Feature Selection for Sales Demand Forecasting Using K-Means Clustering and Naive Bayes Classification Wulandari, Fadhilah Dwi; Lindawati, Lindawati; Fadhli, Mohammad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37046

Abstract

Sales demand forecasting is crucial for inventory optimization in retail, especially for Micro, Small, And Medium Enterprises (MSMEs). This study examines the effect of entropy-based feature selection on the performance of a two-stage machine learning framework comprising K-Means clustering and Naive Bayes classification. The research was conducted on transactional data collected from a footwear MSME in Palembang, Indonesia, covering January to December 2024. Shannon Entropy and Information Gain were applied to identify and retain the most informative features before clustering and classification tasks. Two experimental scenarios were investigated: (1) using all features without selection and (2) applying entropy-based feature selection with Information Gain thresholds of 0.4 and 0.5 for category-based and quantity-based targets, respectively. The first scenario yielded moderate performance, with a Silhouette Score of 0.5747 and a classification accuracy of 96.97%. In contrast, the second scenario demonstrated superior results, achieving a Silhouette Score of 0.6261 and a classification accuracy of 99.49% when quantity sold was used as the target variable. These findings indicate that entropy-based feature selection reduces data dimensionality, enhances clustering compactness, and improves classification accuracy. This research contributes to the field by presenting a practical framework for sales demand forecasting in retail environments. Future work will focus on integrating additional contextual variables, such as seasonal trends and promotions, and validating the system in real-world retail settings
SOSIALISASI DAN SIMULASI KEAMANAN SERVER KERBEROS DAN OPENLDAP DI SMK NEGERI 4 PALEMBANG Lindawati; Fadhli, Mohammad; Soim, Sopian; Deta Mediana, Salwa
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): Aptekmas Volume 7 Nomor 3 2024
Publisher : Politeknik Negeri Sriwijaya

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

Abstract

Computer networks are essential to daily life in the digital age because they link devices and provide distant information access. But technological progress also poses serious concerns to data security. Threats like viruses, malware, and hacker attacks put data's confidentiality and integrity in danger. For this reason, cybersecurity technicians must comprehend and use strong security frameworks. Kerberos, a security protocol that enables entities in a network to safely verify their identities, is one of the solutions that are presented. On the other side, directories are managed via Lightweight Directory Access Protocol (LDAP). In this context, teachers and students at the Vocational High School (SMK) with a focus on computer and network engineering receive instruction and training on network security. It is hoped that by doing this outreach, the community would see how important network security is and take the necessary precautions to safeguard data from intrusions. The outreach efforts were conducted at SMK Negeri 4 Palembang, which is the subject of this study. The findings demonstrate that students are able to test exploitation techniques on the framework used in the apache2 service and have a solid understanding of Kerberos, LDAP, and Firewalld.
Development of a Distributed Gradient Boosting Forest Algorithm with Residual Connections in Data Classification Respati, Rayhan Dhafir; Soim, Sopian; Fadhli, Mohammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4899

Abstract

The growing complexity and volume of data across various domains necessitate machine learning models that are scalable and robust for large-scale classification tasks. Ensemble methods such as Gradient Boosting Decision Trees (GBDT) demonstrate effectiveness; however, they encounter issues concerning scalability and training stability when applied to very deep architectures. This work presents a novel enhancement using residual connections derived from deep neural networks into the Distributed Gradient Boosting Forest (DGBF) algorithm. By enabling direct gradient propagation across layers, residual connections solve the vanishing gradient problem and so improve gradient flow, accelerate convergence, and stabilise the training process. The Residual DGBF model was assessed using seven distinct datasets across the domains of cybersecurity, financial fraud, phishing, and malware detection. The Residual DGBF consistently surpassed the baseline DGBF in terms of accuracy, precision, recall, and F1-score across all datasets. Particularly in datasets marked by imbalanced classes and complex feature interactions, this suggests improved generalisation and higher predictive accuracy. By proving more stable and strong gradients across the depth of the model, layer-wise gradient magnitude analysis supports these improvements and so confirms the effectiveness of residual connections in deep ensemble learning. This work improves ensemble techniques by combining the scalability and interpretability of decision tree ensembles with the residual architecture optimising benefits. The proposed Residual DGBF enables future research on enhanced deep boosting frameworks by offering a strong and scalable method to address challenging real-world classification tasks.
Co-Authors Ade Rosta Paramitha, Nadila Affrylia, Gita Aliffiyah, Muhammad Bayu Arsella, Shendy Cahya Iman, Tri Rizki Ciksadan, Ciksadan Deta Mediana, Salwa Irma Salamah LINDAWATI Lindawati Lindawati Maharani, Fistania Ade Putri Martinus Mujur Rose Mediana, Salwa Deta Merinda, Siska Mujur Rose, Martinus Raihan, Ahmad Respati, Rayhan Dhafir Saifuddin Z.S, Joumil Aidil Saifuddin, Joumil Aidil Salsabila, Meidita Savitri, Yulivia Rhadita Septiani, Dinda Sopian Soim, Sopian Suni, Gina Amanah Suroso SURYANI, TRIA ANANDA Tarnita Rizky Prihandhita Ulima Achva, RA. Jihan Utomo, Tri Binarko Winursito, Yekti Condro Wulandari, Fadhilah Dwi