Publication Search

Computers in one of the network companies at PT. Estrada uses the Fortinet operating system. The final result expected through this implementation is to comprehensively see the capabilities of the firewall on Fortinet in overcoming the problem of blocking social media applications and streaming platforms during working hours. Blocking the application in question is the ability to filter web processes such as Facebook, Instagram, YouTube, etc. In the tests carried out, web filtering was able to block applications on social media and streaming platforms, which proves that the performance of web filtering is quite good. In analyzing web filtering performance, use the office hour rule tool by carrying out the rule schedule in the Fortinet network and displaying all the information in detail. The final result obtained in the network application filtering simulation process using Fortinet is that every network sent cannot be entered (blocked) on both social media applications and streaming platforms.

The rapid development of technology in the era of the Industrial Revolution 4.0 has driven the education sector to continuously adapt to the evolving demands of digital-based industries. One of the key technological innovations supporting this transformation is the Internet of Things (IoT), which enables data acquisition, real-time monitoring, and remote control of systems through internet networks. In response to these developments, a community service program was conducted to enhance the understanding and technical skills of students at SMK Negeri 1 Sindang through the provision and utilization of an IoT Trainer Kit Simulator as a practical learning medium. This activity aimed to bridge the gap between theoretical knowledge and industry-relevant technological applications by introducing students to hands-on IoT system implementation. The program included demonstrations and guided practice on the use of sensors, microcontrollers, and web-based monitoring platforms to simulate real-world industrial scenarios. The results indicate that students showed high enthusiasm and active participation throughout the activity. Moreover, participants were able to grasp the fundamental concepts of IoT systems, understand component integration, and recognize the relevance of IoT applications in supporting automation and digital transformation. Overall, this community service activity contributed positively to strengthening students’ digital competencies and preparedness for the demands of the contemporary industrial and technological landscape.

In demographic studies, population growth refers to changes in the number of inhabitants over a given period, measured by calculating numerical differences in population size and expressing them within specific time units to illustrate patterns of increase or decline. Based on data from Tembalang District in Figures 2025, the population growth rate in Kedungmundu Village rose by 0.51% between 2016 and 2025, with most residents relying on clean water services provided by PDAM Tirta Moedal as their primary source of drinking water. This study aims to estimate clean water demand based on projected population growth and to design a pipeline network system capable of meeting future needs. The analysis of water demand applies population projection methods, including arithmetic, geometric, and least square approaches, to compare their levels of accuracy, while the clean water distribution network is modeled using EPANET 2.0. One of the main challenges faced by PDAM is that service coverage has not yet been fully optimized. The ten-year projection results indicate that the arithmetic method provides a correlation value closest to 1, estimating a future population of 14,904 people with a total clean water requirement of 3.48 liters per second. To support this projected demand, the proposed network design utilizes High Density Polyethylene (HDPE) pipes with diameters of 12, 10, and 8 inches to ensure efficient and sustainable water distribution.

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

The increasing number of Indonesian Migrant Workers (TKW) working abroad, particularly through programs organized by BP2MI, has become a significant concern in managing the labor export process. One of the challenges faced is the uncertainty of the number of TKW to be sent each year, which is influenced by various external and internal factors. Therefore, this study aims to apply artificial neural networks (ANN) with a backpropagation algorithm approach to predict the number of TKW that will be processed by BP2MI. This method was chosen due to its ability to recognize patterns and nonlinear relationships between variables that affect the decision-making process for TKW export. In this study, the data used includes factors such as the number of job seekers, government policies, and the condition of the international labor market. The artificial neural network with the backpropagation algorithm is used to train the model based on existing historical data, with the goal of generating accurate predictions regarding the number of TKW to be processed in the coming years. The results of the tests show that the developed model can provide fairly accurate predictions and can serve as a tool for BP2MI in planning and managing the export of TKW more effectively. With the application of this technology, it is expected that the decision-making process related to TKW export can become more efficient and well-predicted.

The increasing competition in the coffee shop industry in Bandung requires businesses to adopt marketing strategies that emphasize long-term relationships with customers. This study aims to analyze the implementation of relationship marketing strategies in building business networks at Groei Coffee, a community-based café located in Sukapura, Bandung Regency. A descriptive qualitative approach was employed, with data collected through observation, interviews, and documentation. The findings indicate that relationship marketing at Groei Coffee is implemented through consistent product quality, two-way communication with customers, personalized services, and collaboration with campus communities and strategic partners. These strategies have proven effective in enhancing customer loyalty, expanding business networks, and strengthening the café’s competitive position in the food and beverage industry. However, several challenges were identified, including limited resources, price sensitivity among student customers, and dynamic consumer preferences. Therefore, an adaptive and sustainable approach is required to ensure that relationship marketing strategies provide long-term benefits for small and medium-sized enterprises (SMEs).

This community service activity was carried out to overcome the problem of open and uncontrolled internet networks at SMK Islamiyah Serua. This condition poses security risks as well as inefficient bandwidth usage. The solution offered is the implementation of a network gateway system with a user-friendly web-based login portal, so that internet access can be limited only to educators through an authentication mechanism. The implementation method includes needs analysis, system design, implementation using MikroTik RouterOS and HTML/CSS/JavaScript-based web portals, system trials, and socialization to schools. The results showed a significant improvement in network security, bandwidth usage efficiency, and ease of access for teachers. In addition, this activity also increases educators' understanding of the importance of network security in supporting the learning process. The implementation of this system provides sustainable benefits for schools, both from a technical and educational perspective, because it is able to create a safer and more controlled digital environment. Thus, this community service activity not only solves technical problems, but also contributes to improving the quality of educational services at SMK Islamiyah Serua.

Network service disruptions are critical issues that can negatively affect public trust and corporate reputation in the telecommunications industry. As one of the largest telecommunication providers in Indonesia, PT Telkomsel faces significant challenges in managing public issues related to network disturbances. This study aims to analyze how the Public Relations division of PT Telkomsel manages issues in handling network service disruptions in Indonesia.This research employs a qualitative descriptive approach by analyzing secondary data, including literature studies, official corporate communications, and media coverage related to network service disruptions. The findings indicate that PT Telkomsel’s Public Relations implements issue management through several systematic stages, namely issue identification, issue analysis, communication strategy formulation, message dissemination, and evaluation. The communication strategy emphasizes transparency, rapid response, and the use of digital media as the main channel to communicate with the public.Effective issue management enables the company to reduce negative public perceptions and maintain customer trust. This study confirms that strategic and proactive public relations communication plays an important role in managing network service disruption issues and protecting corporate reputation.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

This study aims to analyze the implementation of business networks in improving operational efficiency in small and medium-sized enterprises (SMEs) operating in the outdoor equipment sector. Increasing public interest in outdoor activities has intensified competition among SMEs, requiring them to develop effective collaboration strategies to maintain operational sustainability. This research employs a qualitative descriptive approach with a single case study design conducted at Misala Gear Outdoor, an SME engaged in outdoor equipment rental and sales in Ciwidey, Bandung Regency. Data were collected through in-depth interviews, direct observation, and documentation, and analyzed using descriptive qualitative techniques. The results indicate that the implementation of business networks involving supplier partnerships, outdoor communities, tourism agents, and digital platforms contributes significantly to improving operational efficiency, particularly in inventory management, service delivery, and market reach. Moreover, business networks enable cost control, resource optimization, and service differentiation, which strengthen the enterprise’s competitive position. This study highlights the strategic role of business networks as an operational efficiency driver for SMEs in the outdoor industry and provides practical implications for SME managers in developing collaborative business strategies.

Penelitian ini bertujuan untuk merancang dan merealisasikan antena mikrostrip patch berbentuk segitiga (triangular) yang bekerja pada frekuensi 1800 MHz, sebagai penguat jaringan internet di kawasan Kebun Raya Balikpapan. Lokasi tersebut diketahui memiliki kualitas sinyal yang kurang baik untuk beberapa provider. Antena yang dirancang menggunakan dua konfigurasi, yaitu MIMO 8x8 dengan teknik pencatuan langsung dan array 8x1, yang kemudian disimulasikan menggunakan CST Studio Suite 2019 dan diuji performansinya menggunakan metode Speedtest. Hasil simulasi menunjukkan bahwa antena 8x8 memiliki nilai VSWR sebesar 1,50, return loss -12,96 dB, dan gain 4,06 dBi dengan pola radiasi omnidirectional. Sementara itu, konfigurasi 8x1 array menunjukkan nilai VSWR 1,24, return loss -19,21 dB, dan gain 6,46 dBi, juga dengan pola radiasi omnidirectional. Hasil pengujian di lapangan dengan tiga kondisi tanpa antena eksternal, antena MIMO dan antena array 8x1 didapatkan hasil bahwa antena array 8x1 memberikan kecepatan unduh tertinggi mencapai 17,227 Mbps, unggah 4,072 Mbps, Jitter 34,9 ms  dan packet loss 14,18 % sedangkan antena MIMO 8x8 memberikan kecepatan unduh tertinggi mencapai 13,77 Mbps, unggah 2,462 Mbps, Jitter 49,9 ms  dan packet loss 18,43%. Kesimpulannya, kedua jenis antena eksternal yakni MIMO 8x8 dan array 8x1 mampu meningkatkan performa jaringan secara signifikan dan dapat menjadi solusi efektif untuk daerah dengan sinyal lemah. Antena ini diharapkan dapat memberikan kontribusi terhadap akses internet yang lebih stabil di area public yang sangat luas.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

Data security in web services is a critical aspect that must be considered to prevent information leakage and unauthorized access. This study aims to analyze the security level of the Universitas Andalas Repository site, specifically regarding its vulnerability to sniffing attacks due to the use of the unencrypted HTTP protocol. The research employs a penetration testing method with an experimental approach. The testing process is carried out in three main stages: reconnaissance using Nmap to identify the services and protocols in use, sniffing using Wireshark to capture and analyze data packets, and evaluation of results based on modern web security standards to assess the level of risks found. The results of the study indicate that the Universitas Andalas Repository site still uses the HTTP protocol in its communication process, meaning that all data transmitted between the client and server can be read in plaintext. The Nmap scan results confirm that the web service operates on port 80 without SSL/TLS encryption support. Meanwhile, the packet analysis results using Wireshark show that both HTTP requests and responses can be captured and directly observed, including request parameters, cookies, and other potentially sensitive information. These findings suggest that the site has significant vulnerabilities to sniffing attacks and man-in-the-middle attacks, posing a potential risk to user data security.

Information system security faces serious challenges due to increasingly complex cyber attacks. Intrusion Detection Systems (IDS) require efficient approaches to handle high-dimensional data such as the NSL-KDD dataset with 41 features. This study aims to implement the Genetic Algorithm (GA) for feature selection on the NSL-KDD dataset to improve the efficiency and accuracy of network attack detection. The method used is computational experimental research, involving data preprocessing, GA implementation for feature selection, building a classification model using Random Forest, and performance evaluation based on accuracy, precision, recall, F1-score, and computation time. The results show that GA successfully reduced features from 41 to 12 features (70.7% reduction), significantly improving computational efficiency. However, model accuracy slightly decreased from 0.4973 to 0.4951, indicating that while GA is effective for feature selection, the elimination of certain features may reduce classification capability. The implication of this study is that GA can be used as a tool to simplify intrusion detection models, but it should be combined with parameter optimization and data imbalance handling to achieve more optimal performance.  

Coffe is one of Indonesia’s leading commodities, known for its diverse flavors and aromas. Traditionally, coffee quality assessment is conducted manually through cupping tests performed by expert panelists. However, this method is subjective and requires considerable time and cost. This study aims to implement an Artificial Neural Network (ANN) using the backpropagation algorithm to classify coffee types based on sensory parameters such as flavor, aroma, acidity level, and body. Simulated data were generated from five common Indonesian coffee varieties: Arabica Gayo, Robusta Lampung, Arabica Toraja, Liberica Jambi, and Excelsa. The results show that the ANN-based classification system with a 4-8-1 architecture achieved an accuracy rate of 93% after 500 training epochs, with a final error value of 0.07. The implementation of this method provides an efficient and objective technological alternative to assist the coffee industry in maintaining product quality and automatically identifying coffee types.    

Air bersih merupakan kebutuhan pokok yang sangat penting bagi kehidupan manusia. Seiring dengan pertambahan dan perkembangan penduduk kebutuhan akan air bersih juga semakin meningkat, untuk itu perlu dilakukan analisis terhadap sistem jaringan perpipaan eksisting. Penelitian ini dilakukan dengan bantuan Pamsimas Sendang Lumintu Desa Sendangkulon Kecamatan Kangkung yang bertujuan untuk mengetahui kondisi eksisting sistem jaringan perpipaan Pamsimas Sendang Lumintu untuk tahun 2024 , mengetahui berapa besar kebutuhan air yang dibutuhkan untuk memenuhi pertumbuhan penduduk sampai dengan tahun 2039,  dan menganilisi kondisi eksisting sistem jaringan perpipaan Pamsimas Sendang Lumintu untuk tahun 2039. Metode yang digunakan dalam penelitian ini adalah dengan mengumpulkan data sekunder lalu menganalisis data tersebut dengan program EPANET 2.0 sehingga didapatkan hasil simulasi. Hasil simulasi menunjukan bahwa pada tahun 2024 besar debit kebutuhan rata-rata yaitu sebesar 16,27 liter/detik, kebutuhan harian maksimum 17,90 liter/detik dan kebutuhan jam puncak 24,40 liter/detik. Sedangkan untuk tahun 2039 besar debit kebutuhan rata-rata yaitu sebesar 20,92 liter/detik, kebutuhan harian maksimum 23,01 liter/detik dan kebutuhan jam puncak 31,38 liter/detik. Kondisi hidrolis eksisting sistem jaringan Pamsimas Sendang Lumintu Kecamatan Sendangkulon masih ada yang tidak sesuai kriteria. Kriteria yang dianalisa adalah sisa tekan dan kecepatan air. Pada jam puncak terdapat beberapa titik yang menghasilkan sisa tekan kurang dan kecepatan dibawah kriteria

The quality of user experience (Quality of Experience/QoE) in YouTube video streaming services is highly influenced by the quality of network service (Quality of Service/QoS). QoS measurement is conducted using Wireshark software with parameters such as throughput, packet loss, delay, and jitter, and assessed based on the TIPHON standard. Meanwhile, QoE is evaluated through a survey of 30 respondents and measured using the Likert scale.The analysis results indicate that QoS falls into the "Good" category, with a throughput of 1,545.45 kbps at 51.5%, 0% packet loss, 5.69 ms delay, and 0.0015 ms jitter. Additionally, the average QoE score of 4.55 suggests that the user experience is also considered good. Further analysis reveals a correlation between QoS and QoE, where a more optimal network quality positively impacts user experience. These findings provide insights for service providers to optimize streaming quality to meet user expectations.

Perang siber adalah suatu kondisi konflik dengan menggunakan perkembangan teknologi informasi dan komunikas dimana menjadi sebuah fenomena sosial dalam relasi internasional yang menjadi masalah serius bagi bangsa-bangsa di dunia dalam membangun stabilitas internasional. Mmenghadapi variasi bentuk perang siber, dibutuhkan adanya proses pembangunan nasional berbasis keamanan siber/cyber security sebagaimana telah dilakukan oleh beberapa bangsa-bangsa di dunia. Perang siber sesungguhnya merupakan bentuk dari perang dunia ketiga yang sudah terjadi. Kekosongan dalam literatur hukum internasional membuat model serangan siber semakin menunjukkan kesiapannya untuk terlibat dalam konflik bersenjata. Konflik siber, yang melibatkan serangan terhadap sistem komputer dan jaringan digital, menimbulkan pertanyaan kompleks mengenai bagaimana prinsip-prinsip hukum humaniter yang ada dapat diterapkan dalam konteks konflik ini. Menggunakan metode penelitian yuridis normatif (legal  study  research) yang bersifat kualitatif dengan pendekatan perundang-undangan, konseptual dan kasus dari bahan primer dan sekunder. Tantangan HHi dalam mengatur konflik siber mulai dari definisi, pelacakan pelaku, perlindungan masyarakat, sampai penyesuaian hukum dengan teknologi baru. Diperlukan pembaruan hukum, peningkatan kerja sama antar negara, dan penguatan cara penegakan hukum agar perlindungan kemanusiaan tetap terjaga di zaman digital. HHI dapat diterapkan pada konflik siber tetapi perlu adaptasi melalui instrumen baru atau interpretasi progresif sebagaimana Kasus Rusia-Ukraina yang menjadi bukti urgensi penyesuaian hukum.