Elkom : Jurnal Elektronika dan Komputer merupakan Jurnal yang diterbitkan oleh Universitas Sains &amp;amp;amp; Teknologi Komputer (UNIVERSITAS STEKOM). Jurnal ini terbit 2 kali dalam setahun yaitu pada bulan Juli dan Desember. Misi dari Jurnal ELKOM adalah untuk menyebarluaskan, mengembangkan dan menfasilitasi hasil penelitian mengenai Ilmu bidang Teknik Elektro dan Informatika, sebagai media bagi para dosen, guru, peneliti dan para praktisi dalam bidang teknologi informasi dari seluruh Indonesia, dalam melakukan pertukaran informasi tentang hasil-hasil penelitian terbaru yang telah dilakukan.
Sistem Monitoring Suhu, Api Dan Asap Untuk Pencegahan Kebakaran Pada Ruang Server
The purpose of this research is to produce a temperature, fire, and smoke monitoring system based on Internet of Things (IoT) for fire prevention in the server room at the Personnel and Human Resources Development Agency (BKPSDM) of Indramayu Regency. The research is motivated by the server room monitoring which is still carried out manually without an early warning system, making it ineffective and at risk of delayed response to potential fires, especially outside working hours. The research method used is R&D (Research and Development) by Borg & Gall (1983) with 6 stages. Data collection was conducted through in-depth interviews, direct observation, and document review. The system was developed using an ESP32 microcontroller integrated with a DHT22 sensor for temperature, an MQ-2 sensor for smoke, and a flame sensor for fire. The software was developed using Arduino IDE and connected to the Blynk IoT platform and Telegram Bot API for automatic notifications. Testing was conducted through unit testing and end-to-end testing on seven operational scenarios. Based on the research results, the author successfully created a fire monitoring system for server rooms based on IoT. The test results showed an average temperature error of the DHT22 sensor of ±0.31°C, an average Telegram notification latency of 2.18 seconds, and a 100% success rate in all testing scenarios. The system was able to operate stably for 72 hours and is equipped with a servo motor to automatically open the ventilation when smoke is detected. This system provides ease of remote monitoring via smartphones and becomes a responsive and efficient solution to support the security of digital infrastructure against fire threats.
Copyright (c) 2026 Elkom: Jurnal Elektronika dan Komputer https://creativecommons.org/licenses/by-sa/4.0
Rancang Bangun Sistem Informasi Manajemen Jadwal Konsultasi Guru Bimbingan Dan Konseling Berbasis Web
Guidance and Counseling (BK) services play a vital role in supporting students' academic, personal, social, and career development. However, at MA Darul Ihsan, scheduling counseling sessions is still done manually using logbooks, leading to time inefficiencies, difficulties in checking teacher availability, risks of data loss, and compromised student privacy. This research aims to design and implement a web-based information system for managing counseling schedules; this system enables students to submit and track requests online while assisting counselors and administrators in managing schedules, topics, and session approvals. The system was developed using the Waterfall methodology incorporating UML and ERD modelingand implemented using PHP, HTML, and MySQL. Key features include user login, schedule viewing, topic submission, and status tracking for students, alongside schedule and data management for administrators. Black-box testing confirmed that all primary features function as intended. The system is expected to make the scheduling process faster, more transparent, and more accessible than manual methods, although further development such as notification features and mobile accessibility support is still required.
Penjadwalan Konsultasi; Bimbingan dan Konseling (BK); Web; Waterfall; UML
Sistem Informasi Inventory Barang Berbasis Website Pada PT. Oasis Water International Bogor
PT. Oasis Water International is a company engaged in bottled drinking water (AMDK). This research aims to design and build an information system that can efficiently manage inventory at PT. Oasis Water International, which operates in the bottled water production sector. Currently, inventory management in the company is still conducted manually, using unintegrated recording methods, resulting in inefficiencies, recording errors, and delays in data processing. The method used in this research is the First-In First-Out (FIFO) method to ensure that the first items in are the first items out, thereby minimizing the risk of losses due to expired products. This research also employs the Rapid Application Development (RAD) approach in software development to accelerate the design and implementation process of the system. The results of this research are expected to improve operational efficiency, reduce storage costs, and enhance customer satisfaction by providing accurate and real-time information regarding inventory. With the implementation of this web-based information system, PT. Oasis Water International is expected to address existing issues and improve performance and competitiveness in the bottled drinking water industry
Design of an IoT-Based Water Level Monitoring System for Flood Detection in Ungaran
Flooding is a frequent natural disaster in Indonesia that significantly impacts the socio-economic conditions of the community. In the West Ungaran area of ​​Semarang Regency specifically in Dliwang Village (RT 06/03) there is a small river prone to overflowing during periods of heavy rainfall. This situation highlights the need for a water level monitoring system capable of automatically, accurately, and continuously detecting potential floods. This study aims to design an Internet of Things (IoT) based flood detection and water level monitoring system using an ESP8266 microcontroller integrated with HC-SR04 ultrasonic and raindrop sensors. The system utilizes a buzzer for early warning alarms and an LED indicator to signal flood alert statuses. Sensor data is transmitted in real-time to the Blynk IoT platform, allowing for monitoring via internet connected devices such as smartphones. The design results demonstrate that the system effectively detects changes in water levels and rainfall conditions, providing rapid alerts when water levels reach specific thresholds. Consequently, this system serves as an effective alternative solution to support flood mitigation efforts in flood prone areas.
Rancang Bangun Sistem Informasi Penjualan Berbasis Web pada Toko Rin's Bouquet
Perkembangan teknologi informasi terus mendorong terciptanya sistem yang lebih efektif dan efisien dalam mendukung berbagai aktivitas. Toko Rin’s Bouquet masih menjalankan proses penjualan dan pemesanan buket secara manual melalui WhatsApp sehingga pengelolaan transaksi belum berlangsung secara optimal. Kondisi tersebut menimbulkan beberapa kendala seperti lambatnya konfirmasi pesanan, terbatasnya akses pelanggan terhadap informasi produk, meningkatnya kemungkinan terjadinya kesalahan pencatatan, serta adanya pesanan yang berpotensi tidak tertangani. Terangkat dari permasalahan tersebut penelitian ini bertujuan mengembangkan rancangan dan membangun sistem informasi penjualan berlandaskan web pada Toko Rin’s Bouquet untuk mempermudah proses pemesanan dan pengelolaan data penjualan. Metode pengembangan sistem yang digunakan adalah metode Waterfall yang terdiri dari tahap requirement, design, implementation, verification dan maintenance. Hasil penelitian ini berupa sistem informasi penjualan berlandaskan web guna mengelola produk, melakukan pemesanan online, menghitung ongkos kirim, memproses pembayaran, serta menghasilkan laporan penjualan secara otomatis. Melalui penerapan sistem informasi penjualan berlandaskan web ini, kesalahan pencatatan dapat diminimalisir serta pelayanan dan kualitas pada Toko Rin’s Bouquet dapat ditingkatkan.
Sistem Inspeksi Visual Berbasis Web-camera Menggunakan YOLO untuk Deteksi Real-time Spaghetti defect pada 3D Printing FDM
Print failure remains a major challenge in fused deposition modeling (FDM) 3D printing because it may lead to material waste, production delays, and potential damage to printer components. One frequent failure mode is the spaghetti defect , in which extruded filament forms irregular strands due to poor bed adhesion, object displacement, or extrusion outside the intended printing path. This study develops a low-cost visual inspection system based on a web-camera and Raspberry Pi for real-time detection of spaghetti defect using a YOLO object detection model. The proposed system captures visual data from the printing area, processes image frames locally on the edge device, and displays the detected defect location using a bounding box, class label, and confidence score. The dataset consisted of 100 images, including 50 normal printing images and 50 spaghetti defect images, divided into training, validation, and testing sets using a 70:20:10 ratio. Defect images were annotated using bounding boxes to enable location-based defect detection. The evaluation results show that the model achieved a precision of 91.30%, recall of 88.24%, F1-score of 89.74%, mAP@0.5 of 92.10%, and mAP@0.5:0.95 of 87.30%. Real-time testing also demonstrated that the system could detect spaghetti defect with confidence scores ranging from 0.87 to 0.90. These results indicate that the integration of a web-camera, Raspberry Pi, and YOLO has potential as an economical and portable early monitoring system for FDM 3D printing failures.
Real-Time Human Detection and Face Recognition System Using CCTV Stream and Localhost-Based Monitoring Dashboard
This study presents a real-time human detection and face recognition system that utilizes CCTV video streams and a localhost-based monitoring dashboard. Using a Research and Development (R&D) approach, a computer vision application was designed and implemented on a laptop platform. Human detection was performed using YOLO11n, facial regions were localized with YuNet, and face recognition was carried out using SFace to distinguish registered individuals from unknown persons. The detection results were displayed through a web-based dashboard that provided live video streaming, AI status information, face registration, and detection history records. Performance evaluation was conducted under various conditions, including human and non-human scenarios, registered and unknown faces, dark-room environments, and night-vision mode. The dashboard maintained a preview rate of approximately 20–30 FPS. Experimental results showed that human detection achieved an accuracy of 80%, while face recognition achieved 72% accuracy under the tested conditions. Alert Level 1 was triggered when a person was detected, whereas Alert Level 2 was activated for unknown-face events. The findings demonstrate the potential of integrating lightweight computer vision models into a local surveillance system without relying on cloud infrastructure. Nevertheless, system performance remained dependent on factors such as lighting conditions, camera distance, face orientation, image quality, and available computing resources.
Rancang Bangun Sistem Informasi Pemagang Indonesia Berbasis Web Menggunakan Framework Laravel
Abstract—Japan is one of the main destinations for Indonesian migrant workers participating in internship programs. Ibaraki Prefecture, particularly Kitaibaraki City, hosts many Indonesian interns in the fisheries, agriculture, manufacturing, and construction sectprocesses, customer data recording, trip scheduling, and report preparation manually through WhatsApp and separate worksheets, resulting in problems such as uncentralized data, risk of recording errors, and difficulties in schedule management. This study aims to develop an information website for Indonesian interns in Ibaraki Prefecture, Japan. The website provides information on pre-departure preparation, daily life, rights and obligations, cultural adaptation, local communities, and practical experiences from former and current interns. It is expected to serve as an educational platform, a reliable source of information, and a communication bridge between prospective interns, active interns, alumni, and families in Indonesia. Ultimately, the website is expected to improve the preparedness and well-being of Indonesian interns while strengthening the positive image of the Indonesian community in Japan
Detection of Organic and Inorganic Waste on the Deli River Surface Using Convolutional Neural Networks (CNN)
River pollution caused by waste remains a serious environmental issue, particularly in urban areas such as the Deli River. The process of monitoring and identifying waste types is still largely performed manually, making it inefficient and ineffective. Therefore, this study aims to develop an image-based classification system for organic and inorganic waste using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture. The dataset used in this research consists of surface images of the Deli River captured using a drone camera. The images were processed through preprocessing and data augmentation stages, followed by data splitting, model training, and testing. The CNN model was designed to classify two waste categories, namely organic and inorganic waste. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score as evaluation metrics. The results indicate that the proposed CNN model is able to classify organic and inorganic waste effectively. The system achieved an accuracy of 94%, a precision of 92%, a recall of 98%, and an F1-score of 95%. These results demonstrate that the model has excellent performance and reliability in identifying waste characteristics based on shape, color, and texture features from river surface images. Therefore, the developed system has the potential to be implemented as an automatic and sustainable tool for monitoring the cleanliness of the Deli River.
CNN; MobileNetV2; Classification; Deli River; Drone Images
Design and Implementation of a Web-Based Work Order Auto-Dispose System to Optimize Vendor Workload at CV Anugrah Sejahtera Wibowo, Semarang
Penelitian ini dilatarbelakangi oleh proses pendistribusian Work Order di CV Anugrah Sejahtera Wibowo Semarang yang masih dilakukan secara manual, yaitu melalui pencatatan spreadsheet dan komunikasi pesan instan, tanpa adanya perhitungan beban kerja vendor secara objektif. Tujuan dari penelitian ini adalah merancang dan mengimplementasikan Sistem Auto Dispose Work Order berbasis web yang dapat mendukung pemerataan beban kerja vendor dan monitoring pekerjaan yang lebih terstruktur. Metode pengembangan sistem yang digunakan adalah model Waterfall. Data penelitian diperoleh melalui observasi, wawancara, dokumentasi operasional, dan studi pustaka, kemudian dianalisis melalui analisis kebutuhan sistem, validasi desain, uji coba pengguna, dan Black Box Testing. Sistem diimplementasikan menggunakan PHP, MySQL, dan Bootstrap dengan algoritma Least Connection untuk menentukan vendor penerima Work Order baru berdasarkan jumlah pekerjaan aktif paling sedikit. Hasil penelitian menunjukkan bahwa validasi ahli memperoleh skor 32 dengan kategori Sangat Baik, uji coba pengguna akhir memperoleh rata-rata skor 36,3 dengan kategori Sangat Baik, dan seluruh 12 skenario Black Box Testing menghasilkan keluaran sesuai harapan. Berdasarkan hasil tersebut, sistem dapat mengurangi subjektivitas penugasan, membantu pemerataan beban kerja vendor, meningkatkan monitoring pekerjaan, dan mendukung evaluasi kinerja vendor.
Analisis Penerapan Metode Weighted Product Wp untuk Penilaian Kelayakan Kredit Kendaraan Bermotor
In the credit approval process, prospective customers are first surveyed, after which the Credit Analyst determines whether they are eligible to receive credit. A Credit Analyst is required to work quickly and accurately when analyzing the large number of incoming credit applications. As a result, the possibility of human error cannot be ignored, such as calculation errors, data misinterpretation, and other mistakes. Therefore, to support the decision-making process in determining customer creditworthiness, a computer-based system is needed to facilitate data analysis, evaluate credit applicant criteria, and process data into meaningful information for making decisions regarding semi-structured problems. A Decision Support System (DSS) is an appropriate solution to assist in the selection of motor vehicle credit applicants. The system is designed using the Weighted Product (WP) method, which is one of the methods in Fuzzy Multiple Attribute Decision Making (FMADM). The WP method was chosen because the criteria weighting calculations are relatively simple and not overly complex. The proposed system is expected to assist in the selection of credit applicants, thereby accelerating the credit evaluation process and reducing errors in determining customer creditworthiness. With the implementation of this system, Credit Analysts can more easily identify customers who are eligible to receive motor vehicle credit. In addition, the calculation process for determining the most suitable customers becomes easier and faster. The system also helps management reduce difficulties in selecting the best customers by considering and representing each evaluated criterion, resulting in accurate and reliable decisions.
Credit Analyst; Decision support system; Weighted Product; Credit
Optimasi Algoritma XGBoost Berbasis SMOTE untuk Mengidentifikasi Faktor Logistik Pemicu Pembatalan Pesanan E-Commerce
Unilateral order cancellation by consumers is a major operational challenge in the Indonesian e-commerce industry, directly impacting supply chain inefficiency and inflating logistics costs. Computational modeling to predict this risk often faces hurdles such as class imbalance and vulnerability to data leakage. This study proposes an optimization of the Extreme Gradient Boosting (XGBoost) algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) using a multi-scenario feature analysis approach. The dataset used comprises real transaction records from a major e-commerce platform. Experiments were designed in two scenarios: Scenario 1 included all transactional features, while Scenario 2 excluded the dominant financial feature (Total Pembayaran) to test the model's pure dependency on pre-finalization variables. The test results showed that Scenario 1 yielded a pseudo-accuracy of 99.50% due to data leakage. After reconstruction in Scenario 2, the SMOTE-based XGBoost model produced a stable real performance with an Accuracy Score of 83.96% and an Area Under ROC (AUC) of 86,98%. Through Feature Importance analysis, this study successfully revealed that pure logistics factors, specifically "Ongkos Kirim Dibayar oleh Pembeli" (Shipping Fee Paid by Buyer) with an absolute importance weight of 77.10%, serve as the primary predictor and driver behind consumer order cancellations. These findings provide tactical contributions for e-commerce decision-makers in formulating shipping cost strategies and mitigating early operational risks.
Data Leakage; E-Commerce; Order Cancellation; SMOTE; XGBoost
Evolusi Corner Detection dalam Computer Vision: Analisis Komprehensif Metode Klasik dan Deep Learning
Corner detection merupakan salah satu teknik penting dalam computer vision yang digunakan untuk mendeteksi titik-titik penting pada citra dan mendukung berbagai aplikasi seperti object detection, image registration, image matching, dan medical imaging. Seiring berkembangnya teknologi, berbagai metode corner detection telah dikembangkan mulai dari pendekatan konvensional seperti Harris Corner Detector hingga metode berbasis deep learning. Penelitian ini bertujuan untuk menganalisis perkembangan metode corner detection, mengevaluasi kinerja metode yang digunakan, mengidentifikasi dataset yang digunakan, membandingkan kelebihan dan kekurangan metode konvensional dan deep learning, serta mengidentifikasi tren penelitian terkini. Penelitian dilakukan menggunakan metode Systematic Literature Review (SLR) dengan pendekatan Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Proses pencarian literatur menghasilkan 241 artikel dari berbagai database ilmiah. Setelah melalui proses screening dan evaluasi kelayakan, diperoleh 23 artikel yang digunakan sebagai sumber utama sintesis penelitian dan 118 artikel relevan yang digunakan untuk analisis tren dan pemetaan penelitian. Hasil penelitian menunjukkan bahwa metode berbasis Convolutional Neural Network (CNN) dan pendekatan hybrid mendominasi penelitian terkini karena mampu memberikan akurasi yang lebih tinggi dibandingkan metode konvensional. Selain itu, penerapan corner detection semakin luas pada bidang remote sensing, UAV, dan medical imaging. Temuan ini menunjukkan bahwa penelitian corner detection berkembang menuju pendekatan yang lebih adaptif, akurat, dan robust terhadap berbagai kondisi citra.
Decision Support System for Student Laptop Recommendation Using the Simple Additive Weighting Method Based on Android
The increasing variety of laptop products with diverse specifications and prices has made the selection process more challenging for students. Different academic activities such as coding, gaming, graphic design and editing, 3D engineering modeling, and multitasking require distinct hardware specifications, making it difficult to determine the most suitable laptop manually. This study aims to develop an Android-based Decision Support System (DSS) for laptop recommendations using the Simple Additive Weighting (SAW) method. The system evaluated 25 laptop alternatives based on nine evaluation criteria, namely price, RAM, CPU, GPU, SSD, battery, device weight, screen size, and body material. The SAW method was implemented through decision matrix construction, normalization, weighted preference calculation, and alternative ranking. The results showed that different user profiles produced different recommendation outcomes. The ASUS Zenbook 14 OLED UM3402YA achieved the highest preference value in the coding and multitasking categories, while the Nitro V obtained the highest score in the gaming, graphic design and editing, and 3D engineering modeling categories. The developed Android application provides users with a practical and flexible tool for obtaining laptop recommendations according to their specific needs. The findings indicate that the SAW method is effective in generating objective and personalized laptop recommendations through a multi-criteria decision-making approach.
Comparison of x86, ARM, and RISC-V Architectures for Performance and Energy Efficiency in Modern Computing Systems: A Systematic Literature Review
The rapid growth of cloud computing, artificial intelligence, and energy-efficient computing has increased interest in alternative processor architectures beyond traditional x86 systems. This study aims to compare the characteristics, performance, and energy efficiency of x86, ARM, and RISC-V architectures through a Systematic Literature Review (SLR). Literature was collected from IEEE Xplore, ACM Digital Library, SpringerLink, arXiv, and Google Scholar covering publications from 2021 to 2026. After applying inclusion and exclusion criteria, 20 articles were selected for detailed analysis. The findings indicate that ARM and RISC-V generally provide better performance-per-watt efficiency than x86, particularly in cloud and AI-related workloads. ARM has achieved significant adoption in hyperscale cloud infrastructures due to its lower power consumption and operational costs. Meanwhile, RISC-V demonstrates strong potential through its open-source, royalty-free, and modular architecture, supported by successful hardware implementations on FPGA platforms. However, x86 continues to maintain advantages in software compatibility and ecosystem maturity. In conclusion, no single architecture is universally superior; the choice depends on application requirements, energy constraints, and software ecosystem considerations. These findings provide a comprehensive overview of current processor architecture trends and their implications for future computing systems.
Implementation of the TOPSIS Method in Selecting Outstanding Students at SMKN 11 Semarang
This study discusses the application of the TOPSIS method in selecting outstanding students at SMKN 11 Semarang. The process of selecting outstanding students has been carried out based on manual assessments covering several criteria such as academic grades, discipline, attendance, attitude, and non-academic achievements. This manual assessment has the potential for subjectivity in determining the final results, so a more structured decision-making method is needed. The TOPSIS method is used to solve this problem by considering the proximity of each alternative to the positive ideal solution and the negative ideal solution. The research data was obtained from 30 students who were the alternative assessments. The stages of the TOPSIS method include compiling a decision matrix, normalization, weighting, determining the ideal solution, calculating distances, and calculating preference values. The results show that the TOPSIS method is able to produce objective student rankings based on all criteria used. Students with the highest preference values ​​show more balanced performance in each aspect of the assessment compared to other students. The application of this method helps the decision-making process become more systematic and measurable.
Technique For Order Preference By Similarity To Ideal Solution (TOPSIS)
Artificial Intelligence-Based 2D Virtual Character Design as a Promotional Media to Support Public Information Disclosure at Jambi University
Public information delivery at Universitas Jambi is currently one-directional through social media without automatic response mechanisms, resulting in suboptimal user interaction. This study aims to design and develop a 2D virtual character based on Artificial Intelligence (AI) named JUVIO (Jambi University Virtual Information Officer) as an interactive promotional medium to support public information disclosure at Universitas Jambi. System development was carried out using the Waterfall methodology through requirement analysis, design, implementation, testing, and maintenance stages. The system was built using the Laravel framework with the virtual character rendered through Live2D Cubism SDK and PixiJS, along with an AI mechanism based on Retrieval-Augmented Generation (RAG) using the Gemini 2.0 Flash Lite LLM model through LiteLLM, and a text-to-speech feature using edge-tts and WebSpeech API. Functional testing using Black Box Testing with Equivalence Partitioning involved 4 testers with 88 total scenarios, yielding a 100% success rate and 0% failure rate. These results demonstrate that the JUVIO system is feasible as an interactive promotional medium based on 2D virtual characters and AI to support public information disclosure at Universitas Jambi.
Development of a WhatsApp-Based Disaster Mitigation Chatbot Using n8n Workflow Automation
Indonesia is one of the countries with a high level of disaster risk, making disaster mitigation information essential for improving community preparedness. However, access to disaster mitigation information is often limited by the need to access specific platforms or applications. This study aims to develop a WhatsApp-based disaster mitigation chatbot using n8n workflow automation and an AI Agent to provide disaster mitigation information automatically and interactively. The research employed a prototype development method consisting of requirements identification, rapid planning, prototype design, implementation, testing, and refinement. The developed system integrates WhatsApp as a communication platform, n8n as a workflow automation tool, and an AI Agent equipped with disaster mitigation knowledge domains. Functional testing results indicate that all main system functions operated successfully, including message reception, question processing, response generation, and response delivery. Response testing also showed that the chatbot was able to provide relevant information within the predefined disaster mitigation domains while rejecting questions outside the specified knowledge boundary. The results demonstrate that the proposed chatbot can serve as an accessible and responsive disaster mitigation education medium through a widely used communication platform. Future development may focus on integrating real-time disaster data and early warning notification features.
Rancang Bangun Sistem IoT MCB Kantor dengan Auto Cut-off untuk Efisiensi Energi
The use of electrical energy in office environments that has not been optimally managed often results in waste, especially for lights and air conditioners that remain active outside of operating hours. This study aims to design and build an Internet of Things-based system using ESP32 to control office electricity with a time-based auto cut-off mechanism through a path after the Miniature Circuit Breaker. The system controls the load of lights and air conditioners concisely using relays as actuators, equipped with time synchronization via Network Time Protocol, Real Time Clock DS3231 as a backup time source when offline, DHT11 sensors, OLED displays, input buttons, and a Laravel-based web dashboard for two-way monitoring and control. The research method uses a design approach with stages of requirements analysis, hardware and software design, implementation, integration, and testing using black box testing, integration testing, and durability testing. The test results show that the system is able to activate and deactivate relays according to the operational schedule, distinguish workdays and national holidays, continue to run in conditions without internet, and provide an override mode for manual control. The developed system can support the efficiency of electrical energy use and the implementation of the Internet of Things-based smart office concept.
Auto Cut-off; Efisiensi Energi; ESP32; Internet of Things; MCB
Audit Sistem Pencahayaan pada Gedung Sekolah B Politeknik Negeri Semarang
School Building B of Politeknik Negeri Semarang (POLINES) is an intensively used academic facility that requires a comprehensive evaluation of its lighting energy consumption. A lighting system energy audit was conducted using a quantitative descriptive method through direct measurements with a lux meter and laser meter. The results indicate that only 6 out of 41 rooms met the minimum illuminance standard of SNI 6197:2020, while 5 rooms exceeded the maximum LPD limit of 7.53 W/m², primarily due to the use of inappropriate lamp types and sizes relative to room dimensions, as well as performance degradation from aging lamps. Replacing conventional lamps with LED technology and adjusting the number of luminaires based on illuminance requirement calculations are recommended as corrective measures, which are proven to reduce LPD values below the maximum standard limit while improving visual comfort for building occupants
Household Electricity Consumption Monitoring System for Customer Electricity Bill Validation
Current conventional electrical energy recording systems have limitations in accurately monitoring power consumption fluctuations in real-time. On the other hand, Internet of Things (IoT)-based monitoring solutions are often vulnerable to the risk of data loss during internet connection disruptions on the user's end. This study aims to design a single-phase electrical power consumption monitoring system as a comparison instrument for State Electricity Company (PLN) bills that is robust against network disturbances. The proposed solution is a system based on the ESP32 microcontroller and PZEM-004T sensor, implementing a dual (hybrid) logging method. This system utilizes an SD Card as an independent local storage medium and Google Spreadsheets as a cloud database, which is subsequently visualized through a Grafana dashboard. Processing is managed in parallel using FreeRTOS to prevent program interruptions. Calibration results demonstrate an exceptionally high level of system precision, with a coefficient of determination ($R^2$) of 0.9997 for voltage and 0.9999 for current against standard measuring instruments. Transmission reliability testing over 30 days recorded a 0% data loss rate from a total of 43,200 samples. Furthermore, direct validation against PLN postpaid bills shows a high degree of alignment, where the system's accumulated monthly consumption of 144.541 kWh differed by only 0.541 kWh from the official PLN meter reading (144 kWh). This system is proven effective in preventing data loss while simultaneously functioning as a credible and transparent actual bill comparison instrument for customers.
Analysis of the Service Quality of the Palembang City LRT Transportation Information System Using ITIL 4: Literature Study
The development of information technology drives the improvement of service quality in various sectors, including public transportation. Technology-based transportation services such as the Palembang LRT utilize information systems in their operations, such as electronic ticketing systems and the delivery of travel information to users, making the quality of information technology services an important factor in determining user satisfaction as well as the overall success of the service. analyzing the service quality of the information system at Palembang LRT using the ITIL 4 framework. The method used is a literature review by examining various previous studies that are relevant within the last five years. shows that the implementation of ITIL 4 is able to improve the quality of information technology services, especially through the practices of incident management, problem management, and service request management. The analysis also shows that the aspects of reliability, responsiveness, availability, and user satisfaction are the main indicators in assessing IT service quality. Nevertheless, there are still potential issues such as disruptions in the ticket system and delays in information delivery that can affect the user experience. Therefore, it is recommended to implement ITIL 4 gradually, enhance system monitoring, and optimize the service desk to improve IT service quality. Thus, it is expected that LRT Palembang services can provide a better experience for users and increase public trust in technology-based public transportation.
Sistem Keamanan Pintu Cerdas Berbasis Rfid, Esp32-Cam, Dan Notifikasi Telegram
The security of home access is a critical aspect of asset protection and occupant safety. Conventional security systems using physical keys have weaknesses such as potential key loss, illegal duplication, and a lack of access records. This research develops Smart Door Guardian, an intelligent door security system that integrates RFID technology, the ESP32-CAM microcontroller, a servo motor lock mechanism, a reed switch sensor, and real-time notifications via the Telegram Bot API. The system operates by reading RFID cards; if registered, the servo motor rotates to unlock the door, and the ESP32-CAM captures and sends the user's image to the administrator. Conversely, forced entry attempts are detected by the reed switch, triggering an intruder's alert and photo capture. Test results show an RFID reading accuracy of 98.7%, an average photo notification speed of 2.3 seconds, and a 100% detection accuracy for forced entry at a 5 mm gap. This system provides an effective, low-cost IoT solution for room security.
Optimasi Hyperparameter EfficientNet-B1 untuk Klasifikasi Penyakit Bawang Merah Berbasis Citra Digital
Penyakit bawang merah merupakan salah satu faktor yang dapat menurunkan produktivitas dan kualitas hasil panen. Identifikasi penyakit secara manual sering kali memerlukan waktu dan bergantung pada keahlian pengamat. Penelitian ini bertujuan menganalisis performa arsitektur EfficientNet-B0 dan EfficientNet-B1 dalam klasifikasi penyakit bawang merah berbasis citra digital. Dataset yang digunakan berasal dari Onion Leaf Disease Dataset dan dibagi menjadi 2.700 data pelatihan, 332 data validasi, dan 201 data pengujian. Model dibangun menggunakan pendekatan transfer learning dengan bobot awal ImageNet serta diuji menggunakan variasi learning rate (0,0001 dan 0,001) dan dropout (0,3 dan 0,5). Hasil penelitian menunjukkan bahwa EfficientNet-B1 dengan learning rate 0,001 dan dropout 0,3 memberikan performa terbaik dengan training accuracy 99,94%, validation accuracy 89,13%, dan test accuracy 93,03% serta test loss 0,2572. Analisis confusion matrix menunjukkan bahwa model mampu mengklasifikasikan 187 dari 201 citra pengujian dengan benar. Hasil penelitian membuktikan bahwa EfficientNet-B1 efektif digunakan untuk klasifikasi penyakit bawang merah dan berpotensi mendukung sistem deteksi penyakit tanaman secara otomatis.
Design and Construction of a Website-Based Padel Tournament Management System Using the Incremental Method with the Implementation of Round Robin and Knockout Formats
The rapid growth of padel sports requires a tournament management system capable of managing competitions effectively, accurately, and in an integrated manner. Manual tournament management is still prone to score recording errors, ranking calculation mistakes, match scheduling conflicts, and errors in managing knockout brackets. This study aims to design and implement a web-based padel tournament management system capable of automating tournament management using Round Robin and Knockout formats. The research method used is the Incremental system development method, which is carried out gradually through requirements analysis, system design, implementation, testing, and system evaluation stages. System testing was conducted using the Black Box Testing method to ensure that all system functions operate according to user requirements. The results showed that the system successfully automated padel scoring validation, including two-game difference calculations and 7-6 tiebreak handling, while also managing match scheduling, Round Robin standings, and Knockout brackets automatically. The system was also able to handle seeding processes, bye allocation, and automatic winner advancement to the next round effectively. In conclusion, the developed web-based padel tournament management system successfully improved tournament management efficiency, minimized human error risks, and supported better tournament administrative integrity.
Implementasi Sistem Iot untuk Monitoring dan Deteksi Dini Kerusakan Bearing Motor Induksi
Induction motors are essential components in industrial systems, where bearing failures can cause increased vibration, overheating, reduced performance, and unexpected production downtime. Conventional monitoring methods are generally manual and periodic, limiting the ability to detect early signs of failure in real time. This study aims to develop an Internet of Things (IoT)-based monitoring and early fault detection system for induction motor bearings using a DS18B20 temperature sensor, ADXL345 vibration sensor, ESP32 microcontroller, Blynk cloud platform, and a risk score decision method. The research employed a Research and Development (R&D) approach with a prototyping model to design and evaluate the proposed system. Experimental results showed that the DS18B20 sensor was able to detect temperature changes properly, while the ADXL345 sensor successfully measured vibration changes with readings relatively close to reference instruments. The Blynk platform successfully displayed temperature and vibration monitoring data in real time, although minor delays were observed due to network conditions. System testing under four simulated operating conditions confirmed that the risk score method successfully classified motor conditions into normal, warning, danger, and shutdown categories according to predefined thresholds. The developed system demonstrates the potential to support continuous monitoring and early fault detection of induction motor bearings, contributing to more effective preventive maintenance in industrial applications
ADXL345; Blynk; DS18B20; Induction Motor Bearing; Internet of Things
Prediksi Penjualan Sepeda Motor Terlaris di Astra Honda Brebes Menggunakan Algoritma Naive Bayes
This study aims to apply the Support Vector Machine (SVM) method in predicting motorcycle sales levels at Astra Honda Brebes using historical sales data for the 2020–2025 period. The research dataset consists of 200 sales data that have attributes of motorcycle model, vehicle year, price, transmission type, vehicle type, engine capacity, and sales status. The research process includes data preprocessing, training and testing data division, classification process using RBF kernel, and model evaluation using confusion matrix, accuracy, precision, recall, F1-score, AUC, and cross validation. The results show that the SVM method is able to classify vehicle sales levels with good performance. The accuracy value obtained was 89.50%, precision 87.20%, recall 85.40%, F1-score 86.29%, AUC of 0.91, and cross validation accuracy of 91.20%. These results indicate that the SVM method has good capabilities in distinguishing between best-selling and less-selling vehicle categories. This research is expected to help dealers in determining marketing strategies, managing vehicle stock, and making decisions more effectively, objectively, and based on data.
Support Vector Machine, Data Mining, Machine Learning, Sales Prediction, Data Classification.
Sistem Pendukung Keputusan Pemilihan Ayam Petelur Menggunakan Metode SAW
Selecting the right type of laying hen is crucial to increase the productivity of the Karangasem Village-Owned Enterprise (BUMDes) farm. The manual assessment process tends to be subjective, necessitating a Decision Support System using the Simple Additive Weighting (SAW) method. This study used the criteria of egg productivity, chicken health, feed consumption, endurance, environmental adaptation, egg quality, and chicken mortality rate with alternative breeds Brown, KUB, Leghorn, Lohmann Brown, Hy-Line, and Elba. The website-based system was built using PHP and MySQL. The results showed that the SAW method was able to provide objective assessment and ranking, with Elba chickens obtaining the highest score of 0.9671, thus being recommended as the best type of laying hen.
81 Decision Support System, Simple Additive Weighting, Laying Hens, SAW, BUMDes.
Optimasi Algoritma Support Vector Machine untuk Prediksi Kelulusan Tes Samapta Personel Kodim 0713 Brebes
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Support Vector Machine; Samapta Test; Hyperparameter Optimization; Grid Search; Graduation Prediction
Penerapan Kecerdasan Buatan (AI) dalam Sistem Rekomendasi Produk Pada E-commerce
The rapid advancement of the digital era has fundamentally transformed the electronic commerce landscape, where the abundance of product options frequently causes information overload that hinders consumer decision-making. This study aims to examine the effectiveness of artificial intelligence implementation in product recommendation systems on e-commerce platforms while comparing the performance of various algorithms employed. The research adopted a comparative experimental quantitative approach using 10,000 transaction records, evaluating three primary algorithms, namely Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering, through Precision, Recall, F1-Score, and RMSE metrics. Findings revealed that AI-based systems achieved an average recommendation relevance rate of 84.6%, substantially surpassing conventional systems at only 51.3%. Among the three algorithms tested, Hybrid Filtering demonstrated the highest performance with an F1-Score of 87.9% and the lowest RMSE of 0.231. The hybrid approach also proved most resilient under cold-start and data sparsity conditions compared to other algorithms. This study concludes that integrating artificial intelligence, particularly through a hybrid algorithm, represents the most optimal strategy for improving product recommendation personalization quality and driving sales conversion on large-scale e-commerce platforms.
E-Commerce; Artificial Intelligence; Recommendation System; Hybrid Filtering; Product Personalization