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.
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
Copyright (c) 2026 Elkom: Jurnal Elektronika dan Komputer https://creativecommons.org/licenses/by-sa/4.0
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
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.
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
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
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.
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.
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.
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
<div id="tw-container" class="nev7se" data-cp="1" data-nnttsvi="1" data-sletp="false" data-sm="1" data-ssbp="true" data-sugg-time="500" data-sugg-url="https://clients1.google.com/complete/search" data-uilc="id" data-vil=",af,af-ZA,am,am-ET,ar-EG,ar-AE,ar-KW,ar-QA,ar,ar-IL,ar-JO,ar-LB,ar-PS,az,az-AZ,bg,bg-BG,bn,bn-BD,bn-IN,ca,ca-es,cs,cs-CZ,de,de-DE,de-CH,de-AT,de-LI,en,en-US,en-CA,en-AU,en-NZ,en-GB,en-IN,en-KE,en-TZ,en-NG,en-GH,en-PH,en-ZA,es,es-ES,es-AR,es-UY,es-419,es-BO,es-CL,es-CR,es-CO,es-DO,es-EC,es-GT,es-HN,es-NI,es-PA,es-PE,es-PR,es-PY,es-SV,es-VE,es-MX,es-US,eu,eu-ES,fi,fi-FI,fr,fr-FR,fr-CH,fr-BE,gl,gl-ES,gu,gu-IN,he,he-IL,iw,iw-IL,hu,hu-HU,hy,hy-AM,id,id-ID,is,is-IS,it,it-IT,it-CH,ja,ja-JP,jv,jv-ID,ka,ka-GE,km,km-KH,kn,kn-IN,ko,ko-KR,la,lo,lo-LA,lv,lv-LV,ml,ml-IN,mr,mr-IN,ms,ms-MY,nl,nl-NL,nb,nb-NO,ne,ne-NP,pl,pl-PL,pt,pt-BR,pt-PT,ro,ro-RO,ru,ru-RU,si-LK,sk,sk-SK,sr,sr-RS,su,su-ID,sv,sv-SE,sw,sw-TZ,sw-KE,ta,ta-IN,ta-SG,ta-LK,ta-MY,te,te-IN,tr,tr-TR,ur,ur-PK,ur-IN,yue,yue-HK,yue-Hant-HK,zh-HK,zh,zh-CN,zh-cmn,zh-cmn-CN,zh-Hans,zh-Hans-CN,zh-cmn-Hans,zh-cmn-Hans-CN,cmn-CN,cmn-Hans,cmn-Hans-CN,zh-TW,zh-Hant-TW,cmn-TW,cmn-Hant-TW,zh-cmn-TW,zh-cmn-Hant-TW,zu,zu-ZA,hi,hi-IN,tl,tl-PH,pa,pa-IN"> <pre id="tw-target-text" class="tw-data-text tw-text-large tw-ta" dir="ltr" tabindex="-1" role="text" data-placeholder="Terjemahan" data-ved="2ahUKEwiiyaKuxdGUAxUJSWwGHWdOBxYQ3ewLegQIDRAV" aria-label="Teks terjemahan: Tes Kebugaran Fisik Samapta berfungsi sebagai tolok ukur utama untuk menilai kesiapan fisik personel Tentara Nasional Indonesia (TNI-AD) dan dilakukan secara berkala, menghasilkan catatan kinerja historis yang substansial. Namun, pemanfaatan data tersebut sebagai dasar untuk memprediksi hasil tes di masa mendatang belum diimplementasikan secara sistematis di tingkat Komando Militer Distrik (Kodim). Studi ini bertujuan untuk mengoptimalkan algoritma Support Vector Machine (SVM) untuk meningkatkan akurasi prediksi hasil Tes Samapta bagi personel di Kodim 0713 Brebes. Optimasi dilakukan melalui dua pendekatan utama: penyetelan hyperparameter menggunakan Grid Search Cross Validation untuk mengidentifikasi konstanta regularisasi (C) dan parameter kernel (gamma) yang optimal, dan pemilihan fitur untuk menentukan komponen Samapta yang paling berpengaruh dalam proses prediksi. Fitur input terdiri dari lima komponen tes Samapta — lari 12 menit, pull-up, sit-up, push-up, dan shuttle run — dengan label output yang mewakili kategori skor Samapta sesuai dengan standar militer yang berlaku. Dataset ini berasal dari catatan tes Samapta historis personel Kodim 0713 Brebes di berbagai periode penilaian. Evaluasi model dilakukan menggunakan validasi silang k-fold, mengukur akurasi, presisi, recall, dan skor F1 sebelum dan sesudah optimasi. Hasil eksperimen menunjukkan bahwa optimasi hyperparameter secara signifikan meningkatkan kinerja model dibandingkan dengan baseline SVM dengan parameter default, menghasilkan pengklasifikasi yang lebih andal untuk memprediksi kategori kebugaran fisik personel. Penelitian ini diharapkan dapat menjadi referensi untuk penerapan pembelajaran mesin di dalam unit militer, mendukung program kebugaran fisik berbasis data yang berorientasi pada peningkatan berkelanjutan kesiapan operasional personel."> The Samapta Test is a component of the physical fitness assessment that is mandatory for all military personnel, including personnel of the 0713 Brebes Military District Command (Kodim), as an indicator of physical readiness to carry out duties. The high failure rate of this test encourages the need for a prediction system capable of identifying personnel at risk of failure early. This study aims to build and optimize a prediction model for passing the Samapta Test using the Support Vector Machine (SVM) algorithm. Optimization is carried out through hyperparameter tuning techniques using the Grid Search Cross-Validation method to obtain the optimal combination of kernel parameters, C values, and gamma values. The data used comes from a summary of the Samapta Test results of Kodim 0713 Brebes personnel, including attributes such as age, running, push-up, sit-up, pull-up, and swimming scores. The research stages include data collection, pre-processing, feature selection, SVM model development, parameter optimization, and model performance evaluation using accuracy, precision, recall, and F1-score metrics. The results of the study show that the optimized SVM model is able to significantly increase prediction accuracy compared to the SVM model without optimization, so that it can be used as a decision-making tool for units in designing more targeted and effective personnel physical development programs.
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
Development of Higher Education Key Performance Indicator Monitoring System using Rapid Application Development Method
Key Performance Indicators (KPIs) have a very essential role in higher education institutions in order to maintain the quality standards and to meet the requirements of the accreditation. But manual input and tracking typically results in data silos, data loss and delayed reporting. Furthermore, the system must be integrated with the subsystems that have already been built, which makes the development process longer and more complex. The purpose of this project is to design a university web-based KPI monitoring system by using Rapid Application Development (RAD) method to increase efficiency and reduce data loss. This choice is done because it allows a fast implementation in a real environment with an already existing subsystem. It started development in June 2025 and continued through August. The user input was looped as per criteria. The KPI tracking system has been implemented in one semester already. RAD has already proven to be the right methodology, as it achieved the goal of fast implementation and satisfied functional requirements.
Design and Construction of an Internet of Things-Based Attendance System at PT. Roda Maju Bahagia
This research is intended to develop an Internet of Things (IoT)-based attendance system that enhances the efficiency of attendance data management at PT. Roda Maju Bahagia, where the previous process was still carried out manually. Human error during manual attendance management became one of the main issues, accompanied by inefficient data recording, obstacles in data retrieval and archiving, and limited accuracy in reporting. This research applied a qualitative method using a Research and Development (R&D) approach based on a prototype model. The attendance device/machine was built using a microcontroller circuit. Fingerprint reading results were then sent to a database, and notifications were sent to employees' Telegram accounts. All of this was done via a Wi-Fi or internet connection. The system featured automatic daily and monthly attendance reporting on a local website managed by HR administrators, utilizing attendance data obtained from the fingerprint device. Reports can be downloaded in Excel or PDF format, in tabular format, according to management and company needs. The results indicated that the system was able to record, process, and display attendance reports effectively. This system implementation has improved the efficiency, accuracy, and transparency of attendance records used for payroll calculations, thereby enhancing human resource management performance.
Thermal Behavior Clustering of High-Voltage Electrical Equipment Using K-Means and Fuzzy C-Means
Thermal monitoring of high-voltage electrical equipment is an important aspect of maintaining the reliability and operational safety of electrical power systems. Conventional classification approaches generally require labeled data and are limited in representing transitional thermal conditions. Therefore, this study proposes an unsupervised learning approach using K-Means and Fuzzy C-Means (FCM) clustering methods to analyze thermal behavior patterns in high-voltage electrical equipment based on thermal image features. The proposed model utilizes two main features extracted from thermal images, namely the percentage of white regions (% white) and non-white regions (% non-white), where white regions represent high-temperature areas. A total of 12 thermal images were used in the clustering process. Experimental results showed that the K-Means algorithm converged after only 2 iterations, whereas FCM required 53 iterations to achieve convergence . Both methods successfully identified dominant thermal patterns corresponding to Normal, Warning, and Hazardous conditions. The most extreme thermal condition was observed in data sample 6, which had a white-region percentage of 83.2647% and was consistently classified as Hazardous by both K-Means and FCM . In addition, FCM demonstrated superior capability in representing transitional thermal conditions through membership values. Data sample 3, with a white-region percentage of 56.5476%, was classified as Hazardous by K-Means but categorized as Warning by FCM with a dominant membership value of 0.702543 . These results indicate that FCM provides more flexible thermal behavior representation compared with hard clustering approaches. Overall, the proposed clustering-based approach demonstrates significant potential for real-time thermal condition assessment and predictive maintenance applications in high-voltage electrical equipment.
Thermal Imaging High-Voltage Electrical Equipment K-Means Fuzzy C-Means Clustering Behavior Analysis
Sistem Monitoring Listrik dan Kesehatan Perangkat Videotron Jalan Tol Berbasis IoT
Videotrons on toll roads serve as visual information media to convey traffic conditions, safety warnings, and early warnings to road users. However, the reliability of videotrons is greatly influenced by the stability of the electricity supply and the health of the devices. Electrical disturbances such as unstable voltage, power outages, overheating, and high humidity can cause device damage and disrupt videotron operations. This study aims to design and build an Internet of Things (IoT)-based electrical and videotron device health monitoring system on the Semarang–Solo Toll Road of PT Trans Marga Jateng. The system uses a NodeMCU ESP8266 as the main microcontroller integrated with a PZEM-004T sensor for monitoring electrical parameters and a DHT22 sensor for monitoring temperature and humidity. Data is sent in real-time via the internet to the Blynk & Telegram application as a monitoring and early notification medium. This study uses a Research and Development (R&D) method with a prototype model. The results show that the system is capable of monitoring voltage, current, power, electrical energy, temperature, and humidity in real-time and providing early notification when anomalies occur in certain parameters. This system is expected to improve the operational reliability of videotrons, speed up the handling of disturbances, and reduce device maintenance costs.
IoT; Monitoring listrik; Videotron; NodeMCU ESP8266; PZEM-004T; DHT22; Blynk
Implementasi Convolutional Neural Network berbasis Transfer Learning untuk Klasifikasi Acute Lymphoblastic Leukemia
Leukemia is a cancer that originates in human blood cells. The most common type of leukemia (97%), with an incidence of 4–4.5 cases per 100,000 children per year, is Acute Lymphoblastic Leukemia (ALL). This indicates that leukemia can progress rapidly and become fatal for the patient within a few months. Therefore, a supporting method is needed that can classify blood cells automatically, quickly, and accurately. This method is a Convolutional Neural Network (CNN) using the EfficientNet-B3 architecture as a pre-trained model or for transfer learning. This dataset consists of 3,527 blood cell images that have been preprocessed to a size of 224x224x3 and image enhancement has been applied. The images were trained on the pre-trained model and then combined with Global Average Pooling (GAP), Batch Normalization, Dense, and Softmax layers until the model could classify the images into the ALL or HEM classes. The results of the study show that the EfficientNet-B3 architecture is capable of classifying white blood cell images into the ALL and HEM classes through the transfer learning process. The best hyperparameter configuration for optimal results includes a learning rate of 0.0001 and the RMSProp optimizer. The model achieved the best training accuracy of 100% at epoch 30 and a batch size of 16, while the best testing accuracy was 96% at epoch 50 and a batch size of 16. Additionally, the precision, recall, and F1-score were 96%, 94%, and 95%, respectively.
Pengembangan Sistem Kendali Cerdas Alat Pemberi Isyarat Lalu Lintas (APILL) Berbasis Machine Learning
In modern cities, population growth directly contributes to an increase in the number of vehicles, leading to significant traffic problems and a decline in road service quality and capacity. Conventional traffic light control systems (APILL) that rely on fixed-time scheduling often fail to adapt to the dynamic nature of traffic conditions, potentially exacerbating congestion. This study proposes an innovative approach to traffic management by utilizing the YOLO (You Only Look Once) object detection algorithm. By analyzing CCTV streaming data at intersections, the system dynamically assesses traffic density, identifies vehicle types, and adjusts signal timings in real-time. Leveraging YOLO's ability to perform fast and accurate object detection, the system can respond to traffic conditions in a timely manner. This approach integrates Artificial Intelligence (AI) and Machine Learning techniques to address the urgent need for adaptive traffic management strategies in urban areas. The primary goals of this solution are to reduce congestion, improve traffic flow, and minimize environmental impact. Therefore, the integration of YOLO technology with adaptive traffic signal control algorithms represents a strategic step toward addressing the complex challenges of urban traffic congestion
Smart APILL; Traffic Light; YOLO; Machine Learning; Adaptif
Development of a Digital-Based 360-Degree Performance Appraisal System to Enhance the Objectivity of Employee Evaluation
Employee performance appraisal is a strategic instrument in human resource management that directly influences competency development, job promotion, and remuneration policies. However, conventional top-down appraisal systems are often confronted with issues of subjectivity, evaluator bias, and limited transparency in the evaluation process. This study aims to develop a digital-based 360-degree performance appraisal system that integrates multi-source perspectives, including supervisors, peers, subordinates, and self-assessment, within a unified platform. The study employed an Agile-based system development method using an iterative approach, supported by data collection techniques consisting of literature review, business process observation, and in-depth interviews with stakeholders within higher education institutions. The system was designed using a web-based architecture with role-separated modules, a role-based access control (RBAC) authentication mechanism, and score normalization algorithms to ensure assessment consistency. The testing results indicate that the system successfully improves evaluation objectivity through standardized weighting distribution, enhances transparency through audit trail and real-time reporting features, and accelerates the recapitulation process by up to 78% compared to manual methods. Furthermore, the system effectively reduces single-evaluator bias and strengthens accountability in human resource management decision-making. This study concludes that the digitalization of 360-degree performance appraisal functions not merely as an automation tool, but as a strategic instrument for establishing a fair, transparent, and data-driven evaluation culture.
Perancangan Smart Server System Berbasis MonitoringSuhu, Kelembapan, dan Keamanan Akses di CV GundaraSolusi Bersama Ungaran
Server rack management at CV Gundara Solusi Bersama Ungaran previously relied on conventional room cooling and manual door locking mechanisms without environmental monitoring and access logging. This condition increases the risk of hardware overheating, excessive humidity, and unauthorized physical access. This study proposes a Smart Server System based on the ESP32 microcontroller to integrate temperature and humidity monitoring, automatic thermal control, electronic access security, and cloud-based logging within a single Internet of Things (IoT) platform. The system utilizes an SHT21 sensor, dual DC cooling fans controlled through a relay module, a 4×4 keypad for PIN authentication, a solenoid door lock, an LCD 20×4 I2C display, and Google Sheets integration through an HTTPS REST API. The cooling mechanism activates automatically when the temperature exceeds 27°C or humidity exceeds 65% RH, while access events are recorded in real time to cloud storage. The system was developed using an iterative Research and Development (R&D) prototyping approach and evaluated through functional, performance, and reliability testing. Experimental results show that the proposed system achieved a temperature measurement MAE of 0.220°C, humidity MAE of 0.724% RH, average response times below 3 seconds for cooling and access control operations, logging completeness of 100%, and system uptime of 99.7% during a 72-hour continuous test. The proposed system provides an affordable and integrated solution for thermal management and physical security monitoring of server infrastructure in small and medium enterprises (SMEs).
Load Balancing of 20kV Network Against Power Losses Using ETAP Software of PT PLN (Persero) ULP Cijawura
Load imbalance in the electric power distribution system is one of the problems that can reduce power quality and increase power losses in the network. This study aims to analyze the condition of load imbalance and make efforts to balance the load on the secondary side of the distribution transformer based on field measurement data. The methods used include collecting current data on each phase, calculating the percentage of load imbalance, and evaluating the distribution of the load before and after balancing. The analysis results show that before balancing, there was a significant difference in current between the phases, indicating an unbalanced condition. After the load redistribution, the imbalance value decreased so that the current in each phase became more evenly distributed. This condition has an impact on increasing system efficiency and reducing power losses in the distribution network. Thus, load balancing is an effective step in improving the performance and reliability of the electric power distribution system.
Pengembangan Sistem Backup Server EPrint dengan Ubuntu 24.04 ke Cloud Storage Nextcloud
Managing scientific archive data on the EPrints Server at STP Sahid Surakarta faces issues with manual backups using WinSCP. This procedure leads to potential human error and the risk of data loss due to local storage, thus threatening important institutional information. The purpose of this research is to develop a scheduled automatic backup system for an Ubuntu 24.04-based Eprints server, utilizing two Virtual Private Servers (VPS) and Nextcloud as cloud storage. The development method used is the Software Development Life Cycle (SDLC) Waterfall model, which includes analysis, design, implementation, and testing. The system implementation involves using bash scripts, cron, mysqldump, tar, and rclone for backup and restore functions. The system's functionality is verified through Black Box Testing. The test results show that the automatic backup system works consistently and as planned. Additionally, the restore function successfully recovered the entire data component and rolled back old data, complete with satisfactory activity log recording. This system provides a more efficient and controlled backup solution, addressing the weaknesses of the old system analyzed using the PIECES framework (performance, information, economy, control, efficiency, and service).
Backup System; EPrint; Ubuntu 24.04; Nextcloud; Private Cloud; Automation
Evaluasi dan Manajemen Infrastruktur Jaringan Lokal (LAN) pada Badan Penanggulangan Bencana Daerah (BPBD) Kabupaten Ciamis
The Regional Disaster Management Agency (BPBD) of Ciamis Regency is an emergency institution that heavily relies on internet connectivity to support its operational functions, particularly in accessing disaster reporting applications. However, the existing local area network (LAN) infrastructure faces structural issues, including the absence of a structured failover mechanism, bandwidth management that lacks operational priority, and network segmentation without logical separation at the network layer. This study aims to evaluate the existing LAN infrastructure and formulate improvement recommendations using the first two stages of the Network Development Life Cycle (NDLC): Analysis and Design. Data were collected through direct observation and interviews with network administrators at the site. Evaluation results indicate that the primary issue lies not in bandwidth capacity, but in the governance layer and network distribution architecture. The proposed recommendations include implementing a Dual WAN topology with automatic Netwatch-based failover, tiered Simple Queue based on operational priority, VLAN-based network segmentation, and replacement of the hub with a managed switch. All recommendations are designed to be implemented using existing devices without requiring comprehensive new infrastructure procurement
Bandwidth; Network evaluation; Failover; LAN infrastructure; MikroTik
Perbandingan Metode Random Forest dan Convolutional Neural Network dalam Deteksi Website Phishing pada Lingkungan Universitas XYZ
Phishing attacks are a cybersecurity threat often used to steal sensitive user information through fake websites that resemble legitimate sites. Therefore, this study aims to analyze and compare the performance of the Random Forest and Convolutional Neural Network (CNN) algorithms in detecting phishing websites based on Uniform Resource Locator (URL) features. The dataset used was obtained from Web Application Firewall (WAF) security logs on the network infrastructure at XYZ University, which record URL access activities on the web system. The data was then processed and labeled into two categories: phishing and legitimate websites. The dataset used in this study consists of 549,346 URL records. The research stages include data exploration (Exploratory Data Analysis / EDA), URL text preprocessing, character-based feature extraction and URL tokenization, and model training using the Random Forest algorithm and a 1D Convolutional Neural Network (1D-CNN) architecture. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics as well as confusion matrix analysis. The results showed that the Random Forest model achieved an accuracy of 82.69%, while the 1D-CNN model achieved a higher accuracy of 95.94%. Furthermore, the CNN training process demonstrated a steady increase in accuracy and a decrease in loss values in each epoch. Based on these results, it can be concluded that the deep learning approach using CNN outperforms the Random Forest method in detecting URL-based phishing websites
Phishing Detection; Random Forest;; Convolutional Neural Network; Machine Learning; Engineering
Implementasi Kontrol PID Pada Alat Negative Pressure Wound Therapy
This study aims to implement Proportional-Integral-Derivative (PID) control in a Negative Pressure Wound Therapy (NPWT) device to improve the precision and stability of negative pressure applied to wounds. NPWT is a standard therapy for complex wounds that applies subatmospheric pressure to accelerate healing. The developed system consists of three operating modes, namely continuous, intermittent, and dynamic, to tailor therapy based on wound type and patient needs. The prototype was built using main components including the XGZP101DB1R pressure sensor, ESP32 microcontroller, DC vacuum motor, and LCD Nextion interface. The PID algorithm was optimized through a trial-and-error method to achieve a stable response with minimal overshoot and fast response time. Test results show that the system can operate in a pressure range of -25 mmHg to -150 mmHg with very high accuracy, as indicated by an error percentage below 2% across all modes. PID control proved effective in maintaining pressure according to the setpoint, with a response time ranging from 12.8 to 15.8 seconds depending on the target pressure. Thus, this research successfully developed a precise, stable, and adaptive NPWT system, ready for further testing in clinical applications.