Journal of Students‘ Research in Computer Science
Journal of Students‘ Research in Computer Science (JSRCS) merupakan jurnal ilmiah yang dikelola oleh Lembaga Penelitian, Pengabdian Kepada Masyarakat, dan Publikasi (LPPMP) Universitas Bhayangkara Jakarta Raya dan diterbitkan oleh Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya. Jurnal ini berisi tentang karya ilmiah hasil penelitian mahasiswa bidang ilmu komputer bersama dosen pembimbingnya yang bertemakan: Algoritma, Augmented and Virtual Reality, Bahasa Komputasi, Computer Graphics, Game Teknologi, Mobile Computing, Operating Systems, Pengolahan Citra, Robotika, Sistem Pakar, Soft Computing, Software Engineering, Software Process and Life Cycle, Software Testing and Quality Assurance, System Software, User Experience (UX), User Interface (UI), Artificial Intelligence, Blockchain Technology, Business Intelligence, Cloud Computing, Computer Architecture, Computer Vision, Database Systems, Deep Learning, Human Computer Interaction, Digital Forensic, Internet of Things, IT Security, Machine Learning, Networking, Semantic Web, Sistem Terdistribusi, Systems Engineering, dan Wireless Network. Jurnal ini diterbitkan dua kali setahun (semester genap terbit Mei dan semester ganjil terbit Nopember). Pertama kali terbit Mei 2020 E-ISSN: 2722-290X. sesuai SK ISSN 0005 2722290X/JI.3.1/SK.ISSN/2020.05 (20 Mei 2020). JSRCS is a nationally accredited journal ranked SINTA 5 by the Directorate General of Research Strengthening and Development under Decree No. 225/E/KPT/2022, covering publications from Volume 1, Issue 2 (2020) to Volume 6, Issue 1 (2025). The journal was re-accredited under Decree No. 295/C/C3/KPT/2026 and retains its SINTA 5 status for the period from Volume 6, Issue 2 (2025) through Volume 11, Issue 1 (2030).
Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya
Sistem Keamanan Pintu Rumah Berbasis Internet of Things Dengan Sensor Reed Switch dan Telegram
Home security is an important aspect that requires special attention. This research aims to design and implement a Reed Switch sensor-based home door security system with notification via telegram. This system uses NodeMCU ESP8266 as a microcontroller, magnetic Reed Switch sensor to detect door status, and supporting devices such as LED, buzzer, and telegram application as notification media. The system development method involves hardware design, software programming, with functional testing of the system. The test results show that the system can accurately detect changes in door status (open and closed) and send real-time notifications to users via telegram. The implementation of this system is expected to enhance home security by providing early warning to users.
Copyright (c) 2026 Journal of Students‘ Research in Computer Science
Evaluasi Kinerja Metode Content-Based Filtering pada Sistem Rekomendasi Destinasi Wisata dan Rute Pendakian Berbasis Web
This study aims to evaluate the Content-Based Filtering method in a web-based recommendation system for tourist destinations and hiking routes. The study integrates two domains: 1,621 tourist destinations in Bogor and 126 mountain hiking routes across Java. Tourism data were collected through web scraping from Google Maps, while hiking route data were obtained from Gunungbagging.com and Muncak.id. The attributes used include category, location, ticket price, facilities, rating, distance, elevation gain, estimated duration, difficulty level, and recommended user experience. Hiking duration was estimated using the Naismith Rule. The system constructs a user profile based on user preferences and computes the similarity between the user profile and item characteristics using Cosine Similarity. For tourism data, a Deep Autoencoder was employed to reduce 54 features into 8 latent dimensions, while the Haversine Formula was applied as a geospatial filter to account for location proximity. Evaluation using Precision@10 yielded average scores of 0.80 for hiking route recommendations and 0.85 for tourist destination recommendations. The results indicate that Content-Based Filtering effectively produces relevant and precise recommendations that align with user preferences and supports more objective and efficient decision-making.
Sistem Informasi Penjualan dan Persediaan Obat pada Apotek Sejuk Berbasis UI/UX dan Design Thinking
Sejuk Pharmacy still faces operational challenges because medicine inventory records and sales transactions are managed manually. This condition leads to stock data discrepancies, reporting delays, and a risk of recording errors. This study aims to design a User Interface (UI) and User Experience (UX) for a sales and medicine inventory information system at Sejuk Pharmacy to improve operational efficiency. The method used is Design Thinking, consisting of the empathize, define, ideate, prototype, and testing stages. The result of this study is a prototype of a pharmacy information system that includes medicine data management, sales transactions (Point of Sale), transaction history, and operational reports. The prototype was designed using Figma with a simple and user-friendly interface. The usability testing results indicate that the proposed interface helps users understand system navigation and facilitates inventory management and sales transaction processes.
Arsitektur Mikrolayanan pada Platform Penilaian Risiko Kardiovaskular Menggunakan Vue.js dan Integrasi Vertex AI
Heart disease is a leading cause of death in Indonesia, exacerbated by delayed diagnosis and lack of access to healthcare facilities. This study aimed to develop JantungIn, a cardiovascular risk assessment web platform using a microservices architecture. The system separated the user interface layer using Vue.js, server logic with Hapi.js, and a Supabase relational database. The novelty of this research lay in the real-time integration of a TensorFlow.js prediction model and a Vertex AI Dialogflow CX intelligent virtual assistant. System evaluation using the Black Box method covered positive and negative scenarios on core functionalities, including role authentication for doctors and patients, medical parameter validation, and medical history storage. The testing results showed that the software architecture successfully processed data exchange without functional errors. In conclusion, JantungIn was successfully implemented as a reliable medical decision support system to facilitate early detection of heart disease in the community.
Perbandingan Kinerja Random Forest dan Long Short-Term Memory dalam Prediksi Kedalaman Hiposentrum Gempa di Maluku dan Laut Banda
Hypocenter depth is an important parameter in earthquake analysis because it is related to the earthquake source mechanism and its impact on the Earth’s surface. The Maluku and Banda Sea regions have high seismic activity caused by interactions among active tectonic plates. This study compares the performance of Random Forest and Long Short-Term Memory (LSTM) algorithms in predicting earthquake hypocenter depth using historical earthquake data from the United States Geological Survey (USGS). Model evaluation used MAE, RMSE, and R² metrics. The results show that Random Forest outperformed LSTM, achieving MAE, RMSE, and R² values of 31.94, 62.83, and 0.73, respectively. Meanwhile, LSTM produced MAE of 69.35, RMSE of 119.31, and R² of -0.11. Therefore, Random Forest was considered more effective for predicting earthquake hypocenter depth in the Maluku and Banda Sea regions.
Evaluasi Kinerja Clustering K-Means Menggunakan Elbow Method dan Silhouette Coefficient pada Data Penimbangan Sampah
Bank Sampah Perumahan Sapta Pesona RW 08 Bekasi City has waste weighing data that is still limited to administrative records and has not been optimally used to identify waste characteristic patterns. This study aims to Cluster waste data based on quantity/tonnage and unit price using the K-Means algorithm. The research method used Cross Industry Standard Process for Data Mining (CRISP-DM). The dataset consisted of 810 waste weighing records from January 2024 to December 2025 with item type, quantity/tonnage, and unit price attributes. The number of Clusters was determined using the Elbow Method based on the Sum of Squared Error (SSE), while the Clustering results were evaluated using the Silhouette Coefficient. The results show that the data were grouped into three Clusters with a Silhouette Coefficient value of 0.7844, indicating a strong Cluster structure. Cluster 0 represents waste with low to moderate tonnage and unit price, Cluster 1 represents waste with low tonnage and high unit price, while Cluster 2 represents waste with high tonnage and low to moderate unit price. These results help managers understand waste data characteristics more systematically
Sistem Informasi Penjualan Harian Berbasis Web sebagai Solusi Digitalisasi Pencatatan Keuangan UMKM
Micro, Small, and Medium Enterprises (MSMEs) often face challenges in managing transaction records and financial reports manually, which can lead to recording errors and reduced operational efficiency. This study aims to develop a web-based daily sales information system to manage sales transactions, product data, income, expenses, and profit-loss reports in an integrated manner. The system was developed using the Waterfall method and web-based technology. The results show that the system can assist MSMEs in recording transactions digitally, improving data accuracy, accelerating financial report preparation, and supporting more effective and efficient business decision-making.
Sistem Peringatan Dini Banjir Dengan Menggunakan ESP32 dan Sensor Ultrasonik
The advancement of Internet of Things (IoT) technology has driven its application across various sectors, including flood disaster mitigation. At present, most flood monitoring activities are still conducted manually, resulting in delayed response actions. This research proposes the design of an Android-based flood early warning system utilizing the ESP32 microcontroller as the central controller. The system is equipped with an HC-SR04 ultrasonic sensor to measure water level with high accuracy, a buzzer for local alert signaling, and a Wi-Fi connection to send real-time notifications to an Android application. The operational mechanism begins with the sensor measuring the water surface distance, which is then compared to a predefined threshold. If the water level exceeds this limit, the system activates the alert and sends a notification to the user. This prototype is designed to deliver timely, accurate, and easily accessible information, enabling communities to take preventive measures promptly to minimize the impact of flooding.
Analisis Clustering Data Mahasiswa Berdasarkan Nilai Akademik Menggunakan K-Means
Academic data in higher education are mainly used for administrative purposes, rather than for meaningful insights. Yet, analyzing student grade data can reveal patterns that help institutions evaluate and improve development strategies. This study grouped student data by academic grades using the K-Means Clustering method. Grades from core courses underwent data collection, preprocessing, cluster number selection, and Clustering using K-Means. The results showed K-Means successfully clustered students by performance level. Each cluster reflected a category of academic ability: high, medium, or low. These results can help institutions monitor progress and design better academic guidance. Thus, applying K-Means may be effective for analyzing student academic data in higher education.
Aplikasi Toko Buku Berbasis Web Dengan Fitur Rekomendasi Buku Menggunakan Algoritma Collaborative Filtering
The rapid development of information technology has had a significant impact on various sectors, including the bookselling industry. Many conventional bookstores have begun to switch to digital platforms, simplifying and expanding their sales reach. However, with the wide selection of books available, users often experience difficulty finding books that match their interests and needs. Therefore, this research was conducted with the aim of designing a web-based bookstore application equipped with a personalized book recommendation feature using the PHP programming language and a MySQL Database. The main feature is the implementation of a collaborative filtering algorithm, a recommendation system method that analyzes the similarity of preferences between users based on data from ratings provided. This data is used to predict and recommend books that are likely to be liked by other users with similar preference patterns. The implementation of this algorithm aims to increase the relevance of recommendations and provide a more efficient and interactive book search experience. The results of this test indicate that the system is capable of providing relevant book suggestions that align with user preferences. Furthermore, the implementation of this web-based application also simplifies the transaction process, speeds up book searches, and increases user satisfaction in digital shopping. Thus, this web-based bookstore application that uses a collaborative filtering algorithm can be an effective solution in helping users find books according to their interests and supporting the development of digital bookstores.
Pengujian Blackbox Testing Sistem Informasi Booking Studio Musik Berbasis Web
Digital transformation in the creative industry sector demands the existence of integrated service systems capable of adapting to user needs. However, the music studio booking process that is still done manually often leads to various obstacles, such as scheduling problems, data recording errors, as well as inefficient administrative management. This condition suggests the need for a system that can manage the booking process in a more structured and computerized manner.This study focuses on the development of a web-based music studio booking information system aimed at improving service efficiency as well as facilitating schedule management. The development method used is a waterfall model that includes the stages of needs analysis, designing, implementation, and testing. System testing is carried out using the Black Box Testing method by evaluating the conformance between the input and the output generated. The results of the study show that the developed system is able to perform the main functions well according to the users’ needs. Thus, the system is expected to improve the service quality at music studio service providers.
SADEMIN: Sistem Cerdas Deteksi Kelayakan Makanan dan Minuman Berbasis Sensor Gas dan Internet of Things (IoT)
Food safety is a fundamental aspect of public health; however, the rate of food poisoning in Indonesia remains alarmingly high, with reports indicating 4,711 cases by September 2025. This issue is compounded by the limitations of traditional methods that are subjective and the inability to detect food spoilage in its early stages, during which microorganisms start to emit Volatile Organic Compounds (VOCs) like ammonia and alcohol vapors that human senses cannot identify. The aim of this innovative concept is to create SADEMIN (Smart Food and Beverage Eligibility Detection System) as an intelligent detection system based on the Internet of Things (IoT) for objectively, accurately, and continuously monitoring food freshness. SADEMIN is engineered by incorporating MQ-135 gas sensors and MQ-3 alcohol sensors controlled by the ESP8266 microcontroller. This innovation operates by identifying gas emissions of ammonia and ethanol resulting from food degradation, assigning a "Not Suitable" status when gas levels hit a threshold of ≥ 32% or when alcohol concentrations are detected. The monitoring results are displayed locally on a 16x2 LCD and wirelessly transmitted to the Blynk app on mobile devices. Additionally, the system is equipped with an audio alert feature using the DF Player Mini module as an early warning system. Trial results indicate that SADEMIN has a high validity in distinguishing between suitable and unsuitable sample conditions in line with actual conditions. This innovation is anticipated to reduce the risk of food poisoning and contribute to achieving the SDG goal 3 regarding healthy and prosperous lives.
Pengujian Fungsional Sistem Rental Mobil Berbasis Web Menggunakan Metode Black Box Testing
The development of web technology has supported digital transformation in various service sectors, including vehicle rental services. A web based car rental system was developed to simplify the process of vehicle booking, data management, and transaction handling. To ensure that the system functioned properly and met user requirements, software testing was conducted using the black box testing method. This testing method evaluated the system functionality by examining the outputs generated from user inputs without analyzing the internal structure of the program code. The testing process covered several main features of the system, including user registration, login authentication, vehicle booking, payment confirmation, and administrative data management. The results showed that all tested operated according to the expected outcomes. The system successfully processed valid input and provided appropriate responses to invalid or incomplete input. These findings indicated that the web based car rental system functioned properly and was able to support vehicle rental services effectively.
Evaluasi Sistem Kinerja E-Tracking Pada Aplikasi Transjakarta Menggunakan Metode PIECES
GPS-based E-Tracking technology plays an important role in Transjakarta's operations by ensuring schedule accuracy and providing travel information to passengers. This study aims to evaluate the quality of the E-Tracking system in the Transjakarta application using the PIECES Framework (Performance, Information, Economics, Control, Efficiency, and Service). A mixed-methods approach was employed through a survey of 71 active users and interviews with Command Center officers. The results indicate that the E-Tracking system achieved a very high level of user satisfaction, with an average score of 4.37 (Very Satisfied category). The Information, Economics, Control, and Efficiency dimensions obtained the highest score of 4.40, while the Performance and Service dimensions scored 4.34 and 4.31, respectively. The main issues identified include data latency and the Ghost Bus phenomenon caused by signal disruptions in certain areas. Overall, the E-Tracking system functions effectively in supporting Transjakarta operations, although improvements in infrastructure and data processing are still needed to enhance service quality.
Sistem Informasi Penjualan Mainan Menggunakan Algoritma Single Moving Average Berbasis Web Pada Rumah Hotwheels Karawang
This study discusses the development of a web-based sales information system for Rumah Hotwheels Karawang by implementing the Single Moving Average (SMA) method to forecast inventory requirements. The system was developed to overcome problems related to manual transaction recording and inventory management. The application was developed using the Waterfall method and implemented with PHP and MySQL. Historical sales data were processed using the SMA algorithm to generate sales forecasts that support inventory management decisions. Black-box testing results showed that all system features, including inventory management, sales transactions, reporting, and forecasting, functioned according to user requirements. The system helps improve inventory management efficiency while reducing the risk of stock shortages and overstocking.
Sistem Pakan Otomatis Pada Kolam Lele Sangkuriang Menggunakan Motor Servo dan Wemos D1 R32
The feeding system in Sangkuriang catfish aquaculture requires regular and appropriate feed distribution to support optimal fish growth. This study develops an automatic feeding system using the Wemos D1 R32 microcontroller integrated with a Load Cell sensor, a servo motor, a DC blower, and the Blynk application for remote operation. The research employed a prototyping method that included system design, hardware assembly, and field testing. The results indicate that the device is capable of performing scheduled feeding via Blynk and distributing feed effectively with a dispersion range of approximately 100 cm. These findings demonstrate that the developed system can support improved and sustainable aquaculture technology in automatic feeding management for Sangkuriang catfish cultivation.
Copyright (c) 2025 Journal of Students‘ Research in Computer Science
Analisis Daya dan Performa Sistem Pengamanan Kendaraan Berbasis Internet of Things
A power analysis and performance study of a vehicle safety system combines vehicle contactor control, battery voltage monitoring, and an automatic disconnect feature to maintain power stability. Quantitative system performance measurements using statistical and probability approaches included connection reliability, voltage drop patterns, current distribution, and the relationship between operating time and power consumption. A total of 300 observation data sets were collected from a controlled testing process. The results show that battery voltage drop follows a negative linear trend with a correlation coefficient of -0.54, indicating a moderate relationship between load duration and voltage degradation. Analysis of relay response probabilities and WiFi connectivity showed a 96.3% success rate and a 3.7% probability of connection failure under standard network conditions. The distribution of errors in voltage measurements tended to follow a log-normal pattern based on the Kolmogorov–Smirnov test. The findings confirm that the safety control system performs stably in most test scenarios, but requires specific optimization when peak currents occur
Implementasi Internet of Things (IoT) pada Sistem Pemantauan Kelembapan Udara di Perpustakaan UBSI
Libraries, particularly academic libraries like those at UBSI, store valuable collections of books, manuscripts, and digital archives. The preservation of these physical assets is constantly threatened by environmental factors, primarily fluctuating temperature and humidity. Uncontrolled high humidity accelerates the growth of mold and mildew, leading to irreversible damage to paper-based materials. Manual monitoring systems are often inefficient, costly, and susceptible to human error. This research aimed to develop and implement a real-time, Internet of Things (IoT)-based air humidity monitoring system to ensure optimal environmental conditions for collection preservation in the UBSI Library. The system utilizes the NodeMCU ESP8266 microcontroller and the SHT30 sensor, chosen for its high accuracy and stability, to continuously collect humidity data. This data is transmitted via Wi-Fi to a Firebase real-time database and visualized on a dynamic web dashboard. The prototype was tested for accuracy and reliability, showing minimal deviation (less than 3%) compared to commercial hygrometers. The results confirm that the IoT system successfully provides remote, continuous, and highly accurate monitoring, enabling prompt intervention by library staff when humidity levels exceed the safe threshold (50%–60%). This innovative approach significantly enhances collection preservation efficiency and reduces potential conservation costs. The system built not only successfully collects data, but also processes it into easily understood information, thus fulfilling the initial objective of overcoming the inefficiency of manual monitoring.
Sistem E-Payroll pada Karyawan Yayasan Pendidikan Islam An-Nadwah Menggunakan Algoritma Advanced Encryption Standard (AES) Berbasis Web
The rapid development of information technology encourages educational institutions to improve efficiency and security in administrative management, including payroll systems. This research aims to design a web-based E-Payroll system implemented at the Islamic Education Foundation An-Nadwah by applying the Advanced Encryption Standard (AES) algorithm to ensure the confidentiality of employee salary data. The system was developed using the Rapid Application Development (RAD) method to enable fast and user-responsive development. The implementation results show that the system can effectively encrypt and decrypt salary data, as well as provide real-time and secure payroll reports. System testing using the blackbox method demonstrates that all system functionalities work as expected. This system is expected to enhance efficiency, accuracy, and security in the payroll process within the foundation.
Sistem Pakar Berbasis Whatsapp Bot Dengan Metode Forward Chaining Untuk Diagnosis Dini Gangguan Mental Pada Perkembangan Anak
Mental disorders in children can hinder their development if not detected early. Limited public awareness and a shortage of professionals often lead to undiagnosed cases. This study developed an expert system based on a WhatsApp Bot using the Forward Chaining method to support early diagnosis of children's mental disorders. Built with Node.js and MySQL, the system can identify seven types of disorders: ADHD, anxiety, depression, autism, OCD, eating disorders, and PTSD. Black box testing showed the system functions well and provides accurate results. It is expected to assist parents and educators in early detection and awareness of mental health issues in children.
Implementasi Algoritma Apriori pada Sistem Informasi Penjualan Web
This study focuses to implementing a web-based sales information system leveraging the Apriori algorithm to analyze consumer purchasing patterns at PT. Mura Mitra Sejati. Adopting the Waterfall development methodology, the project progressed systematically through the analysis, design, implementation, testing, and maintenance stages.The system was developed using a native PHP architecture based on the MVC pattern, supported by a MySQL database and Bootstrap for the front-end. Key functionalities of the system include sales transaction recording, real-time inventory management, and powerful association rule mining. Comprehensive black-box testing across all modules—Login, Stock Management, Transactions, Apriori Analysis, and Reporting—confirmed the intended performance of every system function, achieving a 100% success rate.The Apriori algorithm effectively identified strong association rules from 10 transaction datasets, notably revealing the frequent co-purchase of White Paint and 1-inch Brushes, with 40% support and 80% confidence, respectively. Ultimately, the resulting system delivers an efficient, well-structured, and data-driven solution that significantly improves sales management and supports strategic decision-making.
Model Prediksi Kondisi Kesehatan dari Data Medical Check-Up Menggunakan K-Nearest Neighbors dan Decision Tree
Medical Check-Up (MCU) is an essential procedure for the early detection of health disorders. However, manual analysis of MCU results requires time and may be subject to the interpretation of medical personnel. This study aims to develop an automatic classification system to predict health conditions based on MCU results using the K-Nearest Neighbors (KNN) and Decision Tree algorithms. The MCU data used includes blood pressure, body temperature, heart rate, as well as heart and blood pressure assessments. The models were trained and evaluated using the CRISP-DM methodology. The results show that the Decision Tree achieved an accuracy of 91.31%, while KNN achieved an accuracy of 89.75%. This system is implemented as a web-based application with a simple user interface to support the early diagnosis process at RS EMC Cibitung.
Analisis Sentimen Ulasan Produk Sneakers Lokal Pada Tokopedia Menggunakan Algoritma Naïve Bayes dan Support Vector Machine
transformation in various sectors, including the fashion industry, especially sneakers. Sneakers are now a symbol of modern lifestyle and global trends, with brands such as Nike and Adidas dominating the market. However, high prices are an obstacle for many Indonesian consumers. This opens up opportunities for local brands to offer quality products at affordable prices through e-commerce such as Tokopedia, the second highest traffic platform in Indonesia. The research analyzed sentiment from 1,032 consumer reviews of local sneakers from five stores: NAH Project, Aerostreet, Geoff Max, Ventela, and Brodo. The analysis was conducted using Naïve Bayes and Support Vector Machine (SVM) algorithms. The SVM evaluation results produced the highest accuracy of 98%, compared to Naïve Bayes which reached 96%. This best model is implemented in a web-based application to analyze the sentiment of new reviews, to assess the perceived quality and consumer satisfaction of local sneakers products on Tokopedia.
Rancang Bangun Tempat Sampah Otomatis Menggunakan Sensor HC-SR04 Berbasis Arduino Uno
The research on automatic trash bins aims to create an innovative solution for maintaining environmental cleanliness by designing an Arduino-based trash bin. This automatic trash bin is expected to reduce the negative impact of a dirty environment on human health and surrounding cleanliness. The trash bin consists of two main components: hardware and software. The hardware includes an ultrasonic sensor that detects objects at a certain distance and a servo motor that operates the lid of the trash bin. The software uses Arduino IDE to control and manage the functions of the hardware. This research employs a Prototype method, which allows for better control over the design and development process. With this method, researchers can quickly iterate and make improvements based on testing results and feedback. The results show that the automatic trash bin functions as expected. When the ultrasonic sensor detects an object at a distance of 20 cm, the Arduino instructs the servo motor to automatically open the trash bin lid. Conversely, if the object is no longer detected, the lid will close again. The implementation of this Arduino-based automatic trash bin is expected to be an effective step in creating a cleane.
Analisis Sentimen Masyarakat Terhadap PHK di Indonesia Pada Twitter Menggunakan Naïve Bayes dan Support Vector Machine (SVM)
The phenomenon of layoffs in Indonesia has led to various public opinions, especially on social media. This research aims to analyze public sentiment on the layoff issue using data from Twitter, and compare the performance of two text classification algorithms, namely Naïve Bayes and Support Vector Machine. The Knowledge Discovery in Databases approach is used as the research framework, which includes the stages of data selection, text cleaning, transformation, classification, and evaluation. A total of 3,458 tweets were collected and processed through the pre-processing stage, then classified into positive and negative sentiments. Performance assessment was conducted with three scenarios of training and test data sharing: 80:20, 70:30, and 90:10. The results showed that Support Vector Machine gave the highest accuracy of 84.93% in the 90:10 scenario, compared to Naïve Bayes with 82.61% accuracy in the same scenario. Visualization through wordcloud was also used to strengthen the interpretation of dominant words in public opinion. The findings show that classification algorithms can be utilized to understand public perceptions of employment issues and support social data-based decision-making. This research can be further developed by expanding data coverage and evaluating more complex methods to improve classification accuracy.
Pendeteksian dan Klasifikasi Sampah pada Bank Sampah Berbasis Web Menggunakan YOLOv11
The problem of poorly managed household waste management can increase the burden on the environment and reduce the effectiveness of recycling. Waste banks in general still rely on manual systems in sorting waste which is prone to errors and requires more labor. This research aims to develop a web-based waste detection and classification system using the You Only Look Once (YOLO) version 11 yolov11n (nano) method. The research method included downloading the main secondary dataset named R1 Test version 15 from the Roboflow Universe platform, collecting other secondary datasets from internet scraping and manual photography, which resulted in a total of 27,400 images of trash with nine different types, namely bottle, cans, cardboard, cup, foil, food, paper, paper_bag, and plastic.The results show that the yolov11n model is able to detect objects with sufficient accuracy and light computational resources by producing a precision value of 91,7%, recall of 89%, mAP50 of 93,2% and mAP50-95 of 75,8% in all classes. The best model results obtained are integrated into the web using the flask framework.
Implementasi Sistem Monitoring Jaringan Mikrotik Dengan The Dude
The internet service provider, CV. Sekarjaya Computindo, is currently still using a manual monitoring system. This manual monitoring system relies on the Windows command prompt. As a result, detecting network issues takes longer because the administrator must manually search for and ping IP addresses within the network or wait for user reports when network problems occur. In this study, a Mikrotik network monitoring system using The Dude was developed for CV. Sekarjaya Computindo to assist the administrator in monitoring network devices more efficiently, enabling faster identification and resolution of network disruptions. The results of the study show that The Dude monitoring system can quickly monitor the status of network devices when they are up or down due to damage or network disturbances. These statuses are displayed on The Dude’s network maps and real-time notifications are sent via Telegram.
Implementasi K-Means Clustering pada Citra Digital Tomat untuk Identifikasi Kondisi Segar dan Busuk
The manual identification of fresh and rotten tomatoes has still relied on human visual observation, which tends to be inconsistent and time-consuming. This study aimed to develop a tomato image classification system using the K-Means Clustering method based on color, shape, and texture features to automatically identify fresh and rotten conditions. The Dataset consisted of 500 tomato images for training and 60 tomato images for testing, equally representing fresh and rotten conditions. The process involved converting the images into L*a*b and grayscale formats, performing segmentation using K-Means, and extracting shape and texture features for the classification process. The testing results showed that the system successfully classified fresh and rotten tomatoes with an accuracy rate of 95%, with both precision and recall exceeding 93% for each class. These findings indicated that the K-Means method could be effectively applied in tomato image processing to support the sorting process of agricultural products. This research contributed to the development of a digital image-based classification system that could be integrated into smart agriculture systems.
Optimalisasi Pemilihan Laptop Kerja Terbaik dengan Pendekatan Metode AHP dan TOPSIS
The components found in laptops are generally the same as those in personal computers (PCs), but they are designed to be smaller, lighter, and more energy-efficient. As technology has advanced, laptops have also undergone many improvements, including in design, processor speed, memory capacity, feature additions, and efficiency in terms of time and space. Additionally, the prices of laptops today are relatively affordable. PT. Varsindo Kimia Abadi is a company engaged in the chemical industry. Currently, the company is facing a challenge in selecting the right laptop for new employees, considering the many brands and specifications available. Therefore, the author aims to provide the best recommendations for laptop selection. In the data analysis process of this research, two methods are adopted: AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The research results indicate that the best choice for a work laptop is the Asus X441MA with a score of 0.6717, followed by the Lenovo Ideapad IP V130 with a score of 0.5913, and the Asus X407MA as the third alternative with a score of 0.5458.
Algoritma SMART dalam Sistem Penilaian Karyawan Terbaik Berbasis Web
PT Sinar Perkasa Engineering is a leading engineering and manufacturing company that considers employee evaluation a critical component of human resource management. Manual evaluation processes are often time-consuming and prone to human error, potentially leading to inaccuracies in selecting the best employees. This study aims to design a decision support system based on the SMART algorithm to enhance the efficiency and accuracy of employee evaluations. The system was developed using the Waterfall model and designed with the Unified Modeling Language (UML). The results indicate that the implementation of a web-based decision support system significantly improves the speed and accuracy of the evaluation process, reduces the risk of human error, and provides a transparent and objective assessment tool. This research is expected to serve as a foundation for further development of decision support systems, either through collaborative efforts or by comparing alternative decision-making methods.