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 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.
Visualisasi Data untuk Analisis Musik Digital Menggunakan Power BI pada Data Spotify
The development of digital technology has transformed the music industry with the emergence of streaming platforms such as Spotify. This study analyzes digital music data on Spotify using Power BI to identify music trends and user consumption patterns. The dataset consists of 6,300 songs with attributes such as artists, Genres, duration, popularity, and explicit status. Data visualization is employed to determine the artists with the most songs, the most popular Genres, the distribution of song duration, and the proportion of explicit and non-explicit songs. The results show that Metallica has the most songs, rock is the most popular Genre, most songs last between 2 and 6 minutes, and non-explicit songs are dominant. These findings provide insights for musicians, record labels, and streaming platform developers in designing music strategies aligned with listener preferences.
Sistem Informasi Navigasi Wisata Kota Jakarta untuk Menentukan Rute Tercepat Menggunakan Algoritma Dijkstra Berbasis Web
This study aims to design a web-based tourism navigation information system using Dijkstra’s algorithm to determine the fastest rout in Jakarta City. The proposed navigation system assists tourist in planning their trips more efficiently by providing real-time information on the fastest routes, travel distances, and estimated traviel times. By implementing Dijkstra’s algorithm, the system calculates the optimal route based on the starting location from the user’s device and destination data stored in the database. This research employs the waterfall system development method, which inludes the stages of analysis, design, implementation, and testing. The testing results demonstrate that the system accurately provides the fastest routes, enhancing convenience and travel efficiency for tourist.
Penerapan Metode EOQ Pada Sistem Informasi Manajemen Stok Bahan Baku Ayam Broiler Berbasis Web
The Economic Order Quantity (EOQ) method is an important inventory management to determine the optimal order quantity to minimize total inventory costs, including ordering costs, storage costs, and stockouts. Companies use technology to make data management easier. With the increasing demand for chicken meat, efficient raw material management is very important to reduce production costs and maintain chicken health. This study found problems faced by companies as a result of manual recording systems that are prone to errors and data loss. The study aims to improve the accuracy of feed management, reduce waste, and ensure proper nutrition for broiler chickens through the implementation of this system. The results of the implementation of this system show improvements in raw material efficiency analysis and stock monitoring. In addition to helping improve the productivity and sustainability of the broiler chicken farming industry, this study offers solutions for companies to overcome current management problems. The system uses the PHP programming language, the codeigniter framework and MySQL as a database.
Perancangan Aplikasi Augmented Reality 3D Sebagai Media Pembelajaran Rumah Adat Indonesia Dengan Algoritma FAST Corner di SDI Al-Munir Bekasi
Elementary school students still tend to be less interested in learning local culture, one of which is the variety of traditional houses in Indonesia. Students are more interested in global culture that is trending. The main reason is because the learning process that applies is still conventional and monotonous. The teaching methods that are carried out are only delivered verbally and through written media, resulting in low student involvement in the learning process. Many areas that have cultural heritage are at risk of losing valuable cultural assets due to limited interest and preservation efforts. Digital transformation allows for the creation of new innovations in the scope of education. Augmented reality technology can be used as a bridge to connect students with local cultural diversity. AR technology designed on mobile devices is an ideal medium to motivate the younger generation to maintain the sustainability of local culture in a modern way and in accordance with a digital lifestyle. The purpose of this study is to explore the use of 3D Augmented Reality technology collaborated with the FAST corner algorithm in the learning process as an effort to increase student interest in understanding the forms of traditional houses in Indonesia. The development of a waterfall system is a method in developing Android-based applications by utilizing Unity 3D programming. This research produces an Android-based application to recognize various forms of traditional houses in Indonesia which will be applied to 3rd grade elementary school students so that they can experience a more interactive learning experience.
Sistem Monitoring Kekeruhan Air Berbasis Arduino Pada Tangki Penampungan Air
Water is the main human need in daily life. Every house has a storage container (water tank). Water that is stored for a long period of time can change the quality of the water in the water tank. Therefore, the aim of the research is to make it easier to control water in water tanks by using an Arduino-based circuit so that water storage remains clean and does not cause disease. The research uses the Prototype methodology for system development including needs analysis, system design, implementation and system testing. The Arduino-based water turbidity monitoring system is designed with a 16x2 i2c LCD module. The results of research on an Arduino-based water turbidity monitoring system make it easier to control water in water tanks. This system also helps to determine the condition of the water tank, where generally controlling water turbidity and checking water quality is still done manually.
Copyright (c) 2024 Journal of Students‘ Research in Computer Science
Metode Naïve Bayes dan Support Vector Machine untuk Mengolah Sentimen Ulasan dan Komentar di Platform Digital
This study analyzes sentiment from user Reviews of the FLO app, Taman Mini Indonesia Indah (TMII), and public comments on infidelity cases on Instagram, using Naïve Bayes and Support Vector Machine (SVM) algorithms. FLO, an app that helps users track reproductive health, was analyzed based on 1,393 Reviews on Google Play Store. Of these, 796 Reviews expressed positive sentiment, while 597 were negative. Although both Naïve Bayes and SVM achieved an accuracy of 74%, SVM performed better in recall (74%) and precision (71%). For TMII Reviews, the analysis involved 1,616 Google Reviews, with 1,263 showing negative sentiment, indicating complaints about facilities and services, and 353 expressing positive sentiment. SVM outperformed Naïve Bayes, achieving an accuracy of 85% and an f1-score of 87%, compared to Naïve Bayes’ 82% accuracy and 83% f1-score. Additionally, the analysis of 1,200 public comments on Instagram accounts @lambe_turah and @awreceh.id revealed 918 negative comments and 282 positive ones. SVM once again demonstrated superior performance with an accuracy of 91%, precision of 87%, recall of 96%, and an f1-score of 92%, surpassing Naïve Bayes, which achieved an accuracy of 86%. These findings confirm that SVM is more effective for sentiment classification across various digital Platforms, including social issues and service evaluations. The results can be applied to develop public opinion analysis systems that support strategic decision-making and enhance service quality based on user feedback.
Konversi ke Microservices Untuk Peningkatan Layanan Perpustakaan
Most web-based applications are applications that provide services to users. The services themselves often change according to policies that are usually based on user satisfaction information. Unfortunately, existing applications are mostly based on open-source which is mostly monolithic. If there is an addition of a new service, it requires development from the beginning again. Especially when there is an imbalance between one service and another where one service requires large resources while other services do not require too many resources such as processors, network speed, or database size. This study tries to propose a system change in the library based on PHP-MySQL to be based on microservices.
Anticipating Cyber Espionage: Open Source Intelligence (OSINT) Investigation and Cyber Counterintelligence
This research was conducted to analyse the use of OSINT and cyber counterintelligence in investigating cyber espionage operations using Advanced Persistent Threat (APT). Indonesia as one of the victims of cyber espionage conducted by Australia, raises the urgency of preventing cyber espionage. The purpose of this research is to answer the questions of how the utilisation of OSINT in the prevention of cyber espionage and how cyber counterintelligence can prevent cyber espionage. This research uses a qualitative method with case study on APT groups affiliated with China. The results of the analysis of cyber espionage cases conducted by I-SOON and its affiliates, which were then carried out by cyber counterintelligence efforts and investigations through OSINT with dorking techniques, can find a comprehensive picture of cyber espionage operations carried out by I-SOON, including operating practices and the underlying motivation for cyber espionage.
Analisis Clustering K-Means untuk Pemetaan Tingkat Pengangguran Terbuka di Provinsi-Provinsi Indonesia Tahun 2013-2023
This study analyzes unemployment rates in Indonesian provinces using data from the Central Statistics Agency (BPS) for the period 2013-2023 and the K-Means clustering algorithm. The aim is to group regions based on the Open Unemployment Rate (TPT). Two main clusters were produced: one with a high unemployment rate (cluster 0) and one with a low unemployment rate (cluster 1). Cluster 0 consists of 12 provinces, while cluster 1 consists of 22 provinces. The model evaluation shows a Davies-Bouldin Index score of 0.7041, indicating good clustering quality. The clustering results are visualized in the form of a map for easy interpretation. This research is expected to help policymakers design more effective policies in reducing unemployment in Indonesia, provide deep insights into regional differences in terms of unemployment, and support targeted decision-making.
Implementasi Metode Decission Tree Dalam Mengklasifikasi Depresi Menggunakan Rapidminer
Depression has become a serious mental health problem with a significant impact on quality of life and work productivity. This study aims to develop a depression classification model using the Decision Tree method implemented through RapidMiner software. The dataset consists of 2054 data with 11 variables covering demographic aspects, working conditions, and mental health. Data preprocessing is carried out through several stages, including data format conversion, categorical variable transformation using Nominal to Binominal, and numeric data normalization with Z-transformation. Implementing the Decision Tree uses the gain ratio parameter as the criterion, maximum depth 10, and confidence 0.1, and activates the pruning and prepruning features for model optimization. The results showed excellent performance with an accuracy of 97.50%, a weighted mean recall of 92.29%, and a weighted mean precision of 93.49%. The confusion matrix shows that the model successfully identified 1463 non-depression cases and 139 depression cases correctly, with a low misclassification rate.
Metode Analytical Hierarchy Process dalam Seleksi Peserta Olimpiade Sains Nasional Tingkat SD di SDN Jatimekar IV
Technological developments that help in the teaching and learning process in the classroom, when selecting OSN participants, each school sends representatives, the same as SDN Jatimekar IV which also sends student representatives from the school. However, there are often difficulties in determining school representatives. So a decision support system (SPK) is needed to determine which students are appointed by school representatives. The research uses the Analytical Hierarchy Process (AHP) algorithm because it has a model that can see the relationship between criteria data and alternative data which helps in determining which students are worthy of representing the school. Therefore, it is hoped that it will help the school to simplify the selection process for students who will be proposed as school representatives. The research results help the selection process for participants in the National Science Olympiad (OSN) at SDN Jatimekar IV.
Sistem Monitoring dan Penilaian Kinerja Pegawai Politeknik Penerbangan Medan
With the development of the times and the complexity of business demands, organizations need an effective way to measure, manage, and improve employee productivity and performance contributions. The purpose of this study is to monitor and assess employee performance at the Medan Aviation Polytechnic. This research method is quantitative with Software Development Life Cycle with waterfall technique. with sample user admin, pedulitaruna. The results of the study showed that users of 108 employees, the percentage results based on satisfaction with the use of the performance management information system were 9% of employees very satisfied with the use of the performance management information system, 69% of employees satisfied with the use of the performance management information system, 17% of employees neutral in the use of the performance management information system, and 5% of employees dissatisfied with the use of the performance management information system.
Pendekatan Fuzzy Servqual Untuk Mengevaluasi Tingkat Kepuasan Pasien di Praktik Dokter Karunia
Measuring patient satisfaction is one of the main indicators of measuring the quality of service in health facilities. Health service providers have difficulty measuring patient satisfaction, problems that are often complained about based on patient perceptions include late arrival of doctors at the clinic, lack of toilet cleanliness, long examination times, and the attitude of some nurses who are not friendly. The purpose of the study was to evaluate the level of satisfaction of patients who were treated at the Karunia Doctor's Practice using the Fuzzy Servqual method. Service quality was analyzed through five dimensions in the Servqual framework, namely tangible evidence, reliability, responsiveness, assurance, and empathy. The results of the study showed that the gap value of the five dimensions was negative, indicating that the quality of service provided did not meet the expectations of patients who were treated at the Karunia Doctor's Practice. The dimension with the highest gap value was tangible evidence with a value of -0.77, while the lowest gap value was in the reliability dimension. Thus, it can be concluded that the tangible evidence dimension requires special attention to improve service quality.
Penerapan Metode Prototype dalam Perancangan Media Pembelajaran Interaktif
Interactive learning media is technology used in the learning process that allows users to interact actively with lesson material. Users can access it via devices connected to the internet via a web-based learning media platform. This learning platform provides the flexibility to access lessons freely, without being limited to location or time schedule. User needs analysis, prototype design, implementation, and platform testing are part of the research process. One approach that can be used in the system design process is the prototype method. The research objective is to create an interactive learning media platform for computer programming using the prototype method. Test results using the prototype method increase the functionality and responsiveness of the learning platform. To enhance the programming learning experience, the platform offers interactive video tutorial features and achievement or ranking features. This research is successful because it can create a platform that is not only informative but also interesting, which makes learning programming more fun and effective.
Deteksi Emosi Menggunakan Convolutional Neural Network Berdasarkan Ekspresi Wajah
Facial expression recognition is an effective method for identifying someone's emotional expression. Emotional expressions can be recognized from changes in facial expressions, wrinkles on the forehead, blinking of the eyes, or changes in facial skin color. Facial expressions that a person generally has, such as neutral, angry, happy expressions. The problem that often occurs is the subjective assessment of a person's expression. This research examines how artificial intelligence can recognize facial expressions. The facial recognition process in the research uses a Convolutional Neural Network (CNN), which is a deep learning method capable of carrying out an independent learning process for object recognition, object extraction and classification and can be applied to high resolution images that have a nonparametric distribution model. The two main stages in CNN are feature learning and classification. The results of facial expression recognition can be used to detect a person's emotions. This research uses the FER2013 dataset which contains images of happy, sad, angry, afraid, surprised, disgusted and neutral emotions. The data set in the research received tests that had been carried out, namely the percentage of accuracy level in the model was 76%. It is hoped that the classification of emotions resulting from this research can contribute to the development of artificial intelligence technology and as a tool in various fields such as psychology, education and others. For further research, it can be developed further by adding other architectures such as VGG19, MobileNet, and ResNet-50 so that the resulting CNN model is more optimal.