INTI Nusa Mandiri

PENDEKATAN HYBRID TSR-NN UNTUK PERAMALAN INFLOW OUTFLOW UANG KARTAL REGIONAL JAWA TIMUR

2024-11-12T04:33:02+00:00

The availability of currency circulating in society can influence the economic conditions of a country. The need for money increases when religious holidays approach, such as Eid al-Fitr and Christmas, as well as school holidays and the end of the year. Therefore, it is necessary to plan the need for currency, one of which is by forecasting the circulation of currency, both inflow and outflow. Forecasting is done to predict a value in the future based on historical data. This research aim was to predict the inflow and outflow of regional currency in East Java using the hybrid Time Series Regression (TSR) – Neural Network (NN) method. The methods in time series analysis used to predict are increasingly developing, as are hybrid methods, namely methods that combine several models to produce more accurate forecasts. The analysis results obtained show that the prediction of incoming and outgoing cash flows is better using the hybrid TSR-NN method because it produces a smaller RMSE value, namely 1,656.62, with a MAPE of 0.28 compared to the TSR method. The results of this study are expected to contribute to a hybrid approach for forecasting the regional currency inflow and outflow of East Java.

IMPLEMENTASI METODE RAPID APPLICATION DEVELOPMENT DAN PENGUJIAN KATALON PADA APLIKASI LAPORAN BARANG RUSAK

2024-12-03T02:47:03+00:00

The management of State-Owned Assets (BMN) within the Secretariat General of the National Resilience Council faces challenges in recording and reporting damaged assets. The current manual process leads to delays, data inaccuracies, and hinders effective asset maintenance. This study aims to design and develop the Labarus (Laporan Barang Rusak) application as a digital solution to support faster and more accurate recording and monitoring of damaged BMN assets. The Rapid Application Development (RAD) method was used to accelerate the application development process through an iterative approach that allows for prototyping and immediate validation by users. Application testing was conducted using Katalon to ensure functional compliance with requirements and compatibility across various devices. The outcome of this research is a web-based application that enables users to report damaged goods online, track the status of repairs, and automatically generate damage and repair reports. This application reduce recording errors, speed up the repair process, and support the ongoing digital transformation in Setjen Wantannas. This research contributes by developing a web-based application called LABARUS (Laporan Barang Rusak) to replace the slow and error-prone manual processes, thereby enhancing the efficiency and accuracy of asset management in the Setjen Wantannas.

IMPLEMENTASI YOLOV5 UNTUK DETEKSI KARTU DEBIT: STUDI KASUS PADA KLASIFIKASI BRITAMA DAN SIMPEDES

2025-01-23T06:46:15+00:00

This study aims to develop an object detection model based on YOLOv5 to classify debit card types. With the advancement of financial technology, the need for automated systems to identify debit cards has become essential to enhance transaction efficiency and security. The research methodology involves five main stages: dataset collection, data preprocessing through labeling and resizing to 640 x 640, dataset augmentation, YOLOv5 model training, and model evaluation. The dataset used consists of three categories of debit cards, with a total of 300 images. The results demonstrate that the YOLOv5 model achieves excellent performance with a mean average precision (mAP) of 92.7% and an object loss value of 0.08. The high mAP value indicates the model’s capability to accurately recognize objects, while the low object loss value reflects minimal detection errors during testing. In conclusion, YOLOv5 has proven to be reliable for application in debit card detection systems. This study provides significant contributions to the development of automation systems in the financial sector, particularly in improving the efficiency and accuracy of identification processes. It is hoped that this research will serve as a foundation for further studies with broader datasets, the application of more advanced augmentation techniques, and the utilization of more sophisticated hardware to enhance model performance.

IMPLEMENTASI MODEL DeiT UNTUK MEMBEDAKAN GAMBAR BUATAN AI DAN MANUSIA PADA ILUSTRASI ANIMASI 2D

2025-01-18T03:11:28+00:00

The development of artificial intelligence (AI) has influenced various fields, including art and visual design. AI Generative Art, which mimics human styles, has sparked debates on originality, artistic value, as well as legal and ethical challenges. Therefore, methods are needed to distinguish between AI-generated and human-made images, particularly in 2D animation illustrations. This study proposes the use of Data-efficient Image Transformers (DeiT) for image classification. Two models tested are DeiT Base and DeiT Tiny, using a dataset of 6,000 images equally divided between AI and human categories. The dataset is split into training (70%), validation (15%), and testing (15%). Experimental results show that DeiT Base achieves over 95% accuracy with fast convergence and optimal loss function stability. Meanwhile, DeiT Tiny attains around 93% accuracy, being more computationally efficient despite requiring more epochs for stability. Compared to previous models using a larger dataset (11,000 images per category) but achieving only 80% accuracy, DeiT performs better in both accuracy and computational efficiency, even with a smaller dataset. In conclusion, DeiT is effective for classifying 2D animation images. DeiT Base excels in accuracy and convergence speed, while DeiT Tiny is more resource-efficient, making it an ideal choice for environments with computational constraints.

ANALISIS KUALITAS WEBSITE PORTAL MEDIA ONLINE MILENIANEWS.COM MENGGUNAKAN STANDAR ISO 9126

2025-01-13T08:21:10+00:00

Software quality can be assessed based on two main criteria, namely conformance to specifications and the ability to meet user needs. One of the international standards used to assess software quality is ISO 9126, which includes six main aspects: functionality, reliability, usability, efficiency, maintainability, and portability. In this journal, four aspects are taken to examine the quality of an online media portal website milenianews.com. The research methods include black-box testing for functionality, stress testing for reliability, Likert Scale-based questionnaire for usability, and GTMetrix for efficiency. The results showed that the functionality aspect scored 100%, indicating that all functions run according to specifications. The reliability aspect shows a 100% success rate on sessions, pages, and hits, indicating excellent performance under high usage conditions. Usability scored 79%, which falls into the good category, reflecting an interface that is easy to use and understand by users. The efficiency aspect obtained grade B with a performance score of 75% and structure 91%, indicating quite good performance, although there is room for improvement, especially in the load time of 2.5 seconds and total blocking time of 192 ms. Overall, the milenianews.com online media portal has met ISO 9126 quality standards and is declared suitable for use. These results show the importance of implementing international standards-based quality testing to ensure an optimal user experience.

IMPLEMENTASI BAHASA PEMPROGRAMAN PHP DAN MYSQL DALAM PERANCANGAN APLIKASI TICKET CENTER RESERVATION PO. NPM

2025-01-13T10:01:19+00:00

The rapid development of information technology today significantly impacts various sectors, including business, to compete more effectively. One of the important applications of technology is the development of a web-based information system for managing bus departure schedules and ticket reservations. This research aims to design and implement a web-based ticket reservation system for PO. NPM in Bukittinggi City, in order to improve efficiency and address data redundancy issues that occurred in the previous manual system. The system design method used is the Waterfall method. This system is built using PHP as the server-side programming language and MySQL as the database to store ticket reservation data and bus departure schedules. The result of this research is a web-based ticket reservation system that allows passengers to book tickets online without having to visit the ticket counter. The implementation of the web-based information system at PO. NPM can improve passenger comfort, operational efficiency, and better data management. This system also contributes to improving service quality and reducing the potential for errors in data processing.

PERANCANGAN SISTEM INFORMASI WEBSITE PROFILE SEKOLAH SEBAGAI SARANA PROMOSI

2025-01-24T03:51:31+00:00

The development of information technology encourages educational institutions to utilize digital media in supporting promotional activities and information dissemination. This research aims to design a website-based school profile information system that functions as a promotional medium and increases the dissemination of information and expands the reach of school promotion to the community, especially prospective students and parents. This information system is designed to increase transparency, accessibility, and effectiveness in conveying school information to the public. This information system presents information about school profiles, school activities, school facilities, and school contacts in an interactive and structured manner. The Waterfall method is used in system development, which consists of the stages of requirements analysis, design, implementation, testing, and maintenance. In the needs analysis stage, the main information required is identified through interviews and surveys. Then, the interface design was made with attention to the aspects of user-friendliness and responsiveness for various devices. The implementation phase uses the codeigniter framework to build a dynamic and easy-to-manage system. Testing is carried out using the blackbox testing method to ensure that the system functionality runs according to specifications. The results of the study show that this website-based school profile information system is able to present information effectively, increase school visibility, and facilitate interaction with prospective students and parents. This system can also provide added value in building the school's image in the digital era.

PERBANDINGAN MODEL MACHINE LEARNING PADA KLASIFIKASI CURAH HUJAN DI BOGOR

2025-01-24T10:02:53+00:00

Accurate rainfall prediction remains a significant challenge due to the involvement of complex physical processes and its substantial impact on various sectors of society. Rainfall prediction can be performed using classification techniques in Data Mining. Each algorithm employed for rainfall prediction may yield different performance outcomes, depending on factors such as the size of the dataset, the number of missing values, and the meteorological parameters utilized in the study. Selecting the appropriate algorithm for rainfall prediction continues to pose a challenge. This study aims to compare the performance of Naïve Bayes, Decision Tree, and Random Forest in order to identify the best model for classifying rainfall in Bogor Regency. The data utilized in this study includes maximum temperature, minimum temperature, average temperature, average humidity, duration of sunlight exposure, maximum wind speed, average wind speed, maximum wind direction, and rainfall. The dataset spans five years comprising a total 1.825 of data obtained from the Class III Citeko Meteorological Station. The results indicate that Random Forest, when trained with a smaller proportion of data compared to the proportion of test data to be predicted, achieves the best performance, with a precision of 59.1%, recall of 64.3%, and f1-score of 65.5%. This performance is attributed to the ensemble principle employed by Random Forest, which combines multiple weak learner trees to produce a robust learner tree.

OPTIMASI HYBRID INTELLIGENT SYSTEM UNTUK IDENTIFIKASI BUAH: STUDI KASUS PISANG DAN APEL

2025-01-30T04:20:27+00:00

Image processing-based fruit classification is one of the rapidly developing technology applications in the field of digital agriculture. This study aims to develop a fruit identification system, especially yellow bananas, green bananas, and apples, by utilizing the K-Nearest Neighbors (KNN) and Principal Component Analysis (PCA) methods. The background of this study is the need for an accurate automatic system to distinguish fruit types based on visual characteristics, such as color, texture, and shape, to support the distribution and management of agricultural products. The method used in this study involves four main stages: image loading, segmentation, feature extraction, and classification. PCA is used to reduce data dimensions by maintaining relevant main features, while KNN functions for classification based on the closest distance between test data and training data. The dataset used consists of 130 images, with 120 images as training data and 10 images as test data. The results of the study show that the developed system is able to classify all test data with 90% accuracy. This success proves that the combination of PCA and KNN methods is effective in identifying fruit types based on extracted visual characteristics. This system is expected to be the basis for further development in the field of automatic fruit classification.

EVALUASI PENERIMAAN PENGGUNA APLIKASI ADONAI DENGAN PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM)

2024-12-13T08:39:32+00:00

The Adonai application is designed to make it easier to submit insurance and claims for customers by Kosppi employees, but the level of acceptance still needs to be researched. This research uses the Technology Acceptance Model (TAM) to evaluate the influence of perceived usefulness (9.92%) and perceived ease of use (35.04) on application acceptance. Analysis of data from 53 respondents shows that the two variables simultaneously contribute 66.8% to application acceptance. The results of this research support the validation of TAM as a technology evaluation model in the financial services sector and provide practical recommendations to increase the ease of use and benefits of the adonai application.

ANALISIS USER EXPERIENCE DAN INNOVATION RESISTANCE PENGGUNAAN APLIKASI MYPERTAMINA MENGGUNAKAN PENDEKATAN UTAUT2 DAN UCD

2025-01-22T07:32:38+00:00

Pertamina Company launched the MyPertamian Application as a solution to the problem of uneven distribution of subsidized fuel and the implementation of non-cash payment innovations during the pandemic. However, the mismatch between the implementation of the innovation and the habits of the community has led to a rejection of the use of the application. Therefore, with this research, it is expected to identify the factors influencing the rejection of innovation usage using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) approach, with SmartPLS measurement and System Usability Scale (SUS) measurement as the readiness for application usage among 48 respondents through questionnaire completion. The research results show that aspects such as ease of use, usefulness, and perceived value of the MyPertamina application significantly influence user acceptance and the decision to adopt the application. On the other hand, innovation resistance appears in the form of user concerns about change, complexity, and data security. Therefore, design implications and development strategies can be identified to enhance user experience and reduce innovation barriers.

IMPLEMENTASI HYBRID INTELLIGENCE SYSTEM UNTUK KLASIFIKASI BIJI-BIJIAN DENGAN ALGORITMA PCA DAN KNN

2025-02-03T10:33:09+00:00

Food security has become a pressing global issue with the increasing population and food consumption needs. Red kidney beans, peanuts, and sunflower seeds play a crucial role in meeting the nutritional needs of society and serving as raw materials for various industries. This study aims to develop a seed classification system based on the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms. The system is designed to recognize three types of seeds—red kidney beans, peanuts, and sunflower seeds—to improve the efficiency and accuracy of the classification process compared to manual methods. The dataset consists of 58 seed image samples, divided into training data (48 samples) and test data (10 samples). The research stages include image preprocessing (cropping, background removal, and thresholding segmentation), feature extraction using PCA to reduce data dimensionality, and classification with KNN based on Euclidean distance. A value of K=3 is used in the KNN algorithm to determine the proximity between data points. The test results show a classification accuracy of 90%, with 9 out of 10 test data correctly classified. PCA successfully simplified high-dimensional data into two main components without significant information loss, while KNN demonstrated strong capability in distinguishing the three types of seeds. This research contributes to the development of an AI-based automatic classification system for the food industry, with broader potential applications in high-dimensional data processing across various fields.

PREDIKSI HARGA PONSEL BERDASARKAN SPESIFIKASINYA MENGGUNAKAN ALGORITMA LINEAR REGRESSION

2025-02-02T03:54:37+00:00

The rapid advancement of mobile technology tools day by day benefits thousands of smartphone retailers by offering various innovations. This study aims to predict smartphone prices based on their technical features using the linear regression method. The dataset used includes various technical attributes from different smartphone models. The research process involves a data preprocessing stage to clean missing or invalid values and feature transformation to prepare the data for the linear regression process. Subsequently, a linear regression model is developed and tested using cross-validation techniques to evaluate its performance. The metric used to measure the model's prediction accuracy or error is RMSE. The experimental results show an RMSE value of 170.692. The target variable, which is the smartphone price, ranges from the lowest price of 614 to the highest price of 4,361. The RMSE value obtained in this study can be considered fairly good, as it is less than 10% of the actual value or average price. Variables such as RAM, storage size, camera, and processor type significantly influence smartphone prices. However, other factors such as brand and design may also have an impact, albeit to a lesser extent. This study confirms that linear regression can be effectively used to predict smartphone prices based on technical specifications. The findings of this research can assist companies in developing pricing strategies based on smartphone specifications. Additionally, it can help determine which products are suitable for market introduction.

PENERAPAN DECISION TREE DENGAN PENYEIMBANGAN DATA IMBALANCE MENGGUNAKAN UPSAMPLING DALAM PREDIKSI PENYAKIT LIVER

2025-02-02T05:47:51+00:00

Acute liver disease has a significant impact on liver function and is often only detected at an advanced stage due to the lack of patient awareness for early examination. One of the challenges in treating liver disease is the delay in diagnosis, where many patients do not notice the early symptoms until their condition has worsened. Therefore, a predictive system is needed that can identify liver disease patients early on, allowing for regular check-ups and timely treatment. In this study, a classification model was developed using a machine learning approach, specifically the Decision Tree algorithm, by balancing the data in the minority class through upsampling. The research results show that this model is capable of predicting liver disease status with an accuracy rate of 89.22%, a recall of 88.45%, a precision of 83.21%, and an f1-score of 85.78%. In addition, the ROC-AUC value of 0.89 is categorized as a good classification. This model achieved a higher accuracy score than other studies with similar datasets. This system is expected to help improve early detection and expedite the treatment of liver disease patients.

PENERAPAN POLA FIBONACCI UNTUK PENGATURAN QOS (QUALITY OF SERVICE) JARINGAN

2025-02-07T07:28:43+00:00

In managing network quality of service (QoS), this research uses the Fibonacci pattern to optimize delay control and bandwidth allocation. QoS is very important in contemporary network management, especially considering the increasing demand for stable and effective data services. This study prioritizes data based on traffic levels using a Fibonacci algorithm simulation. Each priority is assigned a value corresponding to the Fibonacci sequence, which allows for resource allocation that is more in line with network load.The simulation was conducted under normal and overload conditions. The research results show that conventional methods, such as round-robin and weighted fair queuing, can improve QoS efficiency with the Fibonacci pattern by up to 15%. This improvement primarily focuses on managing important data packets such as real-time communication and video streaming, and reducing latency. Additionally, this technique is better at adapting to traffic changes.The research results show that the Fibonacci pattern can be an innovative method for managing network QoS, especially for complex priority needs. By using the Fibonacci pattern as a data priority management technique, this research helps improve network quality of service (QoS). This method is capable of improving bandwidth allocation efficiency and reducing latency by up to 15% compared to conventional approaches such as Round-Robin and Weighted Fair Queuing. The main contribution of this research is to offer a new approach based on Fibonacci patterns that can be adapted to the dynamics of network traffic.

ANALISIS DISTRIBUSI MINAT MAHASISWA PADA KONSENTRASI INFORMATIKA MENGGUNAKAN PENDEKATAN DATA-DRIVEN DECISION MAKING

2025-02-06T09:30:18+00:00

In the digital era, higher education institutions face the challenge of aligning the curriculum with the dynamic demands of the industry. This research aims to identify patterns of student interest in choosing specialization concentrations in the Informatics Study Program (S1), Universitas Sebelas April, using a data-driven decision-making approach. The study involved 133 5th semester students out of a total population of 500 students in the Computer Science program at Sebelas April University. The respondents were selected because they were at the relevant stage of study to determine the specialization concentration, the results of which provide important recommendations for curriculum optimization and resource allocation.Student specialization survey data were analyzed using descriptive statistics, data visualization, and trend analysis to provide data-driven insights to support more efficient academic planning. The results showed that the concentration of "Computer Science, Software, and Intelligent Systems" was more desirable than "System Security and Computer Networks".

SISTEM DETEKSI GEMPA BERBASIS IOT DENGAN VISUALISASI REAL-TIME DAN NOTIFIKASI CERDAS

2025-01-30T04:26:50+00:00

Indonesia, as a region with high seismic activity, requires a fast, accurate, and reliable disaster mitigation system. However, most existing earthquake detection systems still focus primarily on data collection without automatic notifications, which delays response times in emergency situations. This study develops an Internet of Things (IoT)-based early earthquake detection system that integrates a gyroscope sensor, the ThingSpeak cloud platform, and an Android application to provide real-time information to users. The system detects orientation changes along the X, Y, and Z axes, calculates vibration magnitude through a calibrated algorithm, and sends automatic notifications via WhatsApp to mitigation officers. Testing was conducted through simulations using Wokwi to validate the algorithm and physical implementation in real-world conditions, demonstrating that the system achieves high accuracy in detecting seismic activity, with an average accelerometer magnitude of 3.35 and a gyroscope magnitude of 4.19. Data visualization on ThingSpeak, along with graphical displays in the Android application, enables intuitive and real-time earthquake monitoring. The integration of smart notifications via WhatsApp ensures a fast response from mitigation officers, making it an effective and applicable solution for earthquake risk mitigation.

PENGEMBANGAN SISTEM INFORMASI MONITORING HARIAN MAGANG INDUSTRI PENDIDIKAN TEKNIK MESIN MENGGUNAKAN MODEL 4D

2025-02-17T02:31:27+00:00

The problems encountered in the industrial internship courses that are held conventionally are less actual daily log book recording and limited frequency of monitoring supervisors, resulting in less than optimal internship results. This research aims to develop a daily monitoring information system for industrial internships for Mechanical Engineering Education students at Sebelas Maret University. The research method used is Research and Development (RnD) with 4D models including Define, Design, Develop, and Disseminate. The result of this study is to develop a web-based daily monitoring information system for industrial internships and accommodate the industrial internship process from the preparation stage to the end of the assessment. Based on the analysis results, this information system obtained the "good" category with an average website performance test score of 84.25 on 4 test tools. The system was also rated "highly valid" based on a 98.6% eligibility score from 3 IT experts and 85.6% from 30 trial students.

PROTOTIPE KERAN AIR TANPA SENTUH DAN PENGUKUR SUHU TUBUH OTOMATIS BERBASIS MIKROKONTROLLER ARDUINO UNO

2025-02-17T02:25:17+00:00

The global pandemic requires the development of technological solutions to minimize physical contact, especially in public facilities such as water taps, which can function as a medium for transmitting infectious diseases. This study aims to design and develop a prototype of an automatic water tap integrated with an Arduino Uno microcontroller-based body temperature meter. This system was created to support health protocol efforts when carrying out activities, increasing efficiency and reducing the risk of disease transmission. The research method includes problem identification, literature study, hardware and software component design, prototyping, and functionality testing. The test results obtained show that all components work according to their functions with a high level of accuracy, such as the HC-SR04 ultrasonic sensor, which is able to detect objects at a distance between the object and the sensor <12cm, then the Relay will be active and the Mini Water Pump will pump water automatically, and the Valve on the Solenoid Valve will open, and water will flow automatically through the water tap. The test results on the MLX-90614 temperature sensor also obtained an average difference of only 0.28 ° C compared to the thermometer gun as a comparison.

PENGEMBANGAN SISTEM AUDIT TERINTEGRASI PADA SIAKAD MENGGUNAKAN FRAMEWORK COBIT 5

2025-01-22T07:59:21+00:00

The Academic Information System (SIAKAD) plays a crucial role in supporting academic activities within the university environment. Evaluating the system's maturity is necessary to identify weaknesses and potential improvements. Therefore, periodic audits of SIAKAD are essential to obtain constructive feedback from stakeholders. Conducting regular audits of SIAKAD is more effective when supported by an application integrated with the system. This study aims to develop an audit system integrated with SIAKAD. The COBIT 5 framework is used for auditing SIAKAD, while the audit system is developed using PHP programming language and a MySQL database. The focus of this research is the development of an audit system within the Evaluate, Direct, and Monitor (EDM) domain. This study proposes a new approach to developing an effective SIAKAD audit system by integrating SIAKAD with a COBIT 5-based audit system. The developed audit system has been tested on a limited scale with 20 respondents from the Bantaeng Manufacturing Industry Community Academy. At this stage, the audit system development has only reached the process of summarizing questionnaire results. The calculation of SIAKAD maturity levels has not yet been integrated and is still performed manually. Gap analysis results indicate that SIAKAD’s maturity level remains low, with the following scores:EDM1: 30, EDM2: 20, EDM3: 33.6, EDM4: 16.7, and EDM5: 23.3. Based on these maturity scores, improvements in governance are required, particularly in the EDM2 and EDM4 subdomains.

ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP SKINCARE DENGAN METODE SUPPORT VECTOR MACHINE (SVM)

2025-02-10T04:48:37+00:00

The Originote Hyalucera Moisturizer skincare product has attracted public attention because it offers superior quality at an affordable price. Social media, especially Twitter, is used by consumers to express opinions regarding this product, whether positive, negative, or neutral. However, the large number of reviews with various sentiments can confuse potential consumers in assessing product quality. Therefore, this study aims to understand user perception through sentiment analysis and evaluate the effectiveness of the Support Vector Machine (SVM) algorithm in sentiment classification. A total of 1,820 tweets were collected using the crawling technique with Python. The data undergoes preprocessing, including text cleaning, tokenization, stopword removal, and stemming, reducing it to 902 tweets. Key text features are extracted using Term Frequency-Inverse Document Frequency (TF-IDF). For sentiment classification, this study used the SVM algorithm, which is known as an effective method in text processing. Model evaluation showed good results with an accuracy of 87%, precision of 89%, and recall of 87%. This study provides insight into public perception of The Originote Hyalucera Moisturizer and measures the effectiveness of SVM in social media-based sentiment analysis. The results of the study can be utilized by manufacturers for more targeted marketing strategies, product quality improvement, and more effective communication in responding to opinions on social media. In addition, this study contributes to the development of machine learning-based sentiment analysis methods in the context of skincare products.