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INTI Nusa Mandiri
Published by PPPM Nusa Mandiri
ISSN : 02166933     EISSN : 2685807X     DOI : -
Core Subject : Science,
Computer Science & IT
The INTI Nusa Mandiri Journal is intended as a media for scientific studies on the results of research, thought and analysis-critical studies on the issues of Computer Science, Information Systems and Information Technology, both nationally and internationally. The scientific article in question is in the form of theoretical review and empirical studies of related sciences, which can be accounted for and disseminated nationally and internationally.
Arjuna Subject : -
Articles 475 Documents
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PERBANDINGAN ALGORITMA YOLOV3 DAN YOLOV4 DALAM PENGELOMPOKAN UKURAN TELUR AYAM SECARA REAL TIME Lysheeba Abbygail Sembiring; Brian Fernanda Manik; Jovi Jonathan; Steven Giovano; Reyhan Achmad Rizal
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5699

Abstract

The common problem currently faced by MSMEs producing chicken eggs is the difficulty in calculating the number of eggs and grouping egg sizes where everything is still done manually so that errors often occur and many entrepreneurs often experience losses. To improve and strengthen productivity, management, and marketing in this business, technological innovation is needed. This study aims to detect the number of eggs and group egg sizes based on their type using the Yolov3 and Yolov4 algorithms. Based on the results of the tests carried out, it shows that the Yolov3 and Yolov4 algorithms are able to detect chicken eggs in real time with the best accuracy value obtained by the Yolov3 algorithm. The comparison was carried out using 10 epoch tests with an F1-Score value of 0.89 where the F1-Score value approaching 1 indicates that the system performance has been running well. The results of this classification can be used to create a real time egg calculation application that can help calculate the number of eggs every day by each MSME.
PENGEMBANGAN CHATBOT TELEGRAM FAQ LAYANAN ICT MENGGUNAKAN ALGORITMA RANDOM FOREST DAN METODE WORD2VEC Muhammad Arif Setiyawan; Erina Divaa Kenoya
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.5766

Abstract

In today's digital era, chatbots have become an essential tool for businesses to improve interaction with customers. A responsive and efficient chatbot can help customer service agents be happier, improve customer satisfaction, and resolve issues faster. The study aims to create a  Telegram-based chatbot that uses  the Random Forest algorithm  and the Word2Vec method  to answer questions about ICT services. The development was carried out by collecting a dataset of  questions and answers from FAQs (Frequently Asked Questions) of ICT services. Then,  the Random Forest algorithm  is used to classify the questions. In addition, the Word2Vec method  is used to create vector representations of words in questions and answers. This improves  the chatbot's ability  to understand complex questions. The test results show that  the chatbot gets an accuracy of 91.28%, a precision of 93.56%, a recall of 91.28% and  an F1-Score of 91.42% and can provide relevant and accurate answers to user questions. Therefore, the development of  this chatbot using  the Random Forest algorithm  and the Word2Vec method can be an effective solution to improve customer service in the field of ICT services
EVALUASI PENERIMAAN PENGGUNA APLIKASI ADONAI DENGAN PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM) Fani Nurona Cahya; Hikmatulloh Hikmatulloh; Rangga Pebrianto; Deny Novianti
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6077

Abstract

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.
IMPLEMENTASI METODE RAPID APPLICATION DEVELOPMENT DAN PENGUJIAN KATALON PADA APLIKASI LAPORAN BARANG RUSAK Yayat Ruhiyat; Ridan Nurfalah
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6113

Abstract

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 Rizki Hesananda; Vian Firmansyah
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6155

Abstract

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.
ANALISIS KUALITAS WEBSITE PORTAL MEDIA ONLINE MILENIANEWS.COM MENGGUNAKAN STANDAR ISO 9126 Muhammad Rifqi Firdaus; Yuris Alkhalifi; Dinar Ismunandar; Oky Kurniawan
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6218

Abstract

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 Stefani Hardiyanti Putri; Wizra Aulia
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6283

Abstract

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.
PREDIKSI HARGA PONSEL BERDASARKAN SPESIFIKASINYA MENGGUNAKAN ALGORITMA LINEAR REGRESSION Muhammad Irsyad; Silvy Amelia; Yahya Mara Ardi
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6292

Abstract

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.
PERBANDINGAN MODEL MACHINE LEARNING PADA KLASIFIKASI CURAH HUJAN DI BOGOR I Dewa Gede Loka Maheswara; Ahmad Hanif Al’aziz
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6296

Abstract

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.
ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP SKINCARE DENGAN METODE SUPPORT VECTOR MACHINE (SVM) Dwi Tiyas Novitasari; Mula Agung Barata; Pelangi Eka Yuwita
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6297

Abstract

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.

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