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INDONESIA
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 220 Documents
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ANALISIS KEPUASAN PENGGUNA WEBSITE ORLANSOFT MENGGUNAKAN METODE WEBQUAL 4.0 Nurlela, Siti; Ilham, Muhamad; Supriatna, Supriatna
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5125

Abstract

Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536> F table 2.81 and the significance value is 0.0000011 <0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.
OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN Fitri, Evita; Nugraha, Siti Nurhasanah
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5269

Abstract

Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.
PENERAPAN METODE ASOSIASI PADA ANALISA POLA PEMINJAMAN BUKU PERPUSTAKAAN Amsury, Fachri
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5325

Abstract

The library produces a lot of book loan transaction data every day, but the data has not been maximally utilized due to the limited knowledge of the data, therefore the librarian cannot provide the right book recommendations for readers. The research aims to analyze book loan data by applying the Knowledge Discovery in Database (KDD) method. The research stages are observation and interviews, data selection and data preprocessing, data transformation. Data processing using the apriori algorithm association rule mining approach to provide an overview in seeing the pattern of book loan transactions. This is to provide book recommendations that match the reading interests of library members, so that it can become a reference in the layout of books on the shelf according to the results of the rules formed. The book loan transaction data used is the September period of 2023, the implementation uses the rapidminer application to find association rules. The results obtained as many as 77 rule recommendations with the highest support value of 10.7%, the highest confidence value of 100% and the highest lift value of 14. The rule formed is that if a library member borrows a book by Dale Carneige, the chances that the library member will also borrow a book by George Orwel are 100%. The results obtained can be a reference for the library to provide book recommendations to readers, maintain the availability of book stock and arrange the placement of these books on adjacent shelves.
KNOWLEDGE MANAGEMENT SYSTEM PENGOLAHAN SAMPAH MENGGUNAKAN SOCIALIZATION, EXTERNALIZATION, COMBINATION, INTERNALIZATION MODEL Indriani, Risma; Yanitasari, Yessy; Dedih, Dedih
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.v19i1.4251

Abstract

Garbage is an environmental problem that cannot be avoided, changes in human lifestyles cause an increase in the volume of waste, various ways are carried out to overcome the increase in the volume of waste, one of which is the Reduce, Reuse, Recycle (3R) technique which plays an important role in waste processing and can change waste. to be artistic and economical, to share knowledge about waste management requires a container that can accommodate and share knowledge. In this study, a Knowledge Management System (KMS) was developed using the Knowledge Management Life Cycle (KMSLC) method and capturing knowledge using the Sosialization Externalization Combination Internalization (SECI) model. The results of this study are web-based applications that can accommodate, add and share knowledge in the form of tacit and explicit and change the knowledge formed from the results of individual interactions into documented knowledge which is expected to help organizations manage all knowledge and develop it so that it can improve the abilities and knowledge of members organization for waste management.
SINTESA CITRA DAUN KOPI MENGGUNAKAN GENERATIVE ADVERSARIAL NETWORK PADA DATASET PENYAKIT DAUN KOPI Wildah, Siti Khotimatul; Latif, Abdul; Haryanto, Toto
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.v19i1.5045

Abstract

Coffee, as the second most traded commodity after petroleum, faces production challenges, especially due to pest or disease attacks on coffee leaves. Therefore, it is important to carry out early detection of the disease in order to minimize the risk and apply special treatment. Automatic detection of disease can be done through the application of Computer Vision technology. However, one of the main challenges faced is the limited training dataset. Generative Adversarial Networks (GANs) is a Deep Learning method that is capable of modifying images with high quality. This research aims to synthesize coffee leaf images based on the public Coffee Leaf Disease dataset using the GANs method. Testing was carried out using the RMSProp optimizer, the learning rate was 0.0001 and was carried out for 300 epochs. The architecture built uses 26 layers in the generator model and 15 layers in the discriminator model. The results of the test show that the drilled network obtained an MMSE value of 0.1658, which is not too high because the resulting synthesized image is not very good.
PERANCANGAN AUTENTIKASI MULTI FAKTOR DENGAN PENGENALAN WAJAH DAN FIDO (FAST IDENTITY ONLINE) Atmawijaya, Rizky; Radiyah, Ummu
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.v19i1.5263

Abstract

Digital services based online are assets that need to be safeguarded, especially if the application still uses single-factor authentication vulnerable to cyberattacks and potential data leaks and identity theft. The proposed solution is to implement multi-factor authentication (MFA) utilizing facial recognition, particularly through FaceNet technology. Although facial recognition can provide an additional layer of security, the main challenge is to maintain user privacy even if biometric information might leak. This research aims to create a secure, reliable MFA model that protects the privacy of employees at PT Traspac Makmur Sejahtera. The proposed method involves an MFA system with four factors: knowledge factor (password), biometric factor (facial measurements), ownership factor (OTP) and location factor (optional if facial accuracy is insufficient). The implementation of this MFA model enhances security, reliability, and protects employee privacy. Considering the specific needs of the company, this research can assist the company in monitoring the locations of employees working from home (WFH).
DETEKSI RUPIAH EMISI 2022 UNTUK DISABILITAS NETRA MENGGUNAKAN YOLOV5M DENGAN OUTPUT SUARA Mahfuzh, Muhammad Farhan; Abdillah, Mokhammad Nurkholis; Fatkhurrozi, Bagus
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.v19i1.5295

Abstract

People with visual disabilities have difficulty recognizing rupiah denominations using blind codes due to differences in paper size for each denomination, wrinkled paper, and variations in blind codes for different emission years.. The proposed method uses the YOLOv5m algorithm as well as Google Text to Speech (GTTS) as voice output. The aim of the research is to find a model with the best precision value from YOLOv5m in detecting the 2022 emission rupiah and integrate it into GTTS to produce nominal rupiah sounds. The model was trained with the main image dataset, namely 700 images of rupiah emissions in 2022 taken at an angle of 1200. Next, the model was tested to recognize seven nominal amounts, namely IDR 1,000, IDR 2,000, IDR 5,000, IDR 10,000, IDR 20,000, IDR 50,000, and IDR 100,000. The test results show that the best YOLOv5m model is the one that has been trained using the main dataset (700 images) and supplemented with a multi-class image dataset (250 images) and background images (30 images). This model has a precision value of 82% when testing in real time. This research succeeded in applying the YOLOv5 algorithm which is integrated with Google Text to Speech to detect the image of 2022 emission rupiah banknotes.
SISTEM INFORMASI HOME SERVICE DAN PENJUALAN SPARE PARTS MENGGUNAKAN MODEL WATERFALL Subagio, Heri; Masturoh, Siti
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.v19i1.5553

Abstract

Yamaha Karya Laba Motor is a company specializing in motorcycle maintenance and parts sales. Despite its services, shortcomings persist such as verbal vehicle inspections upon service intake and manual recording of service queues and parts sales. Issues arise concerning inventory monitoring, particularly when parts are depleted, necessitating time-consuming manual checks that hinder mechanics' efficiency. Additionally, the lack of home service and online parts sales further complicates customer convenience. The development of a Home Service and online parts sales application at Yamaha Karya Laba Motor aims to address these challenges by enhancing operational efficiency and customer satisfaction. The application includes features such as transaction management, user administration, parts inventory, reporting, Home Service requests, and online parts sales. These functionalities empower Yamaha Karya Laba Motor employees to efficiently monitor parts availability and generate transaction reports. Simultaneously, customers benefit from streamlined processes, saving time and ensuring convenience. This study underscores the transformative impact of digital solutions in improving operational workflows and enhancing service.
K-BEST SELECTION UNTUK MENINGKATKAN KINERJA ARTIFICIAL NEURAL NETWORK DALAM MEMPREDIKSI RANGE HARGA PONSEL Saelan, M. Rangga Ramadhan; Subekti, Agus
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.v19i1.5554

Abstract

Determining the price of a mobile phone that will be released to the market cannot be based on assumptions alone. This problem can be overcome by utilizing machine learning. In this study, what is predicted is not the exact price, but rather the price range of a cellphone based on the specifications that are its attributes. In machine learning, the Deep Learning ANN model will be used to predict the price range of a mobile phone. To understand the relationship between features and labels, the Univariate feature selection method SelectKBest is used which will calculate the correlation value between features and labels. In this study, the best performance was obtained from the ANN model with feature selection and hyperparameter tuning, the evaluation of performance metrics obtained the highest accuracy of 97.5%. Experiments were conducted by building several models to compare until there was one model that performed well in processing training and validation data. Model evaluation is presented using confusion metrics with various types of performance metrics: accuracy, precision, recall and f1-score. This study also aims to evaluate the effectiveness of the SelectKBest feature selection method in improving model accuracy and testing various hyperparameter configurations to obtain the best performance.
PERBANDINGAN PENERAPAN ALGORITMA DEEP LEARNING DALAM PREDIKSI HARGA EMAS Julianto, Muhammad Fahmi; Iqbal, Muhammad; Hidayat, Wahyutama Fitri; Malau, Yesni
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.v19i1.5559

Abstract

Digital investment is trending because advancements in information technology make access easy through smartphones. Various digital investment instruments attract much interest from the public. Post COVID-19 pandemic, the economic impact of the pandemic is still felt until the end of 2022, requiring people to be smart in managing their finances. Gold investment is considered profitable due to its high value and tendency to increase, unlike the fluctuating stocks. Although easily accessible, investments carry risks, so investors must have sufficient knowledge to maximize profits. This research aims to predict gold prices using several deep learning models, namely Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The dataset used was taken from the Kaggle website, which includes historical gold price data. In this research, various deep learning models were applied and evaluated to determine the best model for predicting gold prices. The results show that the CNN model with Adam optimization and Mean Squared Error (MSE) loss function provides the best performance. The CNN model achieved the lowest Mean Absolute Error (MAE) of 0.004848717761305338, the lowest MSE of 4.3451079619612133, and the lowest Root Mean Squared Error (RMSE) of 0.006591743291392053. These results indicate that the CNN model is more effective in predicting gold prices compared to the ANN, RNN, and LSTM models on the used dataset.

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