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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 475 Documents
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PROTOTIPE KERAN AIR TANPA SENTUH DAN PENGUKUR SUHU TUBUH OTOMATIS BERBASIS MIKROKONTROLLER ARDUINO UNO Firma Firma; Budy Satria; Candra Surya; Sepriano Sepriano; Muhammad Al Ashari; Muhammad Iqbal
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.v19i2.6417

Abstract

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.
PENERAPAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI PRODUKSI TANAMAN PADI Anggi hadi Wijaya
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.v20i1.6438

Abstract

Rice is a staple food crop in Indonesia, including in West Sumatra Province, which plays an important role in national food security. This study aims to develop a rice production prediction model using Artificial Neural Networks (ANN) with the Backpropagation algorithm. Historical rice production data from 2006 to 2023 in 19 regencies/cities in West Sumatra Province were used as the data basis. The research methods include data collection from BPS West Sumatra, data preprocessing, prediction process using the Backpropagation algorithm, and accuracy testing of the prediction results. The results show that ANN with the Backpropagation algorithm can predict rice production with an accuracy rate of 82.56% using an architecture with 16 neurons in the input layer, 9 neurons in the hidden layer, and 1 neuron in the output layer. This prediction model is expected to assist farmers and the government in planning optimal rice production, thereby increasing production and the welfare of farmers in West Sumatra Province. Thus, this research provides significant contributions in supporting decision-making in the agricultural sector, particularly in efforts to enhance food security and the welfare of farmers in the region
IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS DAN KNEAREST NEIGHBORS DALAM KLASIFIKASI TANAMAN JAHE, KUNYIT, DAN LENGKUAS yesibetriana_18 Yesi Betriana Roza; Agung Ramadhanu
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.v20i1.6441

Abstract

Ginger (Zingiber offivinale), turmeric (curcuma longa), and galangal (Alpinia galanga) plants are the result of Indonesia's wealth which has high economic and health value. This type of plant has high economic and health value, so its accurate identification is very important in the agricultural and pharmaceutical fields. By combining image classification methods, PCA, KNN, this research aims to develop a system that can identify ginger, turmeric, and galangal automatically and accurately. It is hoped that this system can not only provide a solution for efficient plant identification, but can also contribute to the management of natural resources and the development of herbal plant-based products in Indonesia. Data collected by taking pictures and then processed using MATLAB. This research aims to identify ginger, turmeric and galangal plants using euclidean distance and extract shape and texture characteristics. Shape feature extraction using RGB, HVS, and Area. This research implements the PCA and K-Nearest Neighbor methods in classifying data. Meanwhile, the KNN method is applied by measuring the closest distance between the test data and the training data. In this research there are labels and attributes, labels taken from the level of fruit maturity and attributes obtained from the results of image feature extraction. These attributes are R(red), G(green), B(blue), H(hue), S(saturation), V(value), Area. The accuracy results obtained from the classification of ginger, turmeric and galangal plants using the KNN method were 80% with a K=3 value obtained from 8 test data with accurate classification, and 20% from 2 test data with inaccurate classification.
SISTEM INFORMASI MANAJEMEN ARSIP PADA DIREKTORAT TEKNOLOGI INFORMASI KEIMIGRASIAN Sani Abdurahman; Juarni Siregar
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.v20i1.6469

Abstract

In this research case study, the storage of letter archives at the Directorate of Immigration Information Technology is still done manually where all letter archive documents are stored by employees on their respective Personal Computers. Records management that is done manually by storing files on Personal Computer rarely has a regular backup mechanism so that if a document is lost it is difficult to recover it. So, an innovation is needed in the form of a website-based archive storage information system. The purpose of this research is to facilitate employees who are appointed as letter archive managers and facilitate the search for archives needed by the leadership. In this study, researchers used the scrum method or model with stages namely product backlog, sprints, scrum meetings and demos. The stages of the Agile method in this study include system analysis, design, development, testing, deployment, system evaluation and maintenance. The programming language used in building archive management information system applications at the directorate of immigration information technology is using the PHP (Hypertext Preprocessor) and JavaScript programming languages. The results showed that the web-based archive management information system has been successfully designed. This system overcomes difficulties in searching for archives, reduces the risk of data loss, and optimizes the management of incoming and outgoing mail archives. With this system, officers in each section can manage the storage of letter archives and enable data management and document searches that were previously time-consuming now become faster and more efficient.
RANCANG BANGUN APLIKASI PENGELOLAAN PPH 21 PADA CV.ECS CONSULTING SERVICES DENGAN PENDEKATAN RAD Aji Yunisyaputra; Syarif Hidayatulloh
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.v20i1.6640

Abstract

Income Tax (PPh) 21 often poses a challenge for companies and employees in managing tax payments efficiently and accurately. CV. ECS Consulting Service, which currently uses Microsoft Excel for PPh 21 calculations, faces risks of errors and time-consuming manual processes. This research aims to develop a web-based PPh 21 calculation application using the Rapid Application Development (RAD) method tailored to the company’s needs. Data collection methods include direct observation and literature study, while the application development method adopts RAD, which is iterative and responsive to changing requirements. The scope of the research includes user interface design, development of calculation algorithms, employee data processing, and ensuring data security. The application was tested using Black Box testing to ensure all features function properly, User Acceptance Testing (UAT) to assess whether it meets user needs, and performance testing to evaluate the website’s speed and stability. Black Box testing was conducted on six cases, and UAT was carried out directly with users. The results showed that the application passed all tests and met the required functionalities. Performance testing also indicated that the system is fairly stable, although further improvement is needed for long-term use or during high traffic.
ANALISIS FAKTOR – FAKTOR PENERIMAAN DAN PENGGUNAAN APLIKASI SEABANK DAN BANK JAGO DENGAN MODEL UTAUT2 Helvina Agil Agil; Rahma Fitria Rahma; Zalfie Ardian Zalfie
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.v20i1.6780

Abstract

In recent years, banking in Indonesia has undergone significant transformation through the use of technology, as reflected in the increase in digital transactions, which reached Rp15,881.53 trillion, a year-on-year growth of 16.15%. This study employs the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to analyze the factors influencing the acceptance and use of Seabank and Bank Jago digital banking applications. This study is a quantitative research using a survey method involving 632 student respondents in North Sumatra up to Lhokseumawe, analyzed using descriptive statistics and hypothesis testing based on Structural Equation Modeling (SEM). The results of the descriptive statistical analysis showed that the average user response to the Seabank app was 87.02% and Bank Jago was 84.82%, both falling into the “strongly agree” category, indicating a positive response. Hypothesis analysis revealed that social influence, facilitating conditions, price value, habits, behavioral intention, and usage behavior significantly influence the acceptance of the Seabank app. For Bank Jago, the significant influencing factors are social influence, price value, habits, behavioral intention, and usage behavior. The findings of this study confirm the applicability of UTAUT2 in the context of digital banking in Indonesia and provide practical insights for app developers and policymakers to encourage the adoption of digital banking services.
REKOMENDASI PEKERJAAN BIDANG EKONOMI : SISTEM REKOMENDASI MENGGUNAKAN CONTENT BASED Abdur Rouf; Hasyim Asy’ari; Maysas Yafi Urrohman; Febriane Devi Rahmawati
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.v20i1.6786

Abstract

The recommendation system was developed to assist students of the Institut Teknologi dan Bisnis Widya Gama Lumajang, particularly those from the Faculty of Economics and Business, in determining their preferred career options. This system helps students by providing various job references that match their individual criteria. The data was collected from a tracer study, which includes information such as academic grades, non-academic achievements, job positions, company names, salaries received. From the total dataset, 1,120 records were deemed valid and used in the research process. The aim of this research is to assist students by providing job recommendations based on similar criteria between current students and alumni. The method applied in this study is quantitative experimental research based on data mining, with the main approach being Content-Based filtering and the MLP (Multi-Layer Perceptron) Classifier algorithm. The data was split into two parts: 65% for training and 35% for testing. This division aims to allow the model to learn from most of the data while also being tested for accuracy using unfamiliar data. The recommendation model was developed using the MLP Classifier algorithm with a hidden_layer_size configuration of 100 neurons and a max_iter of 200 iterations. For the initial test, 10 sample data points were used to evaluate the model’s performance. During training, the loss value was monitored to assess how well the model understood the data and adjusted its internal weights. With this configuration, the system is expected to provide accurate job recommendations based on the user’s profile and academic history.
PENENTUAN PRIORITAS PENGEMBANGAN DESA WISATA RINTISAN KOTA PURWAKARTA MENGGUNAKAN METODE MULTI ATTRIBUTE UTILITY THEORY Lia Fitriyanti; Mochzen Gito Resmi; Dede Irmayanti
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.v20i1.6849

Abstract

A Tourism Village can be understood as a village that organizes tourism activities due to the tourist attractions arising from the characteristics of the local community’s life, including various attractions available within the village itself. Each tourism village needs to be supported by tourist attractions, accessibility, and amenities, which include the potential of cultural tourism as well as natural tourism. The purpose of this research is to design and develop a Decision Support System for Determining the Priority of Pioneer Tourism Village Development using the Multi Attribute Utility Theory (MAUT) method. The urgency of this research lies in the need for DISPORAPARBUD Purwakarta to have a tool to assist in making strategic decisions related to the development of pioneer tourism villages. The methodology used in developing this system is the Waterfall model. The system is designed using PHP with the CodeIgniter 3 framework, MySQL as the database, and Unified Modeling Language (UML) for system modeling. The main criteria used in the MAUT calculation include public facilities, homestay management, local crafts, local arts, and local food. The results of the calculation using the MAUT method show that Batu Nunggal Margaluyu Tourism Village ranks first with a preference value of 100, Sasanakerta Tourism Village ranks second with a value of 96, and Sumbersari Tourism Village ranks third with a value of 90. Therefore, these three tourism villages are the best recommendations to be prioritized in the development of tourism villages in Purwakart
ANALISIS SENTIMEN APLIKASI TIKTOK SHOP SELLER CENTER MENGGUNAKAN NAIVE BAYES, SVM DAN LOGISTIC REGRESSION Elly Indrayuni; Acmad Nurhadi
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.v20i1.6851

Abstract

The rapid growth of e-commerce has driven the emergence of new platforms such as TikTok Shop Seller Center, which is now integrated with Tokopedia. Increasing competition among digital platforms has made service quality and user experience key success factors. In this context, user reviews and feedback serve as crucial data sources that reflect satisfaction, complaints, and expectations toward the application. However, the large and diverse volume of reviews renders manual analysis inefficient. Therefore, an automated approach such as sentiment analysis is required to classify user opinions quickly and accurately. This study aims to perform sentiment analysis on TikTok Shop Seller Center user reviews using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression algorithms to determine the best-performing model. The dataset was obtained from the Kaggle platform and underwent preprocessing, including case folding, tokenization, stemming, and TF-IDF weighting. Model evaluation was conducted using confusion matrix and ROC curve, along with performance metrics such as accuracy, precision, recall, and F1-score. The results show that the SVM algorithm outperformed Naïve Bayes and Logistic Regression, achieving 93.75% accuracy, 93.78% precision, 95.65% recall, 94.70% F1-score, and an AUC of 0.98, categorized as Excellent Classification. Thus, SVM proved to be the most effective algorithm for classifying user review sentiments on TikTok Shop Seller Center.
IMPLEMENTASI METODE EXTREME MENGGUNAKAN ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI JENIS FASHION ALAS KAKI Dina Selvia; Sela Ramadani
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.v20i1.6860

Abstract

This research explores the application of Extreme Learning Machine (ELM) for classifying types of fashion footwear. The increasing number of e-commerce transactions and the use of visual media in marketing demand an efficient and accurate automated system to identify various types of footwear such as shoes, sandals, and slip-ons. Conventional classification systems often encounter challenges in handling variations in shape, color, and lighting conditions in footwear images. ELM, with its unique approach of assigning random weights in the hidden layer, offers a potential solution to these issues. In this study, a classification system was developed consisting of several stages, including the collection of diverse footwear image data, image preprocessing to improve quality and reduce noise, feature extraction relevant for distinguishing footwear types, and finally, classification using the ELM algorithm. The preprocessing process involved color conversion from RGB to HSV to reduce sensitivity to lighting variations, as well as thresholding to produce binary images. Extracted features included geometric characteristics such as area, perimeter, and aspect ratio. The system’s performance was evaluated using standard metrics such as accuracy, precision, and recall. The results showed an accuracy value of 83.3%. In addition, the model evaluation demonstrated very good results: precision reached 83.3%, recall 83.3%, and F1-Score 91%, indicating that ELM is effective in classifying types of fashion footwear. This study contributes to the development of intelligent, efficient, and accurate classification systems for applications in the fashion industry, while also opening opportunities for further research in optimizing ELM parameters and exploring more representative features

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