INTI Nusa Mandiri

SISTEM INFORMASI MANAJEMEN ARSIP PADA DIREKTORAT TEKNOLOGI INFORMASI KEIMIGRASIAN

Sani Abdurahman — 2025-08-13

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 SISTEM INFORMASI MANAJEMEN DISTRIBUSI QURBAN

Nanang Ruhyana — 2025-08-13

One of the most important aspects of Eid al-Adha celebrations is the distribution of sacrificial meat. However, the process of distributing sacrificial meat often faces various challenges, such as inaccurate data collection, difficulty in tracking the amount of sacrificial meat, and a lack of transparency and efficiency. The objective of this study is to design and develop an application that can enhance efficiency, accuracy, and accountability in the distribution of sacrificial meat through the systematic use of information technology. This study employs the waterfall method, which involves several sequential stages: needs analysis, system design, implementation, and testing. This system was developed to support the performance of the sacrificial committee in managing data on sacrificial animals, information on recipients, the distribution process of meat, and the documentation of all activities in a digital and real-time manner. In the user interface (front end), the Next.js/React.js framework is combined with Tailwind CSS to produce a responsive and user-friendly interface. Meanwhile, the server side (back end) was developed using Laravel as a reliable and efficient PHP framework, and MySQL as a database to store all information related to distribution. The result of this research is a web-based application prototype featuring animal sacrifice data collection, beneficiary data recording, and distribution report generation. It is hoped that this application will facilitate more organized and effective distribution of sacrificial meat

IMPLEMENTASI METODE CLUSTERING UNTUK PEMETAAN WILAYAH PRODUKSI DAN EKSPOR KOPI DI INDONESIA

Arya Dwi Saputra — 2025-08-13

Coffee is one of the main agricultural commodities in Indonesia, but the distribution of production and export contribution is still uneven. This study aims to map the patterns of coffee production and export in Indonesia using clustering methods, namely K-Means and Hierarchical Agglomerative Clustering (AHC). The data used includes coffee production by province and regency (2015–2022), as well as coffee export data by destination country (2016–2023), obtained from BDSP and BPS. The system is developed in the form of an interactive website that allows users to upload datasets, select clustering methods, and view analysis results in the form of tables, graphs, and interactive maps. Clustering quality is evaluated using the Silhouette Score and Davies-Bouldin Index (DBI). The testing results show that the optimal number of clusters is two for all datasets, with the highest Silhouette score reaching 0.85 and the lowest DBI of 0.21, indicating good clustering quality. AHC is more effective in analyzing export and provincial-level production data, while K-Means performs better for regency-level data. This system is expected to provide insights into the distribution patterns of coffee production and exports and support decision-making in the agricultural sector, particularly for coffee commodities.

ANALISIS SENTIMEN APLIKASI TIKTOK SHOP SELLER CENTER MENGGUNAKAN NAIVE BAYES, SVM DAN LOGISTIC REGRESSION

Elly Indrayuni — 2025-08-13

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.

ANALISIS FAKTOR – FAKTOR PENERIMAAN DAN PENGGUNAAN APLIKASI SEABANK DAN BANK JAGO DENGAN MODEL UTAUT2

Helvina Agil Agil — 2025-08-13

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.

RANCANG BANGUN APLIKASI PENGELOLAAN PPH 21 PADA CV.ECS CONSULTING SERVICES DENGAN PENDEKATAN RAD

Aji Yunisyaputra — 2025-08-13

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.

REKOMENDASI PEKERJAAN BIDANG EKONOMI : SISTEM REKOMENDASI MENGGUNAKAN CONTENT BASED

Abdur Rouf — 2025-08-13

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.

EVALUASI PENERIMAAN MAHASISWA TERHADAP APLIKASI AKADEMIK MOBILE: PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM)

Muji Ernawati — 2025-08-13

Mobile applications are widely used in educational environments to accelerate various academic and administrative tasks. Their presence has enhanced service effectiveness, expedited decision-making, and improved the digital campus ecosystem. This study was conducted to evaluate the acceptance level of MyNusa Student, a mobile-based academic application for students. The Technology Acceptance Model (TAM) framework was employed in this research to assess students’ acceptance of the MyNusa Student application. A total of 238 respondents, all registered students using the application, provided data for this study. Data analysis was carried out using Structural Equation Modeling (SEM) with a Partial Least Squares (PLS) approach to examine the relationships among variables: Perceived Ease of Use, Perceived Usefulness, Attitude Toward Using, Behavioral Intention to Use, and Actual Usage. The results indicated that all relationships among variables were statistically significant. The most influential relationship was observed between Perceived Ease of Use and Perceived Usefulness, followed by the relationship between Attitude Toward Using and Behavioral Intention to Use, and subsequently, Actual Usage. The findings suggest that the primary elements influencing students’ positive perceptions of the application—which in turn affect their intention and actual usage patterns—are their evaluations of its usefulness and utility. The practical implications highlight the need for continuous improvement in usability and utility aspects, with a focus on enhancing ease of use, optimizing core features such as real-time data updates, and improving technical as well as system security aspects.

RANCANG BANGUN APLIKASI PREDIKSI TAGIHAN AIR BERBASIS WEB MENGGUNAKAN REGRESI LINIER BERGANDA

Arum Fatmawati Arum — 2025-08-21

The management of water billing in the PAMSIMAS service in Sidobandung Village is still conducted manually and does not provide early information regarding bill estimates, often resulting in delayed payments by customers. This study aims to design and develop a web-based water bill prediction application using the Multiple Linear Regression (MLR) method, capable of delivering fast, accurate, and accessible billing estimates. The dataset used in this research consists of historical monthly water usage and billing data from January to December 2024, with a structure comprising 231 rows of customer data and 30 feature columns. The research stages include data preprocessing, model training using MLR, integration of the model into a web-based system, and evaluation of prediction results using the Mean Squared Error (MSE) and R-squared ( ) metrics. Evaluation results showed that the model achieved an MSE of 18,882 and an of 0,8, indicating a fairly good and stable prediction performance. The system allows customers to log in, view predicted water bills for the 13th month based on previous data, and access graphical visualizations of usage and cost trends. Meanwhile, the admin can efficiently manage customer data through a dedicated dashboard. With the implementation of this application, the management and prediction process of water billing becomes more transparent, efficient, and helps customers in planning their water expenses more precisely .

IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS DAN KNEAREST NEIGHBORS DALAM KLASIFIKASI TANAMAN JAHE, KUNYIT, DAN LENGKUAS

yesibetriana_18 Yesi Betriana Roza — 2025-08-21

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.

PENERAPAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI PRODUKSI TANAMAN PADI

Anggi hadi Wijaya — 2025-08-21

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

PENGEMBANGAN SISTEM ROBOT PENJELAJAH BERBASIS MQTT MITIGASI BENCANA DENGAN DUKUNGAN IMAGE PROCESSING

Riadi Marta Dinata — 2025-08-29

Disaster mitigation is a global challenge that requires innovation to enhance the effectiveness of emergency response, particularly in the rapid and safe detection of victims. Although much research focuses on optimizing individual components such as sensors or algorithms, a gap remains in the development of holistically integrated frameworks. This study develops and evaluates an integrated explorer robot system based on Message Queuing Telemetry Transport (MQTT) and artificial intelligence for real-time disaster victim detection. Using a Design Science Research approach, the system architecture integrates an explorer robot based on ESP32-CAM and GPS for data acquisition, a central server running the You Only Look Once (YOLO) algorithm for image analysis, and involves a human operator for critical decision validation. Experimental results show that the system can detect victims with an average accuracy of 87.3% across various simulated scenarios. Communication via the MQTT protocol proved to be highly reliable and efficient, with an average latency of 127 ms and a packet loss rate of only 2.3%, enabling swift coordination between components. This research successfully validates an effective and replicable end-to-end architectural model, thereby presenting a practical blueprint for the development of low-cost Search and Rescue (SAR) robotic systems

PENENTUAN PRIORITAS PENGEMBANGAN DESA WISATA RINTISAN KOTA PURWAKARTA MENGGUNAKAN METODE MULTI ATTRIBUTE UTILITY THEORY

Lia Fitriyanti — 2025-08-29

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

IMPLEMENTASI METODE EXTREME MENGGUNAKAN ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI JENIS FASHION ALAS KAKI

Dina Selvia — 2025-08-29

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

PENINGKATAN AKURASI KNN DALAM PREDIKSI KELULUSAN MAHASISWA MELALUI OPTIMASI PARAMETER PSO

Yessica Fara Desvia — 2025-09-02

Predicting student graduation is a crucial aspect in supporting academic planning and ensuring timely completion of studies. However, no prior research has specifically applied the integration of K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) for graduation prediction using student data. This study aims to evaluate the effectiveness of combining KNN and PSO in improving classification accuracy. The KNN algorithm is used for classification, while PSO is implemented as a feature selection technique to identify the most relevant attributes. A dataset of 750 student records was processed through data preprocessing and attribute weighting using PSO, followed by model training and evaluation with 10-fold cross-validation. The evaluation results show that the KNN+PSO model improves accuracy from 80.91% to 84.31%, along with increases in precision and recall. These findings indicate that PSO enhances the performance of KNN, particularly in identifying students likely to graduate on time

PERANCANGAN SISTEM SIMPAN PINJAM MENGGUNAKAN OBJECT-ORIENTED SOFTWARE ENGINEERING PADA PT. AUTO PRIMA MAKMUR

Moh. Mahar Faisol - — 2025-09-02

PT Auto Prima Makmur Jakarta still faces challenges in managing its savings and loan system, which is currently conducted manually. These problems include recording errors, administrative delays, and a lack of transparency, all of which reduce efficiency, accuracy, and the ability to monitor transactions in real time. This situation also increases the risk of data manipulation and loss, slows down member services, and potentially undermines trust in the company’s credibility. To address these issues, this study aims to design a web-based savings and loan information system that can enhance efficiency, accuracy, security, and transparency in managing employee savings and loans. The proposed system is designed to process transactions, reporting, and monitoring quickly and accurately, while minimizing the risk of errors and data duplication. The development method used is Object-Oriented Software Engineering (OOSE), which emphasizes the use case approach with simple notation yet covers all stages of software engineering. This approach enables the design of a structured, flexible software architecture capable of effectively managing system objects. The analysis results indicate that implementing a web-based system can accelerate savings and loan transactions, simplify reporting and monitoring, and improve data security. In addition, real-time data availability will assist management in making faster and more accurate decisions. Therefore, the implementation of this system will not only digitize the company’s business processes but also improve service quality, strengthen competitiveness, and support the company’s sustainability in facing the challenges of the digital era