Jurnal INTI Nusa Mandiri merupakan jurnal yang diperuntukan keilmuan Computer science (Ilmu Komputer). kajian keilmuan mencakupi tentang komputasi, perangkat lunak (software). Ilmu komputer mencakup beragam topik yang berkaitan dengan komputer, mulai dari analisis data science, algoritma sampai subyek yang lebih konkret seperti bahasa pemrograman, perangkat lunak. Ilmu Komputer lebih menekankan pada pemrograman komputer, dan rekayasa perangkat lunak (software), pemrograman science Jurnal INTI Nusa Mandiri terakreditasi SINTA 4 dan memiliki P-ISSN: 0216-6933 (Media Cetak), E-ISSN: 2685-807X (Media Onlie). Jurnal INTI Nusa Mandiri terbit 1 (satu) tahun sebanyak 2 (dua) kali terbit, pada bulan Februari dan Agustus.
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PERANCANGAN SISTEM INFORMASI E-COMMERCE BERBASIS WEBSITE PADA PT. BIMANTARA SAKTI PERSADA MENGGUNAKAN METODE WATERFALL
The development of digital technology encourages companies to implement information systems with the aim of improving operational effectiveness and competitive ability in the market. PT. Bimantara Sakti Persada, which previously conducted sales processes traditionally, faces challenges such as limitations in marketing reach, the possibility of errors in transaction recording, and delays in preparing sales reports. This study aims to design and create a website-based e-commerce information system by utilizing the Waterfall method as a solution to the existing problems. The Waterfall method is implemented through a series of steps that include needs analysis, system design, implementation, testing, and evaluation and maintenance. The designed system provides various functions such as member registration, product management, shopping cart, payment processing, and transaction verification. The system assessment is carried out by conducting functional testing using the Black Box Testing approach as well as testing the utilization of the system by the admin and sales team. The test results show that all system functions operate according to the specified requirements. Based on usage evaluation results, the system is capable of replacing the manual sales process with a more structured computerized system, thereby facilitating transaction recording and the preparation of sales reports. Comparisons of conditions before and after the system's implementation indicate an increase in efficiency in transaction management as well as ease of access to sales information in real time. Thus, the developed web-based e-commerce information system can support PT. Bimantara Sakti Persada's sales process more effectively and in an integrated manner
Copyright (c) 2026 Mohamad Rifa Yusro, Siti Masturoh http://creativecommons.org/licenses/by-nc/4.0
RANCANG BANGUN SISTEM INFORMASI E-BOOKING PENYEWAAN MOBIL BERBASIS WEB MENGGUNAKAN PENDEKATAN OOAD
The manual system employed by PT. ACM Mahadat—a car rental service company—resulted in significant operational issues, including irregular schedules, data recording errors, and slow service, which contrasted sharply with consumer expectations for efficient and secure services. This study aims to design and build a web-based car rental e-booking information system to simplify the reservation process and manage data in real-time. The chosen methodology is Object-Oriented Analysis and Design (OOAD), a systematic approach that concentrates on the concept of objects as the fundamental system elements. The design documentation utilizes Unified Modeling Language (UML) notation, including the Use Case Diagram (functional requirements), Activity Diagram (process flow), Sequence Diagram (object interactions), and Class Diagram (static system structure). The resulting system is designed to be modular and flexible, offering a user-friendly interface for customers to view, select, and book cars easily. It also helps the Admin manage booking and rental data more efficiently, and its core authentication features are validated through Black Box Testing. This implementation is expected to support the digitalization of PT. ACM Mahadat's business processes and enhance the company's competitiveness in the information technology era.
Copyright (c) 2026 Muamar Fallahi, Ridan Nurfalah, Inka Syaifa Fauzia, Samuel Tarigan http://creativecommons.org/licenses/by-nc/4.0
KLASIFIKASI JENIS IKAN AIR TAWAR MENGGUNAKAN ALGORITMA CNN DAN ARSITEKTUR ALEXNET
Freshwater fish are an important commodity in the fishing industry that requires an accurate classification system. This study aims to develop a freshwater fish classification system using the Convolutional Neural Network (CNN) algorithm with AlexNet architecture, as well as applying data augmentation techniques to improve model accuracy. The dataset used consists of 488 images of five types of freshwater fish, namely catfish, baung fish, tapah fish, juaro fish, and patin fish, which were then augmented into 68,400 images. The model was trained using the Adam optimizer, with a batch size of 16, a learning rate of 1e-5, and 200 epochs. The results of the experiment show that the model achieved a training accuracy of 71.09%, a validation accuracy of 85.00%, and a testing accuracy of 80.29%. Precision reached 0.8310, Recall 0.7909, and F1-score 0.7912, indicating the model's excellent performance in classifying freshwater fish species. This research is expected to support the development of an automatic classification system for the freshwater fisheries industry
Copyright (c) 2026 Ahmad Rizky, Dedy Hermanto http://creativecommons.org/licenses/by-nc/4.0
OPTIMALISASI PREDIKSI JARAK TEMPUH KENDARAAN LISTRIK MENGGUNAKAN XGBOOST DAN FEATURE SELECTION RANDOM FOREST
Electric vehicle (EV) adoption is increasing with increasing awareness of clean energy and environmental concerns. However, range anxiety, the uncertainty in estimating driving range, remains a major barrier. This study aims to develop a predictive model for EV range based on technical specifications to provide more accurate estimates and alleviate user concerns. A Machine Learning approach is applied, using Random Forest for feature selection and XGBoost as the primary prediction algorithm. The dataset consists of 478 EV records with 22 attributes, including battery capacity, efficiency, dimensions, and speed. Key features affecting range prediction include battery_capacity_kWh, efficiency_wh_per_km, and height_mm. The XGBoost model demonstrates strong predictive performance with an R² of 0.978, an MAE of 10.555, and an RMSE of 15.180. These results suggest that combining Random Forest and XGBoost offers a promising solution to improve the accuracy of EV range estimation, potentially reducing range anxiety and supporting wider EV adoption
Copyright (c) 2026 M. Rangga Ramadhan Saelan, Riyan Latifahul Hasanah, Siti Fauziah http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTASI METODE USER CENTERED DESIGN PADA APLIKASI KORAN DIGITAL RADAR KARAWANG
The rapid development of digital technology has required print media to adapt in order to remain relevant and competitive in the information era. Radar Karawang, as a local daily newspaper, needs to undergo digital transformation through the development of an Android-based digital newspaper (e-paper) application. This study aims to design the User Interface (UI) and User Experience (UX) of the Radar Karawang digital newspaper application using the User-Centered Design (UCD) method and to evaluate the usability level of the developed application. The UCD method was implemented through several stages, including user needs analysis, prototype or wireframe design, design evaluation based on user feedback, and application implementation. Data were collected through questionnaires distributed to 13 respondents who represented potential users of the application. Usability evaluation was conducted using the System Usability Scale (SUS) method with a Likert scale to measure ease of use, efficiency, and user satisfaction. The evaluation results show that the Radar Karawang digital newspaper application achieved an average SUS score of 74, which falls into the Acceptable category with a Good adjective rating. Nevertheless, this study has several limitations, particularly related to the limited number of respondents and the use of a single usability evaluation method. Therefore, future research is recommended to involve a larger and more diverse group of respondents and to combine other usability evaluation methods to obtain more comprehensive results
Copyright (c) 2026 Normah Normah, Ainun Zumarniansyah, Agus Echtiyar http://creativecommons.org/licenses/by-nc/4.0
MODEL KLASIFIKASI PENERIMA PROGRAM KARTU INDONESIA PINTAR MENGGUNAKAN METODE XGBOOST
Poverty is one of the major factors contributing to the low quality of education in Indonesia. The Smart Indonesia Program (Program Indonesia Pintar) is a cash assistance program distributed through the Smart Indonesia Card (Kartu Indonesia Pintar/KIP) to support students from economically disadvantaged families, ranging from elementary to higher education levels. This study aims to classify students who are eligible to receive the Smart Indonesia Card using the XGBoost method, with a case study conducted at SMPN 02 Kebun Tebu. The dataset used in this study consists of independent and dependent variables. The independent variables include father’s age, mother’s age, father’s education level, mother’s education level, father’s income, mother’s income, number of family dependents, and students’ academic average scores. The dependent variable is the eligibility status of KIP recipients as the target class. Two classification models were developed using data split ratios of 70:30 and 80:20. The model with a 70:30 data split achieved an accuracy of 0.9048, a precision of 0.9034, a recall of 0.9072, and an F1-score of 0.9053. Meanwhile, the model with an 80:20 data split demonstrated better performance, with an accuracy of 0.9167, a precision of 0.9149, a recall of 0.9189, and an F1-score of 0.9169. The optimal model obtained from this study can be utilized by schools to support policy decision-making in determining eligible Smart Indonesia Card recipients, ensuring that educational assistance is distributed accurately, adequately, and equitably
Copyright (c) 2026 Andi Nurkholis, Styawati Styawati, Susi Susi, Alifah Chairul Munawar http://creativecommons.org/licenses/by-nc/4.0
STUDI KOMPARATIF ALGORITMA C4.5 DAN RANDOM FOREST PADA DIGITALISASI UMKM KABUPATEN TEGAL
Digital transformation has become an essential necessity for Micro, Small, and Medium Enterprises (UMKM) to enhance their competitiveness in the era of Industry 4.0. However, in Tegal Regency, the level of digitalization adoption among MSMEs remains varied and tends to be low, thus requiring further investigation. This study aims to compare the performance of the C4.5 and Random Forest algorithms in classifying the level of digitalization of MSMEs in Tegal Regency. This research employs the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Primary data were collected through questionnaires distributed to 100 MSME respondents and processed using RapidMiner. The results indicate that the Random Forest algorithm demonstrates superior performance, achieving an average F1-score of 89.63%, accuracy of 91.43%, while the C4.5 algorithm records an average F1-score of 86.24%, accuracy of 90%. The highest F1-score for both algorithms is observed in the low digitalization category at 95%, which is consistent with the data distribution showing that the majority of MSMEs (59%) fall within this category. This study systematically integrates the CRISP-DM approach from business understanding to model implementation, resulting in a structured data analysis workflow that can be replicated by local governments or future researchers. Another novelty of this study lies in the finding that although Random Forest exhibits better classification performance than C4.5, the majority of MSMEs remain at a low level of digitalization. These results provide practical contributions as a basis for formulating more targeted and sustainable MSME digitalization policies at the local
Copyright (c) 2026 Gita Iftah Royani, Nur Syifa Amelia, Marlina, Fani Nurona Cahya http://creativecommons.org/licenses/by-nc/4.0
PENINGKATAN AKURASI KNN DALAM PREDIKSI KELULUSAN MAHASISWA MELALUI OPTIMASI PARAMETER PSO
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
Copyright (c) 2025 Yessica Fara Desvia, Febryawan Yuda Pratama, Ganda Wijaya http://creativecommons.org/licenses/by-nc/4.0
PERANCANGAN SISTEM SIMPAN PINJAM MENGGUNAKAN OBJECT-ORIENTED SOFTWARE ENGINEERING PADA PT. AUTO PRIMA MAKMUR
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
Copyright (c) 2025 Moh. Mahar Faisol -, Ridan http://creativecommons.org/licenses/by-nc/4.0
ANALISIS KEPUASAN PENGGUNA WEBSITE LIGA ARLOJI MENGGUNAKAN METODE END USER COMPUTING SATISFACTION (EUCS)
The evolution of information technology plays a significant role in accelerating digital transformation across various sectors, including the wristwatch retail industry. The Liga Arloji website is utilized as a medium for promotion and online transactions, which needs to be evaluated in terms of user satisfaction. This study aims to analyze the level of user satisfaction with the performance of the Liga Arloji website using the End User Computing Satisfaction (EUCS) method, in which the components analyzed include five dimensions: content, accuracy, format, ease of use, and timeliness. The methodology of this study is based on a quantitative approach, using a questionnaire as the data collection instrument distributed to 68 selected respondents. The data were processed using the EUCS calculation formula and tested for validity and reliability using SPSS. Based on the data analysis, the findings show that all EUCS dimensions received scores falling into the “Satisfied” to “Very Satisfied” classification. The content dimension achieved an average score of 4.19, accuracy scored 4.13, format scored 4.18, ease of use scored 4.15, and the timeliness dimension received the highest score of 4.22. These findings indicate that user satisfaction with the website is relatively high, particularly in terms of timely information and presented content, while the system’s reliability and accuracy were rated as moderately satisfactory. This study provides several recommendations, including improving data accuracy and enhancing system reliability. The results are expected to serve as a key reference for the future development of e-commerce-based websites
Copyright (c) 2026 Asni Zalukhu, Siti Nurlela http://creativecommons.org/licenses/by-nc/4.0
PENGEMBANGAN CHATBOT TELEGRAM FAQ LAYANAN ICT MENGGUNAKAN ALGORITMA RANDOM FOREST DAN METODE WORD2VEC
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
Copyright (c) 2025 Muhammad Arif Setiyawan, Erina Divaa Kenoya http://creativecommons.org/licenses/by-nc/4.0
PENENTUAN PRIORITAS PENGEMBANGAN DESA WISATA RINTISAN KOTA PURWAKARTA MENGGUNAKAN METODE MULTI ATTRIBUTE UTILITY THEORY
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
Copyright (c) 2025 Lia Fitriyanti, Mochzen Gito Resmi, Dede Irmayanti http://creativecommons.org/licenses/by-nc/4.0
REKOMENDASI PEKERJAAN BIDANG EKONOMI : SISTEM REKOMENDASI MENGGUNAKAN CONTENT BASED
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.
Copyright (c) 2025 Abdur Rouf, Hasyim Asy’ari , Maysas Yafi Urrohman, Febriane Devi Rahmawati http://creativecommons.org/licenses/by-nc/4.0
PERANCANGAN APLIKASI TIKET IT HELPDESK UNTUK MENINGKATKAN EFEKTIVITAS LAYANAN TEKNOLOGI INFORMASI
Effective and responsive Information Technology (IT) services play a crucial role in supporting organizational operations. However, in practice, ASPAN Company still faces challenges in recording, monitoring, and resolving IT incidents due to the absence of an integrated system. This study aims to design an IT Helpdesk ticketing application that enhances the effectiveness of IT services through a systematic, fast, and measurable incident management process. The research adopts the waterfall methodology and qualitative data collection using a case study approach through direct observation involving IT staff and service users. The result of this study is a web-based application that enables users to create incident report tickets, track ticket status, and provide feedback on the services received. The system also provides performance reports that are useful for service evaluation. The implementation results demonstrate a significant improvement in IT service performance, where the average incident resolution time decreased from 2.5 hours to 1.5 hours. Furthermore, the system facilitates real-time ticket tracking, historical issue management, and structured performance reporting to support IT service evaluation. With the implementation of the IT Helpdesk ticketing application, the IT issue-handling process becomes more efficient, well-documented, and ultimately improves the overall quality of Information Technology services within ASPAN company
Copyright (c) 2025 veron vaskarian karwur, Ramdha, Alvi, Fahmi, rocci, Riyan, Siti Nurhasanah Nugraha http://creativecommons.org/licenses/by-nc/4.0
EVALUASI PENERIMAAN MAHASISWA TERHADAP APLIKASI AKADEMIK MOBILE: PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM)
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.
Copyright (c) 2025 Muji Ernawati, Eni Heni Hermaliani, Evita Fitri, Siti Nurhasanah Nugraha http://creativecommons.org/licenses/by-nc/4.0
RANCANG BANGUN APLIKASI PENGELOLAAN PPH 21 PADA CV.ECS CONSULTING SERVICES DENGAN PENDEKATAN RAD
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.
Copyright (c) 2025 Aji Yunisyaputra, Syarif Hidayatulloh http://creativecommons.org/licenses/by-nc/4.0
ANALISIS FAKTOR – FAKTOR PENERIMAAN DAN PENGGUNAAN APLIKASI SEABANK DAN BANK JAGO DENGAN MODEL UTAUT2
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.
Copyright (c) 2025 Helvina Agil Agil, Rahma Fitria Rahma, Zalfie Ardian Zalfie http://creativecommons.org/licenses/by-nc/4.0
PENGEMBANGAN SISTEM ROBOT PENJELAJAH BERBASIS MQTT MITIGASI BENCANA DENGAN DUKUNGAN IMAGE PROCESSING
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
Copyright (c) 2025 Riadi Marta Dinata, Muhammad Ikrar Yamin, Agus Sofwan, Ariman Ariman, Niko Purnomo Niko http://creativecommons.org/licenses/by-nc/4.0
AI-DRIVEN ACADEMIC SCREENING: PENGEMBANGAN SISTEM REVIEWER OTOMATIS BERBASIS AI AGENT
An artificial intelligence (AI)-based research proposal submission system is an innovative solution to improve efficiency and transparency in the academic selection process. This study develops a web-based system using the Laravel framework integrated with AI Agent to automatically review the title and abstract of lecturers' research proposals. This system is designed with a hybrid training approach, combining Supervised Learning (labeled data) and Reinforcement Learning from Human Feedback (RLHF), and utilizing Natural Language Processing (NLP) techniques for semantic analysis. The implementation results show that the system is able to evaluate research proposals with high accuracy, including checking title-abstract alignment, identifying problem backgrounds, and assessing originality. The system also provides real-time statistics and evaluation records, supporting more objective decision making. The contribution of the research lies in the use of AI to automate academic processes, reduce the workload of human reviewers, and improve the integrity of the research roadmap
Copyright (c) 2026 Verry Riyanto, Andi Saryoko, Anton, Lia Mazia, Nurmalasari, Tati Mardiana http://creativecommons.org/licenses/by-nc/4.0
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN DISTRIBUSI QURBAN
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
Copyright (c) 2025 Nanang Ruhyana, Ani Oktarini Sari, Tati Mardiana, Achmad Bayhaqy, Andri Agung Riyadi , Setiaji Setiaji http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTASI METODE EXTREME MENGGUNAKAN ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI JENIS FASHION ALAS KAKI
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
Copyright (c) 2025 Dina Selvia, Sela Ramadani http://creativecommons.org/licenses/by-nc/4.0
ANALISIS SENTIMEN APLIKASI TIKTOK SHOP SELLER CENTER MENGGUNAKAN NAIVE BAYES, SVM DAN LOGISTIC REGRESSION
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.
Copyright (c) 2025 Elly Indrayuni, Acmad Nurhadi http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTASI METODE CLUSTERING UNTUK PEMETAAN WILAYAH PRODUKSI DAN EKSPOR KOPI DI INDONESIA
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.
Copyright (c) 2025 Arya Dwi Saputra, Jefri Jaya, Teny Handhayani, Manatap Sitorus Dolok Lauro http://creativecommons.org/licenses/by-nc/4.0
RANCANG BANGUN APLIKASI PREDIKSI TAGIHAN AIR BERBASIS WEB MENGGUNAKAN REGRESI LINIER BERGANDA
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 .
Copyright (c) 2025 Arum Fatmawati Arum , Afril Efan Pajri, Sahri http://creativecommons.org/licenses/by-nc/4.0
SISTEM INFORMASI MANAJEMEN ARSIP PADA DIREKTORAT TEKNOLOGI INFORMASI KEIMIGRASIAN
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.
Copyright (c) 2025 Sani Abdurahman, Juarni Siregar http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS DAN KNEAREST NEIGHBORS DALAM KLASIFIKASI TANAMAN JAHE, KUNYIT, DAN LENGKUAS
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.
Copyright (c) 2025 yesibetriana_18 Yesi Betriana Roza, Agung Ramadhanu http://creativecommons.org/licenses/by-nc/4.0
PENERAPAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI PRODUKSI TANAMAN PADI
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
Copyright (c) 2025 Anggi hadi Wijaya http://creativecommons.org/licenses/by-nc/4.0
PENGEMBANGAN SISTEM INFORMASI MONITORING HARIAN MAGANG INDUSTRI PENDIDIKAN TEKNIK MESIN MENGGUNAKAN MODEL 4D
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.
Copyright (c) 2025 Rasyid Sidik, Azizah Nurul Husnaini, Riina Syivarulli, Muhammad Kholil, Ngatou Rohman; Danar Susilo Wijayanto http://creativecommons.org/licenses/by-nc/4.0
PROTOTIPE KERAN AIR TANPA SENTUH DAN PENGUKUR SUHU TUBUH OTOMATIS BERBASIS MIKROKONTROLLER ARDUINO UNO
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.
Copyright (c) 2025 Firma Firma, Budy Satria, Muhammad Al Ashari http://creativecommons.org/licenses/by-nc/4.0
ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP SKINCARE DENGAN METODE SUPPORT VECTOR MACHINE (SVM)
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.
Copyright (c) 2025 Dwi Tiyas Novitasari, Mula Agung Barata, Pelangi Eka Yuwita http://creativecommons.org/licenses/by-nc/4.0