Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen

UI/UX Design of a Tourism Information System in Bima Regency

2026-06-03T01:23:55+00:00

Bima Regency has diverse tourism potential, including natural, cultural, historical, and marine tourism, supported by accommodation and tourism businesses. However, tourism information is still distributed across several platforms, is not centralized, and is not presented completely. This condition makes it difficult for local communities and tourists to obtain information about destinations, facilities, road access, accommodation, events, and tourism news. This study aims to design the UI/UX of a web-based tourism information system for Bima Regency using the Design Thinking method. The research stages include empathize, define, ideate, prototype, and testing. Data were collected through interviews with the Bima Regency Tourism Office and 20 respondents, then analyzed using empathy maps, user personas, affinity diagrams, How Might We, site maps, user flows, and prototype design using Figma. The prototype was evaluated using the System Usability Scale (SUS) with 56 respondents. The resulting prototype includes destination, event, news, accommodation, detailed tourism information, and admin pages for managing tourism content. The SUS test produced an average score of 84.55, categorized as acceptable, grade B, and an excellent adjective rating. These results indicate that the UI/UX design has good usability and can be used as a reference for developing a tourism information system in Bima Regency.

Implementation of Forward Chaining in a Web-Based Expert System for Diagnosing Chili Pepper Plant Diseases

2026-03-30T01:56:16+00:00

Chili plants are among superior horticultural commodities with high economic value and play a crucial role in supporting the agricultural sector in Indonesia. In cultivation practices, chili productivity often declines due to infections caused by various plants diseases. This condition is futher exacerbated by farmers limited technical knowlegde and insufficient accsess to agricultural experts, which frequently caused delays in the diseases identification process. This study aims to develop a website based expert system to assist in diagnosing chili plant diseases by applying forward chaining method. The system operates by processing facts input by users to generate appropriate diseases diagnoses and teatment recommendations. The system’s knowlegde base was constructed through literature studies and interviews with agricultural experts, and was represented in the form of IF-THEN rules. The testing results indicate that the expert system is capable of accurately identifying types of diseases iaffecting chili plants and providing relevant handling recommendations. Therefore, the proposed system is expected to support chili farmers in making more effective decisions, thereby improving chili crop productivity.

Analysis And Design Of A Website-Based Information System For Educational Development Contribution Payments Smks Yapenap

2026-02-12T02:38:19+00:00

SMKS Yapenap still manages tuition fee payments manually using spreadsheets, which increases the risk of recording errors and slows down report preparation. This study aims to analyze and design a website-based tuition fee payment information system to improve operational efficiency and data accuracy. The methodology used is Rapid Application Development (RAD) with Unified Modeling Language (UML) modeling. System requirements analysis is also supported by the PIECES framework to evaluate performance and service aspects. The results of the study are a system design with a centralized database that enables real-time data management. An automatic validation feature is designed to minimize human error and speed up the transaction process. In addition, this portal provides transparency for parents in monitoring payment status independently. In conclusion, the RAD method is effective in producing a professional and accountable financial management solution for SMKS Yapenap.

Benchmarking Machine Learning Models for Intrusion Detection in Higher Education Networks Using CIC-IDS2017

2026-08-24T09:33:02+00:00

The security of university networks faces an increase in attack surfaces due to the high dependence on digital services. Machine learning-based Intrusion Detection System (IDS) is one of the approaches to recognize benign and malicious traffic patterns. This study analyzes the benchmark machine learning model in the CIC-IDS2017 dataset through a literature study approach and comparative analysis. The models compared include Decision Tree, Random Forest, Support Vector Machine, Naive Bayes, Artificial Neural Network, as well as additional benchmarks XGBoost, KNN, and Logistic Regression. Evaluation parameters include accuracy, precision, recall, F1-score, false positive, false negative, and class distribution. The benchmark results showed that Random Forest achieved an accuracy of 99.67% with precision, recall, and malicious class F1-scores of 99.90%, 99.67%, and 99.67%, respectively in one of the published configurations. Another comparative study reported that XGBoost and Random Forest achieved 99.96% accuracy in their experimental configurations. Analysis of the class distribution showed a strong imbalance so that accuracy was not sufficient to assess the effectiveness of the IDS. The study recommends macro-F1 evaluation, per-class recall, false negative, and testing on more representative data before the model is used in a college production environment.

Evaluation of the KAIA-PAY Application Using Nielsen’s Usability Model

2023-07-08T07:08:10+00:00

Web browser applications such as KAIA-PAY have become a major requirement in the digital era for credit agent users. KAIA-PAY facilitates users in serving credit purchases and getting information easily. Although many users were satisfied with the app's performance, some users also expressed their dissatisfaction. Therefore, this study aims to evaluate usability using the Nielsen Usability Model in the KAIA-PAY application to assess user satisfaction and identify existing usability problems. Evaluation includes aspects of ease of learning (learnability), efficiency (efficiency), user's ability to remember (memorability), error rate (errors), user satisfaction (satisfaction), usability (usability), conformity to specifications (Conformity to Specification), and Responsiveness (Responsiveness). The collected data was analyzed using the Smart PLS 4 application to understand the level of usability of the KAIA-PAY application and the factors that influence it. The results of the study found that KAIA-PAY was significantly influenced by the Learnability and Satisfaction variables.

School Website Design for SLB ABC Muhammadiyah Sumedang Using the Design Thinking Method

2025-08-02T17:29:12+00:00

The development of information technology requires educational institutions to adapt to the digital era, including in the dissemination of information to stakeholders. SLB ABC Muhammadiyah Sumedang still relies on conventional methods such as bulletin boards and WhatsApp in disseminating school information, which are considered ineffective and not well documented. This research aims to design a user-centered school website design using the design thinking method in order to meet the needs of digitizing school information optimally. The research method used is design thinking with five main stages: empathize, define, ideate, prototype, and test. Data collection was carried out through in-depth interviews with teachers and school staff as the main users. The data is then analyzed using affinity diagrams, user personas, user journey maps, pain points analysis, and priority matrix to identify the needs and priorities of feature development. The prototype website was designed using Figma with four main pages: Home, Profile, Extracurricular, and Gallery. The prototype evaluation was carried out using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) instruments involving 20 respondents. The results of the study show that the website prototype successfully meets the needs of users very well. The SUS evaluation showed an average score of 74 in the "Good to Excellent" category, while the UEQ evaluation resulted in an average score of 1.98 with an interpretation of "Excellent" that was in the top 10% of results based on international benchmarks. The resulting website design has clear navigation, responsive display, and structured content according to the needs of teachers and staff in managing school information. This research proves that the design thinking approach is effective in producing school website designs that are truly based on user needs. The iterative process in design thinking ensures that every feature and appearance of the website is developed based on empirical data from real users, resulting in a human-centered digital solution that can improve the effectiveness of communication and documentation of activities in the educational environment.

Behavioral Segmentation of Ride-Hailing Users Using the K-Means Clustering Algorithm

2026-07-16T02:09:48+00:00

The rapid growth of the ride-hailing industry has generated massive volumes of transactional data (Big Data), containing valuable information regarding user behavior patterns. One of the major challenges faced by companies is the difficulty of mapping user characteristics at an individual level, resulting in marketing strategies that are often generalized and insufficiently targeted. This study aims to segment ride-hailing service users using the K-Means Clustering algorithm to identify groups of user behavior based on the Frequency, Monetary, and Rating attributes. The research methodology follows the stages of the Knowledge Discovery in Databases (KDD) process, including data identification, data preprocessing, and result interpretation using Orange Data Mining software. The results indicate that four optimal user clusters were formed. Profile analysis identified four user segments: Loyal Customers (frequent users with high satisfaction), Thrifty Customers (cost-conscious yet satisfied users), Risky Customers (frequent users who are dissatisfied with the service), and Passive Customers (users with relatively low transaction activity). This study recommends segment-specific Customer Relationship Management (CRM) strategies, with particular emphasis on service recovery for high-risk users and retention programs for loyal users to maximize the company's profitability.

Comparative Performance Analysis of SVM, LSTM, and IndoBERT for Sentiment Analysis of User Reviews

2026-07-15T04:19:11+00:00

The Maganghub Kemnaker National Internship Program, launched on October 1, 2025, attracted significant public attention while also generating both criticism and praise on the social media platform X. This study compares the performance of three classification algorithms—Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and IndoBERT—for sentiment analysis of public opinions regarding the program. The dataset consists of 6,825 Indonesian-language tweets collected between October and December 2025 after duplicate removal. The data were preprocessed and labeled as positive, neutral, or negative using two approaches: automated labeling with a Hugging Face model and manual annotation. Each algorithm was evaluated under five hyperparameter tuning scenarios using an 80:20 train–test split and assessed with accuracy, precision, recall, and F1-score. The results show that IndoBERT achieved the highest performance, with an accuracy of 85.27% using the Hugging Face–labeled dataset, outperforming SVM (79.05%) and LSTM (78.68%). IndoBERT's superior performance is attributed to its Transformer-based architecture and multi-head self-attention mechanism, which effectively capture bidirectional contextual information, including sarcastic expressions commonly found in social media posts. The findings were further implemented in a web-based dashboard to support interactive public opinion monitoring for policymakers.

Smart Aquaponics Monitoring and Control System Using Internet of Things Technology

2023-07-06T00:53:35+00:00

Aquaponics is an integrated farming system that combines fish farming (aquaculture) with plant cultivation (hydroponics) in a mutually supportive environment. Aquaponics provides an attractive solution in terms of limited land use in urban areas. In its development, the use of Internet of Things (IoT) technology has made a significant contribution in increasing the efficiency and success of aquaponic systems. The method used in this study is to utilize sensors connected to the IoT network, such as ultrasonic and DHT22 sensors to collect data on important parameters in the aquaponic system, such as temperature and water level, and humidity. The sensor will provide information to the Arduino Uno microcontroller, then the microcontroller will order the water pumps, lights and fans to turn on or off. This system is also equipped with a user interface that allows users to monitor and control the aquaponic system in real-time via a mobile device.