JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI

IoT-Based Water Bill Calculation Automation System Prototype for PAMSIMAS

2025-08-30T13:04:58+00:00

Water is an essential resource that supports human life and contributes to improving community welfare. Clean water availability is not only required for basic needs but also influences health and social well-being. The Community-Based Drinking Water and Sanitation Program (PAMSIMAS) aims to expand access to clean water in rural areas. However, water usage recording is still performed manually, which is time-consuming, prone to errors, and inefficient. This study aims to develop a prototype of an automated water billing system based on the Internet of Things (IoT) integrated with a website. The system employs a Water Flow Meter sensor connected to a NodeMCU ESP8266 microcontroller to measure water flow, with data transmitted in real-time via the internet and displayed on the website. The website provides features such as water consumption recording, automatic billing based on applicable tariffs, and monthly usage reporting. The research method includes literature study, field observation, hardware design, software development, and system testing. The test results show that the prototype successfully performs all test scenarios, displaying water usage data in real-time, calculating bills automatically, and storing data in a database with 100% accuracy. Therefore, the developed prototype functions properly and meets the research objectives.

Comparative Analysis Of UI/UX On Shopee, Lazada And Bukalapak Applications Using The Heuristic Evaluation Method

2025-09-30T00:39:21+00:00

E-commerce users in Indonesia have many platform options, such as Shopee, Lazada, and Bukalapak. A comparison of the interface design of these three platforms is needed to determine its impact on user loyalty. The purpose of this study is to conduct a comparative analysis of the UI/UX design of Shopee, Lazada, and Bukalapak applications using the Heuristic Evaluation approach. This study involved 100 respondents who were evaluated using the Heuristic Evaluation Method with manual and digital questionnaire distribution. The research stages include problem identification, observation, interface measurement, and analysis of results and conclusions based on 10 Heuristic Evaluation principles. Shopee excels with the highest scores in Learnability (76%), Efficiency (72%), Memorability (70%), and Satisfaction (70%). Meanwhile, Bukalapak ranks lowest among the three applications with scores on Efficiency (66%) and Satisfaction (66%). Bukalapak needs to improve the quality of its interface to enhance user ability and satisfaction. Overall, the three applications need to continuously improve their interface design to enhance user experience and meet good usability standards.

Comparison Of The Perfomance Of The C4.5 Anda Naïve Bayes Algorithms In Classifiying Lung Cancer

2025-09-04T04:18:36+00:00

Lung cancer is one of the most dangerous diseases with a high mortality rate. Early detection is crucial to increasing the chances of recovery. This study aims to evaluate the performance of the C4.5 and Naïve Bayes algorithms in classifying lung cancer cases. The dataset used is a lung disease prediction dataset from Kaggle, consisting of 30,000 records and 9 attributes. The experimental process was carried out using RapidMiner with the 10-fold cross-validation method, and evaluation was performed using a confusion matrix and t-test. The results showed that the C4.5 algorithm achieved an average accuracy of 94.44%, while Naïve Bayes achieved 87.05%. The t-test result yielded a p-value of 0.000, indicating that the performance difference between the two algorithms is statistically significant, with C4.5 proving to be superior in classifying lung cancer cases. This research is expected to serve as a reference for the development of disease classification systems, particularly in assisting with early and more accurate lung cancer detection.

Application Of The Waterfall Method In The Design Of A Website-Based E-Learning Information System At The Logval Cendekia Community Learning Activity Center (PKBM) Equivalent School

2025-09-02T12:57:44+00:00

Information technology-based education has become an unavoidable necessity in the implementation of education in Indonesia, both in formal and non-formal sectors. One of the most prominent applications is the use of E-learning systems. This study aims to design and develop a web-based E-learning information system for the Community Learning Center (PKBM) Logval Cendekia. The primary objective of this system is to improve the effectiveness and efficiency of the learning process in non-formal education environments, which often face challenges such as limited face-to-face learning time and the absence of integrated learning media. The system development methodology employed in this research is the System Development Life Cycle (SDLC) with the Waterfall model, which consists of several sequential stages: requirement analysis, system design, implementation, testing, and maintenance. Data were collected through observation, interviews, and literature review. The resulting system provides essential features including user account management (admin, teachers, and students), learning material management, assignment uploads and downloads, as well as online quizzes and exercises. The system is web-based to ensure wide accessibility regardless of location and time constraints. System testing was conducted using the Black Box Testing method, focusing on system functionality without evaluating the internal structure of the code. The test results showed that the system operates as expected according to the specified requirements and can be effectively used by all user roles. The implementation of this E-learning system is expected to enhance the quality of education at PKBM Logval Cendekia by reaching a broader range of learners and supporting digital transformation in the non-formal education sector.

Comparison of K-NN and Naive Bayes Algorithms for Identification of Star Fruit Ripeness Using RGB-Based Digital Images

2026-01-03T03:35:32+00:00

Automatically identifying fruit ripeness is an important aspect in the sorting and distribution of agricultural products, including star fruit, which has economic value. This study aims to analyze and compare the performance of two classification algorithms, namely K-Nearest Neighbor (K-NN) and Naive Bayes, in identifying the ripeness level of star fruit based on digital images. The process begins with capturing images of star fruit in three ripeness categories: unripe, semi-ripe, and ripe. Next, color feature extraction is performed using the average value of the Red, Green, and Blue (RGB) components of the image. These features are then used as input for each classification algorithm. Evaluation is carried out based on accuracy, precision, and recall metrics to assess the performance of each model. The results show that both algorithms are able to classify the ripeness level quite well, but the K-NN algorithm shows superior performance compared to Naive Bayes in the context of the dataset used.

Implementation Of Naïve Bayes Algorithm For Sentiment Analysis Of Creator Content On Tiktok Social Media

2025-09-19T05:42:14+00:00

Social media has become a crucial space for content creators to build interactions, build self-image, and generate economic opportunities. TikTok, as a rapidly growing platform, generates a variety of public opinions in the form of comments reflecting positive, negative, and neutral sentiments toward creators. Sentiment analysis using a text mining approach is considered effective for understanding audience opinion in real time. This study applies the Naïve Bayes algorithm to classify the sentiment of TikTok user comments. This method was chosen for its speed and efficiency in text analysis, although it has limitations due to the assumption of independence between features. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics to assess classification performance. The results showed that the Naïve Bayes algorithm was able to classify comments with an accuracy of 84.39%. The precision obtained was 0.862, recall was 0.844, and the F1-score was balanced, proving this method's effectiveness for sentiment analysis on TikTok comments. These findings confirm the potential of Naïve Bayes as a fast and accurate classification method for understanding audience opinion on social media.

Application Of The Random Forest Algorithm For Classification Of Stunting Factors In Toddlers

2025-09-02T18:55:52+00:00

Stunting is a type of growth failurecaused by chronic malnutrition, a condition that causes children toexperience stunted growth at a certain age. The high rate of stuntinghighlights the need for intensive and innovative efforts to address it,including through data-driven and technology-based approaches. This studyaims to determine the accuracy of the Random Forest algorithm to be implemented for stunting risk classification in infants, using SMOTE for data balancing. Evaluation was conducted using accuracy, precision, recall, and F1-Score metrics. The results show that the Random Forest algorithm provides fairly high performancewith an accuracy of 79.17%, precision of 90.54%, recallof 82.43%, and an F1-Score of 86.29%. Based on these results, it can be seen that proper preprocessingplays a significant role in improving classification performance. Based on this,it can be concluded that the Random Forest model is capable of building a classification model that is quite accurate inidentifying stunting status in toddlers. With a total of 10,000 entries, this model successfully recognized patterns in the datawith fairly good performance results.