J-INTECH ( Journal of Information and Technology) https://jurnal.stiki.ac.id/J-INTECH <p data-start="0" data-end="400">J-Intech (Journal of Information and Technology) is a journal published by the Research &amp; Community Service Institute (LPPM) of the Indonesian College of Informatics and Computers, Malang. The scope of this journal includes the fields of Informatics Engineering, Information Systems, and Informatics Management. Its purpose is to accommodate the growing needs in Information Technology development. J-Intech is published twice a year, in June and December, with ISSN (print): 2303-1425 and ISSN (online): 2580-720X. It also has a DOI: <a href="https://doi.org/10.32664/j-intech" target="_new" rel="noopener" data-start="538" data-end="608">https://doi.org/10.32664/j-intech</a>.</p> <p>&nbsp;</p> en-US jurnal@stiki.ac.id (Siti Aminah, S.Si., M.Pd) jurnal@stiki.ac.id (Addin Aditya, S.Kom., M.Kom) Wed, 18 Jun 2025 09:05:34 +0000 OJS 3.1.1.2 http://blogs.law.harvard.edu/tech/rss 60 Application for Mental Health Consultation with Scheduling Function at the Counseling Guidance of Universitas Teknologi Yogyakarta https://jurnal.stiki.ac.id/J-INTECH/article/view/1922 <p>This study developed a mobile-based mental health consultation application at Universitas Teknologi Yogyakarta (UTY) to offer both online and offline counseling services for students. The tool incorporates two primary features online consultations and offline consultation scheduling, designed to improve students' access to mental health support. The system utilizes Flutter for cross-platform mobile application development, Firebase for data management, and Node.js for backend services. The study employs a Research and Development (R&amp;D) methodology encompassing needs analysis, system design, implementation, and testing. The results indicate that the application successfully mitigates obstacles such time limitations, stigma, and restricted accessibility, hence enhancing student involvement with mental health services. The Self-Reporting Questionnaire 20 (SRQ-20) serves as a mental health screening instrument within the application, enabling students to evaluate their mental health status. This program aims to deliver a thorough and accessible solution for mental health counseling at UTY and may serve as a prototype for other universities.</p> Kevina Maydiva Heriansaputri, Moh Ali Romli ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1922 Wed, 18 Jun 2025 07:29:45 +0000 Application of the A-Star Method for Evacuation Routes Using the Long-Range Wide Area Network (LoRaWAN) https://jurnal.stiki.ac.id/J-INTECH/article/view/1923 <p>Optimal evacuation route planning is a crucial factor in disaster mitigation, especially in areas with limited communication infrastructure. This study proposes the application of the A-Star method for evacuation route optimization, supported by the Long-Range Wide Area Network (LoRaWAN) as an emergency communication system. The research methodology includes the development of an A-Star algorithm optimized with an adaptive heuristic function, integration with LoRaWAN-based sensors for real-time road condition monitoring, and simulations in various disaster scenarios. The results show that the developed system can reduce evacuation time by up to 31.4% compared to conventional methods and maintain communication connectivity up to 95% even under emergency conditions. Furthermore, the dynamic adaptation mechanism allows for automatic route changes based on current field conditions, enhancing the effectiveness of the evacuation process. Therefore, the integration of the A-Star method and LoRaWAN network proves to be a reliable and efficient solution for improving public safety during disasters.</p> Charles Daniel, Moh Ali Romli ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1923 Wed, 18 Jun 2025 08:16:05 +0000 Application of Honeypot in Network Security for Detecting Cyber Attacks on IT Infrastructure https://jurnal.stiki.ac.id/J-INTECH/article/view/1924 <p>The high security risks that are susceptible to hacking and exploitation by malicious actors to steal data or information often arise due to a lack of awareness regarding the critical importance of implementing deceptive network security using honeypots. Negligence can create vulnerabilities that are easily exploited, allowing attackers to initiate breaches. A notable network security approach involves using Honeypots, a method that creates a decoy server to mimic an authentic one. Honeypots are deliberately engineered to attract the attention of cyber attackers and facilitate their access to the trap server, thereby enabling the monitoring and analysis of their activities without compromising the integrity of the primary server. To achieve optimal network security, comprehensive testing of Honeypots is essential. This testing process serves as a fundamental metric in evaluating the efficacy and performance of Honeypot systems in mitigating cyber threats.</p> Carlos Susanto, Moh Ali Romli ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1924 Wed, 18 Jun 2025 09:04:59 +0000 Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection https://jurnal.stiki.ac.id/J-INTECH/article/view/1810 <p>Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.</p> Faruk Ulum, Junhai Wang, Dyah Ayu Megawaty, Ari Sulistiyawati, Riska Aryanti, Sumanto Sumanto, Setiawansyah Setiawansyah ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1810 Mon, 23 Jun 2025 07:33:41 +0000 Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification https://jurnal.stiki.ac.id/J-INTECH/article/view/1851 <p>The classification of edible versus poisonous mushrooms presents a critical challenge in the domains of applied biology and public health, particularly due to the serious implications of misidentification. This research employs the UCI Mushroom Dataset to evaluate and compare the effectiveness of several machine learning models, including traditional algorithms like Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes, as well as advanced ensemble techniques such as Stacking and Voting Classifier. Notably, both Random Forest and Stacking achieved flawless accuracy, reaching 100%, underscoring the high predictive capacity of these models in complex categorical scenarios. Conversely, Naïve Bayes exhibited significantly weaker performance—achieving only 59.8% accuracy—likely due to its underlying assumption of feature independence, which does not hold for this dataset. The ensemble learning approaches, including the combination of Stacking and Bagging, not only preserved but also enhanced model robustness and generalization. These methods effectively leverage the complementary strengths of individual learners to yield more accurate and stable predictions while mitigating overfitting risks. Comparative analysis with previous research confirms the consistency of these findings and reinforces the viability of ensemble strategies for handling intricate classification tasks. Overall, this study highlights the importance of algorithm selection tailored to data characteristics and supports the use of ensemble learning to boost predictive reliability.</p> Neny Sulistianingsih, Galih Hendro Martono ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1851 Mon, 23 Jun 2025 08:08:37 +0000 Decision Support System for Selecting BPS Central Tapanuli Partners Using the SMART Method https://jurnal.stiki.ac.id/J-INTECH/article/view/1857 <p>The selection of partners at the Central Statistics Agency (BPS) of Central Tapanuli is a very important process because it determines the quality of supporting staff in census and survey activities. One of the core stages in the selection process is the interview, which functions to directly evaluate the abilities and character of prospective partners. The assessment in the interview covers several main aspects, namely analytical skills, communication, appearance, and politeness. This study aims to design a decision support system based on the SMART (Simple Multi Attribute Rating Technique) method that can help process interview results systematically and objectively. Each criterion in the interview is given a weight based on the level of importance, then the value of each candidate is processed through mathematical calculations that produce a final score. This score is used to determine the candidate's ranking and provide recommendations to the selection committee. The system is developed in the form of a web-based application with a user-friendly interface, and supports data input, value processing, and automatic presentation of results. The implementation results show that the SMART method is able to improve assessment accuracy, reduce subjectivity, and accelerate the decision-making process in partner selection. With this system, the interview process is not only a fairer and more transparent means of assessment, but also supports work efficiency and consistency of selection results in the BPS environment.</p> Adzkia Nur, Ardilla Syahfitri Lubis, Dicky Sambora, Debi Yandra Niska ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1857 Mon, 23 Jun 2025 08:47:41 +0000 Analyzing Students' Interest in Mathematics Through the Implementation of the K-Means Clustering Algorithm https://jurnal.stiki.ac.id/J-INTECH/article/view/1861 <p>This Research is motivated by the importance of understanding students' interest in mathematics, especially in State Junior High School 193 East Jakarta, considering that mathematics is often considered a difficult and frightening subject for some students. Learning interest, which is defined as the tendency of students to pay attention with a feeling of pleasure, has a significant influence on the process and results of student learning. This study aims to identify the level of student interest in mathematics using the K-Means algorithm. This method is used to group students into several clusters based on their level of interest. The results showed that students were divided into three clusters, namely the first cluster with very high interest totaling 193 students with an average Final Semester Exam score of 91.920, the second cluster with low interest totaling 18 students with an average score of 52.333, and the third cluster with high interest totaling 66 students with an average score of 87.606.</p> Dede Wintana, Hamdun Sulaiman, Ramdhan Saepul Rohman, Gunawan Gunawan, Muhammad Abdul Ghani ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1861 Tue, 24 Jun 2025 09:45:37 +0000 Designing a Web-Based Laundry Service Application For Strala Laundry https://jurnal.stiki.ac.id/J-INTECH/article/view/1820 <p>As daily activities become more hectic, the need for efficient laundry services is increasing. Currently, Starla Laundry relies on a manual system for operations, leading to challenges such as storing customer data in logbooks, creating complex reports, and slow transaction processes due to manual calculations. These issues make it difficult for the owner and admin to manage the business effectively amidst growing competition. To address this, a new application is proposed using PHP programming, a MySQL database, and Microsoft Visual Studio Code for data processing. The application aims to improve operational efficiency, simplify data management, and support business growth. It is expected to enhance customer satisfaction and open opportunities to optimize the business in the digital era. This study focuses on designing a laundry service information system using the System Development Life Cycle (SDLC) methodology, which includes planning, analysis, design, and implementation. The system was tested using Black Box Testing to ensure the software meets its requirements. The results confirm that all application functions work as intended, making it ready to streamline Starla Laundry’s operations efficiently and professionally.</p> Cut Mutia, Fauziah Fauziah, Irzon Meiditra, Fitra Yuda, Rizky Rahmansyah ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1820 Sat, 28 Jun 2025 11:44:57 +0000 Application Of K-Nearest Neighbor Algoritma for Customer Review Sentiment Analysis at Ngeboel Vapestore Shop https://jurnal.stiki.ac.id/J-INTECH/article/view/1893 <p>This study applies the K-Nearest Neighbor (K-NN) algorithm to classify customer sentiments from online reviews about Ngeboel Vapestore, a local MSME in the vape industry. A total of 175 reviews from Google Review and Instagram were processed using standard NLP techniques and TF-IDF for feature extraction. The best K-NN model (k=3) achieved 85.4% accuracy. Although Logistic Regression achieved higher accuracy (92.6%), it failed to detect negative sentiment. The findings highlight the potential and limitations of K-NN for sentiment analysis in underexplored MSME contexts like vape retail. The study recommends further model improvements and broader MSME applications.</p> Muhammad Aryanda, Ita Arfyanti, Yulindawati Yulindawati ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1893 Sat, 28 Jun 2025 11:50:55 +0000 Application of K-Nearest Neighbor Algorithm For Sentiment Analysis On Free Fire Online Game Based On Google Play Store Reviews https://jurnal.stiki.ac.id/J-INTECH/article/view/1882 <p>The swift expansion of the digital gaming sector, especially online games like Free Fire, has produced extensive user feedback via platforms like the Google Play Store. This research utilizes the K-Nearest Neighbor (KNN) algorithm to conduct sentiment analysis on 5,000 user reviews, with the goal of assessing its classification effectiveness. Following preprocessing (case folding, Text Cleaning, tokenization, stopword Removal, stemming), the data was converted using TF-IDF and balanced through SMOTE. Experimental findings indicate that KNN attained a peak accuracy of merely 36.53% (at k = 14), reflecting weak performance with high-dimensional textual data. In contrast, Logistic Regression attained a notably higher accuracy of 88%, showcasing its dominance for this task. The results offer perspectives for game developers to assess user feelings and emphasize the significance of selecting suitable machine learning models. Future research should investigate advanced classifiers like SVM, Random Forest, or deep learning methods to enhance accuracy.</p> Dimas Raya, Amelia Yusnita, Ivan Haristyawan ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1882 Mon, 30 Jun 2025 02:32:41 +0000 Development of a Web-Based Decision Support System for Selecting the Optimal Duck Feed Using the Analytic Hierarchy Process (AHP) https://jurnal.stiki.ac.id/J-INTECH/article/view/1918 <p>The selection of optimal livestock feed is essential for improving animal health and productivity. This study developed a web-based Decision Support System (DSS) using the Analytic Hierarchy Process (AHP) to help farmers choose the best feed based on nutritional content, price, and availability, including sub-criteria like protein, energy, fat, minerals, and vitamins. The system ranks alternatives using pairwise comparisons and priority weights. Validation against manual calculations showed high accuracy (correlation coefficient: 0.9987; errors &lt;2.5%), with Fermented Feed (A3) as the top choice (score: 0.3611). Both methods produced identical rankings. The system reduces evaluation time by ~85% while maintaining accuracy, proving AHP’s effectiveness in digital livestock feed management tools.</p> Khaira Nazla, Enrico Vincentius Manurung, M Fauzan Hidayat, Debi Yandra Niska ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1918 Mon, 30 Jun 2025 02:52:30 +0000 Preventive Attendance Record using Photo from Mobile Phone and Printed Paper using CNN https://jurnal.stiki.ac.id/J-INTECH/article/view/1927 <p>Face-based attendance systems are increasingly popular for their ease of use, but they are susceptible to fraud, such as using photos or videos for unauthorized attendance. This study introduces a digital attendance system that combines facial recognition with liveness detection powered by Convolutional Neural Networks (CNN). Liveness verification is achieved by analyzing subtle movements and responses to ambient lighting. The dataset includes 30 facial images, encompassing both authentic and fraudulent samples. Testing demonstrates a facial recognition accuracy of 91.3% and effective spoofing detection in static and dynamic settings. This system provides a secure, fraud-resistant attendance solution ideal for educational and corporate settings. Further enhancements are suggested to improve performance across diverse facial expressions and lighting conditions.</p> Bradika Almandin Almandin Wisesa, Vivin Mahat Putri, Evvin Faristasari, Sirlus Andreanto Jasman Duli ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1927 Mon, 30 Jun 2025 00:00:00 +0000 Decision Support System for Determining Social Assistance Recipients in Petuaran Hilir Village Using the SMART Method https://jurnal.stiki.ac.id/J-INTECH/article/view/1945 <p>The distribution of social assistance in rural areas is a strategic government effort to reduce social inequality and improve the welfare of underprivileged communities. However, in Petuaran Hilir Village, the process of determining aid recipients is still conducted manually, leading to various issues such as a lack of objectivity, potential unfairness, and mistargeting. Therefore, this study aims to design and implement a Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method to determine social assistance recipients in a more systematic and transparent manner. The SMART method was chosen due to its effectiveness in simplifying multi-criteria decision-making and its practicality for implementation at the village level. The system was developed as a web-based application and tested using the black-box method, as well as validated against the manual selection results conducted by village officials. Testing results showed that the system can objectively identify and rank aid recipients based on final scores from five main criteria: income, number of dependents, home ownership status, housing condition, and type of employment. The system achieved 100% consistency with manual selection results and reduced the selection process time by up to 70%, enabling a fairer and more targeted distribution of aid based on systematically calculated scores. By eliminating manual bias in the selection process, the system significantly improves the accuracy of recipient rankings. This study also opens opportunities for further development, such as integrating real-time population data and advanced analytical features to support more responsive social policies.</p> Debi Yandra Niska, Azura Calista Sitorus, Syafira Istiara, Rahma Hidayanti ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1945 Mon, 30 Jun 2025 03:49:50 +0000 Application of Naive Bayes Algorithm for Analysis of User Reviews on Mobile Legends Game: Bang Bang https://jurnal.stiki.ac.id/J-INTECH/article/view/1881 <p>Mobile Legends: Bang Bang is a highly popular MOBA game, especially among students, which generates a large volume of user reviews on the Google Play Store. These reviews provide a valuable data source for understanding user sentiment. This study conducts sentiment analysis on user reviews using three variants of the Naïve Bayes algorithm: BernoulliNB, GaussianNB, and MultinomialNB. From an initial 5,000 reviews collected via web scraping using Python, 4,428 reviews were used after neutral reviews were removed to focus solely on positive and negative sentiments.The preprocessing steps included case folding, word normalization, tokenization, stopword removal, and stemming. Sentiment labeling was carried out using a lexicon-based approach, comparing the frequency of positive and negative words in each review. The dataset was split in an 80:20 ratio for training and testing.The results show that MultinomialNB achieved the highest accuracy at 75%, followed by BernoulliNB with 74%, and GaussianNB with 50%. MultinomialNB demonstrated superior performance in detecting positive sentiments, while BernoulliNB offered more balanced results. GaussianNB performed poorly due to its assumption of normally distributed continuous data, which is unsuitable for text classification. This study concludes that Multinomial Naïve Bayes is the most effective model for sentiment analysis of user reviews when working with word frequency-based representations.</p> Al-Muchlis Syachrul Ramadani Roba, Siti Lailiyah, Amelia Yusnita ##submission.copyrightStatement## http://creativecommons.org/licenses/by-sa/4.0 https://jurnal.stiki.ac.id/J-INTECH/article/view/1881 Mon, 30 Jun 2025 06:05:24 +0000 Development of Geolocation-Based Employee Attendance Application on Android Mobile https://jurnal.stiki.ac.id/J-INTECH/article/view/1890 <p><em>The development of mobile-based systems in Indonesia has provided innovative solutions to improve the efficiency of conventional administrative processes, especially in employee attendance. This research aims to develop an Android-based employee attendance application that is integrated with geolocation technology to enable accurate and real-time attendance monitoring. This system is built using the Waterfall method, which includes the stages of needs analysis, system design, implementation using Flutter and Dart programming language, and testing using black box testing techniques. Black-box testing was conducted on six main functions, resulting in a 94% overall success rate. Most functions achieved a 100% pass rate, but two test cases for attendance check in/out failed due to GPS location inaccuracies, highlighting the impact of device and environmental factors. The average response time was 1.28 seconds, and the average GPS delay was 2.1 seconds. The implementation of real-time notifications and admin verification improved transparency and minimized attendance fraud. The results demonstrate that the application provides an effective and efficient solution for employee attendance management. Future work should focus on enhancing location accuracy, conducting non-functional testing, and expanding features to ensure broader adoption and system robustness.</em></p> Nisa Kurnia, April Lia Hananto, Tukino Tukino, Shofa Shofiah Hilabi ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1890 Mon, 30 Jun 2025 06:37:55 +0000 Development of Academic Community Recommendation System Using Content-Based Filtering at UIN Malang Informatics Engineering Study Program https://jurnal.stiki.ac.id/J-INTECH/article/view/1916 <p>The mismatch between the number and quality of Information and Communication Technology (ICT) talents and industry needs in Indonesia creates significant challenges, especially for Informatics Engineering students who often experience difficulties in determining the appropriate professional field. This research aims to develop a content-based filtering-based academic community recommendation system to help students choose communities that are relevant to their interests, skills and experience. The system uses TF-IDF and cosine similarity methods to match student profiles with community descriptions. Data was collected from 48 students and 10 academic communities in the Informatics Engineering Study Program of UIN Malang, and processed through preprocessing stages before modeling. Evaluation results using the System Usability Scale (SUS) resulted in a score of 76, which is categorized in the “good” level, However, users indicated the need for improved guidance in navigating the system. This system is expected to be an innovative solution to increase student participation in appropriate academic communities, as well as support the development of their potential and readiness for the world of work</p> Abdurrozzaaq Ashshiddiqi Zuhri, Ririen Kusumawati, Muhammad Ainul Yaqin, Aldian Faizzul Anwar, Achmad Fahreza Alif Pahlevi ##submission.copyrightStatement## https://creativecommons.org/licenses/by-sa/4.0/ https://jurnal.stiki.ac.id/J-INTECH/article/view/1916 Mon, 30 Jun 2025 07:01:50 +0000