CogITo Smart Journal https://cogito.unklab.ac.id/index.php/cogito <h2><strong>COGITO SMART JOURNAL</strong></h2> <table style="height: 223px;" width="642" bgcolor="#ffffff"> <tbody> <tr valign="top"> <td style="width: 146.517px;"> <p>Publication<br />DOI <br />ISSN Print<br />ISSN Online<br />Editor in Chief<br />Managing Editor<br />Publisher<br />Accreditation<br />Contact</p> </td> <td style="width: 477.483px;"> <p>: June &amp; December (2 issues /year)<br />: <a title="DOI" href="https://search.crossref.org/?q=cogito+smart+journal" target="_blank" rel="noopener">cogito</a> by <img style="width: 10%;" src="https://ijain.org/public/site/images/apranolo/Crossref_Logo_Stacked_RGB_SMALL.png" /><br />: <a title="ISSN Cetak" href="https://portal.issn.org/resource/ISSN/2541-2221" target="_blank" rel="noopener">2541-2221</a> <br />: <a title="ISSN Online" href="https://portal.issn.org/resource/ISSN/2477-8079" target="_blank" rel="noopener">2477-8079</a> <br />: Andrew T. Liem, Ph.D.<br />: Jacquline Waworundeng, S.T.,M.T.<br />: <a title="Fakultas Ilmu Komputer - Universitas Klabat" href="https://www.unklab.ac.id/fakultas-ilmu-komputer/" target="_blank" rel="noopener">Fakultas Ilmu Komputer - Universitas Klabat </a> <br />: <a title="AKREDITASI " href="https://sinta.kemdikbud.go.id/journals/profile/449" target="_blank" rel="noopener">SINTA 2 (S2)</a><br />: editorial.cogito@unklab.ac.id</p> </td> </tr> </tbody> </table> <p style="text-align: justify;">CogITo Smart Journal is a scientific journal in the field of Computer Science published by the Faculty of Computer Science, Klabat University which is a member of CORIS (Cooperation Research Inter University) and IndoCEISS (Indonesian Computer Electronics and Instrumentation Support Society). Cogito Smart Journal is accredited by Sinta 2 (S2) and is indexed in various important indexing institutions, both national and international.</p> <p style="text-align: justify;">CogITo Smart Journal is published twice a year, namely every June and December. CogITo Smart Journal accepts various new and original manuscripts from research results, library reviews, and book references from the field of Computer Science and Informatics which may be written in English.</p> en-US <span>Authors who publish with this journal agree to the following terms:</span><br /><ol><li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="https://creativecommons.org/licenses/by/4.0/deed.id">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li></ol> editorial.cogito@unklab.ac.id (COGITO SMART JOURNAL - editorial team) oktoverano@unklab.ac.id (Technical Problems only) Tue, 31 Dec 2024 07:09:18 +0000 OJS 3.2.1.4 http://blogs.law.harvard.edu/tech/rss 60 Analysis Comparison of K-Nearest Neighbor, Multi-Layer Perceptron, and Decision Tree Algorithms in Diamond Price Prediction https://cogito.unklab.ac.id/index.php/cogito/article/view/532 <p><em>Diamond price predictions are essential due to the high demand for these gemstones, valued as investments and jewelry. Diamonds are expensive due to their rarity and extraction process. Their prices vary depending on key factors like the diamond's inherent value and secondary factors such as marketing costs, brand names, and market trends. These variations often confuse customers, potentially leading to investment losses. This research aims to help investors determine the true price of diamonds based solely on their intrinsic value, excluding secondary factors. A machine learning approach was utilized to predict diamond prices, focusing on primary determinants. Three models such as Multi-Layer Perceptron (MLP), Decision Tree, and K-Nearest Neighbor (KNN) were compared with manual hyperparameter tuning to identify the best performing algorithm. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Among the models, KNN demonstrated the best results, achieving MAPE, MAE, and MSE values of 1.1%, 0.00038, and 〖2.687 x 10〗^(-6) respectively. This study offers valuable insights for investors by accurately predicting diamond prices based on fundamental attributes, minimizing the impact of secondary factors.</em></p> Ahya Radiatul Kamila, Johanes Fernandes Andry, Adi Wahyu Candra Kusuma, Eko Wahyu Prasetyo, Gerry Hudera Derhass Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/532 Tue, 31 Dec 2024 00:00:00 +0000 Herbs Go Digital: IoT Monitors Temperature and Humidity Automatically https://cogito.unklab.ac.id/index.php/cogito/article/view/621 <p><em>This article aims to demonstrate the use of the Internet of Things (IoT) in a company by installing sensor devices to monitor environmental conditions automatically and continuously. Before IoT devices, monitoring processes relied heavily on inconsistent manual inspections. In the herbal and pharmaceutical industries, temperature and humidity monitoring are essential. The DHT11 and DHT22 sensors are used in conjunction with the ESP32 microcontroller to facilitate real-time temperature monitoring. The collected data is recorded in a MySQL database and displayed through HTML and PHP-based web dashboards. This article compares the performance of both sensors and discusses their potential mass application in enterprises. This IoT system implementation changes the monitoring process from manual to automated and continuous, enabling historical data collection for further analysis. The results show that IoT integration can improve efficiency and accuracy in monitoring enterprise environmental conditions.</em></p> Yosia Adi Susetyo, Hanna Arini Parhusip, Suryasatriya Trihandaru Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/621 Tue, 31 Dec 2024 00:00:00 +0000 A Usability Study of Augmented Reality Indoor Navigation using Handheld Augmented Reality Usability Scale (HARUS) https://cogito.unklab.ac.id/index.php/cogito/article/view/658 <p><em>Augmented Reality (AR) indoor navigation has surfaced as an unprecedented and inventive method of aiding and directing users as they traverse intricate indoor landscapes, including campuses and structures. The efficacy of AR interior navigation system implementation is predominantly contingent upon the ease of use and adeptness of users in engaging with this technology. This study sets out to comprehensively evaluate the usability of AR indoor navigation with a primary focus on the manipulability and comprehensibility aspects of AR technology, assessing how effectively it facilitates navigation within indoor spaces. To achieve this, the Handheld Augmented Reality Usability Scale (HARUS) was used as the framework for evaluation. The research involved the creation of a marker-based AR indoor navigation application called "DutaNavAR," designed specifically for use within the Agape Building at Universitas Kristen Duta Wacana (Duta Wacana Christian University). The evaluation yielded noteworthy results, with the mean manipulability score averaging 75.19 and the mean comprehensibility score averaging 81.63. In summary, the overall average HARUS score obtained was 78.41. This score indicates a high level of user satisfaction with the interaction and overall experience of the indoor navigation application. These findings underscore the positive impact of AR technology in enhancing indoor navigation, emphasizing the usability and user-friendliness of AR solutions in complex indoor environments.</em></p> Matahari Bhakti Nendya, Aditya Wikan Mahastama, Bantolo Setiadi Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/658 Tue, 31 Dec 2024 00:00:00 +0000 Web-Based Village CCTV Information System to Support Smart City in Yogyakarta https://cogito.unklab.ac.id/index.php/cogito/article/view/664 <p><em>Smart cities and safe cities were modern city concepts based on information technology that had been implemented in many major cities in the world. The smart city includes information and communication technology-based city development with good infrastructure integration. One example of smart city implementation was the installation of </em><em>Village </em><em>CCTV in Yogyakarta City. For optimal CCTV operation, an integration system between various parties was required. In that context, the Website was used as a management tool that combined various aspects. This Website was developed using PHP MySQL as a database, JavaScript and PHP programming languages for logic and functionality, HTML as a website structure, and display design using Bootstrap. This research used qualitative methods and produced a </em><em>V</em><em>illage CCTV Website, which had the main objective of providing information about </em><em>V</em><em>illage CCTV efficiently and effectively in managing CCTV management. Users could quickly and accurately access information related to CCTV Village. Thus, this Website became an important tool in improving security, comfort, and surveillance in the village.</em></p> Hanang Prabowo, Dwi Wahyuningrum, Oktavia Dewi Alfiani, Dessy Apriyanti, Adhiyatma Srinarbito Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/664 Tue, 31 Dec 2024 00:00:00 +0000 Prototype Design of Campus Social Media Application using LBSN https://cogito.unklab.ac.id/index.php/cogito/article/view/714 <p><em>Students pursuing higher education are required to actively learn, participate in academic and social activities, and develop critical and innovative thinking. Interactions between students, both with peers and lecturers, play a crucial role in shaping the learning experience on campus. The importance of these interactions prompted the researcher to design an application specifically for the campus environment. The app features chat, adding friends, and finding mates to expand students' social interactions. The find mate feature allows students to find friends randomly, while the maps feature makes it easier for them to find the location of their lecturers and friends in real time. By integrating the concept of a Location-Based Social Network (LBSN), this application is expected to reduce the level of academic stress and improve the quality of interaction among students and lecturers. Students can easily find friends and lecturers, and share locations in real-time. The method is based on the Prototyping Model and the testing is based on the Black-box method. The results are implemented through the Use Case diagrams and graphical user interfaces. The application prototype has been completely designed, however, </em><em>recommendations for future researchers need to consider the application OS version for multiple platforms to broaden the user base.</em></p> Marchel Thimoty Tombeng, Green F. Mandias, Edson Yahuda Putra Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/714 Tue, 31 Dec 2024 00:00:00 +0000 Comparative Analysis of Clustering Approaches in Assessing ChatGPT User Behavior https://cogito.unklab.ac.id/index.php/cogito/article/view/661 <p><em>ChatGPT is an artificial intelligence technology that is widely used and discussed. The technology invites mixed responses from various parties, mainly because of the benefits and risks of its use in multiple fields. Jambi University students also feel the influence of ChatGPT's presence in education. To determine the behavior of Jambi University students in using ChatGPT, four UTAUT variables were used, namely Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC) as independent variables in measuring the behavior of using ChatGPT. Where UTAUT states these four variables have a positive influence on the actual behavior of technology use. This study used K-Means and K-Medoids Clustering to group Jambi University students based on ChatGPT usage behavior. Based on the Silhouette Score calculation, each method's optimal number of clusters is 2. K-Means is considered more optimal in forming 2 clusters because it obtained a Silhouette Score of 0.2123864, higher than K-Medoids, which is 0.1766865</em>.</p> Dedy Setiawan; Daniel Arsa, Lucky Enggrani Fitri, Farah Fadhila Putri Zahardy Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/661 Tue, 31 Dec 2024 00:00:00 +0000 Generalized Linear Mixed-Model Tree for Modeling Dengue Fever Cases https://cogito.unklab.ac.id/index.php/cogito/article/view/715 <p><em>The GLMM tree demonstrates flexibility when applied to complex dataset structures such as multilevel and longitudinal data. However, there has been no assessment of the performance of GLMM trees on panel data structures. This study aims to assess the performance of the GLMM tree on a panel data structure using a case study of dengue fever cases in West Java. The performance evaluation focuses on the accuracy of the model. The dataset includes cross-sectional data from 27 regencies/cities in West Jawa, covering different regions at a single point in time, and time-series data from 2014 to 2022, tracking dengue fever cases over the years. The results of this study show that the GLMM tree model is suitable for panel data that exhibit nuanced or intricate variability unrelated to temporal effects. When developing the incidence rate of the dengue fever model, the GLMM tree separates into two submodels depending on a GRDP growth rate threshold of 5.5%. The GLMM tree model shows significant differences in the incidence rate of dengue fever between regencies/cities. However, the differences in the incidence rate of dengue fever from year to year between the regencies/cities are not significant. It indicates that local factors, such as research predictor variables, are more dominant in influencing the incidence rate than global factors.</em></p> Erwan Setiawan, Khairil Anwar Notodiputro, Bagus Sartono Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/715 Tue, 31 Dec 2024 00:00:00 +0000 Digital Information and Navigation Kiosk Application Based on Progressive Web Apps and Leaflet Technology https://cogito.unklab.ac.id/index.php/cogito/article/view/745 <p style="margin: 0cm; text-align: justify; text-indent: 35.45pt;"><em><span style="font-size: 11.0pt;">Digital information and navigation kiosk applications offer a solution for communities to access updated information and navigate to locations within a specific environment. This app allows users to easily search for and access information about various locations such as lecture halls or faculty residences. The Agile Software Development method was used in the development of the app, facilitating rapid, iterative progress, and quick adjustments based on user feedback. The application provides details regarding various administrative departments and features for searching locations, accessing information, and identifying points of interest. Designed as a Progressive Web App (PWA) and Leaflet Technology, it combines the best features of web and mobile applications, allowing users to access them through a web browser while providing offline capabilities and an app-like user experience. The PWA design ensures that the app is fast, reliable, and can be accessed from any device using a web browser. This enables efficient information dissemination and rapid navigation within the environment.</span></em></p> Stenly Ibrahim Adam, Rolly Junius Lontaan, Vincent Vian Supit, Shyalenn Cerolin Kolibonso Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/745 Tue, 31 Dec 2024 00:00:00 +0000 Sentiment Classification of IT Service Feedback via TF-IDF https://cogito.unklab.ac.id/index.php/cogito/article/view/701 <p><em>Handling user complaints and feedback is a key strategy of Pusintek, the Ministry of Finance of the Republic of Indonesia, to enhance user satisfaction. The challenge faced is the difficulty in accurately analyzing feedback due to differences in comments and categories chosen by users, which requires manual category correction. This study aims to automate feedback comment categorization using classification algorithms. Specifically, Naïve Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN) algorithms were applied to 11,108 user feedback records. The CRISP-DM framework was used, with dataset preparation involving sentiment analysis techniques (cleansing, case folding, normalization, filtering, and tokenization) and Term Frequency-Inverse Document Frequency (TF-IDF) weighting. Accuracy values for each algorithm were evaluated. Results show that the SVM algorithm performed the best, achieving an accuracy of 94.10% and consistently delivering the highest precision, recall, and f1-score across all sentiment categories. This research contributes to the development of an automatic feedback classification system that improves categorization accuracy, minimizes manual intervention, and optimizes user feedback analysis. It is expected to enrich the understanding of text classification and natural language processing techniques and open up opportunities for further research.</em></p> Samidi Samidi, Devy Fatmawati Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/701 Tue, 31 Dec 2024 00:00:00 +0000 Implementation of SAW and AHP in Decision-Making Models for Credit Provision in Cooperatives https://cogito.unklab.ac.id/index.php/cogito/article/view/662 <p>The research aims to overcome the difficulties in selecting the best members of the Al-Amin Independent Corporation, focusing on the challenges faced in determining the best members in the process of giving credit and payments on time. However, many members fail to meet their obligations or fail to pay their contributions smoothly, leading to credit freezes and decreased cooperative income. The cause of a member's failure to pay quotas has not been identified by the current candidate admission selection system. The methods used are Simple Additive Weighting (SAW) and Analytical Hierarchy Process (AHP) applied in the Decision Support System (DSS) model. The results of the research showed the effectiveness of the SAW method in identifying the best and optimal alternative with the highest value on V2 of 4. The AHP method has successfully determined the priority weight and the level of importance for member selection criteria including Activity (0.50), Savings (0.13), Guarantee (0.09), Loan (0.10), Disbursement (0.10), Time Period (0.07). The research provides insight to decision-makers in cooperatives makes important contributions, especially in the granting of credit, and affirms the importance of objective methods in the selection of members.</p> Rojakul Rojakul, Sumardianto Sumardianto, Gandung Triyono Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/662 Tue, 31 Dec 2024 00:00:00 +0000 Analysis Waste Level of Column Reinforcement Work Planning with Software Cutting Optimization https://cogito.unklab.ac.id/index.php/cogito/article/view/813 <p><em>In the era of globalization, construction development in Indonesia has experienced significant acceleration, accompanied by innovation in its implementation methods. One of the main problems in</em> <em>construction projects is material waste, especially in column work. This study aims to analyze the level of waste in column reinforcement work by applying the cutting optimization method using Cutting Optimization Pro software and analyzing the diameter of the reinforcement that produces the greatest waste. The research method used is quantitative by analyzing secondary data through shop drawings and detailed standards from construction projects. The study was conducted on column work from Ground to Floor 5 by calculating material requirements using the Bar Bending Schedule and optimizing cutting patterns through Cutting Optimization Pro Software. The results show that the lowest percentage of waste D16 is </em><em>0%,</em><em> the highest at</em><em> Ø8 is</em><em> 2.653%, and the</em><em> overall average waste is</em><em> 0.916%. This study provides new insights into</em> <em>the importance of innovation in material planning and management in the construction industry. By utilizing optimization software, contractors can improve efficiency and reduce the</em><em> impact of material waste. This study is expected to be a reference for contractors in adopting new technologies in the </em><em>management of material waste</em><em>.</em></p> Sulthanul Auliya Jagad, I Nyoman Dita Pahang Putra Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/813 Tue, 31 Dec 2024 00:00:00 +0000 RIOT.ID: Revolutionizing Running Community Management with Next.js and Gamification https://cogito.unklab.ac.id/index.php/cogito/article/view/764 <p><em>The RIOT Indonesia running community has been actively engaged since 2018, continuing to expand its reach. However, despite this significant growth, the community still relies on manual systems for managing membership and recording activities. Information technology, recognized as a catalyst for transformation across various sectors, including sports, presents an opportunity to enhance the efficiency of community management. This research focuses on the design of the Web RIOT.ID application, a web-based solution that integrates QR Code technology and gamification principles through an Experience Points (XP) system to motivate member participation. The user interface design of the application employs the Design Thinking methodology, ensuring that the solutions developed are tailored to meet user needs. Developed using a Rapid Application Development (RAD) approach, the application leverages Next.js and MongoDB. Evaluation is conducted through black box testing to confirm the application's functionality and its alignment with the established objectives. The RIOT.ID application is expected to serve as a model for other communities aiming to harness information technology to enhance organizational management and member engagement.</em></p> Peter Shaan, Jay Idoan Sihotang Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/764 Tue, 31 Dec 2024 00:00:00 +0000 Evaluation of Data Mining in Heart Failure Disease Classfication https://cogito.unklab.ac.id/index.php/cogito/article/view/726 <p><em>This study evaluates the effectiveness of data mining algorithms in heart failure disease classification. Various algorithms, including Random Forest, Decision Tree C4.5, Gradient Boosted Machine (GBM), and XGBoost, were applied to a heart failure dataset. The dataset was collected from multiple sources and preprocessed to address imbalances using the SMOTE (Synthetic Minority Over-sampling Technique) technique. The results indicate that employing SMOTE and parameter optimization through grid search significantly enhances the performance of these algorithms. XGBoost and GBM demonstrated superior accuracy, precision, and recall in both balanced and imbalanced data scenarios. In balanced data scenarios, XGBoost achieved an accuracy of 98.75% with an error rate of 1.25%, while GBM achieved an accuracy of 98.60% with an error rate of 1.40%. The study confirms that appropriate data preprocessing and parameter optimization are crucial for improving the accuracy of medical data analysis. These findings suggest that XGBoost and GBM are highly effective for heart disease prediction, supporting early diagnosis and timely medical intervention. Future research should explore alternative preprocessing techniques and additional algorithms to further improve prediction outcomes.</em></p> Nurfadlan Afiatuddin, Rahmaddeni Rahmaddeni, Fitri Pratiwi, Rapindra Septia, Heri Hendrawan Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/726 Tue, 31 Dec 2024 00:00:00 +0000 Evolution and Research Opportunities of Digital Forensic Tools: A Bibliometric Analysis https://cogito.unklab.ac.id/index.php/cogito/article/view/675 <p><em>The use of digital technology has increased rapidly, presenting new challenges such as cybercrime, online fraud and money laundering. To address these threats, digital forensic tools have become crucial in investigating and analyzing electronic evidence to combat increasingly complex digital crimes. Therefore, research and development in the field of digital forensics is crucial to address the growing digital security challenges. This study aims to conduct a bibliometric analysis of digital forensic tools research in the business, management and accounting domains over the past ten years, evaluate the evolution of the research, identify promising research opportunities and provide insights into future directions in the field. Bibliometric analysis was conducted with the help of VOSviewer software on 698 Scopus-indexed articles sourced from ScienceDirect during 2014-2023. Based on the network map analysis, it was found that despite much progress, the field continues to evolve and offers many opportunities for further research and innovation in digital forensic tools related to mobile forensics, memory forensics, anti-forensics, malware analysis, cloud forensics, cybersecurity, machine learning and deep learning, and ethics and privacy in forensic investigations.</em></p> Rischi Dwi Syahputri, Alexander Anggono, Prasetyono Prasetyono, Mohamad Djasuli Copyright (c) 2024 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/675 Tue, 31 Dec 2024 00:00:00 +0000 Predicting Stock Market Trends Based on Moving Average Using LSTM Algorithm https://cogito.unklab.ac.id/index.php/cogito/article/view/648 <p><em>Prediction of the stock market is highly needed to assist traders in making decisions. Many methods are used by traders to predict this</em><em> such as technical analysis and moving averages. Moving averages predict stock trends based on the past data of the stock. The disadvantage of using a moving average analysis is the delay in crossover signals. As a solution, a deep learning technique known as LSTM is applied to the moving average strategy in this paper. In this research, the BBCA stock dataset spanning from 2010 to 2018 was utilized. The data was segmented into two parts: 2010-2017 for training data and 2018 for testing data. The training process employed Long Short-Term Memory (LSTM) networks, with the subsequent results being combined with moving average crossover techniques. Validation results indicate that BBCA shows a relatively minimal error. BBCA's average MAPE is 1.1%, and its RMSE is 65.402, classifying it within the "Highly Accurate Forecasting" category. Various combinations of moving average crossovers were tested during model training, with the combination of SMA05 and SMA50 for BBCA yielding the highest profit potential. Stocks that exhibit a downward trend are more likely to incur substantial losses. The model can predict the reversal of trends by predicting the trading signal given by the moving averages.</em></p> Rizki Surya Permana, Veronica Windha Mahyastuty, Nova Eka Budiyanta, Karel Octavianus Bachri, Maria Angela Kartawidjaja Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/648 Tue, 31 Dec 2024 00:00:00 +0000 Prototype Design of IoT-Based Real-time Monitoring and Security System for University Server Room https://cogito.unklab.ac.id/index.php/cogito/article/view/800 <p><em>The server rooms consist of computing devices hosting essential data which is critical for the operation of universities. Ensuring server room environmental stability and security is vital to prevent data loss and service disruptions. This study presents the design of an IoT-based real-time monitoring and security system for university server rooms to help the IT staff monitor the conditions of the server room. The system aims to enhance server room management efficiency while mitigating risks associated with server room issues and unauthorized access. The research is conducted based on a prototyping model which integrates hardware and software. The main focus is on the construction of the prototype device as a monitoring tool to monitor the server room environment based on the sensor parameters. The prototype has sensors to detect temperature, humidity, smoke, flame, dust, and motion as well as a real-time camera to provide continuous environmental monitoring and intrusion detection. On the software side, the functional design is presented using Unified Modeling Language. Data collected by the sensors are transmitted to IoT platforms for further analysis and visualization, enabling remote monitoring and instant notifications. The research result is a hardware prototype designed with an IoT system that is potentially used to monitor the server room environment.</em></p> Jacquline Waworundeng, Hilkia Heart Korompis Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/800 Tue, 31 Dec 2024 00:00:00 +0000 Machine Learning-Based Counseling to Predict Psychological Readiness for Aspiring Entrepreneurs https://cogito.unklab.ac.id/index.php/cogito/article/view/553 <p><em>Machine learning has become an exciting topic in psychology-related research, one of which is counseling psychological readiness for entrepreneurship. An intelligent application developed using a machine learning model to assist the counseling process in measuring a person's psychological readiness for entrepreneurship. This application was generated using the Entrepreneurship Psychological Readiness (EPR) instrument. In this study, to get the most suitable machine learning model, a comparison of 2 (two) machine learning models, namely, Naïve Bayesian (NB) and k-Nearest Neighbor (k-NN), involving 1095 training data. There are 4 (four) prediction classes recommended from the results of counseling: categories not ready for entrepreneurship, given training, guided, and prepared for entrepreneurship.</em> <em>The EPR instrument consists of 33 question items to measure 8 (eight) parameters used as inputs for the prediction process. The data has been randomized, and the experiment has been repeated 5 (five) times to check the consistency of performance of all techniques. 80% of the data was used as training data, and the other 20% was used as testing data. The results of the five (5) trials show that the Naïve Bayesian model provides the most consistent results in predicting a person's psychological readiness for entrepreneurship, with 89.58% accuracy, in testing. Therefore, the Naïve Bayesian model is recommended to be used in psychological counseling to predict a person's readiness for entrepreneurship</em></p> Nesi Syafitri, Syarifah Farradinna, Yudhi Arta, Icha Herawati, Wella Jayanti Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/553 Tue, 31 Dec 2024 00:00:00 +0000 Application of GIS Technology for Determining Sea Fish Capital Potential on Mobile Apps https://cogito.unklab.ac.id/index.php/cogito/article/view/801 <p>The ocean is the central source of livelihood for fishermen. Fishermen depend heavily on their sea catch. Fishermen often need more certainty because, as is often the case, the spot where they go for fishing is not necessarily the site of the fish. Remote sensing technology, such as the Aqua MODIS satellite, provides two vital advantages: the exact location of the fish and, importantly, guidance to the exact spot of the fish. This guidance can be a reassuring tool for fishermen. Using GIS technology on smartphones, fishermen can ensure the fish's position. The distribution of sea surface temperature and chlorophyll-a is analyzed to determine the coordinates. The purpose of this research is to use the React Native platform to create a mobile application that uses Geographic Information System (GIS) and Global Positioning System (GPS) technologies and is accessible through smartphones. This research adopts a prototyping model method consisting of several stages: Information Gathering, Quick Planning, Quick Design, Software Development, Testing and Deployment, and Results. The application developed can run smoothly from the log-in stage to predictive visualization of fish positions in the ocean. However, further research needs to be done to improve some features.</p> Reynoldus Andrias Sahulata, Putri Brillian, Gabriela Lahengke, Yoshua Shandy Yudha Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/801 Tue, 31 Dec 2024 00:00:00 +0000 Connecting Tutors and Students: A Mobile Application Designed with Design Thinking https://cogito.unklab.ac.id/index.php/cogito/article/view/814 <p><em>The rapid advancement of information technology has transformed education globally, but in regions like Manado, Indonesia, the lack of platforms connecting private tutors with students creates inefficiencies. Students face difficulties in finding affordable tutoring services, while tutors struggle with marketing and building trust. This study aims to design and evaluate the user interface (UI) and user experience (UX) of a mobile application addressing these challenges using the Design Thinking methodology. Through five stages—Empathize, Define, Ideate, Prototype, and Test—key pain points were identified, including scheduling inefficiencies, trust issues, and geographical constraints. Solutions like flexible scheduling, integrated promotional tools, and rating systems were proposed. Prototypes, developed using Figma, were tested through usability evaluations across four scenarios. Key findings include: Scenario 3 (notifying a tutor) showed optimal performance with a task completion time of 2 seconds, no miss-clicks, and a usability score of 100; Scenario 1 (finding courses via maps) had a 95 usability score with an 8% miss-click rate; Scenario 2 (finding schedules) showed a 25% miss-click rate and a usability score of 80; and Scenario 4 (checking notifications) faced significant challenges, with a 50% miss-click rate and a usability score of 75. These results underscore the effectiveness of Design Thinking in addressing the needs of users and provide valuable insights for improving educational platforms in underserved regions. The findings suggest that while the mobile app holds great potential for improving educational access, further refinements are needed, particularly in navigation and notification features.</em></p> Joe Yuan Mambu, Junior Lakat, George Morris William Tangka Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/814 Tue, 31 Dec 2024 00:00:00 +0000 Aligning Information Technology Governance with Business Goals Using the COBIT 2019 Framework: A Case Study of a Innovation Consultancy Firm https://cogito.unklab.ac.id/index.php/cogito/article/view/799 <p>Technological advancements have ushered the world into a new era, particularly in the realm of information. In this context, information technology is considered a crucial tool to support and enhance corporate management, enabling companies to compete in the market. One common approach to managing information technology is by using IT governance frameworks such as COBIT (Control Objectives for Information and Related Technology) 2019. To date, there has been no research or evaluation conducted on the IT performance of Meet Ventures, Pte. Ltd., leaving the maturity level of information technology implementation in the company unclear. This project aims to explore the use of information systems and information technology at Meet Ventures, Pte. Ltd. in managing their operations and data. The project's approach involves a literature study on COBIT 2019 design factors. Based on the application of COBIT 2019, the prioritized objectives identified are BAI03 – Manage Solutions Identification and Build, BAI06 – Manage IT Changes, and MEA01 – Monitor, Evaluate, Assess Performance and Conformance<em>.</em> The assessment of various design factors in COBIT 2019 indicates that Meet Ventures, Pte. Ltd. has a strong focus on innovation, differentiation, and customer service. They also demonstrate a commitment to legal compliance and external regulations, a customer service culture, and effective risk management.</p> Wilsen Grivin Mokodaser, Joe Yuan Mambu, Hartiny Koapaha, Erienika Lompoliu Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/799 Tue, 31 Dec 2024 00:00:00 +0000 Sentiment Analysis and Topic Detection on Post-Pandemic Healthcare Challenges: A Comparative Study of Twitter Data in the US and Indonesia https://cogito.unklab.ac.id/index.php/cogito/article/view/819 <p><em>This study examines public sentiment and key topics in Twitter discussions regarding the COVID-19 vaccine and the Omicron variant in the US and Indonesia. The importance of this research lies in understanding people's changing views on vaccination, especially in light of new virus variants. Using sentiment analysis with VADER and topic modeling with Latent Dirichlet Allocation (LDA), this research analyzes 637,367 tweets from the US and 91,679 tweets from Indonesia collected over two months from January 21 to February 21, 2022. The results reveal that US discussions on vaccines are predominantly positive, while those on Omicron are mostly negative. In contrast, discussions in Indonesia are largely neutral, followed by positive sentiment. Additionally, five main topics were identified for each country, with the US showing a broader range of vaccine-related discussions. These findings suggest that while the vaccine is seen as a source of hope in both countries, factors such as literacy, socioeconomic status, and education contribute to negative sentiment and vaccine resistance.</em></p> George Morris William Tangka, Ibrena Reghuella Chrisanti, Jacquline Waworundeng, Raissa Camilla Maringka, Green Arther Sandag Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/819 Tue, 31 Dec 2024 00:00:00 +0000 MRI Image Analysis for Alzheimer’s Disease Detection Using Transfer Learning: VGGNet vs. EfficientNet https://cogito.unklab.ac.id/index.php/cogito/article/view/836 <p style="text-align: justify; text-indent: 36.0pt; margin: 0cm 0cm 12.0pt 0cm;"><em><span lang="IN" style="font-size: 11.0pt;">This study focuses on developing an effective Alzheimer's disease (AD) classification model using MRI images and transfer learning. This research targets individuals aged 65 and above who are affected by the predominant form of dementia and utilizes an Alzheimer's Disease MRI Image dataset from Kaggle. Model selection involved options like EfficientNetB1, B3, B5, B7, VGG16, and VGG19. Two scenarios with distinct batch sizes (10 and 20) were explored in the model creation process. Evaluation, using a confusion matrix, determined that the EfficientNetB5 model yielded the highest accuracy at 99.22%, surpassing other models such as EfficientNetB1, B3, B7, VGG16, and VGG19. Notably, this research highlights the superior performance of EfficientNet over VGGNet in transfer learning for analyzing Alzheimer's disease MRI images. The study concludes with the implementation of a simple web system for testing model outcomes. Overall, the investigation underscores the efficacy of Convolutional Neural Network (CNN) modeling in Alzheimer's disease analysis and identifies EfficientNetB5 as the optimal model for accurate classification.</span></em></p> Green Arther Sandag, Eleonora Djamal, George Morris William Tangka, Semmy Wellem Taju Copyright (c) 2025 CogITo Smart Journal http://creativecommons.org/licenses/by/4.0 https://cogito.unklab.ac.id/index.php/cogito/article/view/836 Tue, 31 Dec 2024 00:00:00 +0000