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CogITo Smart Journal
Published by Universitas Klabat
ISSN : 25412221     EISSN : 24778079     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering
CogITo Smart Journal adalah jurnal ilmiah di bidang Ilmu Komputer yang diterbitkan oleh Fakultas Ilmu Komputer Universitas Klabat anggota CORIS (Cooperation Research Inter University) dan IndoCEISS (Indonesian Computer Electronics and Instrumentation Support Society). CogITo Smart Journal dua kali setahun, yaitu setiap bulan Juni dan Desember. CogITo Smart Journal menerima berbagai naskah yang sifatnya baru dan asli dari hasil penelitian, telaah pustaka, dan resensi buku dari bidang Ilmu Komputer dan Informatika yang boleh ditulis dalam Bahasa Indonesia atau Bahasa Inggris. Kata CogITo berasal dari Bahasa Latin yang berarti I Think. Sehihngga CogITo Smart berarti I Think Smart dalam Bahasa Inggris.
Arjuna Subject : -
Articles 318 Documents
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Evolution and Research Opportunities of Digital Forensic Tools: A Bibliometric Analysis Dwi Syahputri, Rischi; Anggono, Alexander; Prasetyono, Prasetyono; Djasuli, Mohamad
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.675.474-485

Abstract

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.
Sentiment Classification of IT Service Feedback via TF-IDF Samidi, Samidi; Fatmawati, Devy
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.701.403-417

Abstract

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.
U-MATE : Student And Lecturer Location-Based Social Network Aplication Tombeng, Marchel Thimoty; Mandias, Green F.; Putra, Edson Yahuda
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.714.352-365

Abstract

Students are individuals who pursue higher educcation, have an important role as agents of change and future leaders in various sectors. They 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. Student interactions with lecturers also have an impact on learning motivation and the lecture experience. The importance of these interactions prompted the researcher to design an application specifically for the campus environment. The app features chat, add friend, and find mate 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 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 with lecturers. Through this application, students can more easily find friends and lecturers, and share locations in real-time. The questionnaire results from Klabat University students showed the need for this kind of application
Generalized Linear Mixed-Model Tree for Modeling Dengue Fever Cases Setiawan, Erwan; Notodiputro, Khairil Anwar; Sartono, Bagus
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.715.380-392

Abstract

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.
Evaluation of Data Mining in Heart Failure Disease Classfication: Afiatuddin, Nurfadlan; Rahmaddeni, Rahmaddeni; Pratiwi, Fitri; Septia, Rapindra; Hendrawan, Heri
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.726.460-473

Abstract

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.
Digital Information and Navigation Kiosk Application Based on Progressive Web Apps and Leaflet Technology Adam, Stenly Ibrahim; Lontaan, Rolly Junius; Supit, Vincent Vian; Kolibonso, Shyalenn Cerolin
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.745.393-402

Abstract

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.
RIOT.ID: Revolutionizing Running Community Management with Next.js and Gamification Shaan, Peter; Sihotang, Jay Idoan
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.764.447-459

Abstract

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.
Analysis Waste Level of Column Reinforcement Work Planning with Software Cutting Optimization Jagad, Sulthanul Auliya; Putra, I Nyoman Dita Pahang
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.813.433-446

Abstract

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 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 0%, the highest at Ø8 is 2.653%, and the overall average waste is 0.916%. This study provides new insights into the importance of innovation in material planning and management in the construction industry. By utilizing optimization software, contractors can improve efficiency and reduce the impact of material waste. This study is expected to be a reference for contractors in adopting new technologies in the management of material waste.
A Machine Learning-Based Ambiguous Alphabet Recognition for Indonesian Sign Language System (SIBI) Purbolingga, Yoan; Ridwan, Ahmad; Putri, Dila Marta
CogITo Smart Journal Vol. 11 No. 1 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i1.816.1-14

Abstract

One of the communication problems in deaf people is the inhibition of verbal communication. This is due to the limited hearing function which has an impact on the imperfection of language sound reception. To communicate with deaf people, extraordinary communication is needed so that the meaning of the conversation can be conveyed properly. Sign language is the main communication medium for deaf people. However, in the use of sign language, there are ambiguous letters, namely “D “,“E“,“M“,“N“,“R“, “S“, and “U“. This research uses the chain code method to identify and reconstruct the shape of hand gesture objects. Then, to solve the problem of ambiguity of alphabet letters, an artificial intelligence method, namely K-Nearest Neighbors (K-NN), is used. The sample used consists of 350 real-time images with variations in object recognition accuracy. Based on the research using chain code and K-NN classification method, it can be concluded that the recognition of ambiguous letters in sign language has 245 training data for K-NN which has 88.76% accuracy, and 105 test data with 90% accuracy. This test data is divided into seven letters: “D“, “E”, “M”, “R” and “U” at 100%, and “N” and “S” at 98.88%.
Prototype of IoT-Based Temperature and Humidity Monitoring and Controlling System for Broiler Chicken Coops Lengkong, Oktoverano; Tombeng, Marchel Thimoty; Tasidjawa, Jeniffer Linda; Birahy, Brian Gustaf
CogITo Smart Journal Vol. 11 No. 1 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i1.866.15-26

Abstract

Temperature and humidity are critical factors that influence the health and productivity of broiler chickens. Manual monitoring and control of coop conditions are often ineffective and inefficient, leading to a decline in production quality. This research aims to develop a prototype IoT-based monitoring and controlling system for temperature and humidity in broiler chicken coops. The system employs a DHT22 sensor to measure temperature and humidity, a Wemos D1 R1 microcontroller for data processing, and the Blynk application as a user interface for real-time monitoring and notifications. The Evolutionary Prototyping method is applied in the development of this system to allow gradual adjustments based on user needs. Testing results show that the prototype can monitor temperature and humidity in real-time and automatically activate fans or lights when the temperature is outside the optimal range. With this system, farmers can monitor coop conditions remotely, simplifying farm management.

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