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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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Designing User Interface (UI) And User Experience (UX) of a Sport Space Rental Application using Design Thinking Method Raissa Maringka; Cherry Lumingkewas
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.692.613-624

Abstract

This study aims to enhance the design and streamline the process of renting sports facilities through the development of a user interface (UI) and user experience (UX) for a sport space rental application. Utilizing the Design Thinking method, the research addresses inefficiencies in the current manual booking process and proposes innovative solutions, including search features, user reviews, availability notifications, and direct booking options. The state of the art in this study is represented by the application of user-centric design principles and iterative prototyping to meet the evolving needs of sports enthusiasts. Usability testing, conducted through detailed task scenarios on the MAZE platform, yielded positive results, with an average completion rate of 80% and insights into areas for improvement. The findings suggest that the proposed UI/UX design significantly enhances the efficiency and user experience of renting sports facilities, providing a more convenient and engaging platform for users.
Deep Learning for Peak Load Duration Curve Forecasting George Morris William Tangka; Lidya Chitra Laoh
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.694.603-612

Abstract

As the energy landscape changes towards renewable energy sources and smart grid technologies, accurate prediction of peak load duration curve (PLDC) becomes crucial to ensure power system stability. The background to this research is the urgent need for more effective prediction methods to manage increasingly complex energy loads. This research presents a leading-edge approach to PLDC prediction, leveraging Deep Learning, a subsection of artificial intelligence. Focusing on data from the Taiwan State Electric Company, this study uses a Long Short-Term Memory (LSTM) network to capture complex load patterns. The LSTM model, consisting of two layers and trained on 2019-2020 data, demonstrated excellent accuracy with a Mean Absolute Percentage Error (MAPE) as low as 0.03%. These results confirm the potential of Deep Learning to revolutionize PLDC predictions in complex energy systems. These research recommendations involve exploring diverse datasets, integrating real-time data streams, and conducting comparative analyses for more reliable prediction methodologies. The benefits of this research include providing relevant insights for sustainable energy resource management amidst a dynamic energy landscape.
Comparative Analysis of Lung Cancer Classification Models Using EfficientNet and ResNet on CT-Scan Lung Images Green Arther Sandag; Deo Timothy Kabo
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.706.680-691

Abstract

This study investigates the classification of lung cancer, a major global cause of mortality. The accurate diagnosis and classification of lung cancer through CT-Scan images demand significant expertise, precision, and time to ensure appropriate treatment for patients. Transfer learning has emerged as a beneficial technology to aid in this process by effectively classifying lung cancer-related patterns in CT-Scan images. In this research, a dataset of 1,000 lung CT-Scan images, divided into four categories—Adenocarcinoma, Large Cell, Squamous, and Normal—was employed. The study evaluated several transfer learning models, including ResNet50, ResNet101, EfficientNetB1, EfficientNetB3, EfficientNetB5, and EfficientNetB7. The findings revealed that the EfficientNetB3 model outperformed the others, achieving an accuracy of 97.78%, a precision of 97.34%, a recall of 98.33%, and an F1-Score of 97.78%. These results demonstrate that the EfficientNetB3 model enhances the accuracy of lung cancer classification in CT-Scan images more effectively than other transfer learning models. This research underscores the significant potential of EfficientNetB3 in facilitating early diagnosis, advancing the integration of machine learning in medical practices, and providing critical insights for the selection of transfer learning models in clinical applications. The implications of these findings suggest a substantial impact on improving diagnostic processes and outcomes in lung cancer management.
IoT-based Environmental Monitoring with Data Analysis of Temperature, Humidity, and Air Quality Jacquline Waworundeng
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.708.692-705

Abstract

Environment monitoring has been linked to the use of the IoT. To raise awareness for the environment, the IoT system is built as an instrument tool based on a Prototyping model with an experimental approach. The hardware consists of sensors, microcontrollers, a Wi-Fi modem, powered with solar cells, and electricity integrated with IoT platforms Blynk and ThingSpeak. The prototype detectors were installed in two different locations at the Universitas Klabat. The IoT systems can store data, display information, and send push notifications as alerts to the user’s smartphone when critical conditions emerge. In the two locations for a specified time of May 2023, the data analysis shows average temperatures are 28,39˚C and 28,44˚C, where 28˚C is the optimal value. The average humidity shows 90,18%RH and 85,28%RH. These humidity values are critical because the humidity outside 40-60%RH can significantly impact health. The average air quality shows 59,62 AQI as “moderate” and 3.7 AQI as “good”. While “good” air quality is the best, “moderate” is safe because only when a value higher than 100 is unhealthy. The IoT system can help to monitor and provide real-time information about the environmental parameters.
Analysis Comparison of K-Nearest Neighbor, Multi-Layer Perceptron, and Decision Tree Algorithms in Diamond Price Prediction Kamila, Ahya Radiatul; Andry, Johanes Fernandes; Kusuma, Adi Wahyu Candra; Prasetyo, Eko Wahyu; Derhass, Gerry Hudera
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.v10i2.532.298-311

Abstract

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.
Predicting Stock Market Trends Based on Moving Average Using LSTM Algorithm Permana, Rizki Surya; Mahyastuty, Veronica Windha; Budiyanta, Nova Eka; Bachri, Karel Octavianus; Kartawidjaja, Maria Angela
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.v10i2.648.486-495

Abstract

Prediction of the stock market is highly needed to assist traders in making decisions. Many methods are used by traders to predict this 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.
A Usability Study of Augmented Reality Indoor Navigation using Handheld Augmented Reality Usability Scale (HARUS) Nendya, Matahari Bhakti; Mahastama, Aditya Wikan; Setiadi, Bantolo
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.v10i2.658.326-338

Abstract

Indoor Navigasi barbasis Augmented Reality (AR) telah muncul sebagai metode yang belum pernah ada sebelumnya dan inventif dalam membantu dan mengarahkan pengguna saat mereka melintasi lanskap dalam ruangan yang rumit, termasuk kampus dan bangunan. Efektifitas penerapan sistem navigasi indoor berbasis AR sebagian besar bergantung pada kemudahan penggunaan dan kemahiran pengguna dalam menggunakan teknologi ini. Studi ini bertujuan untuk mengevaluasi secara komprehensif kegunaan navigasi dalam ruangan berbasis AR dengan fokus utama pada aspek manipulability dan comprehenesibility teknologi AR, menilai seberapa efektif teknologi tersebut memfasilitasi navigasi dalam ruang dalam ruangan. Untuk mencapai hal ini, Handheld Augmented Reality Usability Scale (HARUS) digunakan sebagai kerangka evaluasi. Penelitian ini melibatkan pembuatan aplikasi navigasi dalam ruangan berbasis AR menggunakan penanda "DutaNavAR," yang dirancang khusus untuk digunakan di Gedung Agape di Universitas Kristen Duta Wacana. Evaluasi tersebut memberikan hasil yang patut dicatat, dengan rata-rata skor manipulability sebesar 75,19 dan rata-rata skor comprehensibility sebesar 81,63. Secara ringkas, rata-rata keseluruhan skor HARUS yang diperoleh adalah 78,41. Skor ini menunjukkan tingkat kepuasan pengguna yang tinggi terhadap interaksi dan pengalaman keseluruhan aplikasi navigasi dalam ruangan. Temuan ini menggarisbawahi dampak positif teknologi AR dalam meningkatkan navigasi dalam ruangan, yang mana lebih menekankan kegunaan dan kemudahan penggunaan aplikasi berbasis AR dalam dalam ruangan yang kompleks.
Comparative Analysis of Clustering Approaches in Assessing ChatGPT User Behavior Setiawan, Dedy; Arsa, Daniel; Enggrani Fitri, Lucky; Fadhila Putri Zahardy, Farah
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.v10i2.661.366-379

Abstract

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.
Implementation of SAW and AHP in Decision-Making Models for Credit Provision in Cooperatives Rojakul, Rojakul; Sumardianto, Sumardianto; Triyono, Gandung
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.v10i2.662.418-432

Abstract

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.
Web-Based Village CCTV Information System to Support Smart City in Yogyakarta Prabowo, Hanang; Wahyuningrum, Dwi; Dewi Alfiani, Oktavia; Apriyanti, Dessy; Srinarbito, Adhiyatma
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.v10i2.664.339-351

Abstract

Smart city dan safe city merupakan konsep kota modern berbasis teknologi informasi yang telah diterapkan di banyak kota besar di dunia. Smart city mencakup pengembangan kota berbasis teknologi informasi dan komunikasi dengan integrasi infrastruktur yang baik. Salah satu contoh penerapannya adalah pemasangan CCTV Kampung di Kota Yogyakarta. Untuk pengoperasian CCTV yang optimal, diperlukan sistem yang terintegrasi antar berbagai stakeholder. Dalam konteks tersebut, sistem informasi berbasis website dapat digunakan sebagai media untuk menggabungkan berbagai aspek. Website ini dikembangkan dengan menggunakan PHP MySQL sebagai basis data, bahasa pemrograman JavaScript dan PHP untuk logika dan fungsionalitas, HTML sebagai struktur website, dan desain tampilan menggunakan Bootstrap. Penelitian ini menggunakan metode kualitatif dan menghasilkan Website CCTV Kampung yang mempunyai tujuan utama memberikan informasi tentang CCTV secara efisien dan efektif terutama dalam hal pengelolaan CCTV. Pengguna dapat mengakses informasi terkait CCTV kampung dengan cepat dan akurat. Dengan demikian, sistem ini menjadi sarana penting dalam meningkatkan keamanan, kenyamanan, dan pengawasan di desa.

Page 30 of 32 | Total Record : 318

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