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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 336 Documents
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Perancangan UI/UX Untuk Aplikasi Kedai Online Menggunakan Metode Design Thinking Rinny Rantung; Joe Yuan Mambu
CogITo Smart Journal Vol. 9 No. 2 (2023): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v9i2.585.396-410

Abstract

Penelitian ini bertujuan untuk merancang desain antarmuka pengguna (UI/UX) pada aplikasi mobile sebuah kedai makanan dan minuman yang menyediakan program loyalitas bagi pengguna dan penjual. Metode penelitian yang digunakan adalah Design Thinking, dengan lima tahapan yang terinci: empatize, define, ideate, prototype, dan test. Hasilnya adalah aplikasi dengan UI/UX yang memfasilitasi UMKM dalam meningkatkan retensi pelanggan dan mempermudah pembeli dalam memesan. Testing task scenario memberikan hasil usability score 81, menunjukkan tingkat usability yang baik. Implikasi praktisnya adalah aplikasi ini dapat menjadi solusi inovatif sesuai kebutuhan dan harapan pengguna, serta membantu UMKM dalam mengembangkan bisnis mereka. Penelitian ini membuktikan bahwa penerapan Design Thinking dalam pengembangan aplikasi mobile untuk UMKM menghasilkan desain yang berfokus pada pengguna dan solusi inovatif. Hal ini menunjukkan kontribusi penelitian ini terhadap pengembangan aplikasi yang sesuai dengan kebutuhan pasar dan membantu UMKM dalam bersaing di era digital.
Sistem Presensi Berbasis Kode QR untuk Pelacakan Kontak Pasca-Pandemi Imam Husni Al Amin; Veronica Lusiana; Budi Hartono; Dimas Wahyu
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.v10i1.490.436-450

Abstract

The COVID-19 pandemic has brought about profound changes in daily life and introduced new challenges to public health maintenance. Despite ongoing uncertainties, with certain regions easing or completely lifting community activity restrictions, persistent concerns about the virus's continued threat prompt the need for robust health monitoring measures. In this context, the use of contact tracing apps becomes pivotal for organizing individuals' movements, monitoring social distancing, and ensuring adherence to health protocols. This study introduces a QR code-based attendance system, a meticulously designed web and Android application aimed at efficiently and accurately tracking individuals' whereabouts. The system leverages QR code scanning technology, and to enhance security, employs the Advanced Encryption Standard (AES) method for data encryption. This ensures the safe encryption of sensitive data, preserving its confidentiality and integrity during transmission and storage. The research outcome is a versatile application facilitating seamless access across diverse locations, allowing real-time tracking of individuals' presence. This capability proves crucial for contact tracing efforts in the event of positive cases, contributing to the implementation of post-pandemic security and surveillance policies. The system's design and features align with the evolving landscape of the pandemic, emphasizing adaptability and comprehensive support for public health initiatives.
APLIKASI FORUM TRANSLASI BAHASA INDONESIA-MANADO BERBASIS WEB MENGGUNAKAN METODE EXTREME PROGRAMMING Vivi Peggie Rantung; Abimanyu Marvie Dwisuprapto; Ferdinan Ivan Sangkop
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.v10i1.530.451-463

Abstract

Manado language (Manadonese) is one of the languages that is registered in the ISO 639-3 standard system with the code of xmm and used by 3.320.000 users. In daily life, Manadonese is commonly used verbally. In its oral use, Manadonese becomes a low-resource language, meaning it lacks text-based resources, which makes it difficult to develop various linguistic-based technologies to preserve Manadonese. This research aimed to provide a data source to develop advanced linguistic-based technology. The methodology used for this research is extreme programming (XP). The result of this research is a Web-Based Application Of Indonesia-Manado Translation Forum with various functions, such as uploading Indonesian articles that will be translated into Manadonese, from this diverse amount of translation the best of it will be chosen to be processed every word and sentence that will be saved in the database. The application is tested with Acceptance Testing as an indicator and the result shows that the forum managed to accomplish the goal set up before.
Analisis Komparatif Algoritma Clustering Data Kinerja Anggaran Pemerintah Isnen Hadi Al Ghozali; Ibnu Afan; Triardani Lestari
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.v10i1.611.578-591

Abstract

The government's budget performance is a benchmark for the government's success in optimizing people's money to achieve national goals. Even though performance measurement has reached the Work Unit level, the data formed still do not have a specific grouping, in the sense of unstructured data. The purpose of this research is to find the best clustering algorithm for classifying budget performance data. The data used is budget performance data for 19,460 Indonesian Government Work Units. The data is sourced from the SMART application and the OM SPAN application. This research uses a comparative study approach for the K-Means algorithm, DBSCAN, and agglomerative hierarchical clustering (AHC). Evaluation of the clustering results formed using the Davies-Bouldin Index (DBI) method. The AHC algorithm with k = 6 achieved the lowest DBI value of 0.3583472. The DBI value for the DBSCAN algorithm with MinPts = 10 is 0.5398259. However, the AHC algorithm is not good in terms of ease of implementation. Therefore, the K-means algorithm with parameters k = 10 is the best alternative. The K-Means algorithm gets a DBI value of 1.052678. The K-Means algorithm produces 10 clusters. Based on knowledge extraction, it is determined that cluster 2 and cluster 5 are ideal clusters in terms of budget performance. While the clusters that require attention are cluster 1, cluster 3, cluster 4, and cluster 8.
Hybridization Model for Air Pollution Prediction Using Time Series Data Roni Yunis; Andri Andri; Djoni Djoni
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.v10i1.619.422-435

Abstract

In recent years, data science analysis, particularly time series predictions, has been widely employed across various industrial sectors. However, time series data presents high complexity, especially in seasonal patterns such as monthly, daily, or hourly fluctuations. Irregular fluctuations and external factors increasingly challenge accurate predictions. Therefore, this research proposes a hybrid approach combining SVR-SARIMA, SVR-Prophet, LSTM-SARIMA, and LSTM-Prophet to enhance time series prediction accuracy. This study followed the OSEMN methodology approach: gathering data, cleaning data, exploring data, developing models, and interpreting crucial aspects of problem-solving. Seasonal effect predictions indicated a rise in SO2 and NO2 during dry and rainy seasons until the next two years (average daily increments of 0.0831 μg/m3 for SO2 and 0.0516 μg/m3 for NO2). Estimates suggest a decrease in the order of three particles. The evaluation showed that the SVR model performed better compared to the other three models (RMSE 7.765, MAE 5.477, and MAPE 0.261). The best-performing hybrid model was LSTM-Prophet (99.74% accuracy) with RMSE 12.319, MAE 12.057, and MAPE 0.259 values.
Structural Equation Modeling in E-Commerce Application Users: Case Study of Shopee Ronny H. Walean; Douglass Rasuh; Cliford R. Ratulangi
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.v10i1.622.464-477

Abstract

In line with current developments, especially advances in technology that are increasingly advancing, it facilitates all community activities in buying and selling goods, with the existence of e-commerce now people no longer need to go directly to the store to make these transactions. One of the e-commerce applications that are widely used today is a platform for shopping on the internet called Shopee which is easy to use. This study aims to see what factors are included in the form of acceptance by users of e-commerce applications, especially at Shopee, using Davis' (1989) Technology Acceptance Model (TAM) methodology. In this study, the analysis was conducted on 220 respondents who often or have used the Shopee application using the SEM PLS data analysis tool and a quantitative approach with the SmartPLS application. The results demonstrate the pathway through which users' attitudes toward usage are shaped by their perceptions of usefulness and ease of use, subsequently influencing their behavioral intentions, and ultimately impacting their actual usage behavior on the e-commerce application, namely Shopee. This study comprehensively elucidates the interrelationship among each Technology Acceptance Model (TAM) variable examined. Overall, the application of TAM in investigating the use of Shopee is validated through the findings of this study
Enhancing Machine Learning Model Performance in Addressing Class Imbalance Lucky Lhaura Van FC; M. Khairul Anam; Muhammad Bambang Firdaus; Yogi Yunefri; Nadya Alinda Rahmi
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.v10i1.626.478-490

Abstract

This research aims to investigate methods for handling class imbalance in machine learning models, with a focus on the Support Vector Machine (SVM) algorithm. We apply oversampling (SMOTE) and undersampling techniques to a dataset with class imbalance and evaluate the performance of SVM using these methods. Experiments are conducted using data from Twitter social media regarding the 2024 general electionsThe findings indicate that incorporating SMOTE effectively enhances the performance of SVM models, particularly within the SVM Polynomial variant. However, the use of undersampling shows limited impact on improving SVM model performance. This study provides valuable insights for researchers and practitioners in choosing the appropriate strategy for handling class imbalance in machine learning models.
Pemanfaatan Algoritma Machine Learning dan Long-Short Term Memory untuk Prediksi Dini Diabetes Yuri Pamungkas; Meiliana Dwi Cahya; Endah Indriastuti
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.v10i1.630.491-506

Abstract

Diabetes, a chronic condition, affects numerous populations. Poor insulin production from the pancreas combined with high blood sugar levels can result in the onset of diabetes. Diabetes can be caused by numerous factors. Observe and prevent these factors to reduce the high prevalence of diabetes. This study concentrates on medical record data for determining diabetes risk factors via statistical correlation analysis. These factors will be utilized as machine learning and LSTM input parameters for diabetes prediction. The factors analyzed include blood glucose levels, HbA1c levels, age, BMI, hypertension, heart disease, smoking habits, and gender. Based on the research results, we found that glucose levels (>137 mg/dL) and HbA1c levels (>6.5%) are the main benchmarks in diagnosing diabetes. It is also supported by the correlation value, which is relatively high (0.42 and 0.40, respectively) compared to other factors. Increasing age and BMI also increase the risk of developing diabetes. Comorbidities (such as hypertension or heart disease) and smoking habits can worsen the condition of people with diabetes. Meanwhile (based on gender), women are more at risk of developing diabetes than men because their body mass index increases during the monthly cycle. Apart from that, there is a tendency for blood sugar levels in women to increase in the last two weeks before menstruation. Based on the prediction results, the highest levels of accuracy, sensitivity, and F1 score were obtained (96.97%, 99.97%, and 98.37%) using the LSTM method. This performance shows that LSTM is relatively good for the diabetes prediction process based on existing factors/parameters.
IT Management Shapes Marketing Using React Native at Gold Konveksi Yesri Elva; Sepsa Nur Rahman; Hezy Kurnia
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.v10i1.631.528-540

Abstract

Gold Konveksi is a small and medium-sized enterprise (SME) engaged in the garment industry that has not yet fully utilized technology in its marketing and product promotion. As a result, Gold Konveksi's market reach has been limited. This study aims to analyze the responses from questionnaires completed by respondents, with a specific focus on respondent profiles to generate brief demographic information. The objective of this study is to assess the readiness of both employees and customers of Gold Konveksi towards the adoption of new technology. The methods used in this research include data collection through questionnaires filled out by 247 respondents. The validity and reliability of the questionnaire instruments were tested using SPSS data processing. The validity test involved applying the r-value formula, compared to the r-table value, to determine validity. The reliability test was conducted using Cronbach's Alpha value compared to the reliability threshold. Additionally, the Technology Readiness Index (TRI) was calculated to measure user readiness in adopting new technology. The results of the study indicated a high level of readiness among users. The program interface was evaluated, and system testing was conducted using black box testing to ensure its functionality. Overall, the findings of the research show a high level of readiness among users in adopting new technology, which is expected to enhance transactions and sales data recording at Gold Konveksi.
Does Audit Software Adoption Matter? Evidence from Local CPA Firms in Indonesia Judith Sinaga
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.v10i1.635.507-517

Abstract

The integration of technology in the audit practice is widely used nowadays.  IT-based audits are applied since most of the accounting transactions are done computerized.  This paper aims to assess the adoption of audit software by local CPA Firms in Jakarta and Medan, Indonesia. This research used the descriptive method with the quantitative approach. The research was conducted at five local CPA firms in Jakarta and Medan, with a research sample of 63 auditors.  Data collections were done through questionnaires. Descriptive statistics, correlation, and regression were used to analyze data. The results of this study showed that perceived benefit and company readiness have positive and significant effects on the adoption of audit software.  Adoption risk has a negative and significant effect on the adoption of audit software, and external pressure has no significant effect on the adoption of audit software. This research provides added value to all local CPA firms and makes audits more efficient and effective.  It encourages all local firms to conduct the audit in a more advanced method through the use of audit software. This research corroborates the previous research in a different context (types of business and place).  It focused more on a partnership type of business.

Page 28 of 34 | Total Record : 336

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