Article Per Year (5 Year)
p-Index From 2021 - 2026
2.994
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Teika CogITo Smart Journal Jurnal Riset Informatika Jurnal Informasi dan Teknologi Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Global South Review KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Jikom: Jurnal Informatika dan Komputer Servitium Smart Journal CSRID
George Morris William Tangka
Universitas Klabat
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : CogITo Smart Journal

 1 
Tata Kelola Teknologi Informasi Menggunakan Framework COBIT 2019 Pada Perusahaan PT. Pelindo TPK Bitung George Morris William Tangka; Erienika Lompoliu
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.v9i2.577.355-367

Abstract

Tata kelola teknologi informasi (TI) merupakan aspek yang sangat krusial bagi perusahaan dalam mengelola aset TI yang dimilikinya. Penelitian ini menyelidiki implementasi COBIT 2019 di PT. Pelindo TPK Bitung, sebuah perusahaan logistik dan penyimpanan, dengan tujuan meningkatkan tata kelola Teknologi Informasi (TI) perusahaannya. Menghadapi tantangan dalam manajemen TI, perusahaan mengadopsi COBIT 2019 untuk meningkatkan efisiensi operasional, mengurangi risiko keamanan, dan memastikan kepatuhan regulasi. Studi ini menggunakan pendekatan terstruktur dengan memanfaatkan COBIT 2019 Design Toolkit, dan melibatkan tinjauan literatur, wawancara, serta evaluasi sistematis terhadap tata kelola TI PT. Pelindo TPK Bitung. Temuan dari wawancara pertama menyoroti fokus perusahaan pada pertumbuhan, inovasi, dan peran TI yang strategis, sekaligus tantangan dalam mengintegrasikan TI dan operasional. Sasaran utama tata kelola, yaitu DSS05 - Managed Security Services, mencapai tingkat kemampuan 3, menunjukkan keberhasilan yang signifikan, meskipun terdapat celah pada aspek keamanan yang memerlukan peninjauan kebijakan segera. Sebagai kesimpulan, implementasi COBIT 2019 di PT. Pelindo TPK Bitung telah mencapai kesuksesan yang signifikan, mencapai tingkat kemampuan 3, dengan saran untuk menjaga dan meningkatkan manajemen keamanan. Penelitian ini memberikan wawasan tentang implementasi COBIT 2019, memberikan pemahaman menyeluruh tentang dampaknya pada tata kelola TI dalam konteks organisasi yang spesifik.
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.
Connecting Tutors and Students: A Mobile Application Designed with Design Thinking Mambu, Joe Yuan; Lakat, Junior; Tangka, George Morris William
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.v10i2.814.533-547

Abstract

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.
Sentiment Analysis and Topic Detection on Post-Pandemic Healthcare Challenges: A Comparative Study of Twitter Data in the US and Indonesia Tangka, George Morris William; Chrisanti, Ibrena Reghuella; Waworundeng, Jacquline; Maringka, Raissa Camilla; Sandag, Green Arther
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.v10i2.819.561-579

Abstract

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.
MRI Image Analysis for Alzheimer’s Disease Detection Using Transfer Learning: VGGNet vs. EfficientNet Sandag, Green Arther; Djamal, Eleonora; Tangka, George Morris William; Taju, Semmy Wellem
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.v10i2.836.580-592

Abstract

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.
Co-Authors Biya, Antares Nathan Andrew Cherry Lumingkewas Chrisanti, Ibrena Reghuella Delviolin, Ellie Ophelia Djamal, Eleonora Edson Yahuda Putra Erienika Meiling Lompoliu Fanny Soewignyo Green Arther Sandag Izaac, Bryan Vallentino Jacquline Waworundeng Joe Yuan Mambu JUNIOR LAKAT Kolibu, Mario Jonatan Lahamendu, Irene Gloria Lidya Chitra Laoh Lumingkewas, Edwin Maringka, Raissa Ngangk, Stivia Yuliefri Lulij Ramadhana, Rizky Semmy Wellem Taju Sondakh, Debby Erce Stenly Ibrahim Adam Stenly Richard Pungus Tonny Irianto Soewignyo Wilsen Grivin Mokodaser