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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

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Tata Kelola Teknologi Informasi Menggunakan Framework COBIT 2019 Pada PT. Kobexindo Tractor Tbk. Lumingkewas, Cherry; Mambu, Joe Yuan; Tangka, George Morris William
Techno.Com Vol. 23 No. 4 (2024): November 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i4.11708

Abstract

Tata kelola Teknologi Informasi (TI) adalah kerangka kerja yang digunakan organisasi untuk mengelola dan mengendalikan sumber daya TI secara efektif, dengan tujuan memaksimalkan nilai bisnis. PT. Kobexindo Tractor TBK menghadapi tantangan dalam mengoptimalkan manajemen TI mereka, khususnya dalam pengelolaan data penting. Penelitian ini bertujuan untuk menerapkan kerangka COBIT 2019 guna meningkatkan tata kelola TI di PT. Kobexindo Tractor TBK, sehingga layanan data kritis dapat dikelola secara efisien. Temuan menunjukkan bahwa perusahaan belum melakukan penilaian menyeluruh terhadap tata kelola TI dan menghadapi tantangan dalam manajemen data pesanan dan informasi stok. PT. Kobexindo Tractor TBK memerlukan proses tata kelola TI yang lebih terstruktur untuk memastikan penggunaan teknologi yang efektif dan efisien dalam mencapai tujuan bisnis. Proses tinjauan pustaka mengidentifikasi kesenjangan dalam penelitian saat ini, memberikan dasar untuk penelitian yang lebih komprehensif. Setelah analisis tujuan BAI05 dan BAI11, ditemukan hasil yang berbeda. Pada BAI05, pertanyaan mencapai level 2 dengan 75%, di bawah 85%. Demikian juga, pada BAI11, pertanyaan mencapai level 2 dengan 66%. Dengan temuan ini, penelitian ini didukung landasan teoretis yang kuat, memastikan analisis yang mendalam terhadap masalah tata kelola TI.   Kata kunci: Tata Kelola Teknologi Informasi, COBIT 2019, Manjemen Informasi
Desain UI/UX Aplikasi Unklab Mobile Student Dengan Metode Design Thinking Biya, Antares Nathan Andrew; Kolibu, Mario Jonatan; Mambu, Joe Yuan; Tangka, George Morris William
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 10 No 2 (2024): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v10i2.3362

Abstract

The Unklab Mobile Student application is an essential platform for Universitas Klabat students to access various academic services. However, the app currently suffers from shortcomings in features and design, leading to user dissatisfaction. This research employs a Design Thinking approach to redesign the application to be more responsive, intuitive, and visually appealing. Through a series of research methods, including interviews, questionnaires, observation, and data analysis, we gain a comprehensive understanding of user needs and existing problems. The analysis results are then used to formulate improved solutions, which include the development of efficient search features, a more intuitive layout, and enhanced design responsiveness. The resulting app prototype incorporates 17 features and is subjected to usability testing. The results demonstrate a significant increase in user satisfaction, with a high success rate of 60 in each tested task, indicating positive outcomes. This confirms the effectiveness of the Design Thinking approach in designing an application that better meets user needs.
Designing an Intuitive UI/UX for Laundry and Household Cleaning Services Using a User-Centered Design Thinking Approach Tangka, George Morris William; Mambu, Joe Yuan; Putra, Edson Yahuda
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6443

Abstract

The growing demand for footwear and accessory care reflects an increasing awareness of cleanliness and personal appearance. This study focuses on a cleaning service business specializing in shoes, bags, and strollers, with shoes as the primary focus. Operational challenges, including inefficient customer communication and delays due to high demand, impact customer satisfaction. Using the Design Thinking method, this research develops a user-centered User Interface (UI) and User Experience (UX) for the business's application. The Design Thinking process—empathize, define, ideate, prototype, and test—helped identify pain points and generate tailored solutions, such as improved navigation and task flow. Usability testing involved 18 participants performing key tasks, including booking services and tracking orders, with success rates and error metrics as evaluation criteria. The testing yielded a 70.6% task completion success rate, indicating improved service efficiency. However, the 54.4% misclick rate, higher than typical benchmarks for similar applications (30–40%), highlights significant navigation challenges. Future iterations will focus on refining the interface layout and enhancing task clarity to reduce errors and improve usability. These findings emphasize the value of iterative, user-centered design in addressing operational inefficiencies and enhancing the customer experience.
Optimizing IT Governance in BTS.id: A COBIT 2019-Based Analysis of Design Factors Tangka, George Morris William; Lompoliu, Erienika
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1997

Abstract

Effective IT governance is critical for organizations to align technology with business objectives while ensuring risk management, compliance, and operational efficiency. As a technology-driven company, BTS.id faces challenges in managing IT risks, optimizing governance structures, and ensuring seamless alignment between IT initiatives and business strategy. This study analyzes IT governance implementation at BTS.id using the COBIT 2019 framework, focusing on assessing the organization's governance maturity level and identifying key design factors that influence IT governance effectiveness. The research employs document analysis, interviews, and surveys with key stakeholders to evaluate governance and management objectives, design factors, and capability levels. The findings indicate that while BTS.id has implemented IT governance practices, gaps remain in achieving an optimal governance structure. The highest priority areas include structured IT change management (BAI07), enterprise architecture (APO03), and project management (BAI11, BAI02), while risk management (APO12) and performance monitoring (MEA01) play a crucial supporting role. However, lower-priority governance objectives highlight areas for improvement, particularly in security management, vendor relationships, and compliance monitoring. The study underscores the importance of a structured approach to IT governance, emphasizing continuous performance monitoring, enhanced risk management, and strategic IT alignment.
FORECASTING HEALTH INSURANCE PAYER INCOME: A COMPARATIVE ANALYSIS OF DECISION TREE AND SVR ALGORITHMS Mokodaser, Wilsen Grivin; Soewignyo, Tonny Irianto; Tangka, George Morris William; Soewignyo, Fanny
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2466.493 KB) | DOI: 10.34288/jri.v7i3.369

Abstract

An insurance company is a type of non-bank financial institution that protects clients from risks and collects premiums over a certain period, these facts provide an overview of the insurance business and highlight its role in the economy, this study evaluated the performance difference between the Decision Tree Regressor and Support Vector Regression (SVR) in predicting insurance payer income. The Decision Tree model demonstrated strong predictive accuracy, achieving a Mean Absolute Error (MAE) of approximately 57 million and an R-squared (R²) value of 0.896, meaning it could explain around 89.6% of the variance in the data. Additionally, the model maintained high consistency, as evidenced by 5-fold cross-validation scores ranging from 0.908 to 0.967, indicating strong generalization and low risk of overfitting. In contrast, the SVR model significantly underperformed. It recorded a much higher MAE of over 237 million and a large Mean Squared Error (MSE), reflecting substantial deviations from the actual values. Its R² score of -0.299 suggests that SVR performed worse than a naive mean predictor, failing to identify meaningful patterns. This poor performance was consistent across all cross-validation folds, which also produced negative R² scores. The SVR model’s inadequacy is likely due to the large scale of the income data and the lack of proper preprocessing, such as normalization, or parameter tuning. Overall, these findings clearly demonstrate that the Decision Tree Regressor is a more suitable, accurate, and stable model for predicting insurance payer income.
Analysis of User Satisfaction Level of Google Application Classroom Using the ECUS Method Putra, Edson Yahuda; Lahamendu, Irene Gloria; Ngangk, Stivia Yuliefri Lulij; Adam, Stenly Ibrahim; Tangka, George Morris William
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Despite wide adoption during the COVID‑19 pandemic, Google Classroom’s long‑term acceptance in Indonesian higher education remains under‑examined. This study measures end‑user satisfaction using the five‑factor End‑User Computing Satisfaction (EUCS) framework. A cross‑sectional survey captured 247 valid responses from undergraduate students at Universitas Klabat who had used Google Classroom for at least one semester. Twenty Likert‑scaled items (4 per EUCS dimension) were adapted from Doll & Torkzadeh (1988) and checked for reliability (Cronbach’s α) and validity. Multiple‑linear regression assessed the partial effect of each EUCS factor on overall satisfaction, while descriptive statistics profiled satisfaction levels. Four dimensions—Content (β = 0.299, p < 0.001), Ease of Use (β = 0.268), Format (β = 0.182), and Timeliness (β = 0.222)—significantly predict satisfaction (Adj. R² = 0.682). Accuracy (β = 0.009, p = 0.841) is non‑significant, likely due to low internal consistency (α = 0.429). Overall, 69.6 % of respondents report being satisfied or very satisfied with Google Classroom. Content richness, intuitive interface, presentation quality, and timely feedback drive student satisfaction, whereas perceived accuracy warrants instrument refinement. Findings inform LMS developers and university decision‑makers on prioritised enhancement areas.
Classification of Indonesian Undergraduate Students’ Awareness Level of Phishing Attacks using Decision Tree Algorithm Tangka, George Morris William; Putra, Edson Yahuda
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7859

Abstract

Phishing remains a dominant cyber-crime vector in higher-education settings, yet most Indonesian campus studies stop at descriptive awareness surveys. This study sets out (i) to build a fully interpretable predictive model that can classify students’ phishing-awareness levels from a concise questionnaire and (ii) to demonstrate how the model’s rules can be mapped to established behavioural theory for targeted educational intervention. Guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM), we transformed a ten-item phishing-awareness instrument into a 153 × 10 binary matrix drawn from 153 undergraduate responses (82 male; 71 female) and analysed the data with a cost-complexity–pruned Classification-and-Regression Tree (CART). The optimal tree (depth = 5, 19 leaves) achieved 94.9 % accuracy, 93.4 % recall, 95.8 % precision, and a 0.971 ROC-AUC under stratified 10-fold cross-validation—metrics comparable to ensemble methods but obtained with a glass-box structure that exposes explicit IF-THEN rules. The three most salient splits—URL-domain mismatch, urgency cues, and misconceptions about the HTTPS lock icon—directly align with Protection Motivation Theory constructs, providing actionable targets for micro-learning modules. Because the dataset originates from a single campus and governance prerequisites (fairness audit, GDPR impact assessment, SOP alignment) are pending, the model will run in “shadow mode” next term to collect longitudinal evidence and monitor concept drift. Overall, the findings show that concise, theory-grounded instruments combined with pruned decision trees can achieve high predictive power and immediate pedagogical value without sacrificing transparency.
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