Article Per Year (5 Year)
p-Index From 2021 - 2026
4.801
P-Index
This Author published in this journals
All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Transformatika Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Bakti Cendana: Jurnal Pengabdian Masyarakat INSERT: Information System and Emerging Technology Journal Jurnal Dialektika Informatika (Detika) HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal Of Information And Technology Unimor (JITU) Transformasi Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat J-Icon : Jurnal Komputer dan Informatika Jurnal Pengabdian Masyarakat Bangsa Jurnal Inovatif Wira Wacana Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Genitri: Jurnal Pengabdian Masyarakat Bidang Kesehatan Jurnal Indonesia : Manajemen Informatika dan Komunikasi International Journal of Educational Technology and Society Jurnal Sistem Informasi (JASISFO) Global Science: Journal of Information Technology and Computer Science
Tiwuk Widiastuti
Universitas Nusa Cendana
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
Utilizing Digital Technology in Church Youth Counseling: The "Teman Baomong" Platform Reaches the Unreached in the GMIT Classis in East Kupang City Tiwuk Widiastuti; Adriana Fanggidae; Yuliyanto T. Polly; D.M Sihotang; N.D Rumlaklak; Marselino K.P. Abdi Keraf
JURNAL TEPAT : Teknologi Terapan untuk Pengabdian Masyarakat Vol 8 No 2 (2025): Collaboration for Accelerated Community Achievement
Publisher : Faculty of Engineering UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jurnal_tepat.v8i2.623

Abstract

Adolescent mental health in East Nusa Tenggara Province has become an urgent concern due to increasing cases of emotional mental disorders and limited access to psychological services. To address this issue, the community service team partnered with the Klasis GMIT East Kupang City to develop the Teman Baomong Digital Platform as a web-based mental health consultation service for approximately 12,000 youth under the Klasis. This program aims to improve mental health literacy and access to psychological support that is safe, affordable, and stigma-free through online consultations with psychologists, religious leaders, and peer counselors, as well as the provision of educational content. The implementation methods included: (1) development of the Teman Baomong platform, (2) training of 35 peer counselors, (3) mental health education for 121 adolescents, and (4) provision of individual online consultation services. Evaluation was conducted through pre- and post-tests and a User Acceptance Test (UAT) to assess user acceptance of the platform. The results showed a significant increase in adolescent mental health literacy by 39.8% and enhanced involvement of peer counselors in community assistance. The UAT results from 50 respondents indicated an average score of 94%, demonstrating that the platform is perceived as highly effective and feasible for use. The highest-rated aspect was ease of use (98%), while the lowest was access speed (83.6%) due to internet network limitations in several congregation areas. In conclusion, the implementation of the Teman Baomong platform has effectively improved access to mental health services and literacy among adolescents, and it has strong potential for sustainable development within church and community support ecosystems in East Nusa Tenggara.
Interpretable Feature Interaction Mining in High-Dimensional Clinical Data Using Hybrid Tree–Neural Models Tiwuk Widiastuti; Berlien Richard; Manjaruni Maryo Indra
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 1 (2026): March: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i1.182

Abstract

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance. In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems
Integrating Semantic Computing and Predictive Analytics to Enhance Reliability and Scalability of Global Information Systems Agus Wantoro; Adhie Thyo Priandika; Tiwuk Widiastuti; Yulaikha Mar’atullatifah; Krisna Widi Nugraha; Dwi Utari Iswavigra
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.196

Abstract

Global information systems (GIS) are essential for managing large scale data across industries such as healthcare, finance, and urban planning. As the volume and complexity of data continue to grow, there is an increasing need for systems that can handle these demands while maintaining reliability and scalability. This research explores the integration of semantic computing and predictive analytics as a solution to improve the performance of GIS. Semantic computing, through the use of ontologies and standardized data models, enhances data interoperability, allowing systems to interpret and exchange data meaningfully across diverse platforms. On the other hand, predictive analytics uses statistical methods and machine learning models to forecast system behavior and optimize resource allocation, ensuring systems remain adaptive under varying loads. By integrating these two methodologies, this study demonstrates how they can address key challenges in global information systems, such as fault tolerance, system adaptability, and real time decision making. The results show significant improvements in system reliability and scalability, as well as better performance under high data volumes and diverse user interactions. The integrated approach was tested in several use cases, including urban planning, healthcare, and supply chain management, with results indicating that systems utilizing both semantic computing and predictive analytics are more resilient, accurate, and efficient. This paper discusses the practical implications of this integration for global scale applications and suggests future research directions, including the incorporation of emerging technologies like blockchain and artificial intelligence to further enhance the capabilities of GIS.
Co-Authors Adi Sebastianus Molla Adriana Fanggidae Adriana Fanggidae Agus Setyobudi Agus Wantoro Ahmad Taufik Ardean Raflian Arfan Y Mauko Baun, Diandra Berlien Richard Bertha S. Djahi Bertha Selviana Djahi Bertha Selviana Djahi Bertha Veronika Da Silva Pinto Bloemhard, Putri E D.M Sihotang Derwin R Sina Derwin R Sina Derwin Rony Sina, Derwin Dewantoro Lase Djahi, Bertha S. Djahi, Bertha Selviana Dumanauw, Yesaya Evanmarch Dwi C Djahilape Dwi Utari Iswavigra Emerensye S. Y. Pandie Emerensye Sofia Yublina Pandie Emerensye Sofia Yublina Pandie febby, jurgan Fios, Ignasius Kristoforus Siuk Firman Pratama Hanna Florenci Tapikap Immanuel K P Rini Inggrid Raga Djara Juan Rizky Mannuel Ledoh Kabosu, Maria Inansintia Elvira Kornelis Letelay Krisna Widi Nugraha Lehot, Fransisco Ronaldo Lestari, Ayu Triyuni Lete, Patrisius Remby Lobo, Franklin Anugrah Steveinson Mage, Marnon Yolinda Chrisma Manjaruni Maryo Indra Maria Louise Ludgardis Muku Marnon C. Y Mage Marselino K.P. Abdi Keraf Marylin S. Junias Meiton Boru Meiton Boru Meiton Boru Metkono, Denni Irvanto Missa, Wanto I Mola, Sebastian Adi Santoso Mola, Sebastianus Adi Santosa Mustakim Sahdan N.D Rumlaklak Naatonis, Djohan Rudolf Andriano Nabuasa, Yelly Yosiana Nelci D Rumlaklak Nelci Dessy Rumlaklak Nelcy Rumlaklak Ngefak, Videl Richard Nita Novita Non, Erwin T. W. Nunes, Ingratcia Pa, Bernard Jose Adrian Junio Ajilo Priandika, Adhie Thyo Ratu, Nalfayo Christian Romy O. D. Djami Rumlaklak, Nelci D. Rumlaklak, Nelci Dessy Safitri, Aisyah Rizki Sani, Michelle Sarinah Basri K Sebastianus A S Mola Sebastianus Adi Santoso Mola Sihotang, Dony Martinus Sina, Derwin R. Sintha Lisa Purimahua Suhada, Dimas Tabelak, Dion Stekiko Melfin Tarus, Karen N.V Tas'au, Emilia Thimothy Ariel Masangin Tokan, Diana Inda Carmilla Triyanto Umanailo, Ali Umasangadji, Fachry Muhammad yelly y nabuasa Yoshua Patriot Thundericco Yulaikha Mar’atullatifah Yulianto Triwahyuadi Polly Yuliyanto T. Polly