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All Journal Jurnal Pekommas Jurnas Nasional Teknologi dan Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Technology and Its Utilization Widya Teknik
Wikarsa, Liza
Universitas Katolik De La Salle Manado
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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Applied E-Learning of Kolintang Musical Instruments Case Study: University of De La Salle Manado Liza Wikarsa; Debby Paseru; Vianry Teguh Pangemanan
Widya Teknik Vol. 15 No. 1 (2016)
Publisher : Fakultas Teknik, Universitas Katolik Widya Mandala Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33508/wt.v15i1.1195

Abstract

Kolintang (Indonesian’s xylophones) is popular nationwide as the traditional music instruments from Minahasa, a regency in North Sulawesi. It is usually played in ancestor worshipping rituals as it was believed that Kolintang had a close relationship with the traditional belief of North Sulawesi's natives and as their culture. Currently, there are several Kolintang applications developed that run either on Windows or Android operating system. They also use different types of controllers like a web camera, touch screen, and keyboard. Unfortunately, these applications are mostly intended for advanced users who have knowledge and skills in playing the Kolintang instruments. In addition, there remains a general lack of research on how to play the five instruments of Kolintang. Thus, this research will develop an e-learning application for the Kolintang musical instruments that are best suited the needs of novice and advanced users. An evaluation framework is developed to assess the quality and efficiency of learning objects of this application on the selected users by incorporating the Unified Theory of Acceptance and Use of Technology (UTAUT). Four criteria identified in this framework that are learning goal alignment, presentation design, interaction usability, and accessibility. The findings revealed that this e-learning application can help both novice and advance users to play the five instruments of Kolintang with ease and smoothness. The learning process can be done at the individual’s choice of pace and time. Key words: E-learning, Kolintang, Multimedia, North Sulawesi
Improving Coronary Heart Disease Detection Using K-Means Clustering Techniques Sanger, Junaidy; Wikarsa, Liza; Taulu, Angelica
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.107-117

Abstract

The heart is a crucial organ in the cardiovascular system, playing a key role in blood circulation and supplying oxygen and nutrients to the body. Cardiovascular diseases, particularly coronary heart disease (CHD), are the leading cause of death worldwide. In Indonesia, especially in North Sulawesi, the high prevalence of CHD is indicative of the effects of an unhealthy lifestyle. This study employs the K-Means clustering method to identify the early risk of CHD based on eight common symptoms, including chest pain, nausea, shortness of breath, heartburn, a history of hypertension, obesity, diabetes, and genetics. This innovative approach integrates these early warning signs and categorizes the risk into three groups: low CHD risk (C1), moderate CHD risk (C2), and high CHD risk (C3). The detection results are provided based on responses collected through a questionnaire within an application, aiming to raise awareness of CHD and encourage users to seek further health evaluations and adopt healthier lifestyles.
Improving News Text Classification Using a Hybrid C5.0-KNN Model Wikarsa, Liza; Ngenget, Algy; Tumewu , Andrew; Kalempouw , Miracle; Oley , Edgard
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11478

Abstract

In the digital era, the overwhelming volume of online news far exceeds readers’ ability to manually filter information, necessitating automated text classification. However, achieving high classification accuracy remains challenging, especially in low-resource languages like IndonesianThe C5.0 decision tree and K-Nearest Neighbors (KNN) offer complementary strengths but have not yet been jointly utilized for Indonesian news classification; therefore, this study proposes a hybrid C5.0–KNN model designed to enhance news classification performance. A dataset of 1.700 articles was collected from four Indonesian online news, namely CNN Indonesia, Okezone, Tribun Jakarta, and Tribun Jabar, covering five topical categories, namely economy/ekonomi, technology/teknologi, sport/olahraga, entertainment/hiburan, or life style/gaya hidup). The data underwent preprocessing and TF-IDF weighing before classification with the hybrid model. In this approach, C5.0 first generates interpretable decision rules, and KNN then refines borderline cases, combining rule-based and instance-based methods. The findings revealed that the hybrid model achieved a highest accuracy of 0.8847 (using 25% test data and k=5), outperforming standalone C5.0 (0.7426) and KNN (0.8735). Notably, it attained 100% recall for “sport/olahraga” and an F1-score of 0.89 for “entertainment/hiburan”. These results demonstrate the model’s novelty, efficiency, and strong potential for real-world news classification in low-resource language contexts, offering practical value for journalists, analysts, and media monitoring systems.
Co-Authors Debby Paseru Junaidy Budi Sanger Kairupan, Indah Kalempouw , Miracle Kembuan, Audreyvia Kurnia, Henrico Lengkey, Chrisdityra Loha, Christ Henry Michael George Sumampouw Ngenget, Algy Oley , Edgard Rumondor, Axl Marselino Sombouwadil , Ticoalu Steven Pandelaki Sumajouw, Karen Taulu, Angelica Thomas Ch. Suwanto Tore, Christofel Mario Tumewu , Andrew Vianry Teguh Pangemanan