qdal88 mahjong ways 2 mahjong ways 2 slot anti rungkad slot gacor gacor4d slotgacor4d situs slot online slot gacor 2026 qdal88 sakuratoto2 totoagung gacor4d toto slot slotgacor4d slotgacor4d login gacor4d qdal88 situs toto sakuratoto2 https://journal.sttcipasung.ac.id/index.php/CTP <p><strong>Cipasung Techno Pesantren: Scientific Journal</strong> is a collection of scientific articles which are resulted from community services, original research, or literature reviews in the fields of engineering, technology, and applied sciences. For details about the <strong>Focus and Scope</strong> of this journal, please <a href="https://journal.sttcipasung.ac.id/index.php/CTP/focusandscopes"><strong>click here</strong></a>.</p> <p><strong>Cipasung Techno Pesantren: Scientific Journal</strong> is published twice a year by the<strong> Institute for Research and Community Services (LPPM)</strong> of<strong> Sekolah Tinggi Teknologi Cipasung</strong>. <span style="font-weight: 400;">The journal has been registered at the Indonesian ISSN National Center with <strong>p-ISSN 1978-0842 </strong>and <strong>e-ISSN 2963-1114</strong>.</span></p> <p>The article published in the <strong>Cipasung Techno Pesantren: Scientific Journal</strong> is an original manuscript that has never been published before. All articles to be published in the <strong>Cipasung Techno Pesantren: Scientific Journal</strong> must follow the <a href="https://drive.google.com/drive/u/5/folders/1PUn0xGPcC1RItDRR38gcWCSNZbNkkulf" target="_blank" rel="noopener">Template</a> and guidelines for writing and publication that the journal manager has determined. All the submitted articles will be checked for plagiarism using Turnitin and then reviewed by the <strong>Double-Blind Review Process</strong>.</p> LPPM Sekolah Tinggi Teknologi Cipasung en-US Cipasung Techno Pesantren: Scientific Journal 1978-0842 https://journal.sttcipasung.ac.id/index.php/CTP/article/view/104 <p><em>One of the global health problems today is diabetes, the prevalence of which continues to increase and therefore requires an effective method for its classification. The purpose of this study is the implementation of Orange Data Mining in the classification of diabetes using the Decision Tree method. The selection of these specifications is due to the fact that the resulting model is easy to understand and can be interpreted. The data analyzed were taken from a public diabetes dataset that includes various health attributes. The analysis process was carried out through preprocessing, splitting, and Juvenile Decision Tree model training. The results showed that the Decision Tree model achieved an accuracy of up to 85% with adequate sensitivity and specificity. decision. Therefore, the conclusion of the study is that increasing the accuracy and quality of diabetes classification can be achieved by the Decision Tree method in Orange Data Mining.</em></p> FAJAR FAJAR WIDIANTO Copyright (c) 2025 FAJAR FAJAR WIDIANTO https://creativecommons.org/licenses/by/4.0 2025-11-20 2025-11-20 19 2 https://journal.sttcipasung.ac.id/index.php/CTP/article/view/116 <p><em>The development of digital technology has brought significant changes to communication patterns, including the dissemination of Islamic da’wah. Social media, particularly Facebook, has become a strategic medium for distributing religious messages. This study aims to analyze da’wah content disseminated through the Facebook platform and to evaluate the forms, characteristics, and effectiveness of digital da’wah messages. Using a qualitative approach with content analysis methods, this research examines several popular da’wah accounts on Facebook over a three-month period. The findings indicate that digital da’wah on Facebook employs short narratives, appealing visuals, and thematic approaches relevant to contemporary issues. Although digital da’wah expands audience reach, challenges related to content validity and public response remain key concerns. This study recommends strengthening digital da’wah literacy to enhance the effectiveness of Islamic da’wah in the digital era.</em></p> ANDI SULANJANI Copyright (c) 2025 ANDI SULANJANI https://creativecommons.org/licenses/by/4.0 2025-11-20 2025-11-20 19 2 https://journal.sttcipasung.ac.id/index.php/CTP/article/view/105 <p><em><span style="font-weight: 400;">The transition from the Fourth Industrial Revolution (Industry 4.0) to Society 5.0 has significantly shifted the skills demanded in the labor market, urging Indonesian workers to adapt to more relevant digital competencies. This study aims to develop an optimized machine learning model to map the skills gap between job seekers and the demands of the digital labor market. Clustering using the K-Means algorithm was applied to group applicants based on demographic profiles and skills, followed by an analysis of skill gaps in each cluster. The results identified two primary clusters: experienced applicants needing reskilling and younger applicants requiring upskilling. Training recommendations were formulated based on the most in-demand skills not widely possessed by applicants, such as JavaScript, Django, and UI/UX. These findings serve as a foundation for formulating more precise, adaptive, and data-driven digital human capital development policies.</span></em></p> Resta Maolina Maora Copyright (c) 2025 Resta Maolina Maora https://creativecommons.org/licenses/by/4.0 2025-11-20 2025-11-20 19 2 https://journal.sttcipasung.ac.id/index.php/CTP/article/view/109 <p><em>Divorce is one of the social issues that has an impact on life, from adults to children. Divorce is influenced by many factors, ranging from economics, incompatibility, to the availability of technology. Therefore, appropriate efforts are needed to reduce </em><em>the divorce rate. One approach that can be used is clustering using the BIRCH </em>(<em>Balanced Iterative Reducing and Clustering Using Hierarcic</em><em>s</em>)<em>&nbsp;</em><em>algorithm based on divorce factors across all provinces in Indonesia. The study utilised 10 clusters, ranging from cluster 0 to 9. The research method employed was KDD. Data proc</em><em>essing was conducted using Google Colab with the Python programming language. Based on the clustering results, different dominant factors were identified for each cluster. Cluster 2 has the most members, with 6 provinces, where the causes of divorce are ab</em><em>andonment by the spouse and gambling. This study produced a system for applying the BIRCH algorithm to divorce data, which can serve as a reference for the government to reduce divorce rates based on triggering factors in each region</em>.</p> Adittia Fathah Copyright (c) 2025 Adittia Fathah https://creativecommons.org/licenses/by/4.0 2025-11-20 2025-11-20 19 2