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All Journal Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) CogITo Smart Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Pengabdian Kepada Masyarakat (Mediteg) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics KLIK: Kajian Ilmiah Informatika dan Komputer Servitium Smart Journal
Sondakh, Debby Erce
Fakultas Ilmu Komputer, Universitas Klabat
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Social Sciences

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Meningkatkan Literasi AI dan Kesadaran Etika Digital melalui Edukasi Interaktif bagi Pelajar Sekolah Menengah Atas Pungus, Stenly Richard; Sondakh, Debby Erce; Liem, Andrew Tanny; Adam, Stenly Ibrahim; Mambu, Joe Yuan Yulian; Tombeng, Marchel Timothy
Servitium Smart Journal Vol 3 No 2 (2025): Servitium Smart Journal
Publisher : Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/servitium.v4i1.38

Abstract

This Community Service Program was conducted at SMA Negeri 2 Kota Bitung with the aim of enhancing students’ literacy on the basic concepts of Artificial Intelligence (AI), while also fostering awareness regarding ethical and privacy issues in the use of AI-driven technologies in educational settings. The approach employed was an interactive workshop consisting of AI introduction, discussions on digital ethics and privacy, and practical training in designing effective and responsible AI prompts. A total of 27 eleventh-grade students from various academic tracks actively participated in this program. The results of pre-tests and post-tests indicated a significant improvement in students’ understanding of how AI works and its ethical implications. Furthermore, students demonstrated high enthusiasm in crafting contextually relevant prompts for learning purposes. This initiative shows that integrating AI education with a contextual and reflective learning approach can be an effective strategy to develop a generation of students who are not only technologically literate but also ethically conscious in their digital behavior. The program is recommended for replication in other schools as part of a broader effort to strengthen national digital literacy
Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm Sondakh, Debby Erce; Maringka, Raissa C; Ayorbaba, Ferlien P; Mangi, Joanne S. C. B. T.; Pungus, Stenly Richard
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1721

Abstract

As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.
Co-Authors Alan Hubert Frederick Sanger Andreuw Vandy Lengkong Andrew Tanny Liem Anggreiny Claudia Anjelita Ferensca Kaminang Arwin E. T. Pangaila Ayorbaba, Ferlien P Dipta Divakara Pius Purwadaria Edson Yahuda Putra Erwin Janpermadi Favian Joshua Mulumbot George Morris William Tangka Janto Motulo, Janto Jian Kezia Tesalonica Yuune Jimmy H Moedjahedy Joe Yuan Mambu Julio Kolopitawondal, Julio Lidya Chitra Laoh Mambu, Joe Yuan Yulian Mangi, Joanne S. C. B. T. Marchel Thimoty Tombeng Maringka, Raissa Michelle G. Tene Oktoverano Lengkong Prizilia Runtukahu Renaldy Richard Lawongan Reymon Rotikan Reyvan Sabbathinno Solis Rinny Cherill Rantung Rizky Saroinsong Rolly Junius Lontaan Sahulata, Reynoldus Semmy Wellem Taju Stenly Ibrahim Adam Stenly Richard Pungus Stephanie Runtuwene Syalom Gabriela Wagey Tombeng, Marchel Timothy