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All Journal ELKHA : Jurnal Teknik Elektro Indonesian Journal on Software Engineering (IJSE) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Engagement: Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Paramita, Cinantya
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

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Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti; Ahmed, Foez
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5207

Abstract

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity.
Development of E-Catalog Design as a Promotional Medium to Support Digital Transformation and Professional Certification for Batik Communities Amalia, Amalia; Paramita, Cinantya; Izzhati, Dwi Nurul; Tjahyono, Rudi; Syamwil, Rodia; Febrian, Nanda Dwi; Nugroho, Adi
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 2 (2025): November 2025
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v9i2.2116

Abstract

The Indonesian government is encouraging the digitalization of public procurement to strengthen the use of domestic products and services, including competency certification. The LSP Batik is a professional certification institution that assesses the competencies of batik artisans and communities. They face challenges due to limited digitalization and restricted access to certification information. This project aims to develop an e-catalog design for the certification schemes and to enhance the understanding of digital procurement. A Participatory Design approach was adopted, actively engaging both end-users and key stakeholders across all phases of the co-creation and evaluation process. The project culminated in the development of an e-catalog integrated with the government's e-procurement portal, designed to modernize the services of LSP Batik. The design evaluation yielded high suitability scores for illustration and contrast (100%), followed by the completeness of information (88%), and branding and clarity of information (75%). Furthermore, user assessment indicated a high degree of comprehension in product search functionality (100%), while understanding of product comparison (63%) and showcase development (50%) was moderate, suggesting areas for future improvement.
Co-Authors Abu Salam Adhitya Nugraha Adi Nugroho Ahmed, Foez Amalia Amalia Andreas Wilson Setiawan Astuti, Yani Parti Catur Supriyanto Dwi Eko Waluyo Dwi Nurul Izzhati Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Fauzi Adi Rafrastara Febrian, Nanda Dwi Finoe, Albireo Musyaffa Guruh Fajar Shidik Hayu Wikan Kinasih Hussein, Jasim Nadheer Junta Zeniarja Kencana, Lisdi Inu Manaf, Humam Wajdi Muljono Muljono Mulyanto, Edy Nohan, Rajendra Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Pergiwati, Dewi Pradana, Naufal Arsyaputra Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Rafsanjani, Muhammad Ivan Rahadian, Arief Ramadhan Rakhmat Sani Rodia Syamwil Saputra, Filmada Ocky Savicevic, Anamarija Jurcev Shier Nee Saw Simbolon, Calvin Samuel Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja Syamsiar, Syamsiar Tanjung, Mohamad Aprilian Tasleem, Faathir El Tjahyono, Rudi Triono, Justin Matthew Widi Utomo, Emanuel Pinesthi Wildanil Ghozi ‘Aini, Fariida Qurrota