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All Journal Jupiter Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Resona : Jurnal Ilmiah Pengabdian Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JSiI (Jurnal Sistem Informasi) Journal of Information Systems and Informatics JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) The Journal of Indonesia Sustainable Development Planning (JISDeP) Jurnal Teknologi Informasi dan Komunikasi IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Ilmu Informasi, Perpustakaan, dan Kearsipan IJCOSIN : Indonesian Journal of Community Service and Innovation Journal of Vocational, Informatics and Computer Education Governance IT Adoption and Technology Advance
Rona Nisa Sofia Amriza
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences Other

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Segmentation of Adult Respondents’ Well-being Profiles Based on Daily Stress, Social Networks, Personal Resources, and Lifestyle Using Clustering Method Ajang Sopandi; Siti Ummi Masruroh; Neneng Tati Sumiati; Cindy Rahayu; Rona Nisa Sofia Amriza; Doni Febrian
The Journal of Indonesia Sustainable Development Planning Vol 7 No 1 (2026): April
Publisher : Pusbindiklatren Bappenas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46456/jisdep.v7i1.1071

Abstract

Existing literature on the topic of wellbeing mostly utilized scales and methods that are abstract and variable-centered yet assume homogeneity within the population being studied. This research utilizes a person-centered approach to classify the sample of 15,977 adults from a large-scale online survey about their wellbeing according to variables related to their stress, social networks, personal resources, and lifestyle. Factor analysis of mixed data (FAMD) is performed to reduce 22 variables of mixed types to 14 principal components that account for 77.51% of the variance in the data. Using these components, eight segments of well-being are classified by K-Means clustering and validated using Silhouette analysis. These segments range from those with low levels of stress, high levels of meditation, and clear goals for their lives to those with high levels of stress, no sense of accomplishment in their careers, and few social connections outside of work. Interestingly, another variable that was revealed as significantly different within each of the stress levels groups was the notion of whether or not the individual feels like they have enough money to cover their needs. Finally, the methods used in this research can be replicated to evaluate the wellbeing of the general population and to inform the creation of interventions to improve the lives of those with certain types of wellbeing profiles.
PEMBERDAYAAN PEREMPUAN DESA MELALUI PEMANFAATAN MEDIA SOSIAL: STUDI KASUS KADER PKK DAN WANI LEMPER DESA AMPELSARI Khairun Nisa Meiah Ngafidin; Sarah Astiti; Dwi Mustika Kusumawardani; Sisilia Thya Safitri; Rona Nisa Sofia Amriza; Sukmadiningtyas Sukmadiningtyas
RESONA : Jurnal Ilmiah Pengabdian Masyarakat Vol 10, No 1 (2026)
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah (LPPI) Universitas Muhammadiyah Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35906/resona.v10i1.2913

Abstract

Perkembangan teknologi digital dan platform media sosial menawarkan peluang transformatif bagi pemberdayaan sosial-ekonomi perempuan di wilayah pedesaan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk menjembatani kesenjangan digital (digital divide) dan meningkatkan kompetensi teknologi pada 30 orang kader Pemberdayaan Kesejahteraan Keluarga (PKK) dan komunitas WANI LEmPER di Desa Ampelsari, Kabupaten Kebumen. Pelaksanaan dilakukan melalui metode workshop dan pendampingan partisipatif pada 12 Maret 2026, mencakup materi literasi digital dasar, strategi branding, content planning, hingga praktik penyuntingan video menggunakan InShot. Berdasarkan analisis data kuesioner evaluasi, kegiatan ini terbukti efektif mengatasi hambatan psikologis (“gagap digital”) dengan 100% responden menyatakan kepuasan (Setuju/Sangat Setuju) terhadap kebermanfaatan pelatihan. Pendekatan Technology Acceptance Model (TAM) dan Resource-Based View (RBV) digunakan untuk menganalisis bagaimana kompetensi teknologi mengubah media sosial menjadi modal strategis tak berwujud. Hasil analisis menunjukkan bahwa intervensi literasi digital berhasil memfasilitasi pemberdayaan multidimensi (personal, sosial, dan ekonomi) serta membangun ketahanan wirausaha (entrepreneurial resilience). Rekomendasi tindak lanjut berfokus pada pelatihan public speaking dan penulisan jurnalistik guna memaksimalkan partisipasi UMKM perempuan dalam Social Commerce.  Abstract. The advancement of digital technology and social media platforms presents transformative opportunities for the socio-economic empowerment of women in rural areas. This community service initiative aims to bridge the digital divide and enhance the technological competencies of 30 cadres from the Family Welfare Empowerment (PKK) organization and the WANI LEmPER community in Ampelsari Village, Kebumen Regency. The program was implemented through workshops and participatory mentoring on March 12, 2026, covering topics ranging from fundamental digital literacy, branding strategies, and content planning, to practical video editing using the InShot application. Based on the analysis of evaluation questionnaire data, the intervention proved highly effective in overcoming psychological barriers (digital anxiety), with 100% of the respondents expressing satisfaction (Agree/Strongly Agree) regarding the utility of the training. The Technology Acceptance Model (TAM) and Resource-Based View (RBV) approaches were utilized to analyze how technological competencies transform social media utilization into an intangible strategic asset. The analytical findings indicate that the digital literacy intervention successfully facilitated multidimensional empowerment—encompassing personal, social, and economic dimensions—while fostering entrepreneurial resilience. Future recommendations focus on conducting advanced training in public speaking and journalistic writing to maximize the participation of women-led MSMEs within the Social Commerce landscape.
PCOS Classification Using Random Forest, Recursive Feature Elimination, and Explainable AI Syifa Ayu Salsabila Putri; Rona Nisa Sofia Amriza
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1603

Abstract

Ovary Syndrome (PCOS) is an endocrine-related condition predominantly affecting women during their childbearing years who experience delayed diagnosis due to the limitations of conventional methods that require laboratory tests and imaging procedures that are relatively costly and time-consuming. This study develops a PCOS classification model based on a clinical dataset of 541 patients with 42 clinical attributes using the random forest algorithm with Recursive Feature Elimination (RFE) feature selection and an Explainable AI (XAI) approach. The research pipeline comprised several sequential stages: problem identification, data collection, preprocessing, data splitting, feature selection, model training and testing, evaluation, and SHAP-based explainability analysis. Performance was evaluated using Accuracy, Precision, Recall, and F1-score, and compared between two models, namely RF+CF and RF+RFE, where RF+RFE was identified as the best-performing model. The XAI approach using SHAP (SHapley Additive exPlanations) was applied to identify and explain the contribution of clinical variables to the classification results. The best model, RF+RFE, achieved an accuracy of 92.66%, precision of 93.75%, recall of 83.33%, and F1-score of 88.24%, demonstrating superior performance compared to RF+CF. As this study relies on a single dataset, broader validation across multiple centers is recommended before clinical deployment. This model is intended as a screening-support approach and has not been validated as a clinical diagnostic tool. The findings are anticipated to serve as a foundation for building data-driven early screening tools and clinical decision-making support systems.
Implementation of Customer Relationship Management Based on Visitor Segmentation at the General Sudirman Museum to Support Digital Promotion: Penelitian ini mengimplementasikan sistem Customer Relationship Management berbasis segmentasi pengunjung pada Museum Panglima Besar TNI Jenderal Sudirman menggunakan metode K-Means Clustering dan platform Odoo CRM. Dari 50 data kuesioner dan 270 data Kaggle, diperoleh dua segmen pengunjung dengan karakteristik perilaku digital berbeda sebagai dasar strate Shahifa Sajadiyah; Marsya Valeria valemar; Bunga Ramadhani; Rona Nisa Sofia Amriza
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11237

Abstract

Museum Panglima Besar TNI Jenderal Sudirman is a historical tourism destination in Purwokerto, Central Java, that does not yet have a structured visitor data management system, resulting in limited visitor information being recorded. Based on museum reports from 2024 to 2025, recorded data only includes daily visitor counts, visit dates, adult and child categories, and ticket and parking revenue, with no demographic information available. This study implements a Customer Relationship Management system based on visitor segmentation to support digital promotion strategies. Visitor data was collected through questionnaires distributed to 50 museum visitors using purposive sampling. A public Kaggle dataset of 270 rows served as training data, while the 50 questionnaire responses served as testing data. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, yielding K=2 as optimal with a Silhouette Score of 0.2611. Clustering results identified two segments: Cluster 0 with 37 respondents dominated by visitors aged 15 to 30 years actively using TikTok and Instagram, and Cluster 1 with 13 respondents dominated by adults over 30 years primarily using Facebook and WhatsApp. Results were implemented into Odoo CRM as a structured visitor data management foundation. This study contributes by combining Odoo CRM implementation, K-Means Clustering, and digital promotion strategy recommendations for a historical museum in Indonesia.
Machine Learning-Based Multi-Class Scholarship Classification with a Streamlit Prototype Siti Nabila Ariza Fitri; Rona Nisa Sofia Amriza; M. Yoka Fathoni
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1673

Abstract

The scholarship selection process at a private university is still conducted manually by reviewing scholarship applicant documents individually, resulting in a time-consuming process and potential inconsistencies in evaluation. This study aims to develop a multi-class Machine Learning-based classification model to support scholarship classification based on recipient criteria and evaluating model performance using accuracy, precision, recall, F1-score, and confusion matrix metrics. This study applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework consisting of six stages Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. The dataset consists of 408 historical scholarship recipient records categorized into four scholarship classes. A key contribution of this study is the integration of the CRISP-DM methodology with feature importance analysis using Random Forest Feature Importances, class balancing using SMOTE, and machine learning classifiers, including Random Forest, SVM, and Naïve Bayes, within a unified predictive modelling framework. Based on the evaluation results, the Support Vector Machine algorithm achieved the best performance with an accuracy of 77% and a macro F1-score of 63%, followed by Random Forest at 76% and Naïve Bayes at 70%. The SVM model was then implemented as a Streamlit-based web to support scholarship recommendations and is not intended as a final scholarship approval system. This research contributes to the development of an efficient, data-driven scholarship classification support system for higher education institutions.
Machine Learning-Based Website for Student Psychological Assessment with Support Vector Machine and Rapid Application Development Rachmat Taufik; Rona Nisa Sofia Amriza; Aditya Dwi Putro Wicaksono
Journal of Vocational, Informatics and Computer Education Vol 4, No 3 (2026): September 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i3.890

Abstract

Purpose – This study aims to develop a web-based DCM assessment system that helps guidance and counseling teachers process student assessment data systematically and document follow-up needs.Methods – Rapid Application Development was used to create the system. Data were collected from 616 students using a 200-item Problem Checklist across seven domains. DCM scores and predicates were calculated using percentage-threshold rules. SVM was added as a response-pattern classification layer and compared with six other classifiers. The selected SVM model used an RBF kernel, C = 1.0, gamma = scale, one-vs-rest decision function, and probability output.Findings – Support Vector Machine achieved the best aggregate performance, with an accuracy of 0.8871, precision of 0.884993, recall of 0.887097, and F1-score of 0.883355. Usability testing produced a System Usability Scale score of 86.72, while User Acceptance Testing with three guidance and counseling teachers reached a 100% success rate.Research implications – The system combines deterministic DCM scoring and data-driven response-pattern classification to support counselor review of student assessment records and documented follow-up considerations. Because the evaluation used one school, imbalanced labels, and no actual predicate A samples, the model output should be used as a decision-support indicator and not as evidence of improved counseling outcomes, workload reduction, service acceleration, or prioritization accuracy.Originality – This study integrates rule-based DCM scoring, SVM-based response-pattern classification, and a web-based school counseling workflow in the Indonesian secondary school context.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Achmad Rizky Fambudianto Aditya Dwi Putro Wicaksono Ahmad Mukhlis Jundulloh Ajang Sopandi Artika Arista Bunga Ramadhani Chhoun Seachhing Cindy Rahayu Citra Wiguna Darmansah Dedy Agung Prabowo Didi Supriyadi Doni Febrian Dwi Januarita Dwi Januarita A.K Dwi Januarita Ardianing Kusuma Dwi Mustika Kusumawardani, Dwi Mustika Fadhilla, Cut Alna Fitriana, Rian Gunawan, Chichi Rizka Intan Azizah Intan Azizah Jonatan Maruli Butarbutar Khairun Nisa Meiah Ngafidin Logiandani Logiandani M Yoka Fathoni M. Yoka Fathoni Marsya Valeria valemar Maulana Wildan Rifaldi Mega Aprilia Fajriati Muhamad Nur Gunawan Nafdik Zaydan Raushanfikar Neneng Tati Sumiati Nurlaila, Ayu Annisa Nurul Fitriani Rachmat Taufik Raihan Zidane Ramadhan Safhira Nanda Rahmadhani Sarah Astiti S.Kom., M.MT Shahifa Sajadiyah Sisilia Thya Safitri Siti Nabila Ariza Fitri Siti Nur Amalia Siti Ummi Masruroh Siti Ummi Masruroh Sukmadiningtyas Sukmadiningtyas Sukmadiningtyas Syifa Ayu Salsabila Putri Tanzil Azim Titi Farhanah Toni Anwar Wijiasih, Tsania Maulidia Wiwit Ratnasari Wiwit Ratnasari Yogo Dwi Prasetyo