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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Sintesis Tasyri` : Jurnal Tarbiyah-Syari`ah-Islamiyah Joyful Learning Journal JURNAL PEMBELAJARAN DAN MATEMATIKA SIGMA (JPMS) Jurnal Bio-Geo Material Dan Energi Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Digital Business and Innovation Management Indonesian Journal of Humanities and Social Sciences Journal Basic Education Skills
Safitri, Anita
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Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Model Ensemble untuk Prediksi Risiko Diabetes dengan Pertimbangan Efisiensi Biaya Qosimah, Rofiatul; Dhenabayu, Riska; Kautsar, Achmad; Safitri, Anita
JOM Vol 6 No 4 (2025): Indonesian Journal of Humanities and Social Sciences , December
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/ijhass.v6i4.8505

Abstract

This study addresses the growing global burden of diabetes by evaluating whether ensemble-based machine learning models can support reliable and cost-efficient early risk prediction. Moving beyond accuracy-centered evaluation, the study integrates cost-sensitive threshold optimization and probability calibration to enhance clinical relevance. Random Forest and XGBoost are evaluated using two datasets with contrasting population characteristics. Model performance is examined in terms of discriminative ability, calibration quality, and total misclassification cost. The results indicate that while XGBoost remains competitive on small-scale datasets, Random Forest provides more stable calibration and more consistent cost efficiency. These findings suggest that cost-sensitive and calibrated ensemble approaches have the potential to support more rational and economically efficient diabetes screening policies.  
STRATEGI KOMUNIKASI IKATAN MAHASISWA MINANG (IMAMI) UNIVERSITAS RIAU DALAM MEMPERTAHANKAN EKSISTENSI KESENIAN RANDAI MINANGKABAU DI UNIVERSITAS RIAU Safitri, Anita; Salam, Noor Efni
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Vol 12: Edisi II Juli - Desember 2025
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Riau

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Abstract

The Minangkabau randai art form is a cultural heritage that is currently experiencing decline due to westernization. Amidst the challenges of modernization, the Minangkabau Student Association (IMAMI) of Riau University strives to maintain the existence of randai art through a communication strategy by making IMAMI a forum for Minangkabau students to learn about Minangkabau culture. The purpose of this study is to examine how IMAMI's choice of communicators, audiences, and delivery media are used, and how reactions emerge to IMAMI's communication strategy. This study uses a descriptive qualitative approach. Informants were selected using purposive sampling, with a total of six informants, namely the chairperson of IMAMI, the head of the arts division of IMAMI, randai arts trainers and dancers, IMAMI alumni, and the general public (UNRI students). Data collection in this study was conducted through interviews, observation, and documentation. To ensure data validity, the researcher used source and method triangulation. The results of this study show that: 1) IMAMI selects communicators who understand randai art and participate in randai art performances so that they not only act as messengers, but also become representatives of Minangkabau culture. 2) Minang students at the University of Riau are the main target audience for introducing randai art. Determining this audience is important for the success of the communication strategy. 3) Informative, persuasive, and educational messages are used to attract the interest of students at the University of Riau. 4) Utilizing social media platforms such as Instagram and TikTok to disseminate information and communicate directly, for example by holding live performances. 5) The response generated by the audience through the enthusiasm of students for randai art, as well as the emergence of pride in Minangkabau culture. Keywords: Communication Strategy, IMAMI, Minangkabau Randai art
DIGITAL PAWN SERVICE AND FINANCIAL LITERACY: THE MEDIATING ROLE OF IT-ENABLED Auji, Shonifah Dzikro; Safitri, Anita
SIBATIK JOURNAL: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Vol. 5 No. 2 (2026)
Publisher : Penerbit Lafadz Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/sibatik.v5i2.4404

Abstract

Indonesia faces a financial inclusion-literacy paradox, with 80% inclusion versus 66% literacy per 2025 OJK SNLIK data, amid rapid FinTech growth including digital pawn services for micro-entrepreneurs. This study aims to examine financial literacy's mediating role between digital pawn service adoption and financial inclusion outcomes in emerging markets. Employing Systematic Literature Review (SLR) methodology per PRISMA 2020 guidelines, the population comprises peer-reviewed articles, proceedings, and grey literature (2021-2025) from Google Scholar, Scopus, and Web of Science, focusing on Indonesia/Southeast Asia. From initial searches, 45 studies were included after title/abstract screening, full-text review, and quality appraisal using quantitative/qualitative checklists. Data extraction covered study characteristics, findings, and effect sizes; thematic analysis and narrative synthesis via directed content analysis assessed mediation pathways. Results reveal financial literacy significantly mediates this relationship (indirect effect β=0.30, p<0.001 via SEM-PLS), amplifying direct adoption benefits like cost reduction (15-25%) and 31-38% higher account ownership, though trust deficits and regulatory gaps persist. In conclusion, policymakers must integrate literacy modules into platforms for sustainable inclusion, prioritizing OJK-led partnerships.
Optimasi Ambang Batas Sensitif Biaya untuk Pemeliharaan Prediktif Menggunakan CSTOLR, CSTORF, dan CSTOXGB: Cost-Sensitive Threshold Optimization for Predictive Maintenance Using CSTOLR, CSTORF, and CSTOXGB Rahman, Abi Rafdi Hasbur; Dhenabayu, Riska; Kautsar, Achmad; Safitri, Anita
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 4 (2026): MALCOM October 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i4.2846

Abstract

Ketidakseimbangan kelas dalam Predictive Maintenance (PdM) dapat menyebabkan model Machine Learning bias terhadap kelas mayoritas sehingga kegagalan mesin tidak terdeteksi. Risiko meningkat karena False Negative (FN) menimbulkan kerugian lebih besar dibandingkan False Positive (FP). Penelitian ini mengevaluasi integrasi Cost-Sensitive Learning (CS) dan Threshold Optimization (TO) untuk meminimalkan Total Misclassification Cost (TMC) pada berbagai rasio biaya FN terhadap FP. CS menerapkan pembobotan biaya selama pelatihan, sedangkan TO menentukan ambang keputusan optimal berdasarkan data validasi. Logistic Regression (LR), Random Forest (RF), dan Extreme Gradient Boosting (XGBoost) dikembangkan menjadi CSTOLR, CSTORF, dan CSTOXGB melalui kombinasi kedua pendekatan tersebut. Eksperimen menggunakan dataset sintetis AI4I 2020 Predictive Maintenance dengan rasio biaya 5:1, 10:1, 20:1, dan 50:1. Kinerja dibandingkan pada model tanpa pembobotan biaya, dengan pembobotan biaya, serta kombinasi pembobotan dan optimasi ambang batas. Hasil menunjukkan integrasi CS dan TO konsisten menurunkan TMC pada ketiga model. CSTOXGB menghasilkan TMC terendah sebesar 41 pada rasio 10:1, dengan ambang optimal 0,38 dan satu FN, atau turun 51,2% dibandingkan XGBoost dasar. Model ini juga mencatat TMC terendah atau setara pada skenario. CSTOLR mengurangi FN secara signifikan, tetapi menghasilkan FP tinggi. Temuan ini menunjukkan efektivitas integrasi CS dan TO dalam menangani klasifikasi PdM dengan biaya kesalahan asimetris.
SHAP-Interpretable Cross-Platform Machine Learning for Classifying High-Performing Influencers Using Ratio-Based Engagement Metrics Hakim, Muchammad Luqmanul; Dhenabayu, Riska; Arifah, Ika Diyah Candra; Safitri, Anita
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 4 (2026): MALCOM October 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i4.2887

Abstract

This study aims to develop and evaluate a Shapley Additive exPlanations (SHAP)-interpretable cross-platform machine learning framework for classifying high-performing influencer segments on TikTok and YouTube using ratio-based engagement metrics. The study addresses limitations of follower or subscriber-based evaluation by constructing a platform-stratified performance label and applying leakage-controlled feature selection to reduce circular prediction bias. A total of 2,000 influencer records, consisting of 1,000 TikTok and 1,000 YouTube influencers, were analyzed using Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Model performance was evaluated using accuracy, macro precision, macro recall, macro F1-score, and Receiver Operating Characteristic–Area Under the Curve (ROC-AUC), while SHAP was used to interpret feature contributions. The tuned CatBoost model produced the highest observed performance, with 0.9925 accuracy, 0.9904 macro F1-score, and 0.9995 ROC-AUC. Additional validation showed stable model ranking across alternative thresholds, while proxy leakage ablation and cross-platform transfer indicated that results should be interpreted as internal classification consistency rather than full platform-independent generalization. SHAP analysis showed that ratio-based engagement variables, particularly like ratio and engagement rate, contributed more strongly than raw audience size. The findings demonstrate that influencer performance can be represented as an interpretable
Co-Authors Ahmad Marzuki Akbar, Muhammad Farhan Amaliyah, Putri Arifah, Ika Diyah Candra Arrosyad, M. Iqbal Auji, Shonifah Dzikro Aulia, Putri Faza Budhiraja, Irsyad Efni Noor Salam Elistantia, Eliga Emma Marsella Febbyanti, Adellyna Cantica Hakim, Muchammad Luqmanul Kautsar, Achmad M. Mukhid Mashuri Maulidana, Royhan Aditya Mubarok, Achmat Muhtarom, H.Ali Nasir, A. Muhajir Novita, Lidya pengelola, pengelola Pinanda, Lula Purnomo, Marcello Hamasias Dwi Greatajoy Qosimah, Rofiatul Rahman, Abi Rafdi Hasbur Riska Dhenabayu Sembiring, Sugihana Syamsuriyawati, Syamsuriyawati Zahroh, Syifaul Aini