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All Journal IKRA-ITH ABDIMAS Indonesian Journal of Data and Science Jurnal Pengabdian kepada Masyarakat Bina Darma International Journal of Artificial Intelligence in Medical Issues Journal Digital Technology Trend
Rezania Agramanisti Azdy
Universitas Bina Darma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Other

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Depression Risk Prediction Among Teenagers Using Explainable Machine Learning and Imbalanced Behavioral Data Rudi Setiawan; Effan Najwaini; Rezania Agramanisti Azdy; Rasmiati Rasyid
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v4i1.436

Abstract

Adolescent depression has become an important public health concern, particularly in relation to increasing digital media exposure, lifestyle changes, and psychosocial pressure. This study proposes an explainable machine learning framework for predicting depression risk among teenagers using social media usage, lifestyle behavior, and psychosocial indicators. The dataset consisted of 1,200 records with 13 variables, including age, gender, daily social media hours, platform usage, sleep hours, screen time before sleep, academic performance, physical activity, social interaction level, stress level, anxiety level, addiction level, and depression label. The target variable was highly imbalanced, with 1,169 samples categorized as non-depression and only 31 samples categorized as depression risk. Several machine learning models were evaluated, including Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting. The experiments compared two feature settings, namely behavioral-only features and full features, combined with three imbalance handling strategies: no imbalance treatment, class weighting, and SMOTE. Model performance was evaluated using accuracy, precision, recall, F1-score, balanced accuracy, ROC-AUC, PR-AUC, Cohen’s Kappa, MAE, and RMSE. The results showed that the full-feature setting substantially outperformed the behavioral-only setting. The best performance was achieved by Random Forest using full features without imbalance handling, producing perfect classification results with accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC of 1.0000. Permutation importance analysis identified sleep hours, stress level, anxiety level, and daily social media hours as the most influential predictors. These findings indicate that teenage depression risk in this dataset is strongly associated with sleep behavior and psychosocial conditions, in addition to social media exposure. Although the model achieved excellent performance, the result should be interpreted cautiously due to the small number of positive depression-risk samples and the possibility of highly separable label patterns. Therefore, the proposed approach should be positioned as an early risk screening framework rather than a clinical diagnostic tool
Akselerasi Daya Saing IKM Pengrajin Kebung Thikai Melalui Diversifikasi Produk dan Penguatan Brand Identity Berbasis Digital Marketing Febrianty Febrianty; Lestari Wuryanti; Rezania Agramanisti Azdy; Galih Iman Pramujati; Faiz Akram Ahmad Naufal
IKRA-ITH ABDIMAS Vol. 9 No. 3 (2025): Jurnal IKRAITH-ABDIMAS Vol 9 No 3 November 2025
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

IKM Pengrajin “Kebung Thikai” merupakan salah satu pelaku industri kreatif di Kabupaten Tanggamus yang berfokus pada produksi kain tenun khas Lampung. Dalam pengembangannya, IKM ini masih menghadapi sejumlah kendala, antara lain keterbatasan peralatan produksi yang sudah tidak efisien, kurangnya inovasi desain yang hanya terpusat pada perlengkapan adat, serta belum optimalnya pemanfaatan strategi pemasaran digital. Program pengabdian ini bertujuan untuk mempercepat peningkatan daya saing mitra melalui diversifikasi produk dan penguatan brand identity yang didukung oleh penerapan digital marketing. Kegiatan dilaksanakan selama delapan bulan dengan pendekatan pelatihan dan pendampingan langsung yang mencakup pelatihan teknologi produksi, pengembangan desain produk turunan (tas, vest, dan outerwear), serta perancangan identitas merek yang konsisten dan representatif. Hasil kegiatan menunjukkan peningkatan signifikan pada aspek produksi dengan capaian 84,83% melalui diversifikasi produk serta peningkatan kemampuan pemasaran digital sebesar 85%. Program ini berhasil mentransformasi citra visual IKM Pengrajin Kebung Thikai, memperkuat keunggulan kompetitif berbasis budaya lokal, dan meningkatkan efisiensi operasional melalui penerapan teknologi yang tepat guna.
Development of a Decision Tree Classifier for Breast Cancer Diagnosis Using Fine Needle Aspirate Data Agus Halid; I Gusti Ngurah Wikranta Arsa; Rezania Agramanisti Azdy; Agus Aan Jiwa Permana
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.202

Abstract

Breast cancer is one of the leading causes of mortality among women globally, necessitating early and accurate detection to improve survival rates. This study leverages machine learning to develop a decision tree classifier for distinguishing between benign and malignant breast masses using the Kaggle Breast Cancer FNA dataset. The dataset underwent rigorous pre-processing, including the removal of irrelevant columns, data cleaning, label encoding, and feature scaling. The model was evaluated using 5-fold cross-validation, achieving an average accuracy of 84.0%, with a test set accuracy of 83.72%. Performance metrics such as precision, recall, and F1-score further validated the model's robustness, with an overall accuracy of 90.24% on the test set. The decision tree classifier demonstrated high interpretability, making it a practical tool for aiding clinical decision-making. While the results are promising, the study highlights opportunities for improvement, including the use of ensemble methods and larger datasets to enhance generalizability. This research contributes to the growing body of evidence supporting machine learning applications in medical diagnostics, particularly in breast cancer detection.
Mengajarkan Literasi dan Numerasi melalui Pembuatan Poster Berita Anti-Hoax Menggunakan Canva Tiara Berliani; Leon A. Abdillah; Indra Gunawan; Putri Septiani; Febby Shania Utami; Anggieta Fatia Sari; Rezania Agramanisti Azdy; Yeni Ernawati
Jurnal Pengabdian kepada Masyarakat Bina Darma Vol. 5 No. 1 (2025): Jurnal Pengabdian kepada Masyarakat Universitas Bina Darma
Publisher : DRPM-UBD

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/2mz1vj71

Abstract

Kemajuan teknologi dan informasi yang sangat pesat membawa dampak yang besar dalam peradaban manusia. Kemajuan Digitalisasi ini memberikan pengaruh yang postif dan juga negatif terutama dalam berita dan informasi salah satu contohnya adalah berita hoax. Artikel jurnal ini akan memberikan pengajaran literasi dan numerasi kepada peserta didik dalam menyikapi berita hoax dalam sebuah karya berupa poster. Metode yang dilakukan dalam kegiatan pengabdian kepada masyarakat ini adalah partisifatif, diskusi, dan praktek langsung dalam pembuatan poters anti berita hoax. Dalam membuat poster siswa diberikan arahan dalam penggunaan aplikasi canva sebagai platform pembuatan poster dan siswa diberikan kebebasan dalam mengekpresikan imajinasi mereka dalam membuat poster berita anti hoax. Dampak dari pengajaran pembuatan poster anti hoax ini meningkatkan kreativitas dan daya pikir peserta didik dalam mendesain suatu poster selain itu, peserta didik jiuga mendapatkan pengetahuan bagaimana menyikapi berita hoax dalam situasi kemajuan teknologi dan informasi sekarang ini.
Perbandingan Kinerja Algoritma Decision Tree dan Naive Bayes dalam Klasifikasi Risiko Diabetes Menggunakan Dataset Pima Indians Diabetes Berdasarkan Nilai Akurasi dan Confusion Matrix Destry Widiani Nurul Ummi; Alex Wijaya; Rezania Agramanisti Azdy
Journal Digital Technology Trend Vol. 5 No. 2 (2026): December
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jdtt.v5i2.473

Abstract

This study compares the performance of Decision Tree and Naive Bayes algorithms in classifying diabetes mellitus risk using the Pima Indians Diabetes Dataset (768 female patient medical records, 8 predictor attributes) without additional optimization techniques. The methodology includes data preprocessing, data splitting (80% training and 20% testing ratio), model construction, and evaluation based on accuracy, confusion matrix, and classification report, reinforced by Stratified 5-Fold Cross Validation. Hold-Out Validation results show that Decision Tree achieved an accuracy of 83.77%, while Naive Bayes achieved 76.62%. In Stratified 5-Fold Cross Validation, Decision Tree recorded an average accuracy of 85.56% (std: 0.0339), whereas Naive Bayes achieved 75.77% (std: 0.0492). Confusion matrix and classification report evaluations further confirm the superiority of Decision Tree across precision, recall, and f1-score metrics. In conclusion, the Decision Tree algorithm delivers superior and more stable performance compared to Naive Bayes in diabetes risk classification.
Implementasi TF-IDF dan Cosine Similarity pada Sistem Manajemen Arsip Berbasis Web di Polrestabes Palembang Seto Prasetyo; Edi Surya Negara; Rezania Agramanisti Azdy
Journal Digital Technology Trend Vol. 5 No. 2 (2026): December
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jdtt.v5i2.480

Abstract

The advancement of information technology has driven significant transformation in archive management through web-based systems designed to enhance document retrieval efficiency. These systems employ Term Frequency-Inverse Document Frequency (TF-IDF) weighting and Cosine Similarity to produce relevance-based search results and automated recommendations. However, system effectiveness depends not only on algorithmic sophistication but also on the user experience perceived by archive officers and unit leaders. This study aims to evaluate the user experience of a web-based archive management system implementing TF-IDF and Cosine Similarity at Polrestabes Palembang. The research employs a qualitative descriptive approach grounded in user experience principles across the dimensions of usability, engagement, and satisfaction. Data collection was conducted through task success observation and probing interviews supported by a Smileyometer and a simplified adaptation of the System Usability Scale (SUS). The findings revealed that although prior technical testing demonstrated high retrieval accuracy (Precision 91.30%, Recall 88.45%, and F1-Score 89.85%), this user experience evaluation identified several interface improvement needs, particularly regarding navigation clarity, terminology localization, and search result presentation.  
Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment I Gede Iwan Sudipa; Rezania Agramanisti Azdy; Ika Arfiani; Nicodemus Mardanus Setiohardjo; Sumiyatun
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.125

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for fruit classification and quality assessment, aiming to enhance agricultural practices through machine learning. Employing a comprehensive dataset that encapsulates various fruit attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality, the research leverages a 5-fold cross-validation method to ensure the reliability and generalizability of the KNN model's performance. The findings reveal that the KNN algorithm demonstrates high accuracy, precision, recall, and F1-Score across all metrics, indicating its efficacy in classifying fruits and predicting their quality accurately. These results not only validate the algorithm's potential in agricultural applications but also align with existing research on machine learning's capability to tackle complex classification problems. The study's discussions extend to the practical implications of implementing a KNN-based model in the agricultural sector, highlighting the possibility of revolutionizing quality control and inventory management processes. Moreover, the research contributes to the field by confirming the hypothesis regarding the effectiveness of KNN in agricultural settings and lays the foundation for future explorations that could integrate multiple machine learning techniques for enhanced outcomes. Recommendations for subsequent studies include expanding the dataset and exploring algorithmic synergies, aiming to further the advancements in agricultural technology and machine learning applications.
Co-Authors Agus Aan Jiwa Permana Agus Halid Alex Wijaya Anggieta Fatia Sari Destry Widiani Nurul Ummi Edi Surya Negara Effan Najwaini Faiz Akram Ahmad Naufal Febby Shania Utami Febrianty, Febrianty Galih Iman Pramujati I Gede Iwan Sudipa I Gusti Ngurah Wikranta Arsa Ika Arfiani Indra Gunawan Leon Andretti Abdillah Lestari Wuryanti Nicodemus Mardanus Setiohardjo Putri Septiani Rasmiati Rasyid Setiawan, Rudi Seto Prasetyo Sumiyatun Tiara Berliani Yeni Ernawati