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All Journal Jurnal Ilmiah Informatika Komputer Journal of Oceanography Jurnal Elemen Jurnal Ilmiah KOMPUTASI JURNAL PENDIDIKAN TAMBUSAI Jurnal Matematika UNAND Shihatuna : Jurnal Pengabdian Kesehatan Masyarakat Jurnal Ilmiah Swara MaNajemen (Swara Mahasiswa Manajemen) Jurnal Ilmu dan Inovasi Fisika Jurnal Penelitian Teknologi Informasi dan Sains Jurnal Imiah Pengabdian Pada Masyarakat (JIPM) Yarsi Dental Journal Jurnal Ragam Pengabdian Journal of Data Science Methods and Applications
Anisa Oktaviani
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Religion Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Classification of Depression Labels Among Adolescents Using TabNet Classifier Based on the Interaction of Social Media Addiction, Stress, and Anxiety Factors Diokta Redho Lastin; Anisa Oktaviani; Puji Zulaikasari
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 1 (2026): : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i1.4561

Abstract

Adolescent depression is an important mental health concern associated with psychological conditions and excessive digital media use. The extreme imbalance of depression labels in behavioral datasets can reduce the ability of classification models to recognize minority cases. This study aims to develop a TabNet-based deep learning model for classifying adolescent depression labels using social media addiction, stress, anxiety, and related behavioral features. The study used a secondary dataset consisting of 1,200 adolescent samples. Data preprocessing included categorical feature encoding, stratified training and testing data splitting, feature standardization, and the application of the Synthetic Minority Oversampling Technique (SMOTE) to the training data. The TabNet Classifier was trained using Cross-Entropy Loss and the Adam optimizer with a step-decay learning rate and early stopping mechanism. The experimental results showed an accuracy of 99.15%, precision of 98.32%, recall of 100%, F1-score of 99.15%, and ROC AUC of 1.0000, with optimal performance achieved at epoch 53. These findings indicate that TabNet can effectively learn psychological and digital behavioral patterns for adolescent depression label classification. The proposed approach provides a potential computational framework for data-driven mental health risk classification, although further validation using diverse empirical datasets is required.
Calendar Variation Time Series Analysis for Forecasting the Number of Domestic Passengers of Juanda International Airport Surabaya Putriaji Hendikawati; Anisa Oktaviani
Jurnal Matematika UNAND Vol. 15 No. 3 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.3.302-318.2026

Abstract

This study focuses on forecasting the number of domestic transportation passengers at Juanda International Airport using a time series model by including exogenous variables of calendar variation effects of Eid Al-Fitr and Eid Al-Adha holidays. Data of the number of domestic passengers at Juanda International Airport from the Statistics Indonesia (BDS) was analyzed for the period January 2014 to May 2024. The models used include Dummy Regression, Trend Regression, ARIMA, SARIMA, ARIMAX, and SARIMAX. The analysis results show that the ARIMAX model produces the most accurate prediction. Juanda Airport passenger data is optimal in the ARIMAX ([1,11],1,1) model with the exogenous variable Eid al-Fitr dummy which produces MAPE:7.49; RMSE: 41,133.26; and MAE: 34.119,06. The results of the analysis show that including exogenous variables in the form of calendar variations in the forecasting model can improve prediction accuracy.
Perbandingan Algoritma Naïve Bayes, Decision Tree, KNN, dan Random Forest Untuk Memprediksi Data Penduduk Penerima BPJS Di Lampung Timur Rachma Annisa W.P; Aprizal, Dede; Dewi, Riana Kristina; Ayuandita, Devi Sari; Oktaviani, Anisa; Azzahra, Marcella
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

This study aims to predict population data in Lampung Timur using various classification algorithms. The algorithms used include Naive Bayes, k-Nearest Neighbors (k-NN), Decision Tree, and Random Forest. The dataset used was derived from population data processed with RapidMiner. The data was processed using steps such as reading from Excel files, data duplication, and model training with the aforementioned algorithms. Evaluation results show that the Naive Bayes algorithm has the highest accuracy of 86.89% with good precision and recall for both BPJS and UMUM classes. Additional analysis indicates that from the dataset used, there are 1924 residents who have BPJS and 1960 residents who do not have BPJS. These results suggest that the Naive Bayes algorithm performs best in predicting population data in Lampung Timur and that there is still a significant number of residents who do not utilize BPJS services. Implementing this classification algorithm can aid in better decision-making regarding the distribution of BPJS services in Lampung Timur.
Family Support and Self-Agency in the Psychosocial Adaptation of Chronic Kidney Failure Patients Undergoing Hemodialysis: Literature Review Oktaviani, Anisa; Budhiana, Johan; Novianty, Lia
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus, Sustainable Development Goals (SDGs): Multidisciplinary Perspectiv
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/kmpfkv03

Abstract

Patients with chronic kidney disease (CKD) undergoing hemodialysis often face various physical and psychosocial problems that can affect their anxiety levels and quality of life. Important factors in helping patients adapt psychosocially to their condition and treatment are family support and self-efficacy, such as self-efficacy and self-care. Focusing on quality of life and anxiety levels, this study investigates the role of family support and self-efficacy in the psychosocial adaptation of CKD patients undergoing hemodialysis. This study uses a literature review of ten relevant scientific articles published between 2021 and 2025, which were analyzed descriptively and obtained from the Google Scholar, PubMed, ScienceDirect, and MDPI databases. Descriptive analysis was performed on selected articles. The results of the literature review showed that good family support, high levels of self-efficacy, and self-care abilities were associated with lower anxiety levels and better quality of life in CKD patients undergoing hemodialysis. These findings confirm that improving psychosocial adaptation can be achieved through the use of a holistic nursing approach that involves the family and strengthens the patient's self-agency
Co-Authors Agus Mulyana Anisa, Putri Mutiara Aprizal, Dede Aulia Ramadhani, Putri Zahra Aulia, Azfa Ayi Bahtiar Ayuandita, Devi Sari Azzahra, Marcella Budhiana, Johan Dewi, Riana Kristina Diokta Redho Lastin Ersi, Dewi Hamidah, Nilna Hawlatul Hidayah, Rizka Putri Hustinawati Hustinawati Lilik Maslukah M. Bachtiar Safrudin Marwah, Syifaul Maulani, Chaerita Milah, Hiarotul Mohammad Dadan Sundawan, Mohammad Dadan Muh. Yusuf Novianty, Lia Nurfianti NURHALIMAH Nurismalatri, Nurismalatri Puji Zulaikasari Putriaji Hendikawati Rachma Annisa W.P Saenih, Saenih Sahara, Cici Citria Salimah, Najwa Nur Qolbi Salsabil, Siti Aldora Surya Amami Pramuditya Yolce Oktiara