Article Per Year (5 Year)
p-Index From 2021 - 2026
1.683
P-Index
This Author published in this journals
All Journal Elektron Jurnal Ilmiah Jurnal Momentum JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INVOTEK: Jurnal Inovasi Vokasional dan Teknologi INOVTEK Polbeng - Seri Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi dan Pendidikan Jurnal Tekinkom (Teknik Informasi dan Komputer) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Testing dan Implementasi Sistem Informasi
Indri Rahmayuni
Politeknik Negeri Padang
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
An Expert System for Early Detection of Mental Health Conditions Using Certainty Factor and DASS-42 Indri Rahmayuni; Yance Sonatha; Tsalsabila Jilhan Haura; Fazrol Rozi
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2214

Abstract

Mental health problems such as depression, anxiety, and stress continue to increase in many countries, while access to professional services is still limited. Many digital screening systems use fixed scoring methods and do not consider uncertainty in user responses. This study developed a web-based expert system by combining the Depression Anxiety Stress Scales (DASS-42) and the Certainty Factor (CF) method to represent uncertainty in overlapping emotional symptoms and provide more flexible screening results. The knowledge base was prepared through consultation with a licensed clinical psychologist and converted into 42 production rules based on the DASS-42 items. Each rule was assigned a confidence value according to expert judgment. The system uses forward chaining to combine active rules and calculate confidence scores for depression, anxiety, and stress at the same time. System evaluation was conducted using 50 community cases aged 18–35 years and compared with independent expert assessment. The overall accuracy reached 86% (43 of 50 cases). The accuracy for each category was 88.2% for depression, 82.3% for anxiety, and 87.5% for stress. Most classification errors occurred between anxiety and stress, which may be related to overlapping symptoms in the DASS-42 instrument. The findings indicate that the proposed system can support early mental health screening through interpretable confidence-based results. However, this study used a limited dataset and only one expert in knowledge development. The system is intended as a screening support tool and not as a replacement for clinical diagnosis.
Customer Churn Prediction in Waste Banks Using XGBoost and SMOTE Indri Rahmayuni; Rahmi Putri Kurnia; Yance Sonatha; Yulherniwati Yulherniwati; Afcha Arel Pratama
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7811

Abstract

Customer retention has become an important challenge in waste bank programs because declining member participation may reduce operational sustainability and weaken community-based waste management initiatives. However, churn prediction studies in non-commercial environmental programs such as waste banks remain limited. This study proposes a machine learning approach for customer churn prediction using operational transaction data from a waste bank managed by the Environmental Agency of Padang City, Indonesia. The dataset consisted of 34,188 transaction records representing 1,015 members collected between May 2024 and April 2026. Customer behavioral features were constructed from transaction history indicators, while class imbalance was handled using SMOTE and churn classification was performed using XGBoost under a leakage-aware customer-level train-test separation, ensuring a realistic evaluation on previously unseen members. Experimental results showed that the proposed model achieved an accuracy of 0.84, an F1-score of 0.73, and a ROC AUC value of 0.898. Feature analysis revealed that recency and transaction frequency were among the strongest predictors of churn behavior. The findings demonstrate the potential of machine learning to support participation monitoring and sustainability management in community-based waste bank systems.  
Co-Authors - Yulherniwati Afcha Arel Pratama Alde Alanda, Alde Aldo Erianda Andres Saputra Cilvia Chairunnisa Dede Rahman Defni Defni Deni Satria Deny Kurniawan Fazrol Rozi, Fazrol Hidra Amnur Humaira, Humaira Ikhsan Yusda Iswandi Saputra Junaldi Junaldi Lastri Fatika Sari Maitsa Nabila Meri Azmi Rahmi Putri Kurnia Rasyidah, - Rika Idmayanti Syukria Fiki Tsalsabila Jilhan Haura Yance Sonatha Yasri Aldi Yogi Febrian Wibawa