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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Informatika (JUTIF) Unnes Journal of Mathematics Jurnal Abdi Negeri
Sanusi, Ratna Nur Mustika
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Optimization Artificial Neural Network (ANN) Models with Adam Optimizer to Improve Customer Satisfaction Business Banking Prediction Ifriza, Yahya Nur; Mandaya, Yusuf Wisnu; Sanusi, Ratna Nur Mustika; Febriyanto, Hendra; Jabbar, Abdul; Kamaruddin, Azlina
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4776

Abstract

Customer satisfaction prediction is critical for business banking to retain clients and optimize services, yet existing models struggle with imbalanced data and suboptimal convergence. Traditional approaches lack adaptive learning mechanisms, limiting accuracy in real-world applications. This study developed an optimized Artificial Neural Network (ANN) model using the Adam algorithm to improve prediction accuracy for banking customer satisfaction. We trained an ANN on the Santander Customer Satisfaction Dataset (76,019 entries, 371 features) with Adam optimization. Preprocessing included normalization, removal of quasi-constant features, and an 80-20 train-test split. Adam’s adaptive learning rates and momentum were leveraged to address gradient instability. The model achieved 95.82% accuracy, 99.99% precision, 95.83% recall, a 97.87% F1-score, and 0.82 AUC, outperforming traditional optimizers like SGD. Training loss reduced by 30% with faster convergence. This work demonstrates Adam’s efficacy in handling imbalanced banking data, providing a scalable framework for customer analytics. The results advance computer science applications in fintech by integrating adaptive optimization with deep learning for high-stakes decision-making. This research contributes to the growing body of knowledge in machine learning applications for business analytics and provides a valuable framework for improving customer satisfaction prediction models in various industries and the advancement of deep learning applications in business intelligence, particularly in banking service quality prediction.
Regional Prioritization for Free Nutritious Food Programs through Social Data Integration and Public Sentiment Analysis Using K-Means and NLP Sanusi, Ratna Nur Mustika; Wijaya, Galih Kusuma; Harwanti, Nur Achmey Selgi
Unnes Journal of Mathematics Vol. 14 No. 1 (2025): Unnes Journal of Mathematics Volume 1, 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.25750

Abstract

This study evaluates Indonesia's Free Nutritious Food Program (MBG) through an innovative dual-method approach combining geospatial clustering and sentiment analysis. Cluster analysis of 38 provinces identified three distinct priority zones: high-priority (Eastern Indonesia), medium-priority (Central Indonesia), and low-priority (Java-Bali-West Sumatra), revealing significant regional disparities. Parallel sentiment analysis of 1,358 social media posts showed 76.6% negative perceptions dominated by food safety concerns ("poisoning," "toxic"), contrasting with 23.4% positive feedback highlighting nutritional benefits. The study makes three key contributions: First, it demonstrates the disconnect between regional needs and implementation quality. Second, it introduces an integrated monitoring framework combining cluster mapping with real-time sentiment tracking. Third, it proposes actionable solutions including a rapid-response task force and targeted communication strategies. These findings provide policymakers with evidence-based tools to simultaneously address geographical inequities and improve program execution in nutrition interventions.
A Data Mining Approach to Wage Inequality Analysis in Indonesia: A Clustering Study Using Fuzzy C-Means Harwanti, Nur Achmey Selgi; Hendikawati, Putriaji; Sanusi, Ratna Nur Mustika; Pratama, Alfian Adi
Unnes Journal of Mathematics Vol. 13 No. 2 (2024): Unnes Journal of Mathematics Volume 2, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.25755

Abstract

This study aims to cluster Indonesian provinces based on the average wage structure of workers across 17 economic sectors using the Fuzzy C-Means (FCM) method. The wage data underwent preprocessing steps including missing value imputation using the median, logarithmic transformation to reduce skewness, and Z-Score standardization to ensure uniform data scaling. The evaluation of the number of clusters and fuzziness values was conducted using the Silhouette coefficient and Fuzzy Partition Coefficient (FPC), with the best results achieved at three clusters and a fuzziness value of 1.3. Further analysis using Principal Component Analysis (PCA) provided visualization of the clusters, while radar charts illustrated wage characteristics by sector within each cluster. The clustering results reveal significant economic disparities among provinces: Cluster 1 consists of provinces with the highest wages dominated by high-value-added sectors such as mining and finance; Cluster 0 shows a balanced wage distribution reflecting a transitional economy; and Cluster 2 includes provinces with the lowest wages facing structural challenges. These findings offer a comprehensive overview of regional economic diversity in Indonesia and can serve as a basis for policy-making aimed at more equitable economic development.
SIMULATION OF THE SARIMA MODEL WITH THREE-WAY ANOVA AND ITS APPLICATION IN FORECASTING LARGE CHILLIES PRICES IN FIVE PROVINCES ON JAVA ISLAND Sanusi, Ratna Nur Mustika; Susetyo, Budi; Syafitri, Utami Dyah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (337.62 KB) | DOI: 10.30598/barekengvol17iss1pp0253-0262

Abstract

Commodities that become potential in the Horticulture Sub-sector are large chilies, so supply and prices must be controlled. One of the efforts that can be made is to predict the price of large chili in the future. However, forecasting is sometimes constrained by several things, such as small sample sizes and outliers. The effect of several factors on the parameter estimation bias can be determined by experimental design by simulating the data obtained from the generation results with several scenarios. The results of the analysis show that all factors have a significant effect on the magnitude of the parameter bias, so that all factors can affect forecasting results. When applying forecasting methods to actual data, paying attention to these three factors is necessary. The application of actual data using the SARIMA method gives good results. It can be seen from the RMSE and MAPE values ​​, which tend to be small. Based on the forecast results for the following 12 periods, it is estimated that the price of big chili in 2022 in five provinces will still fluctuate. The high price of chili in five provinces is predicted to reach its highest in the first three months of 2022. The highest price is predicted to occur in DIY Province in February, which is Rp. 74.230.00/kg. However, from the middle to the end of the year, prices will tend to fall and stabilize. The price will be the lowest in Middle Java Province in December, which is Rp. 20,689.00/Kg.
Transformasi Digital Komunitas Abrasea Indonesia untuk Penguatan Peran Konservasi Pesisir di Jawa Tengah Sanusi, Ratna Nur Mustika; Ifriza, Yahya Nur; Febriyanto, Hendra
Jurnal Abdi Negeri Vol 3 No 2 (2025): September 2025
Publisher : Informa Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63350/jan.v3i2.25

Abstract

Coastal abrasion in Central Java seriously threatens social, economic, and environmental sustainability. In response to this urgency, the Abrasea Indonesia Community was established as a new initiative for community-based conservation. However, as a newly formed organization, the effectiveness of its outreach programs and communication remains limited due to the absence of a structured digital platform. Therefore, this community service initiative aims to transform the community's operations by developing the digital platform abrasea.org as a hub for centralized information and increased public participation. The method employed is a participatory approach that actively involves community members in the planning and development of the platform, combined with quantitative and qualitative approaches to measure its success. The results show that within three months after its launch, the platform successfully increased website visitors by 75% and mobilized 80 new volunteers through online registration. This outcome strengthens institutional capacity and shifts the community's social behavior in organizing and publicizing activities. Thus, the digital platform has proven effective as a catalyst for expanding outreach and reinforcing the role of the Abrasea Indonesia Community in creating more sustainable conservation impacts since its inception.  
Co-Authors Abdul Jabbar Budi Susetyo Febriyanto, Hendra Harwanti, Nur Achmey Selgi Kamaruddin, Azlina Pratama, Alfian Adi Putriaji Hendikawati Utami Dyah Syafitri Wijaya, Galih Kusuma Yahya Nur Ifriza Yusuf Wisnu Mandaya