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All Journal Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Manajemen Riset Inovasi Maeswara: Jurnal Riset Ilmu Manajemen dan Kewirausahaan Jurnal Penelitian Teknologi Informasi dan Sains Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Nila Rusiardi Jayanti
Unknown Affiliation
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Evaluation of the Implementation of E-Government Public Service Aduan Konten Using E-Govqual, Importance Performance Analysis and Heuristic Evaluation (case study: Ministry of Communication and Information, APTIKA directorate) Nila Rusiardi Jayanti; Gerry Firmansyah; Nenden Siti Fatonah; Budi Tjahjono; Habibullah Akbar
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 5, No 3 (2022): Budapest International Research and Critics Institute August
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i3.6640

Abstract

In the current condition of society that is critical in responding to everything, more public services are needed professional, effective, simple, transparent, timely, responsive. Efforts to improve the quality of public services cannot be separated from service evaluation. In order to improve the quality of public services, the Directorate General of Informatics Applications of the Ministry of Communications and Informatics established the Public Service for Aduan konten at the Directorate of Information Application Control (PAI) as a pilot project. So to evaluate the quality of public services, a bureaucratic reform program is carried out at the PAI Directorate through efforts to develop a zone of territorial integrity free from corruption and a clean bureaucratic area to serve. One of the evaluations carried out is for measuring service performance as mandated in the Regulation of the Minister of Administrative Reform Number 14 of 2017 concerning the Community Satisfaction Survey (SKM) on the Implementation of Public Services. This study aims to determine the service quality of the Content Complaint website using the e-Govqual method, while the IPA and heuristic evaluations are to determine the attributes that are priorities for improving service quality, as recommendations to public service providers for Aduan konten. To assess the service quality of the content complaint website, 6 dimensions and 21 e-Govqual attributes are used. Of the 300 respondents who were used as research samples, this study shows the results of the analysis of the level of conformity of the 6 dimensions are 98.03% (<100%) meaning that the public services provided by the Aduan konten website are not satisfactory to users or still not in accordance with user expectations. The result of the average value of the gap between expectations and performance shows the number -0.05 or < 0. With this gap, it can be said that the quality of public service performance of Aduan konten perceived by the public still does not meet what is expected. Attributes that need improvement are those in quadrant A (3 attributes) and quadrant C (8 attributes). Recommendations are given based on the literature/theory for attributes that need to be improved to improve the quality of public services for Aduan konten.
Optimalisasi Strategi Pemasaran Melalui Analisis RFM pada Dataset Transaksi Ritel Menggunakan Python Andy Hermawan; Nila Rusiardi Jayanti; Aji Saputra; Cahaya Tambunan; Dzaky Muhammad Baihaqi; Muhammad Alif Syahreza; Zacharia Bachtiar
Jurnal Manajemen Riset Inovasi Vol. 2 No. 4 (2024): Oktober : Jurnal Manajemen Riset Inovasi
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/mri.v2i4.3246

Abstract

This study aims to optimize marketing strategies through RFM (Recency, Frequency, Monetary) analysis on a retail transaction dataset obtained from Kaggle. The dataset contains 64,682 transactions from 5,242 SKUs involving 22,625 customers over one year. Data cleaning and RFM analysis were conducted to segment customers based on recency, frequency, and monetary values. The findings reveal that customers were segmented into groups such as Champions, Loyal Customers, and At Risk. These segments provide valuable insights for developing targeted marketing strategies, such as loyalty programs for high-value customers and retention campaigns for at-risk customers. The study demonstrates that RFM analysis is effective in identifying valuable customer segments and optimizing marketing efforts based on customer behavior. This approach can increase customer retention and improve the return on investment (ROI) in marketing campaigns.
Analisis Segmentasi Pelanggan Berbasis RFM dan Evaluasi Efektivitas Kampanye Pemasaran untuk Meningkatkan Retensi Andy Hermawan; Fachmi Aditama; Lintang Rizki Ramadhani; Nuur Muhammad Ilham; Aji Saputra; Nila Rusiardi Jayanti
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 2 No. 4 (2024): November : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v2i4.400

Abstract

This research implements RFM (Recency, Frequency, Monetary) analysis to perform customer segmentation and evaluate the effectiveness of marketing campaigns in a retail company. Using a Kaggle dataset, this study identifies customers based on purchasing behaviour and assesses marketing campaign responses for each segment. The analysis reveals that Loyal, VIP, and New Customer segments showed the highest responses, especially in Campaign 6. The findings emphasize the importance of targeting resources on effective segments and campaigns to optimize marketing strategies and maximize ROI. Personalized campaigns based on segmentation can enhance customer retention and align product offerings with customer needs.
Prediksi Klaim Asuransi Perjalanan Menggunakan Machine Learning untuk Optimasi Manajemen Risiko Andy Hermawan; Nila Rusiardi Jayanti; Adam Praharsya Rahmadian; Muhammad Hafizh Bayhaqi; Amira Afdhal; Kerin Aurelia
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 3 No. 2 (2025): April : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v3i2.2476

Abstract

Travel insurance provides financial protection for individuals during their trips, both domestically and internationally. With the increasing demand for travel insurance, insurance companies face challenges in efficiently managing claims. This study aims to develop a predictive model to classify whether an insurance policy will be claimed based on historical customer and transaction data. This research utilizes a dataset containing various features related to travel and policyholders, such as agent type, distribution channel, insurance product, travel duration, and premium amount. The methods used include data exploration, feature processing, and the application of machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. Experimental results indicate that the XGBoost model performs the best, achieving the highest accuracy compared to other models. With this predictive model, insurance companies can optimize claim evaluation processes, reduce fraud risks, and improve operational efficiency in handling travel insurance claims.
TOWR Stock Forecasting From 2021-2025 Using Machine Learning Andy Hermawan; Angga Sukma Budi Darmawan; Muhammad Iqbal; Mochammad Rivan Akhsa; Nila Rusiardi Jayanti; Zidan Amukti Rajendra
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 3 No. 1 (2025): JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v3i1.3136

Abstract

Accurate stock price forecasting is a crucial yet challenging task due to the complex and dynamic nature of financial markets. This study employs the Prophet model to predict the stock prices of PT Sarana Menara Nusantara Tbk (TOWR) from 2021 to 2025. The research leverages historical stock data, incorporating dividend distribution dates and Annual General Meeting (AGM) events as external regressors to enhance predictive accuracy. The model was developed using machine learning-based time series forecasting, with hyperparameter tuning applied to optimize performance. The evaluation metrics indicate a Mean Absolute Error (MAE) of Rp49.92 and a Mean Absolute Percentage Error (MAPE) of 6.47%, demonstrating the model’s robustness in capturing long-term stock price trends. The findings suggest that stock prices exhibit significant movements around dividend announcement periods and AGM events, highlighting the impact of corporate actions on market behavior. This study reinforces the importance of incorporating fundamental financial indicators into forecasting models to improve decision-making for investors and financial analysts. The results offer practical implications for investment strategy formulation, risk management, and market trend analysis.
Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python Andy Hermawan; Nila Rusiardi Jayanti; Aji Saputra; Army Putera Parta; Muhammad Abizar Algiffary Thahir; Taufiqurrahman Taufiqurrahman
Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan Vol. 2 No. 5 (2024): OKTOBER : Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/maeswara.v2i5.1283

Abstract

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.
Co-Authors Adam Praharsya Rahmadian Aji Saputra Amira Afdhal Andy Hermawan Angga Sukma Budi Darmawan Army Putera Parta Budi Tjahjono cahaya Tambunan Dzaky Muhammad Baihaqi Fachmi Aditama Gerry Firmansyah Habibullah Akbar Kerin Aurelia Lintang Rizki Ramadhani Mochammad Rivan Akhsa Muhammad Abizar Algiffary Thahir Muhammad Alif Syahreza Muhammad Hafizh Bayhaqi Muhammad Iqbal Nenden Siti Fatonah Nuur Muhammad Ilham Taufiqurrahman Taufiqurrahman Zacharia Bachtiar Zidan Amukti Rajendra