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Assessing public satisfaction of public service application using supervised machine learning Zharif Mustaqim, Ilham; Melani Puspasari, Hasna; Tri Utami, Avita; Syalevi, Rahmad; Ruldeviyani, Yova
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1608-1618

The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.

Factor analysis influencing Mobile JKN user experience using sentiment analysis Al Qahar, Muhammad Yazid; Ruldeviyani, Yova; Mukharomah, Ulfah Nur; Fidyawan, Miftahul Agtamas; Putra, Ramadhoni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1782-1793

Social security administration for health or Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan), as a public legal entity, has a critical role in the health of the Indonesian population. BPJS Kesehatan introduced the Mobile national health insurance or jaminan kesehatan nasional (JKN) application to enhance its services, enabling Indonesians to access it directly. Nevertheless, the rating of the Mobile JKN application on the Google Play Store has shown a gradual decline over time. Therefore, this study was conducted to analyze the factors influencing the user experience of the Mobile JKN application, utilizing the review data obtained from the Google Play Store. Sentiment analysis using the Naïve Bayes (NB) classification model and support vector machine (SVM) combined with synthetic minority oversampling technique (SMOTE) and slang word replacement. The results obtained an accuracy value of 93.33%, precision of 93.76%, recall of 93.33%, and F1-score of 93.43%. A further analysis was conducted using online service quality factors to obtain the main factors influencing the experience of Mobile JKN application users. The evaluation findings revealed that factors of security, ease of use, and timeliness are three fundamental aspects that should be given immediate attention by BPJS Kesehatan while improving the Mobile JKN application in the future.

Evaluation of Indonesia’s police public service platforms through sentiment and thematic analysis Melani Puspasari, Hasna; Zharif Mustaqim, Ilham; Tri Utami, Avita; Syalevi, Rahmad; Ruldeviyani, Yova
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1596-1607

The Indonesian national police (Polri) offer public services through mobile apps: Digital korlantas polri (DigiKorlantas) and samsat digital nasional (SIGNAL). Sentiment analysis gauges public perceptions, serving as a basis for e-government evaluation using user ratings and comments from app stores. Keyword relevance is assessed via feature extraction and Naïve Bayes classification. Thematic analysis is implemented using N-grams methods to identify the factors affecting the effectiveness based on user experiences. The accuracy of the model reaches 81.09% where it indicates a high performance. DigiKorlantas acquires slightly more negative reviews in comparation with positive reviews which are 51% and 49% respectively. In contrast, positive sentiment is dominant on SIGNAL which reach 58%, compared with negative sentiment that in 42%. N-grams reveal similar review patterns for both apps. Some of the solutions are Korlantas Polri should enhance the verification functionality with several techniques such as retinex algorithms or optical character recognition pipeline and increase the capacity of supporting server then releasing an updated version of application to address errors or bugs. This analysis can be alternative evaluation by the Polri to measure the success of the application and find out the continuous improvement of the process and the system.

Analisis Tingkat Kematangan Open Government Data Menggunakan OD-MM di Pemerintah Provinsi Aceh Sudarwono, Dianto Adwoko; Prastowo, Rahardito Dio; Ruldeviyani, Yova; Widoyono, Bambang
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 3 (September 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i3.988

Pemerintah Indonesia telah memulai inisiatif open data sejak tahun 2008 dengan menerbitkan Undang-undang tentang Keterbukaan Informasi Publik. Gerakan Open Government Indonesia (OGI) yang meluncurkan Rencana Aksi Nasional (RAN) Open Government yang pertama pada tahun 2012. Implementasi Portal Open Data di Pemerintah Aceh dimulai tahun 2018 dengan tujuan optimalisasi penggunaan data dan informasi publik dalam pembangunan Aceh yang lebih baik. Namun berdasarkan data yang dianalisis bahwa terdapat beberapa kendala dalam pelaksanaan Portal Open Data seperti kekurangan SDM yang terampil, ketidakmampuan untuk mengumpulkan dan mengintegrasikan data yang relevan, kelemahan dalam keamanan data, sehingga belum dapat dipastikan apakah proses OGD telah berjalan dengan optimal atau belum. Oleh sebab itu penting dilakukan pengukuran tingkat kematangan Open Government Data (OGD) pada Pemerintah Aceh. Pengukuran tingkat kematangan menggunakan Open Data Maturity Model (OD-MM), dengan memberikan kuesioner kepada 12 pengelola Portal Open Data Aceh. Dari hasil pengukuran diperoleh hasil bahwa tingkat kematangan OGD Aceh berada pada level 3 dari skor maksimal 4. Sebanyak 22 rekomendasi perbaikan disampaikan untuk mengembangkan tingkat kematangan OGD Aceh ke level yang lebih tinggi. Selain itu juga dilakukan simulasi fitur roadmap generator pada OD-MM yang dapat digunakan sebagai alat self-assessment kedepannya.

Analisis Tingkat Kematangan Open Government Data Menggunakan OD-MM di Pemerintah Provinsi Aceh Sudarwono, Dianto Adwoko; Prastowo, Rahardito Dio; Ruldeviyani, Yova; Widoyono, Bambang
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 3 (September 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i3.988

Pemerintah Indonesia telah memulai inisiatif open data sejak tahun 2008 dengan menerbitkan Undang-undang tentang Keterbukaan Informasi Publik. Gerakan Open Government Indonesia (OGI) yang meluncurkan Rencana Aksi Nasional (RAN) Open Government yang pertama pada tahun 2012. Implementasi Portal Open Data di Pemerintah Aceh dimulai tahun 2018 dengan tujuan optimalisasi penggunaan data dan informasi publik dalam pembangunan Aceh yang lebih baik. Namun berdasarkan data yang dianalisis bahwa terdapat beberapa kendala dalam pelaksanaan Portal Open Data seperti kekurangan SDM yang terampil, ketidakmampuan untuk mengumpulkan dan mengintegrasikan data yang relevan, kelemahan dalam keamanan data, sehingga belum dapat dipastikan apakah proses OGD telah berjalan dengan optimal atau belum. Oleh sebab itu penting dilakukan pengukuran tingkat kematangan Open Government Data (OGD) pada Pemerintah Aceh. Pengukuran tingkat kematangan menggunakan Open Data Maturity Model (OD-MM), dengan memberikan kuesioner kepada 12 pengelola Portal Open Data Aceh. Dari hasil pengukuran diperoleh hasil bahwa tingkat kematangan OGD Aceh berada pada level 3 dari skor maksimal 4. Sebanyak 22 rekomendasi perbaikan disampaikan untuk mengembangkan tingkat kematangan OGD Aceh ke level yang lebih tinggi. Selain itu juga dilakukan simulasi fitur roadmap generator pada OD-MM yang dapat digunakan sebagai alat self-assessment kedepannya.

Comparative Analysis of Multicriteria Inventory Classification and Forecasing: A Case Study in PT XYZ Purwandaru, Dhanang; Ruldeviyani, Yova; Nugraheni, Sani; Prisillia, Galuh
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1014

One crucial aspect of supply chain management is inventory management. Inefficient inventory management can lead to various issues, such as product expiration, where a high number of items in the warehouse either have expired or are approaching expiration. This issue is experienced by a distribution SME in Indonesia, PT XYZ. Without such classifications, it becomes challenging to predict demand and manage stock levels efficiently. Therefore, the aim of this study is to classify inventory to identify the most important items to business and make a forecasting model of sales quantity to predict inventory replenishment using machine learning algorithms. To advance our research, we adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For inventory classification, we conducted a hybrid approach that combined TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ABC analysis (A: high-value items, B: medium-value items, and C: low-value items). The data employed in this study comprised secondary data, including purchase orders, sales orders, and stock movement records. The result reveals that 11 of the total 383 items under class A are important items for business. After obtaining labels from the ABC Analysis, we proceed to train models using KNN, SVC, and Random Forest for predicting inventory classification. Notably, the Random Forest model showcased remarkable performance and outperformed the rest of the models, achieving an accuracy of 99.21%. For inventory forecasting ARIMA displays a competitive performance with RMSE value 5.305 and MAE value 3.476, indicating a relatively accurate prediction with lower forecasting errors than two other models

Comparative Analysis of Multicriteria Inventory Classification and Forecasing: A Case Study in PT XYZ Purwandaru, Dhanang; Ruldeviyani, Yova; Nugraheni, Sani; Prisillia, Galuh
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1014

One crucial aspect of supply chain management is inventory management. Inefficient inventory management can lead to various issues, such as product expiration, where a high number of items in the warehouse either have expired or are approaching expiration. This issue is experienced by a distribution SME in Indonesia, PT XYZ. Without such classifications, it becomes challenging to predict demand and manage stock levels efficiently. Therefore, the aim of this study is to classify inventory to identify the most important items to business and make a forecasting model of sales quantity to predict inventory replenishment using machine learning algorithms. To advance our research, we adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For inventory classification, we conducted a hybrid approach that combined TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ABC analysis (A: high-value items, B: medium-value items, and C: low-value items). The data employed in this study comprised secondary data, including purchase orders, sales orders, and stock movement records. The result reveals that 11 of the total 383 items under class A are important items for business. After obtaining labels from the ABC Analysis, we proceed to train models using KNN, SVC, and Random Forest for predicting inventory classification. Notably, the Random Forest model showcased remarkable performance and outperformed the rest of the models, achieving an accuracy of 99.21%. For inventory forecasting ARIMA displays a competitive performance with RMSE value 5.305 and MAE value 3.476, indicating a relatively accurate prediction with lower forecasting errors than two other models

User sentiment dynamics in social media: a comparative analysis of X and Threads Khairunnas, Rezki; Pagua, Jeri Apriansyah; Fitriya, Ghina; Ruldeviyani, Yova
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp447-456

This research examines the dynamics of user sentiment and its correlation with the usage factors of applications in the context of the competition between X (formerly Twitter) and Threads, a social media application under the umbrella of Meta. Through sentiment analysis of user reviews on the Google Play Store and App Store, the study aims to identify the key factors contributing to a significant decline in user engagement with Threads and the return of users to X. The method employed in this research is the support vector machine (SVM) for sentiment classification of reviews. The study then correlates the classified sentiments with application usage factors: usability, features, design, and support. The research findings indicate user sentiment influences user engagement, especially in features and design. The research concludes with insights regarding implications for application developers and suggests directions for future research.

Data Governance Improvement Strategy for Peer-to-Peer Lending Sharia in Indonesia: Study Case PT ABC Priastomo, Ristyo Yogi; Ruldeviyani, Yova; Gunawan, Adi; Al Haq, Muhammad Hezby; Utami, Aisyah Nurlita
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4233

Abstract—Financial Technology (fintech) is a company that supervised by the Indonesian Financial Services Authority (OJK) and fintech associations which has strict regulations. Well-defined data management can support organizations to comply with mandatory regulations. This research was conducted on a sharia peer-to-peer lending fintech in Indonesia with the aim of solving data governance problems in organizations by measure of Data Governance Maturity Level to get recommendations strategies to improve the implementation of data governance in the organization. The measurement was carried out using IBM Data Governance Maturity Model Framework. After validation and finalization of the assessment, the results showed that the average score was 2.47. It's shown that currently at the Managed level. Some domains need to be improved in the future, data value creation, data organizational structure and awareness, data policies and rules and data stewardship.

The Optimizing Data Quality in Interagency Data Sharing: A Framework Kurniawati, Monica Vivi; Zulmy, Mohamad Faisal; Ruldeviyani, Yova
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i1.1310

In the modern landscape of government operations, characterized by a shift towards openness, inclusivity, and interagency collaboration driven by the pursuit of public value and evidence-based policy making, the importance of interagency data sharing (IDS) is unmistakable. Despite the evident benefits of information exchange among government agencies, challenges persist, especially concerning nuanced considerations of data quality. This study aims to bridge this critical gap by proposing a specialized framework for IDS within government agencies. This framework, crafted to proactively address data quality considerations throughout the entire lifecycle, transcends traditional approaches and seeks to offer insights for fostering effective practices in interagency data sharing. Positioned at the nexus of evolving government operations, the research underscores the necessity for strategic frameworks prioritizing data quality to support collaborative and effective evidence-driven decision-making.

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