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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi CommIT (Communication & Information Technology) Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Komtika (Komputasi dan Informatika) Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika Ekonomi Bisnis Computer Science and Information Technologies Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pendidikan Indonesia (Japendi) J-Icon : Jurnal Komputer dan Informatika Jurnal Informatika Ekonomi Bisnis Jurnal Sistem Informasi The Indonesian Journal of Computer Science
Yova Ruldeviyani
Fakultas Ilmu Komputer, Universitas Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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MASTER DATA MANAGEMENT IMPLEMENTATION IN DISTRIBUTED INFORMATION SYSTEM CASE STUDY DIRECTORATE GENERAL OF TAX, MINISTRY OF FINANCE OF REPUBLIC OF INDONESIA Aris Budi Santoso; Yoga Pamungkas; Yova Ruldeviyani
Jurnal Sistem Informasi Vol. 15 No. 1 (2019): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.734 KB) | DOI: 10.21609/jsi.v15i1.779

Abstract

Information system architecture of Directorate General of Tax (DGT) is centralized with distributed data. The main problem are replication of master and reference data which spread among applications which vary on data structure and the synchronization jobs that spread in many locations and not well managed. Therefore, Master Data Management (MDM) needs to be implemented with accordance to characteristic of centralized distributed information system. Master data management maturity evaluation is conducted using MDM maturity model (MD3M) Spruit dan Pietzka, the result is Data Protection, Data Quality and Maintenance topic have maturity level 3 or defined process stage, while Data Model, Usage and Ownership topic have maturity level 2 or repeatable stage. Solutions to solve MDM issues and to enhance the master data management maturity level are proposed based on Data Management Body of Knowledge (DMBOK). DGT’s MDM issues are related to insufficiency of policy and architecture of MDM system. Policy and architectural approach of centralized MDM system is required to solve that issues. Proposed solution involves the use of data virtualization to enable implementation of centralized system of MDM without consolidate all master and reference data into new database.
ANALYSIS OF EFFECTS OF APP PERMISSION CONCERNS ON INTENTIONS TO DISCLOSE PERSONAL INFORMATION: A CASE STUDY OF MONEY TRANSFER SERVICE APP Azis Amirulbahar; Yova Ruldeviyani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4316

Abstract

Data growth increased alongside the rise of mobile app users in financial services. In Indonesia, the number of financial services application downloads reached 24 million by the end of 2022, with a 28.72 percent increase in transactions. However, this growth also brings issues regarding the potential misuse of personal information, although according to the Personal Data Protection Act (UU PDP) in Indonesia, personal data is protected and kept confidential when accessed by another party. This prompts users to be more cautious in disclosing personal information. On the other hand, users are faced with risks to personal data that can be accessed by service providers, one of which is through app permissions. This research focuses on the influence of App Permission Concerns on users' intentions to disclose their personal information, with a case study of a money transfer services app in Indonesia, namely Flip, that received numerous negative reviews about users' data privacy concerns, especially when verifying using an identity card. The study uses a quantitative approach with PLS-SEM for data analysis. Convenience sampling was used, and data were collected via a questionnaire distributed through Google Forms on social media from May 9 to May 21, 2023 and a total of 224 respondents were obtained. The results of this study indicate that App Permission Concerns have a significant influence on Privacy Fatigue, Privacy Awareness, Privacy Concern and Trust. Trust significantly influences Intention to Disclose. This research is expected to contribute to future studies on app permissions and mobile app feature development.
Sentiment Analysis of Nanovest Investment Application Using Naive Bayes Algorithm Lelianto Eko Pradana; Yova Ruldeviyani
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62302

Abstract

Various applications provide simple ways for individuals interested in investing in crypto assets or stocks - both domestic and international - to do so. One of the companies in this industry, Nanovest, has launched the Nanovest investment application. Since its release in 2022, numerous positive and negative responses have been on Google Play, the App Store, and Twitter. However, Nanovest faces two main problems regarding the use of its application. First, they often receive complaints submitted to the operational team, indicating dissatisfaction or problems faced by users. Second, Nanovest has never conducted formal research regarding user experience in using their application. This indicates a lack of understanding of the perspectives, needs and challenges faced by users. This study tries to find out how the public responds to the Nanovest application through a sentiment analysis. This study used tweet and review data from January 1, 2022, to February 17, 2023. The data underwent sentiment analysis, employing the Naïve Bayes algorithm, and were classified into positive and negative sentiments. The findings revealed that 96.07% of the sentiments expressed towards Nanovest were positive, while 22.11% were negative, with these percentages calculated based on the total number of sentiments detected in the data. To evaluate the model's performance, a 10-fold cross-validation approach was utilized alongside the Naïve Bayes algorithm, resulting in an impressive accuracy rate of 94.8391%. This positive sentiment suggests that users are highly favorable towards the crypto assets and global stock investment services offered by the Nanovest application. Nevertheless, 3.93% of users still expressed dissatisfaction with the app due to some flaws that existed when Nanovest was initially launched. Based on the results that have been obtained and analyzed for the development team, it is recommended to make three improvements, namely reducing application size to minimize memory usage, increasing overall application performance, and increasing access speed across all features to allow application users to access more efficiently. It is recommended for the product team and stakeholders to consider developing the Candlestick chart feature into the application. This also increases the competitiveness of the Nanovest application against other applications.
ANALISIS SENTIMEN TERHADAP PENGGUNAAN APLIKASI MERDEKA MENGAJAR DI GOOGLE PLAY STORE Farhan, Muhammad; Ramayuda, Muhammad Davin; Ruldeviyani, Yova
J-Icon : Jurnal Komputer dan Informatika Vol 12 No 1 (2024): Maret 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v12i1.14303

Abstract

To provide a solution to the learning loss that has occurred in the education sector in Indonesia since the COVID-19 pandemic, the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) released the "Merdeka" curriculum. To assist teachers in obtaining references, inspiration, and understanding of the "Merdeka" curriculum, Kemendikbudristek launched the "Merdeka Mengajar" platform, which can be downloaded from the Google Play Store. However, the utilization of this application has not yet reached the target number of users expected. To determine the achieved number of users, the analysis process stages are carried out, namely data collection, data pre-processing (pre-processing), data labeling (labeling), word extraction, classification, classification evaluation, sentiment analysis using the naïve Bayes model, and Support Vector Machine (SVM). The research results, using n-gram implementation with the naive Bayes and SVM models, show that the accuracy level generated by each model is 86% and 91%, respectively. Sentiment analysis indicates that 3,225 (57%) user reviews are positive, while 2,421 (43%) are negative. Overall, it can be concluded that the sentiment regarding the use of the independent teaching application is positive. Meanwhile, several factors causing users to provide negative reviews include difficulties during activation, incomplete learning modules, and requests to release the application on IOS.
Tantangan Data Quality pada Pelaporan Penyakit Menular: Studi Kasus Pelayanan Publik Kesehatan di Indonesia Hendry, Darell; Devina, Fakhira; Nabasya, Oristania Wahyu; Ruldeviyani, Yova
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 2 (2024): April 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i2.1215

Abstract

Data management in the process of reporting infectious diseases in public health services presents a significant challenge. The quality of the generated reports is crucial as they are utilized for various strategic decision-making purposes. However, the successful management of data quality depends on understanding the existing reporting processes and the challenges faced. This study aims to explore the main challenges encountered during the COVID-19 reporting process, using a case study of public health services, and to identify the processes occurring in the field. Recommendations for practitioners are also provided. This research employed interview methods and analyzed them using thematic analysis, following the standards of the Consolidated Criteria for Reporting Qualitative Research (COREQ). Interviews were conducted with experts from public health centers and health departments in Indonesia. The challenges identified from 124 codes encompass issues related to data accuracy, data consistency, data timeliness, and data completeness. Recommendations have been formulated based on data management body of knowledge and critical success factors related to data quality
Improvement Master Data Management : Case Study Of The Directorate General Of The Religious Courts Of The Supreme Court Of The Republic Of Indonesia Alfiandi, Rama; Ruldeviyani, Yova
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13194

Abstract

Implementation of Master Data Management (MDM) in an organization aims to help the process of consolidating and integrating various master data sources into one separate source of truth, as well as helping to overcome data complexity that occurs in the process of synchronizing, consolidating and cleaning data from redundancy. The main obstacle at DG Badilag is that data is spread across various systems, in different formats, and is not well integrated. The aim of this research is to improve master data management at the Directorate General of Badilag using MD3M which has an impact on a more transparent, efficient and just justice system . To improve master data management, it is necessary to measure the maturity level with use Master Data Management Maturity Model (MD3M) by Spruit and Pietzka. The results of the assessment show that the MDM maturity level at the Directorate General of Badilag is 0 with an implementation level of 73% (48 out of a total of 65) of implemented capabilities. From these results, recommendations were prepared to increase the maturity level of the Directorate General of Badilag to level 2 in the three designated focus areas, namely strengthening data management in all aspects, from data structure, data quality and data protection. DG Badilag already has awareness in master data management and can increase MDM maturity to a higher level by implementing capabilities that have not been implemented and implementing activities that have not been implemented.
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Co-Authors Achmad Nizar Hidayanto Adi Gunawan, Adi Afif Gunung , Muhammad Ahmad Hendra Maulana Ahmad Syaifulloh Imron Al Adawiyah, Rabiah Al Haq, Muhammad Hezby Al Qahar, Muhammad Yazid Aldiansah Prayogi Alfiandi, Rama Aloysius Prastowo Setyo Nugroho Amanda Ghaisani Andro Harjanto Arif Hidayat Aris Budi Santoso Astagina, Shania Eriadhani Azis Amirulbahar Brillianto, Bramanti Desiana Nurul Maftuhah Devina, Fakhira Faris Salbari Fathurahman Ma'ruf Hudoarma Fidyawan, Miftahul Agtamas Fitriya, Ghina Hafiz , Muhammad Halida Ernita Handayani, Putu Wuri Hendry, Darell Hizqil, Ahmad I Made Kurniawan Putra Ines Dwi Andini Jeri Apriansyah Pagua, Jeri Apriansyah Juliansyah, Mohamad Denis Khairunnas, Rezki Khairunnaziri, Muhammad Krisna Maria Rosita Dewi Kurniawati, Monica Vivi Layungsari Layungsari Layungsari Layungsari Layungsari Layungsari Lelianto Eko Pradana Lia Ellyanti Mahsa Elvina Rahmawyanet Melani Puspasari, Hasna Muhammad Farhan Mukharomah, Ulfah Nur Nabasya, Oristania Wahyu Noverina Alfiany Nugraheni, Sani Prastowo, Rahardito Dio Pratiwi, Aprilia Priastomo, Ristyo Yogi Prisillia, Galuh Puja Putri Abdullah Purwandaru, Dhanang Putra, Ramadhoni Rahmad Mulyadi Rahman, Henry Aulia Rahmi Julianasari Raksaka Indra Alhaqq Ramayuda, Muhammad Davin Ratna Yulika Go Rina Rahmawati Sidiq, Darmawan Sudarwono, Dianto Adwoko Sulaeman, Achmad Firmansyah Syalevi, Rahmad Tri Broto Siswoyo Tri Utami, Avita Utami, Aisyah Nurlita Widoyono, Bambang Yoga Pamungkas Yudho Giri Sucahyo Yudho Giri Sucahyo Yudistira, Ricko Dwiki Yuli Astuti Zharif Mustaqim, Ilham Zulmy, Mohamad Faisal