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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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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.
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

Abstract

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

Abstract

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

Abstract

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.
Sentiment analysis of online licensing service quality in the energy and mineral resources sector of the Republic of Indonesia Hizqil, Ahmad; Ruldeviyani, Yova
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p63-71

Abstract

The Ministry of Energy and Mineral Resources of the Republic of Indonesia regularly assessed public satisfaction with its online licensing services. User rated their satisfaction at 3.42 on a scale of 4, below the organization's average of 3.53. Evaluating public service performance is crucial for quality improvement. Previous research relied solely on survey data to assess public satisfaction. This study goes further by analyzing user feedback in text form from an online licensing application to identify negative aspects of the service that need enhancement. The dataset spanned September 2019 to February 2023, with 24,112 entries. The choice of classification methods on the highest accuracy values among decision tree, random forest, naive bayes, stochastic gradient descent, logistic regression (LR), and k-nearest neighbor. The text data was converted into numerical form using CountVectorizer and term frequency-inverse document frequency (TF-IDF) techniques, along with unigrams and bigrams for dividing sentences into word segments. LR bigram CountVectorizer ranked highest with 89% for average precision, F1-score, and recall, compared to the other five classification methods. The sentiment analysis polarity level was 36.2% negative. Negative sentiment revealed expectations from the public to the ministry to improve the top three aspects: system, mechanism, and procedure; infrastructure and facilities; and service specification product types.
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 (IJCS)
Publisher : AI Society & STMIK Indonesia

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

Abstract

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

Abstract

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.
Pengukuran Tingkat Kesadaran Keamanan Informasi Pegawai Pada Instansi Pemerintah Krisna Maria Rosita Dewi; Yudistira, Ricko Dwiki; Ruldeviyani, Yova
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3950

Abstract

Human Resources (HR) have become a factor that affects the success of e-government implementation in the era of rapid technological development. However, HR also play a role in creating information security risks for the organization. In order to deal with information security risks, BMKG implemented an information security management system by implementing ISO/IEC 27001:2013 in 2020. It also plans to implement it at three locations of the BMKG Regional Office. Therefore, this study aims to measure employees' level of information security awareness at the three locations using the Knowledge, Attitude, Behavior (KAB) concept and the HAIS-Q method combined with the information asset management sub-area of the KAMI’s Index. The results showed that employees' level of information security awareness is classified in the good category with a value of 81.67%. Some areas that need to be improved are password management (79.81%), mobile computing (75.59%), and incident reporting (79.81%). Regional Center A has the lowest employee information security awareness level, with an average score of 79.75%.
Peningkatan Kualitas Data Talent Karyawan pada Human Capital Management PT XYZ Sulaeman, Achmad Firmansyah; Ruldeviyani, Yova
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4628

Abstract

The importance of high quality data is a top priority for PT XYZ’s Human Capital Management (HCM) in handling Talent Management, Career Management, and Employee Performance Management. information security audit revealed several issues, such as delayed data updates and inconsistencies across functions. To address these issue, an assessment of the Data Quality Management (DQM) maturity level is needed to evaluate the implementation of consistency, accuracy, and integrity. This study uses David Loshin’s framework, with the results explained referring to DQM guidelines in DMBOK. Results show DQM maturity level is at level 2 (Repeatable), with an average score of 2.3. Three dimensions with the lowest scores are the main focus for improvement, which Data Quality Expectations (1.6), Data Quality Protocols (1.8) and Data Quality Technology (1.2). Recommendations from this study focus on enhancing these dimensions to improve data quality and address the issues highlighted in information security audit.
Evaluating Mdm Maturity In Human Capital Data: Case Study of Jasa Marga Juliansyah, Mohamad Denis; Ruldeviyani, Yova; Rahman, Henry Aulia; Astagina, Shania Eriadhani
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.871

Abstract

Effective Master Data Management (MDM) is essential for organizations to ensure data consistency, accuracy, and accessibility, particularly within intricate operational settings. This study assesses the maturity level of MDM for human capital data at PT Jasa Marga (Persero) Tbk, Indonesia’s leading state-owned toll road developer and operator. Human capital data is currently managed through two primary applications: SAP for the main company and the JM-Click Human Capital Information System (HCIS) for its subsidiaries. This separation has led to data silos and inconsistencies, diminishing the reliability and accessibility of critical human capital data. Utilizing the Master Data Management Maturity Model (MD3M) by Spruit and Pietzka, this research investigates five main topics and 13 focus areas within the organization. Data collection involved questionnaires and interviews with key experts. Results indicate that PT Jasa Marga has implemented 76.92% of necessary MDM capabilities, with Data Protection and Usage & Ownership scoring the highest maturity level of 5. However, Data Quality is at level 2, indicating a need for major enhancements in data maintenance and consistency across subsidiary data models. This study provides actionable recommendations for improving data quality, aligning data models, and integrating advanced technologies, stressing the importance of continuous MDM improvements to better support the organization’s strategic objectives and operational demands.
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