Article Per Year (5 Year)
p-Index From 2021 - 2026
2.884
P-Index
This Author published in this journals
All Journal SINTECH (Science and Information Technology) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Information Systems and Informatics Cyber Security dan Forensik Digital (CSFD) Journal of Dinda : Data Science, Information Technology, and Data Analytics International Journal Software Engineering and Computer Science (IJSECS) Journal of Intelligent Software Systems Journal Of Artificial Intelligence And Software Engineering
Bambang Purnomosidi Dwi Putranto
Universitas Teknologi Digital Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
Polynomial Regression Method and Support Vector Machine Method for Predicting Disease Covid-19 in Indonesia Bambang Purnomosidi Dwi Putranto; Moh. Abdul Kholik; Muhammad Agung Nugroho; Danny Kriestanto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.931

Abstract

The COVID-19 pandemic has become a major threat to the entire country. According to the WHO report, COVID-19 is a severe acute respiratory syndrome transmitted through respiratory droplets resulting from direct contact with patients. This study of data history is then processed using data mining prediction methods, namely the Polynomial Regression method compared to the Support Vector Machine method. Of the two methods will be sought the most accurate method by testing accuracy with MAE, MSE, and also MAPE to get the results of covid-19 predictions in Indonesia. Based on the comparison of test results through various scenarios against both methods, the Polynomial Regression method obtained the smallest test value, resulting in an accuracy value of MAE = 4146.025749867596, MSE = 19031800.02642069, MAPE = 0.006174164877416524. Polynomial regression is the best-recommended method
A Decision Model to Support the Selection of SENKOM Personnel Using the Profile Matching Method with the Capability of Cyber Security I Nyoman Oka Semadi; Domy Kristomo; Bambang Purnomosidi
Journal of Intelligent Software Systems Vol 2, No 2 (2023): December 2023
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i2.1135

Abstract

The very rapid development of information technology has brought tactical and strategic advantages, but it can also be a potential attack from opposing parties on the information and communication systems and networks used, thus opening the way for the emergence of a new war, namely cyber warfare. Cyber attacks are a new threat to Adisutjipto Air Base, which targets vital parts that can impact the organization and make the command and control system ineffective and inefficient. One of the important elements of Adisutjipto Lanud in facing cyber attacks is the readiness of data and communication network security personnel. In the direct or conventional personnel selection process, it is not possible to see the abilities possessed by prospective data security personnel, both in terms of skills, management aspects, analytical aspects, competency weight, and so on. A decision support system can be used to assist decision-making based on existing criteria. This research is limited to only considering the selection of personnel who will become members of komlek or senkom who are responsible for data security and communications networks at Adisutjipto Air Base. In this research, the method used is the profile matching method. The concept of the profile matching method is to compare the selection using the conventional method with the decision support system method in selecting komlek/senkom personnel as cyber security personnel so that differences in competency can be identified, also called GAP (Gross Across Product). The smaller the GAP produced, the greater the weight of the value. large, this means that personnel who meet the requirements have a greater chance of someone occupying that position. The final result of this research is to obtain ranking information for each cyber security candidate based on profile matching calculations to be able to carry out tasks optimally in securing data and networks at Adisutjipto Air Base.
Model Blockchain Untuk Pembayaran Lintas Batas Negara Bagi UMKM Putranto, Bambang Purnomosidi Dwi; Kartadie, Rikie; Astuti, Femi Dwi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.6736

Abstract

UMKM (Usaha Mikro, Kecil, dan Menengah) memainkan peran krusial dalam perekonomian Indonesia, dengan sekitar 64,2 juta unit usaha yang menyerap 97% tenaga kerja. Di tengah dampak pandemi global, pemerintah meluncurkan Pemulihan Ekonomi Nasional, dengan UMKM sebagai fokus utama. Digitalisasi menjadi penting untuk meningkatkan ekspor dan memudahkan pembayaran lintas batas yang aman dan cepat. Hal ini juga menjadi bagian dari agenda Presidensi G20 Indonesia, yang menekankan pengembangan pembayaran lintas negara. Penelitian ini menggunakan pendekatan metode pencarian solusi (solution-seeking) untuk menghasilkan berbagai artefak desain, termasuk arsitektur menggunakan bahasa pemodelan ArchiMate, algoritma, model, dan peranti lunak untuk pembayaran lintas batas. Kami mengadopsi Blockchain Layer 1 Stellar, yang menawarkan protokol terbuka dan terdesentralisasi untuk transaksi mata uang digital dan fiat dengan biaya rendah. Hasil akhir dari penelitian ini adalah model peranti lunak yang memungkinkan pembayaran lintas negara menggunakan Blockchain Stellar, dengan pengembangan yang mengikuti metodologi Disciplined Agile Delivery (DAD). Dengan demikian, penelitian ini berkontribusi pada upaya digitalisasi UMKM dalam menghadapi tantangan ekonomi dan meningkatkan konektivitas global melalui pembayaran lintas batas yang lebih efisien.
Current Trends and Future Directions of Big Data in Commerce: A Bibliometric Analysis Based on Scopus Almagribi, Ahmad Bilal; Putranto, Bambang Purnomosidi Dwi
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 2 (2025): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i2.6098

Abstract

Big data provides significant benefits across various sectors, including commerce. However, there remained a gap in bibliometric studies examining big data within the context of commerce, leaving research development in this field unclear. This study aimed to address this gap by conducting a bibliometric investigation into researchers' contributions to big data in commerce, including their affiliations and countries of origin. Additionally, the study sought to identify the most productive journals and highlight relevant and under-researched topics within this field. A bibliometric analysis approach was employed, analyzing 396 Scopus-indexed documents and using VOSviewer visualization to identify major recurring issues in the literature. The findings revealed that in 2021, the number of publications on big data in commerce peaked at 97 documents. Maalla, A., from Guangzhou College of Technology and Business, China, emerged as the most prolific author, while China led in publication output with 308 documents. The Journal of Physics Conference Series was identified as the most productive source. Computer Science was the most explored discipline, indicating a strong integration of technology with commerce. Keyword analysis divided research focus into four main clusters: analytical technology, platform optimization, supply chain management, and marketing strategy optimization. These findings provide a foundation for future research to explore areas such as Customer Experience Management, Blockchain Technology, Cloud Computing, Predictive Analytics, and Customer Segmentation, thereby enriching the academic literature and offering practical contributions to data-driven commerce.
Comprehensive Lakehouse Data Architecture Model for College Accreditation Nenen Isnaeni; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani; Siti Khomsah
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1759

Abstract

Accreditation is an assessment activity that determines the feasibility of study programs at a university. College accreditation data comes from various sources and includes multiple data types: semi-structured, unstructured, or structured. Over time, the volume of data will continue to grow and develop, so there is a possibility of data redundancy and a long time to collect the data needed for accreditation activities. The solution is integrating data. This research aims to design a data architecture to facilitate the management of university accreditation data using the Lakehouse data architecture model. All data types can be stored on one platform in the Lakehouse data architecture. In this research, the identification, integration, and data transformation process for university accreditation data is carried out. The data used in this research is academic data in which there are with. The study's results provide an overview of the data flow process in the Lakehouse data architecture model to help better manage university accreditation data. This architecture also supports real-time data analysis so that the accreditation process can be carried out more effectively and efficiently. Keywords: accreditation, data analysis, data architecture, data lakehouse, data warehouse
RDBMS Scoring Analysis: Billing System Efficiency Solution (Case Study at PT. XYZ) Jaswadi, Jaswadi; Dwi Putranto, Bambang Purnomosidi; Redjeki, Sri
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6372

Abstract

Penggunaan Oracle exadata pada sistem billing di PT. XYZ membutuhkan biaya ATS (Annual Technical Support) dan ACS (Advanced Customer Service) yang besar sehingga dibutuhkan pencarian software database RDBMS yang sesuai untuk penyimpanan dan pengolahan data dalam proses generate billing di PT. XYZ. Oleh karena itu pada penelitian dilakukan analisis penentuan pemilihan database RDBMS (Relational Database Management System). Metode yang digunakan adalah metode scoring untuk membandingkan 3 database RDMS (Oracle non-exadata, EDB Postgres, dan 11DB Postgres) berdasarkan 5 kriteria yang meliputi penyiapan infrastruktur, migrasi data, tingkat akurasi hitung billing, durasi hitung billing, dan waktu pengerjaan. Hasil penelitian menunjukkan bahwa score database Oracle memiliki nilai paling tinggi di antara database yang lain yaitu sebesar 95, kemudian disusul oleh EDB Postgres dan 11DB Postgres dengan score secara berurutan yaitu sebesar 84 dan 57. Berdasarkan hal tersebut, maka dapat disimpulkan bahwa Oracle non-exadata memiliki performance yang paling tinggi di antara database yang lain. 
Spatio-Temporal Graph Neural Network for Solar Irradiance Prediction: A Case Study in Nganjuk, Indonesia Nurcahyo, Agung Wilis; Putranto, Bambang Purnomosidi Dwi
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1424

Abstract

Solar energy utilization in tropical regions is strongly influenced by the accuracy of solar irradiance estimation, which is affected by temporal variability and spatial atmospheric interactions. Conventional forecasting approaches commonly rely on single-station time-series models, limiting their ability to capture regional dependencies. This study proposes a spatio-temporal modeling framework based on Graph Neural Networks (GNN) to estimate solar irradiance by explicitly incorporating spatial relationships among observation sites. The study focuses on Sawahan Subdistrict, Nganjuk Regency, Indonesia, using solar irradiance data collected from five Automatic Weather Stations (AWS) operated by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) during 2024. Each station is represented as a graph node, with spatial connections constructed based on geographical distance, while temporal dependencies are modeled using Long Short-Term Memory (LSTM). Experimental results show that the proposed model achieves a Mean Absolute Error (MAE) of 102.64 W/m², a Root Mean Squared Error (RMSE) of 166.76 W/m², and an R² value of 0.6446 for the target location. These findings demonstrate that GNN-based spatial aggregation improves estimation stability and accuracy, providing practical support for localized solar energy assessment in tropical regions.
Apache Spark for Business and Financial Data Engineering: A Systematic Literature Review Ahmad Bilal Almagribi; Bambang Purnomosidi Dwi Putranto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5419

Abstract

This paper is an SLR that maps the application of Apache Spark in data engineering in the business and finance domains. Practitioners and researchers alike would find it interesting to know how Apache Spark has been applied to solve big data problems as organizations continue to deal with large volumes of data. By analyzing publications from the Scopus database for 2021-2025, we try to find trends and methodologies currently in use as well as gaps in research existing in the field. It was found that Apache Spark is primarily used for sentiment analysis and trend analysis on social media, particularly Twitter, since its real-time processing capability can help understand market dynamics and consumer behavior. The platform carries out predictive tasks like predicting customer churn or pricing financial assets (stocks, bonds, options), proving its versatility across different business applications. Also, this platform is popular for anomaly detection such as transaction fraud with efficiency and cost being the main drivers of adoption. The landscape is not monolithic since some studies propose alternative platforms indicating that Apache Spark may not be the best option for every scenario. Based on our findings, we suggest future research directions that would push the boundaries of the field: using social media data sources other than Twitter for more general market sentiment, applying more varied algorithms to improve prediction accuracy, and extending Spark's application into new areas like currency exchange rate forecasting, credit risk analysis, Anti-Money Laundering (AML) detection as well as Data Lakehouse architecture implementation. These recommendations are meant to steer researchers toward uncharted territories where significant value could be unlocked for business and finance with the help of Apache Spark.
Co-Authors Ahmad Bilal Almagribi Almagribi, Ahmad Bilal Andre Argisitawan Andriyani, Widyastuti Anwarudin Anwarudin Arma Fauzi Bagas Triaji Cucut Hariz Pratomo Danny Kriestanto Dommy Kristomo Domy Kristomo Faizal Makhrus Faizal Makhrus Femi Dwi Astuti Hamdani Hamdani Hendra Maryanto Heri Muhrial I Nyoman Oka Semadi Kuindra Iriyanta Moh. Abdul Kholik Muhammad Guntara Muhammad Najib Nenen Isnaeni Nugroho, Muhammad Agung Nurcahyo, Agung Wilis Rikie Kartadie Robertus Saptoto Robertus Saptoto Siti Khomsah, Siti Sri Redjeki Widyastuti Andriyani