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All Journal Jurnal Sarjana Teknik Informatika JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research CCIT (Creative Communication and Innovative Technology) Journal IJIIS: International Journal of Informatics and Information Systems Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse

Prasetio, Agung Budi

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Author-ID : 1729354

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Articles

Decision Support System Design Structural Promotion Civil Apparatus Using AHP and TOPSIS Methods Muhamad Yusuf; Kusrini Kusrini; Agung Budi Prasetio
CCIT (Creative Communication and Innovative Technology) Journal Vol 14 No 2 (2021): CCIT JOURNAL
Publisher : Universitas Raharja

The quality of the performance of the State Civil Apparatus (ASN) is a very important resource to be able to determine the capacity of the Regional Apparatus Organization (OPD). One of the efforts to improve the quality of OPD performance is the promotion of positions. Promotion of an award given for work performance and dedication of a civil servant, as well as being excited to improve work performance and loyalty. Therefore, it is necessary to determine a promotion. The weighting method in this study uses the Analytical Hirarchy Process. This study also compares this method with the Technique for Ordering Preference based on Similarities with Ideal Solutions. The resulting criteria are formal and informal. Consists of formal sub-criteria consisting of formal education, position experience, class rank, technical competence, managerial competence and socio-cultural competence. Then for the informal sub-criteria consisting of discipline, innovation, creativity, ideas for institutional functions, the ability to collaborate and work in teams, loyalty, responsibility, leadership, ability to communicate well and recommendations at the provincial and / or ministerial level. Furthermore, calculations are carried out using the AHP and TOPSIS methods for data for 2018 which means 1 position, in 2019 means 2 positions, and in 2020 means 4 positions. In one position consists of 3 ASN alternatives. After comparing the accuracy level of the AHP and TOPSIS methods with experts, the results of the AHP method are better in making recommendations for structural promotion of echelon IV ASN by producing a perfect score of 100% and a TOPSIS value of 71.4%.

ANALISIS TINGKAT KEMATANGAN SISTEM INFORMASI MANAJEMEN AKADEMIK DAN KEMAHASISWAAN IAIN PALANGKA RAYA MENGGUNAKAN COBIT 5 Pamungkas, Sapto; Kusrini, Kusrini; Prasetio, Agung Budi
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 11 No 2 (2021): September 2021
Publisher : STMIK Palangkaraya

The use of Information Technology (IT) at a university is needed at this time. There are many advantages in using IT and have a good impact on a university. The use of IT in a university has different roles according to their needs. The utilization of IT in Higher Education theoretically provides appropriate and efficient administration systems (Fernandes Andry and Christianto, 2018). IAIN Palangka Raya is one of the universities that has implemented IT, in this case, the academic and student management information system. The information system at IAIN Palangka Raya is one of the IT applications that is often used to carry out IT governance, however, there are several problems that occur in the information system, including features or tools that are not yet functional. For this reason, the researcher will analyze the information system to get the level of maturity and recommendations so that IAIN Palangka Raya can follow up on the results of this analysis and improve it to get the goal of good information system governance. In this study, the COBIT 5 framework is used with a focus on the EDM domain (Evaluate, Direct, and Monitor). The results of the analysis showed that the capability level was 2.86 (established), which means that the process was carried out, achieved goals, and well managed. With a balance value of 0.96, which means that the expected distance with the current distance is not too far so that features or tools that are not yet functioning are functional. So that the academic and student management information system of IAIN Palangka Raya runs according to its goals and is better.

Segmentasi Luka Diabetes Menggunakan Algoritma Contour Image Processing Roshandri, Wien Fitrian; Utami, Ema; Prasetio, Agung Budi
Jurnal Sarjana Teknik Informatika Vol. 9 No. 2 (2021): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v9i2.20226

Pengukuran luas luka pada penderita diabetes masih menggunakan cara manual dengan penggaris luka. Sedangkan penggaris yang ditempelkan keluka akan menjadi contaminated agent yang dapat menularkan infeksi pada penderita lain. Metode pengukuran digital diperlukan agar masalah tersebut bisa terselesaikan. Tetapi untuk memperjelas batas antara luka dan kulit diperlukan ketelitian dan akurasi yang tinggi. Untuk itu diperlukan metode pencitraan yang dapat melakukan segmentasi antara batas luka dan kulit paada pasien diabetes berbasis digital yang dinamakan digital planimetry. Penelitian ini menggunakan algoritma contour image processing dari nilai hue, saturation, value (HSV). Kemudian melakukan iterasi sebanyak 5 kali dan filter gamma. Sehingga mendapatkan hasil segmentasi luka. Kesimpulan akhir dari penelitian ini adalah segementasi dengan metode ini belum dapat melakukan segementasi luka dengan baik dan diperlukan tambahan nilai masking yang lebih luas, akan tetapi hasil iterasi ke 5 mendapatkan error terkecil yaitu 0.002%. Pencitraan digital yang dilakukan dalam penelitian ini dapat dikembangkan untuk menjadi alat ukur luas luka pasien diabetes berbasis digital.

Interpretable Product Recommendation through Association Rule Mining: An Apriori-Based Analysis on Retail Transaction Data Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala bin
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.252

The rapid growth of e-commerce has generated vast amounts of transactional data, creating opportunities for data-driven decision-making in retail environments. This study presents an interpretable product recommendation approach based on association rule mining using the Apriori algorithm. Unlike complex black-box recommender models, the proposed method emphasizes transparency and explainability in identifying purchasing relationships. The Groceries dataset comprising 38,765 transactions was analyzed to discover frequent itemsets and generate actionable association rules. After applying minimum thresholds of 0.02 for support and 0.4 for confidence, a total of 67 frequent itemsets and 45 strong rules were obtained. The rule {whole milk, sausage, rolls/buns} → {yogurt} achieved the highest lift value of 1.66, revealing meaningful co-purchasing behavior. Visualization tools, including heatmaps and network graphs, were employed to illustrate rule strength and product interconnections, facilitating business interpretation. The findings demonstrate that interpretable rule-based recommendations can effectively support product bundling, cross-selling, and retail layout strategies. This study highlights the continuing relevance of Apriori in creating transparent, data-driven insights and proposes future integration with hybrid models to address personalization and scalability challenges in modern recommendation systems.

Distributed Denial Of Service (DDOS) Attack Detection On Zigbee Protocol Using Naive Bayes Algoritm Masud, Ibnu; Kusrini, Kusrini; Prasetio, Agung Budi
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Distributed Denial of Service or better known as DDoS is an attempted attack from several computer systems that target a server so that the amount of traffic becomes too high so that the server cannot handle the request. DDoS is usually done by using several computer systems that are used as sources of attacks. So they attack one server through several computers so that the amount of traffic can also be higher. A DDoS attack is like a traffic jam that prevents a driver from reaching their desired destination on time. According to data, 33% of businesses in the world have fallen victim to DDoS attacks. DDoS is hard to trace. Some types of DDoS attacks can be very powerful and even reach speeds of 1.35 Tbps. Additionally, DDoS attacks can cause losses of $ 40,000 per hour if they occur. ZigBee is a standard from IEEE 802.15.4 for data communication on personal consumer devices as well as for business scale. ZigBee is designed with low power consumption and works for low level personal networks. ZigBee devices are commonly used to control another device or as a wireless sensor. ZigBee has a feature which is able to manage its own network, or manage data exchange on the network [1]. Another advantage of ZigBee is that it requires low power, so it can be used as a wireless control device which only needs to be installed once, because only one battery can make ZigBee last up to a year. In addition, ZigBee also has a "mesh" network topology so that it can form a wider network and more reliable data. In the previous research of Muhammad Aziz, Rusydi Umar, Faizin Ridho (2019) based on the results of the analysis carried out that the attack information that has been detected by the IDS based on signatures needs to be reviewed for accuracy using classification with statistical calculations. Based on the analysis and testing carried out with the artificial neural network method, it was found that the accuracy was 95.2381%. The neural network method can be applied in the field of network forensics in determining accurate results and helping to strengthen evidence at trial. The NaÃ¯ve Bayes model performed relatively poor overall and produced the lowest accuracy score of this study (45%) when trained with the CICDDoS2019 dataset [47]. For the same model, precision was 66% and recall was 54%, meaning that almost half the time, the model misses to identify threats.Â

Model TangselPay Receipts Using the UTAUT 2 Method Ikhwanudin, Aolia; Kusrini, Kusrini; Prasetio, Agung Budi
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.803

The South Tangerang City Government launched a digital financial service called TangselPay. This payment instrument will function as a means of paying levies and other transactions paid by taxpayers, TangselPay is basically a service from the South Tangerang City Government which is accessed via cellular phones (cell phones/smartphones) with the main aim of providing convenience to taxpayers in making levy payments. . . , so that taxpayers do not need to pay cash to the officer. This study aims to determine what factors influence people's interest in using TangselPay services in South Tangerang. The research model used is a modified model of Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). Data collection using purposive sampling method with the number of respondents in this study as many as 116 people in our market Pamulang. The data analysis technique in this study used Structural Equation Modeling (SEM) with SmartPLS version 3.3.3 software. The results of the analysis illustrate that the variables of Performance Expectations (PE) and Facilitation Condition (FC) have a positive effect on Use behavior and interest in use have a positive effect on usage behavior. While the variables of business expectations, social influence and hedonic motivation do not have a direct effect.

Predicting Customer Conversion in Digital Marketing: Analyzing the Impact of Engagement Metrics Using Logistic Regression, Decision Trees, and Random Forests Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala bin
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.34

This research explores the impact of engagement metrics on predicting customer conversion rates within digital marketing, employing three advanced predictive modeling techniques: Logistic Regression, Decision Trees, and Random Forests. Using a comprehensive dataset of 8,000 customer interactions, the study evaluates critical engagement metrics such as PagesPerVisit, TimeOnSite, and EmailClicks to determine their influence on conversion outcomes. The results indicate that PagesPerVisit and TimeOnSite are the most significant predictors of customer conversion, with the Random Forest model outperforming others, achieving an accuracy of 87.1% and an ROC-AUC score of 0.6979. The Logistic Regression model demonstrated the highest recall for the conversion class at 99.8%, but its performance in predicting non-conversions was less robust, highlighting the challenges of imbalanced datasets. Decision Trees, while offering valuable interpretability, showed a lower accuracy of 79.6% and struggled with precision in identifying non-conversions. These findings suggest that enhancing on-site customer engagement and refining email marketing strategies are pivotal for improving conversion rates. The study contributes to the field of digital marketing analytics by providing empirical evidence on the relative importance of various engagement metrics and offering practical insights for optimizing digital marketing strategies. Additionally, it highlights the benefits of using ensemble methods like Random Forests to achieve more balanced and accurate predictions in customer conversion scenarios.

Blockchain Node Classification Predicting Node Behavior Using Machine Learning Prasetio, Agung Budi; Purbo, Ono Widodo
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.42

Blockchain technology has emerged as a secure and decentralized framework for digital transactions; however, its open and pseudonymous nature also presents significant challenges related to fraudulent activities and malicious nodes. This study investigates the application of machine learning models for blockchain node classification and fraud detection, evaluating three models: Random Forest, XGBoost, and Neural Network. The research leverages a dataset of 10,000 blockchain transactions with 16 attributes, including transaction fees, block scores, stake distribution rates, and coinage. The results demonstrate that machine learning models can effectively classify blockchain nodes with high accuracy. Among the evaluated models, the Neural Network classifier outperformed the others, achieving an accuracy of 95.3%, precision of 95.1%, recall of 95.6%, and an F1-score of 95.3%. Comparatively, XGBoost achieved an accuracy of 94.1%, while Random Forest scored 92.4%. Feature importance analysis highlighted Block Score (0.38), Transaction Fee (ETH) (0.30), and Stake Distribution Rate (0.15) as the most significant factors influencing classification outcomes. Furthermore, confusion matrix analysis revealed that the Neural Network model produced 4780 true positives and 4440 true negatives, with only 200 false positives and 580 false negatives, demonstrating its robustness in identifying fraudulent nodes. Despite these promising results, real-world deployment presents several challenges, including the evolving nature of fraudulent strategies, real-time detection requirements, and scalability concerns. Future research should explore real-time learning techniques, integration of network-based features, decentralized fraud detection mechanisms, and cross-chain anomaly detection to improve model adaptability and effectiveness. By advancing these methods, machine learning-driven fraud detection can contribute to a safer, more transparent, and resilient blockchain ecosystem.

Scam Detection in Metaverse Platforms Through Advanced Machine Learning Techniques Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.19

The rapid expansion of metaverse environments has introduced novel opportunities and challenges, particularly concerning user security and trust. This study investigates the application of machine learning techniques to detect scam activities within the metaverse by analyzing user behaviors and interaction patterns. Using a comprehensive dataset, we evaluated three machine learning models—Random Forest, Support Vector Machine (SVM), and Neural Network—for their effectiveness in identifying scams. The Neural Network model achieved the highest performance, with an accuracy of 91%, a recall of 92%, and an AUC of 95%, making it the most reliable solution for this task. Feature importance analysis revealed that attributes such as the number of transactions and average transaction value significantly contribute to scam detection. Hyperparameter optimization further improved model performance, demonstrating the potential of fine-tuned architectures in handling high-dimensional datasets. Despite the Neural Network’s superior performance, its computational complexity highlights the need for lightweight implementations for real-time applications. This research contributes to the growing field of metaverse security by providing a robust framework for scam detection using machine learning. Future work should focus on expanding datasets to capture multi-platform behaviors, incorporating explainable AI (XAI) for improved interpretability, and enhancing model efficiency. These advancements will ensure safer and more trustworthy metaverse ecosystems for users worldwide.

Co-Authors Aboobaider, Burhanuddin bin Mohd Ahmad, Asmala Ahmad, Asmala bin Ema Utami Ikhwanudin, Aolia Kusrini Kusrini Kusrini, Kusrini Masud, Ibnu Muhamad Yusuf Pamungkas, Sapto Purbo, Ono Widodo Roshandri, Wien Fitrian

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