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All Journal Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sisforma: Journal of Information Systems PROCEEDING IC-ITECHS 2014 Bulletin of Electrical Engineering and Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Technomedia Journal International Journal of Economics, Business and Accounting Research (IJEBAR) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Informatika dan Sistem Informasi (JUISI) IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Informatika (JUTIF) Teknika Journal of Applied Data Sciences Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Pena Dimas JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Adi Suryaputra Paramita
Universitas Ciputra Surabaya
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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Fine-Grained Sentiment Analysis Approach on Customer Reviews Based on Aspect-Level Emotion Detection Paramita, Adi Suryaputra; Jusak, Jusak
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.964

Abstract

In the era of digital platforms, customer reviews constitute a vital resource for understanding user sentiment and perception toward products and services. Traditional sentiment analysis methods predominantly operate at the document or sentence level, often missing fine-grained emotional cues tied to specific product or service aspects. To address this limitation, this study proposes a novel Fine-Grained Sentiment Analysis (FGSA) framework that performs aspect-level sentiment classification using a joint learning approach. The proposed model employs a hybrid deep learning architecture that integrates transformer-based contextual encoders with Bidirectional Long Short-Term Memory (Bi-LSTM) layers. This design allows the model to capture both rich contextual semantics and sequential dependencies a combination that has not been widely adopted in existing FGSA research. Additionally, we introduce a new annotated dataset of 5,000 customer reviews spanning multiple domains (electronics, food and beverages, and general services), enabling robust training and evaluation. Experimental results show that the model outperforms standard baselines, achieving an F1-score of 82.0% for aspect extraction and an accuracy of 79.8% for sentiment classification. Further analysis reveals consistent patterns, such as positive sentiments linked to design and quality, and negative sentiments associated with customer service and delivery. These insights highlight the practical value of aspect-level sentiment modelling. The key contribution of this work is the integration of a transformer-Bi-LSTM joint architecture for aspect-based sentiment analysis, supported by a domain-diverse benchmark dataset. This framework enhances the interpretability and granularity of sentiment insights and sets a foundation for future research in multilingual and multimodal contexts.
Rancang Bangun Sistem Informasi Afiliasi Penjualan Tiket Seminar berbasis Website menggunakan Framework Laravel Pratama, Dimas Kurnia; Paramita, Adi Suryaputra
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penjualan tiket sebuah acara dilakukan secara konvensional adalah dengan proses pertemuan antara penjual dan pembeli. Proses konvensional tersebut memiliki beberapa kendala, dimulai dari kontrol hasil penjualan tiket pada tiap tenaga penjual dan perhitungan pembagian hasil dari penjualan tiket (afiliasi). Kegiatan yang diselenggarakan di Indonesia sangat banyak mulai dari seminar, workshop dan camp. Dimana pada kegiatan tersebut membutuhkan data peserta pada saat proses pendaftaran, data tersebut diperlukan untuk pembuatan sertifikat pada akhir kegiatan. Kemajuan penggunaan teknologi internet pada sebagian besar masyarakat yang berada pada kota-kota besar pada saat ini sudah menjadi kebutuhan sehari-hari. Meninjau dari hal tersebut, teknologi internet dapat dimanfaatkan dengan untuk perhitungan afiliasi penjualan tiket acara pada internal perusahaan, sehingga mempermudah tenaga penjual untuk mengetahui akumulasi hasil yang didapat. Pada penelitian ini akan dikembangkan sebuah sistem penjualan tiket berbasis website sebagai sistem afiliasi penjualan tiket untuk mempermudah perhitungan dan kontrol hasil penjualan. Sistem yang dibangun menggunakan Framework Laravel dimana Framework tersebut berbasis PHP yang open source dan memiliki konsep model-view-controller. Sistem juga menggunakan database yang berfungsi menampung data-data acara, lokasi, tanggal dan konfirmasi peserta. Tujuan dari penelitian ini adalah sistem afiliasi diharapkan dapat menjadi solusi bagi penyelenggara acara dapat menggunakan sistem ini untuk membantu dalam perhitungan afiliasi penjualan tiket. Hasil akhir dari penelitian ini, sistem afiliasi mampu menjembatani kebutuhan penyelenggara acara dan penjual lepas dalam perhitungan penjualan tiket acara. AbstractConventional way to sell a ticket is by direct meeting with a customer. However there has been an obstacle starting from monitoring the result of ticket sales to every salesperson and do the calculation of revenue share from ticket sales (affiliation). Workshop training event that been held in Indonesia is numerous, whether it's a talkshow, workshop or camp. Where this workshop event need the data of participant at registration, which will used as a certificate submission at the end of the event. For the most part of indonesian especially big city, internet usability has became a daily part of their life. According to those factor, intenet usability and technology equity can be use as internet usability to calculate event ticket sales affiliation at internal company. Thus facilitate salesperson to understand the accumulation of the ticket sales. System that develop using web application as ticket sales affiliation system to simplify the calculation and monitoring sales result.To develop the system, researcher used laravel framework which it is a ipen source framework based on PHP  languange, using a model view controller concepts. Systems also used database to save the event data, location, date and participant confirmation. The purpose of this research is affiliation system that hopefully can be a solution to  all event entrepreneur  to help calculate the ticket sales affiliation. Based on research result, affiliation system is able to mediate the needs between eo and sales in event ticket sales calculation.
Comparison of K-Means and DBSCAN Algorithms for Customer Segmentation in E-commerce Paramita, Adi Suryaputra; Hariguna, Taqwa
Journal of Digital Market and Digital Currency Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i1.3

Abstract

Customer segmentation is crucial for e-commerce businesses to effectively target and engage specific customer groups. This study compares the effectiveness of two popular clustering algorithms, K-Means and DBSCAN, in segmenting e-commerce customers. The primary objective is to evaluate and contrast these algorithms to determine which provides more meaningful and actionable customer segments. The methodology involves analyzing a comprehensive e-commerce customer dataset, which includes various features such as customer ID, gender, age, city, membership type, total spend, items purchased, average rating, discount applied, days since last purchase, and satisfaction level. Initial data preprocessing steps include handling missing values, encoding categorical variables, and normalizing numerical features. Both K-Means and DBSCAN algorithms are implemented, and their performance is evaluated using metrics such as silhouette score, Davies-Bouldin index, and Calinski-Harabasz score. The results indicate that K-Means achieved a silhouette score of 0.546, a Davies-Bouldin index of 0.655, and a Calinski-Harabasz score of 552.9. In contrast, DBSCAN achieved a higher silhouette score of 0.680, a Davies-Bouldin index of 1.344, and a Calinski-Harabasz score of 1123.9. These findings suggest that while DBSCAN performs better in terms of silhouette score, indicating more distinctly separated clusters, its higher Davies-Bouldin index reflects fewer compact clusters. The discussion highlights that K-Means is suitable for applications requiring clear and well-defined segments of customers, as it produces balanced cluster sizes. DBSCAN, with its strength in identifying clusters of varying densities and handling noise, is more effective in detecting niche markets and unique customer behaviors. This study's findings have significant practical implications for e-commerce businesses looking to enhance their customer segmentation strategies. In conclusion, both K-Means and DBSCAN demonstrate their respective strengths and weaknesses in clustering the e-commerce customer dataset. The choice of algorithm should be based on the specific requirements of the segmentation task. Future research could explore hybrid methods combining the strengths of both algorithms and incorporate additional data sources for a more comprehensive analysis.
Analysis of Gas Fee Patterns in Blockchain Transactions - A Case Study on Ethereum Smart Contracts Paramitha, Adi Suryaputra; Tarigan, Masmur
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.v2i3.41

Abstract

Gas fees play a crucial role in Ethereum blockchain transactions, directly affecting the cost and efficiency of decentralized applications. This study analyzes gas fee patterns across transaction types, temporal trends, and anomalous behaviors using a dataset of 1,000 Ethereum transactions. The results reveal that the average gas price was 120.5 Gwei, with a standard deviation of 45.2 Gwei, highlighting significant variability. Smart contract functions exhibited varying gas usage, with mint operations consuming the highest average gas (1,500,000 units) compared to approve (1,200,000 units) and transfer (800,000 units). A positive correlation (r = 0.65) was observed between gas price and value transferred, suggesting that higher-value transactions often incur elevated gas fees. Temporal analysis showed predictable patterns, with peak gas prices occurring between 13:00 - 17:00 UTC during high network activity and lower prices between 02:00 - 06:00 UTC. Additionally, anomaly detection identified 15 outlier transactions, including one with an unusually high gas price of 500 Gwei, reflecting network congestion or prioritization strategies. These findings provide actionable insights for optimizing transaction costs and improving smart contract efficiency. Future research could explore layer-2 scaling solutions, alternative fee mechanisms, and machine learning approaches for gas price prediction. This study contributes to a deeper understanding of Ethereum’s gas fee dynamics, offering valuable guidance for developers, users, and researchers in the blockchain ecosystem.
Predicting Player Performance in Valorant E-Sports using Random Forest Algorithm: A Data Mining Approach for Analyzing Match and Agent Data in Virtual Environments Paramita, Adi Suryaputra; Jusak, Jusak
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.39

Abstract

This study presents a data-driven approach to predict player performance in Valorant, an increasingly popular e-sport, using a Random Forest machine learning model. As e-sports continue to evolve within the metaverse, the need for strategic optimization and player selection has become critical. By analyzing a dataset containing player statistics from the Valorant Champion Tour (VCT), we aimed to predict player Rating, a key performance indicator. The dataset includes various metrics such as Kills Per Round, Average Combat Score (ACS), Clutch Success Ratio, and Kills:Deaths. After preprocessing the data, which involved handling missing values and feature engineering, the dataset was split into training and testing sets (80% and 20%, respectively). The Random Forest model, with 100 estimators and a maximum depth of 10, was trained on the processed data. The model's performance was evaluated using regression metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The results demonstrated that the model could predict player performance with a high degree of accuracy, with an R² value of 0.8831, meaning it explained 88.31% of the variance in player ratings. Furthermore, Kills Per Round emerged as the most significant feature, followed by Kill, Assist, Trade, Survive Ratio and Average Damage Per Round. These insights suggest that key metrics like kills and damage output are crucial for predicting player success. This study not only provides a comprehensive framework for predicting Valorant player performance but also demonstrates the potential of data mining in optimizing e-sports strategies. The findings contribute to the growing body of research on virtual gaming environments and offer actionable insights for teams in the metaverse, enabling data-driven decision-making to enhance performance and strategic outcomes.
Interpretable Temporal Risk Modeling for Contributor Inactivity Prediction: A Comparative Study of Tree-Based Ensembles Adi Suryaputra Paramita; Indra Maryati; Christian Christian; Elizabeth Nathania Witanto; Auezova Raya Tileubaevna; Choo Wou Onn
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1311

Abstract

This study aims to develop an interpretable temporal risk modeling framework for predicting contributor inactivity in collaborative development environments, thereby supporting sustained participation and improving productivity. The research focuses on contributor activity data collected from a collaborative software development platform, in which participation histories are represented by temporal engagement features that capture activity recency, participation intensity, and contribution patterns over time. To model inactivity risk, several tree-based ensemble learning algorithms, including Random Forest, XGBoost, LightGBM, and a stacking ensemble, are employed and evaluated under imbalanced classification conditions. Experimental results demonstrate strong predictive performance across models, with Random Forest achieving the highest AUC of 0.9401, while XGBoost obtains the best Matthews Correlation Coefficient (0.7353). The novelty of this study lies in prioritizing structured temporal behavioral representation through normalized temporal engagement features rather than increasing model complexity, enabling more interpretable inactivity risk modeling. The findings provide practical implications for collaborative platform managers by enabling early identification of contributor disengagement, supporting sustained participation, improving productivity, and facilitating continuous product innovation.
Fault-Tolerant Telegram Bot Architecture for Odoo 14: Validated Production Reporting in Flexible Packaging Tarigan, Masmur; Paramita, Adi Suryaputra; Dewi, Deshinta Arrova
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5515

Abstract

In flexible-packaging manufacturing, manual reporting dramatically delays synchronization with the ERP — and that means operational  latency and traceability issues. The proposed work is the design, implementation, and validation of a fault-tolerant Telegram bot interconnected with Odoo 14 for six production departments. Our bot architecture that combines conversational workflows with schema-based validation and XML-RPC for slow, large payloads, enables accurate and  timely reporting. In a four-week pilot with 1,066 production entries, we achieved 98.7% field completeness and lowered reporting latency to less than 2 minutes. Manual  baselines received 75% more requests for corrections. At disconnected state, the layered middleware of the system abstracted retry logic and media ingestion. Both SDG 9 (Resilient infrastructure, including ) and SDG 12 (Continue to reduce production waste at source, including consumables) are connected to the work presented here which evidence the feasibility of automatic conversational interfaces with a computer in the manufacturing informatics domain, and provide pathways towards scalable digital transformation and sustainability in the small-to-medium industry sector.
Machine Learning-Based Fraud Detection in E-Commerce Transactions Evelyn Evelyn; Adi Suryaputra Paramita
International Journal of Informatics and Information Systems Vol. 9 No. 1: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.295

Abstract

The rapid growth of e-commerce has heightened fraud risks, demanding advanced fraud detection solutions. This study evaluates five machine learning models Logistic Regression, SVM, KNN, Random Forest, and Gradient Boosting for detecting fraudulent transactions in e-commerce environments. The models were assessed based on accuracy, precision, recall, F1-score, ROC-AUC, and error-related indicators. Results indicate that ensemble-based models, particularly Gradient Boosting and Random Forest, consistently outperform linear models like Logistic Regression, achieving superior balance between precision and recall. Gradient Boosting emerged as the top performer, with the highest accuracy (0.9763), F1-score (0.9765), and ROC-AUC (0.9880), while maintaining a low false negative rate (4.38%). These findings suggest that machine learning models, particularly ensemble methods, provide robust and efficient fraud detection frameworks. The study emphasizes the importance of using recall and F1-score as primary metrics to balance fraud detection sensitivity and operational efficiency.
Co-Authors Adrie Oktavio Aloysius Putra Amanda Teonata Arief Ramadhan Arief Ramadhan Auezova Raya Tileubaevna Choo Wou Onn Christian Christian christian christian Dana Indra Sensuse Dewi, Deshinta Arrova Elizabeth Nathania Witanto Evelyn Evelyn EVELYN EVELYN Harjanto Prabowo Harjanto Prabowo Hendry Cipta Husada Indra Maryati Isviandhy, Gwynneth Jusak Jusak Kartika Gianina Tileng Laura Mahendratta Tjahjono Marco Febriadi Kokasih Minny Elisa Yanggah Nicoline Odelia, Lisandra Philbert Lukito Setiawan Pratama, Dimas Kurnia Rinabi Tanamal Taqwa Hariguna Tarigan, Masmur Tony Antonio Trianggoro Wiradinata Winata, Shalomeira Valencia Yosua Setyawan Soekamto Yulius Hari