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All Journal Proceedings of KNASTIK JAM : Jurnal Aplikasi Manajemen PROCEEDING IC-ITECHS 2014 Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Humaniora Jurnal Entrepreneur dan Entrepreneurship International Journal of Economics, Business and Accounting Research (IJEBAR) Jurnal Informatika dan Sistem Informasi Jurnal Performa : Jurnal Manajemen dan Start-up Bisnis Aiti: Jurnal Teknologi Informasi Teknika Journal of Applied Data Sciences Journal of Accounting and Finance Management (JAFM) Prosiding Seminar Nasional Universitas Ma Chung
Trianggoro Wiradinata
Universitas Ciputra Surabaya, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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A comparative study on classification models for stock rating prediction Yap, Justin; Wiradinata, Trianggoro
AITI Vol 21 No 1 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i1.140-151

Abstract

The digital transformation in the stockbroker industry has led to a significant increase in retail investors, who often lack the expertise to analyse stocks thoroughly. This research addresses the challenge by proposing a classification model to predict stock ratings such as "Reduce", "Hold", "Moderate Buy", and "Buy”, allowing retail investors to make informed decisions. The data analysed is collected from the S&P 500 index through web scraping using Beautiful Soup, resulting in a dataset used for training and testing the classification model. Popular stock indicators are used as attributes in predicting the rating of the stock, which includes the Exchange, Price, Volume, Market Cap, ROE, ROA, P/E Ratio, EPS, Annual Sales, Net Income, Net Margins, and PB Ratio of the stock. The models selected for classification include K-Nearest Neighbors (k-NN), Gaussian Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Random Forest. GridSearch is employed to maximize each algorithm's parameters for optimal performance. Results indicate that the k-NN model outperforms others, achieving the highest accuracy (0.618644) and weighted F1-score (0.605011). However, all models exhibit relatively low accuracy, suggesting the complexity of predicting stock ratings due to external factors not considered in the study.
Penerapan Model CRISP-DM pada Analisis Pendapatan Menggunakan Metode Klasifikasi Savitri, Tjok Istri Vicky; Wibowo, Wilbert Bryan; Wiradinata, Trianggoro
Prosiding Seminar Nasional Universitas Ma Chung (Informatika & Sistem Informasi Bahasa dan Seni
Publisher : Ma Chung Press

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

Abstract

Di era globalisasi ini, prediksi klasifikasi pendapatan dibutuhkan untuk membantu pemerintah dalam mengalokasikan sumber daya untuk berbagai layanan publik, pembangunan infrastruktur, kesehatan, pendidikan, dan program sosial lainnya. Dengan memahami pola pendapatan dan kebutuhan masyarakat, pemerintah dapat merencanakan dan mendistribusikan anggaran secara lebih efektif dan efisien, serta memastikan bahwa layanan dan program yang disediakan tepat sasaran dan memberikan manfaat maksimal bagi masyarakat. Data Census Income mencakup berbagai atribut demografis dan ekonomi, termasuk usia, jenis kelamin, pendidikan, status pernikahan, pekerjaan, ras, jam kerja per minggu, dan asal negara. Penelitian ini menggunakan teknik machine learning untuk mengklasifikasikan individu berdasarkan tingkat pendapatan mereka, apakah di atas atau di bawah $50.000 per tahun. Metode klasifikasi yang digunakan meliputi Logistic Regression, K-Nearest Neighbors (KNN), dan Naive Bayes. Penelitian ini menggunakan sebanyak 30.162 data dengan pembagian 80% sebagai data latih dan 20% sebagai data tes. Hasil penelitian menunjukkan akurasi untuk Logistic Regression sebesar 81%, KNN sebesar 79%, dan Naive Bayes sebesar 77%. Hasil penelitian juga menunjukkan bahwa faktor-faktor seperti tingkat pendidikan, jam kerja per minggu, dan jenis pekerjaan memiliki pengaruh signifikan terhadap pendapatan individu. Temuan ini dapat membantu pemerintah dan pembuat kebijakan dalam merumuskan strategi untuk mengurangi kesenjangan pendapatan dan meningkatkan kesejahteraan ekonomi masyarakat. Dapat disimpulkan bahwa penggunaan Logistic Regression terbukti paling akurat dalam memprediksi pendapatan.
Online Measuring Feature for Batik Size Prediction using Mobile Device: A Potential Application for a Novelty Technology Wiradinata, Trianggoro; Saputri, Theresia Ratih Dewi; Sutanto, Richard Evan; Soekamto, Yosua Setyawan
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.121

Abstract

The garment industry, particularly the batik sector, has experienced significant growth in Indonesia, coinciding with a rise in the number of online consumers who purchase batik products through e-Marketplaces. The observed upward trend in growth has seemingly presented certain obstacles, particularly in relation to product alignment and product information dissemination. Typically, batik entrepreneurs originate from micro, small, and medium enterprises (MSMEs) that have not adhered to global norms. Consequently, the sizes of batik products offered for sale sometimes exhibit inconsistencies. The issue of size discrepancies poses challenges for online consumers seeking to purchase batik products through e-commerce platforms. An effective approach to address this issue involves employing a smartphone camera to anticipate body proportions, specifically the length and width of those engaged in online shopping. Subsequently, by the utilization of machine learning techniques, the optimal batik size can be determined. The UKURIN feature was created as a response to a specific requirement. However, it is essential to establish a methodology for investigating the elements that impact the intention to use this feature. This will enable developers to prioritize their feature development strategies more effectively. A total of 179 participants completed an online questionnaire, and subsequent analysis was conducted utilizing the Extended Technology Acceptance Model framework. The findings indicate that Perceived Usefulness emerged as the most influential factor. Consequently, when designing and developing the novelty feature of UKURIN, it is imperative for designers and application developers to prioritize the benefits aspect.
Enhancing Online Batik Shopping Experience through Live Streaming Commerce and the LYFY Application Wiradinata, Trianggoro; Wibowo, Wilbert Bryan; Oktian, Yustus Eko; Maryati, Indra; Soekamto, Yosua Setyawan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.504

Abstract

Online batik shopping often results in buyer dissatisfaction due to discrepancies between product descriptions and the actual items received. Static images and text on e-marketplace platforms are insufficient to convey the intricate details of batik designs, leading to mismatches in customer expectations. To mitigate this issue, Live Streaming Commerce (LSC) features, such as those on Shopee Live, allow sellers to showcase products in real-time, providing more accurate representations. However, sellers face challenges in managing overwhelming volume of comments during live streams, making it difficult to prioritize important queries. LYFY, a comment management app developed to streamline these interactions, aims to address this problem by improving the quality of interaction between live streamers and prospective buyers through filtering important comments. This study examines the determinants affecting the adoption of LYFY by online batik vendors. The research integrates the Task-Technology Fit (TTF), Technology Acceptance Model (TAM), and Expectation-Confirmation Model (ECM) frameworks to evaluate LYFY's performance in fulfilling user requirements. Data were collected from 243 respondents with LSC experience, and the research model underwent evaluation through Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model exhibited high reliability and validity, with values surpassing the suggested thresholds, thereby providing solid support for subsequent analysis. Key factors such as TTF, confirmation, perceived usefulness, ease of use, and satisfaction were examined to determine their impact on user adoption. The analysis revealed that TTF has the strongest influence on confirmation, perceived usefulness, satisfaction, and individual performance. Additionally, perceived ease of use and confirmation substantially influence continuance intentions and satisfaction. These results suggest that enhancing LYFY's task-technology fit and simplifying its user interface are crucial for improving user satisfaction and adoption. By addressing these areas, LYFY can better support live stream sellers, reduce product expectation discrepancies, and improve overall customer experience, particularly in the online batik market.
The Effect of Consumer Trust and Perceived Risk on e-Wallet Adoption: Consideration for Technology Startup Entrepreneurs: Consideration for Technology Startup Entrepreneurs Krisnawati, Melisa; Wienadi, Jessica; Wiradinata, Trianggoro
Jurnal Entrepreneur dan Entrepreneurship Vol. 10 No. 2 (2021): Jurnal Entrepreneur dan Entrepreneurship
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/jee.v10i2.2212

Abstract

The use of electronic wallet (e-Wallet) has been increasingly popular due to the growth of electronic commerce because e-Wallet offers practicality and secure way of paying. However, data shows that payment by bank transfers are still the most popular payment method compared to e-Wallet. This study aims to analyze the effect of perceived usefulness, ease of use, consumer trust, and perceived risk towards the adoption of various kinds of e-wallet in Indonesia. The grounding theory of this study is using the Technology Acceptance Model (TAM) which is currently still very relevant in measuring antecedents toward technology adoption. This study collects data from 128 respondents using a self-administered questionnaire using purposive (judgmental) sampling. Among data collected there were 3 responds detected as outliers, hence removed. Multiple Regression Analysis was then performed using python programming language to determine significant factors. The finding of this study shows all antecedents are significantly affecting Intention to Adopt e-Wallet payment with Perceived Usefulness as the dominant factor. The finding is expected to inspire e-Wallet developers, integrators, and digital entrepreneurs to pay more attention towards the Perceived Usefulness factor.
LyFy: Enhancing Batik E-Commerce Live Streaming Through Real-Time Chat Filtering and Product Recommendation Oktian, Yustus Eko; Setiawan, Eugene Abigail; Wiradinata, Trianggoro; Maryati, Indra; Soekamto, Yosua Setyawan
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1104

Abstract

Live streaming has emerged as an essential tool for e-commerce, allowing sellers to engage with potential customers in real-time. However, the massive influx of comments during these sessions often includes a mix of useful product-related queries and irrelevant or distracting messages, which can overwhelm the presenter and reduce the effectiveness of the stream. In this paper, we propose LyFy, a browser-based extension designed to filter live chat messages and provide personalized product recommendations in real-time, specifically applied in Batik e-commerce to support the preservation and promotion of this unique cultural heritage of Indonesia. Our system uses a combination of natural language processing (NLP) and machine learning models to identify relevant comments, group similar queries, and offer product suggestions based on viewers' interests. We demonstrate the effectiveness of this system through a prototype implementation and evaluate its performance with qualitative feedback from streamers and users. The evaluation results indicate high user satisfaction, with over 51% of respondents rating LyFy as highly effective and 52% as highly efficient, making it a valuable tool for enhancing e-commerce live streaming interactions.
EFFECT OF ORGANIZATIONAL COMPETENCE, ORGANIZATIONAL SUPPORT, AND ORGANIZATIONAL PRODUCTIVITY TOWARDS ADOPTION OF FINANCIAL TECHNOLOGY Herdinata, Christian; Wiradinata, Trianggoro; Christian, Sonata; Setiobudi, Auditia
Jurnal Aplikasi Manajemen Vol. 17 No. 4 (2019)
Publisher : Universitas Brawijaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jam.2019.017.04.05

Abstract

The rapid development of internet technology with affordable prices raises new opportunities for micro, small, and medium enterprises (SMEs) in East Java in creating product innovation and marketing development. Various studies in the field of entrepreneurship and information technology have been carried out, but not many studies specifically examine the level of adoption of the Financial Technology system for business creation and development. This study focuses on the influence of organizational competence, organizational support, organizational productivity on financial technology adoption. This study uses a quantitative approach with a sample of SME businesses that use financial technology system adoption in the business that is run. The sample chosen using purposive sampling technique and selected 402 SMEs in East Java with the data analysis technique used is multiple linear regression techniques. The results of this study found that the influence of organizational competence, organizational support, and organizational productivity had a significant influence on financial technology adaptation.
Bridge Scorekeeping Automation: An iOS Application to Improve Tournament Scoring Accuracy and Efficiency Mahazoya, Aqilla Shahbani; Wiradinata, Trianggoro
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5136

Abstract

Scoring in bridge tournaments is still largely dominated by traditional methods such as manual score sheets and specialized devices like Bridgemate. While widely used, these approaches present significant limitations—manual scorekeeping is prone to human error, and Bridgemate devices are often costly and not accessible to all organizers. To address these challenges, Bridge Team Comparator was developed as an iOS-based application offering a more accurate, efficient, and affordable scoring solution for bridge tournaments. Designed with a user-friendly interface for both novice and experienced players, the application supports real-time score entry, automatic calculation of International Match Points (IMP), and efficient result summaries. The development process adopted the Challenge-Based Learning (CBL) framework through the phases of Engage, Investigate, and Act, focusing on simplifying the bridge scoring process. User testing involved bridge athletes from Universitas Negeri Malang, Universitas Brawijaya, and the Sidoarjo bridge community. Results demonstrated a reduction in scoring errors by 8–36% and increased efficiency compared to conventional methods. In addition to offering a cost-effective alternative to commercial devices, the application contributes to the digital transformation of scoring systems in traditional sports. With potential for cross-platform development, Bridge Team Comparator opens new opportunities for broader adoption within the global bridge community.
Enhancing Web Security and Performance with Hybrid Stateless Authentication Mario, Benedictus; Wiradinata, Trianggoro; Christian, Christian
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9251

Abstract

Ensuring operational integrity across industries and protecting sensitive data require strong authentication systems. This paper presents a novel hybrid stateless authentication method that integrates binary payloads, token specifications, and database solutions. By employing a distinctive expiration policy, our proposed approach overcomes limitations inherent in traditional token revocation strategies while achieving token verification speeds that are up to 86 times faster than conventional statefull session-based methods. Overall, through uniformed benchmarking experiments and a comprehensive review of the literature substantiate the performance and security advantages of our method. Ultimately, this hybrid technique offers a more scalable and secure framework for authentication management, enabling efficient and flexible deployment in high-demand distributed environments.
Usability Evaluation on “LYFY” as an e-Marketplace Tool for the National Batik Industry Witanto, Elizabeth Nathania; Permana, Belinda Putri Adi; Wiradinata, Trianggoro; Oktian, Yustus Eko; Maryati, Indra
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.11858

Abstract

In the current digital era, e-commerce has emerged as a primary choice for many business actors, particularly Micro, Small, and Medium Enterprises (MSMEs) specializing in batik products. Live streaming commerce has gained popularity as an effective marketing strategy, enabling direct interaction between sellers and consumers. However, challenges such as audience engagement and the need for prompt responses to questions or comments during live sessions pose significant issues that need to be addressed. To assist MSMEs optimize this strategy, the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies can provide innovative solutions. LYFY, an AI-driven e-marketplace platform, was developed to support batik MSMEs by facilitating product promotion, enhancing consumer interaction, and enabling efficient transactions through features such as live streaming and size prediction tools. To assess the effectiveness of LYFY's user interface and overall experience, a usability evaluation was conducted using two standardized frameworks: ISO 9241-11 and the Usability Metric for User Experience (UMUX-Lite). The finding suggests that LYFY provides a high-quality user experience and is well-positioned to support the digital transformation of Indonesia’s batik SMEs.
Co-Authors Adi Suryaputra Paramita Adrie Oktavio Amanda Teonata Astrid Kusumowidagdo Auditia Setiobudi christian christian Christian Herdinata Christina Theodora Loman Cicilia Larasati Rembulan Dyah Kusuma Wardhani Elia Rapha Tister Nursalim Elizabeth Nathania Witanto Endi Sarwoko Feby Putri Candi Felicia Graciella Harianti, Lintang Indra Maryati Krisnawati, Melisa Mahazoya, Aqilla Shahbani Mario, Benedictus Melania Rahadiyanti Michael Christian Chandra Michelle Chandra Permana, Belinda Putri Adi Rinabi Tanamal Savitri, Tjok Istri Vicky Setiawan, Eugene Abigail Sonata Christian Sutanto, Richard Evan Theresia Ratih Dewi Saputri Tony Antonio Wibowo, Wilbert Bryan Wienadi, Jessica Yap, Justin Yosua Setyawan Soekamto Yustus Eko Oktian