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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Decode: Jurnal Pendidikan Teknologi Informasi
Giat Karyono
STMIK AMIKOM Purwokerto
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Analisis Sentimen Terhadap Ulasan Google Play Store Aplikasi Lazada, Shopee, dan Tokopedia Menggunakan Algoritma IndoBERT Aisy, Afra Rihadatul; Karyono, Giat
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8745

Abstract

The growth of e-commerce has generated many user reviews, which are an important source for understanding consumer satisfaction and perceptions. However, manual analysis of unstructured reviews that use informal language is ineffective. In addition, conventional sentiment analysis approaches are often unable to capture the linguistic variations of the Indonesian language. This study uses the IndoBERT contextual language model to classify the sentiment of e-commerce application reviews on Shopee, Tokopedia, and Lazada. Data was collected through web scraping, amounting to 12,000 data points, with 4,000 for each application, labeled based on ratings, processed through preprocessing stages, balanced using Random Oversampling, and trained for three-class sentiment classification. The evaluation showed an Macro F1-Score of 0.90, indicating strong performance across all sentiment classes, including minority classes. These results confirm the effectiveness of IndoBERT in handling data imbalance in Indonesian sentiment analysis.
WhatsApp Hybrid Chatbot Architecture Rasa-DeepSeek: Design and Performance Evaluation Had, Iqbaluddin Syam; Utomo, Fandy Setyo; Karyono, Giat; Kinding, Dwi Putriana Nuramanah
Sistemasi: Jurnal Sistem Informasi Vol 15, No 2 (2026): 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.v15i2.5791

Abstract

This study designed and evaluated a hybrid chatbot for a domain-specific application by addressing two main issues: limited NLU coverage and the variability of latency and cost when all queries are routed directly to an LLM. The proposed solution integrates a deterministic Rasa-based pipeline with a DeepSeek fallback mechanism. In this architecture, Rasa handles NLU processing, rules, stories, and context storage for mk and jk, while the LLM is only invoked when the NLU confidence score falls below a defined threshold. The methodology includes end-to-end implementation through a Node.js bridge connected to Rasa, functional testing to validate the intent–entity–action flow, and performance testing using load (stress) testing across two access paths: the Rasa REST endpoint and the Node-to-Rasa bridge. Meanwhile, the LLM pipeline was profiled separately through instrumented action calls. The results indicate that domain-specific conversations were successfully answered using curated knowledge, and both deterministic access paths met the service level objective (SLO), achieving a median latency of approximately 32 milliseconds with no observed errors. This study contributes by demonstrating that a hybrid chatbot architecture separating deterministic and generative pipelines can maintain SLO compliance in domain-specific settings. In addition, it highlights limitations of LLMs in understanding domain ontologies, reinforcing the need for semantic guardrails.
Pemodelan Literasi TIK dan Kualitas Sistem untuk Memprediksi Kepuasan Pengguna: Peran Mediasi Kegunaan dan Moderasi Kesiapan Digital Ibrahim, Farrel; Karyono, Giat; Karyono, Purwadi; Nurfaizal, Yusmedi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Di era transformasi digital yang berlangsung cepat, kepuasan pengguna terhadap sistem teknologi tidak lagi semata-mata ditentukan oleh kinerja teknis, tetapi juga oleh kesiapan kognitif dan kontekstual pengguna. Penelitian ini mengkaji hubungan antara literasi TIK, kualitas sistem, persepsi kegunaan, dan kepuasan pengguna, serta menelaah peran mediasi dari persepsi kegunaan dan moderasi dari kesiapan digital. Pendekatan kuantitatif digunakan melalui pemodelan persamaan struktural Partial Least Squares (PLS-SEM) berdasarkan data dari 100 pengguna platform layanan digital. Hasil menunjukkan bahwa literasi TIK dan kualitas sistem secara signifikan meningkatkan persepsi kegunaan dan langsung memengaruhi kepuasan pengguna. Persepsi kegunaan juga terbukti memediasi pengaruh literasi TIK dan kualitas sistem terhadap kepuasan. Selain itu, kesiapan digital memoderasi secara positif pengaruh persepsi kegunaan terhadap kepuasan, menunjukkan bahwa pengguna dengan kesiapan lebih tinggi memperoleh manfaat lebih besar dari sistem yang dianggap berguna. Temuan ini memperkuat pengembangan model TAM melalui integrasi dimensi kognitif, teknologi, dan kontekstual, serta memberikan implikasi praktis bagi desain sistem akademik digital yang responsif terhadap kesiapan pengguna.   Abstract In the era of rapid digital transformation, user satisfaction with technological systems is no longer solely determined by technical performance, but also by users’ cognitive and contextual readiness. This study examines the relationships among ICT literacy, system quality, perceived usefulness, and user satisfaction, while exploring the mediating role of perceived usefulness and the moderating role of digital readiness. A quantitative approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) based on data from 100 users of digital service platforms. The results indicate that ICT literacy and system quality significantly enhance perceived usefulness and directly influence user satisfaction. Perceived usefulness was also found to mediate the effects of ICT literacy and system quality on satisfaction. Moreover, digital readiness positively moderates the influence of perceived usefulness on user satisfaction, suggesting that users with higher readiness levels gain greater benefits from systems they perceive as useful. These findings strengthen the development of the TAM framework through the integration of cognitive, technological, and contextual dimensions, while also offering practical implications for the design of academic digital systems that are responsive to users’ readiness levels.
Studi Komparasi Kinerja Algoritma AdaBoost dan CatBoost dalam Prediksi Perilaku Pembelian Pelanggan Kafilla, Princess Iqlima; Utomo, Fandy Setyo; Karyono, Giat
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.7947

Abstract

Customer purchase behavior is a crucial factor in the development of effective marketing strategies. By leveraging predictive analytics, businesses can personalize recommendations, optimize marketing campaigns and improve user experience, ultimately contributing to increased conversion rates and customer retention. This research compares the performance of AdaBoost and CatBoost algorithms in predicting customer purchase behavior. The dataset used includes demographic attributes and customer behavior history, allowing for comprehensive analysis. The results showed that CatBoost performed better overall with an accuracy of 94%, while AdaBoost showed higher recall and F1-score values in the positive class. This study concludes that both algorithms have reliability in predicting customer behavior, where CatBoost is superior in handling categorical features, while AdaBoost offers good adaptability on simpler datasets. As a next step, future research can explore the implementation of these models in real-time scenarios.
Co-Authors Aisy, Afra Rihadatul Andrianto Andrianto Berlilana Berlilana Dwi Putriana Nuramanah Kinding Fandy Setyo Utomo Had, Iqbaluddin Syam Hermanto, Aldy Agil Ibrahim, Farrel Imam Tahyudin Kafilla, Princess Iqlima Karyono, Purwadi Nurmalitasari, Gupita Pambudi, Rahmat Purwadi Purwadi Putri, Dewi Kartika Rujianto Eko Saputro Taqwa Hariguna Turino, Turino Wahid, Arif Mu'amar Yusmedi Nurfaizal