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All Journal Journal of ICT Research and Applications JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika SemanTIK : Teknik Informasi Syntax Literate: Jurnal Ilmiah Indonesia JURNAL EDUCATION AND DEVELOPMENT Jurnal Teknologi Sistem Informasi dan Aplikasi JSiI (Jurnal Sistem Informasi) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) Jurnal Telematika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Journal La Multiapp Prosiding Konferensi Nasional PKM-CSR Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Legal and Cultural Analytics (JLCA) Jurnal Teknologi dan Manajemen Industri Terapan Journal of Internet and Software Engineering Jurnal Indonesia Sosial Sains eProceedings of Engineering Equivalent: Jurnal Ilmiah Sosial Teknik Jurnal INFOTEL The Indonesian Journal of Computer Science Multidisciplinary Indonesian Center Journal Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika Journal of Production, Enterprise, and Industrial Applications Proceeding of Community Service and Engagement
Sinung Suakanto
Universitas Telkom
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Pengembangan Chatbot E-Commerce Berbasis Large Language Model Dengan Pendekatan Retrieval-Augmented Generation Untuk Mendukung Automated Query Resolution Dan Order Processing Muhammad Haris Sitompul; Nur Ichsan Utama; Sinung Suakanto
eProceedings of Engineering Vol. 13 No. 1 (2026): Februari 2026
Publisher : eProceedings of Engineering

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

Abstract

Seiring dengan pesatnya perkembangan teknologi informasi dan komunikasi di era digital, lanskap bisnis global mengalami perubahan yang signifikan, salah satunya transformasi digital melalui platform e-commerce. Namun, seiring meningkatnya jumlah produk dan kebutuhan informasi yang akurat bagi pelanggan, tantangan muncul dalam menyediakan layanan pelanggan yang responsif dan relevan. Penelitian ini mengusulkan pengembangan chatbot e-commerce dengan pendekatan Retrieval-Augmented Generation (RAG), yang menggabungkan kemampuan Large Language Model (LLM) dengan sistem pencarian dokumen berbasis vektor. Model LLM yang digunakan adalah Llama-3.3-70B-Instruct, yang telah ditingkatkan kemampuannya dengan menambahkan informasi relevan melalui pencarian semantik terhadap knowledge base yang disimpan dalam vector storage berupa FAISS. Dengan pendekatan ini, chatbot mampu memberikan jawaban berbasis data aktual tanpa perlu melakukan fine-tuning, serta meminimalkan munculnya jawaban yang bersifat asumsi atau spekulatif. Hasil implementasi sistem menunjukkan bahwa integrasi LLM dan RAG dapat meningkatkan efisiensi layanan pelanggan dalam platform e-commerce. Hal ini dibuktikan melalui evaluasi mengunakan performance metrics dengan hasil skor metrik yang cukup tinggi, sehingga menunjukkan bahwa chatbot mampu memberikan jawaban yang akurat dan relevan sesuai kebutuhan pengguna. Kata kunci — e-commerce, chatbot, Large Language Model, Retrieval-Augmented Generation, LLaMA, FAISS
LLM-Based Interview Bot for Student Big Five Assessment and Career Recommendation Sang Dara Parameswari; Muharman Lubis; Sinung Suakanto; Jan M. Pawlowski
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1456

Abstract

The development of Artificial Intelligence (AI) and Natural Language Processing (NLP) offers new opportunities to make psychological assessments more interactive and meaningful. However, personality tests such as the International Personality Item Pool – Big Five Factor Markers (IPIP-BFM-50) still rely on static self-report questionnaires, which may limit engagement and contextual interpretation. This study proposes an InterviewBot-based Big Five Personality system (IB-B5P) that combines rule-based IPIP scoring with Large Language Model (LLM)-driven conversational assessment using GPT-3.5 Turbo. The system generates both quantitative personality scores and qualitative narrative profiles. Evaluation results show moderate to strong correlations (r = 0.31–0.71) between IB-B5P and IPIP scores, with Openness and Extraversion showing statistically significant relationships. These findings suggest that the hybrid rule–LLM approach can approximate IPIP tendencies while providing richer context-aware interpretations. The novelty of this study lies in integrating LLM-based conversational intelligence with a standardized psychometric framework, with potential applications in career guidance, educational counseling, and digital psychological assessment in higher education.
Studi Perbandingan Naïve Bayes dan Support Vector Machine (SVM) dalam Analisis Sentimen Pengguna Metaverse Sang Dara Parameswari; Muharman Lubis; Sinung Suakanto; Yumna Zahran Ramadhan; Raisyah Nurul Amanah; Revyolla Ananta Dila
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 4 No. 3 (2025): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v4i3.1122

Abstract

Penelitian ini bertujuan mengevaluasi persepsi publik di Indonesia terhadap isu metaverse melalui analisis sentimen berbasis text mining. Metaverse, yang memadukan media sosial, permainan daring, augmented reality (AR), virtual reality (VR), serta aset digital seperti cryptocurrency, semakin mendapat perhatian sejak pengumuman perubahan nama Facebook menjadi Meta pada tahun 2021 dan memunculkan beragam opini publik. Data diperoleh dari Twitter (X) dan dianalisis menggunakan dua algoritma klasifikasi teks, yaitu Naïve Bayes dan Support Vector Machine (SVM). Dalam penerapannya, Naïve Bayes menggunakan fungsi MultinomialNB, sedangkan SVM dijalankan dengan LinearSVC yang lebih sesuai untuk data teks berdimensi tinggi. Hasil penelitian menunjukkan bahwa SVM memberikan kinerja lebih baik dengan akurasi 78,3% dan Macro-F1 78,3%, dibandingkan Naïve Bayes yang memperoleh akurasi 72,4% dan Macro-F1 sebesar 60,2%. Selain itu, SVM lebih seimbang dalam mengenali seluruh kelas sentimen, khususnya kategori negatif, sementara Naïve Bayes tetap relevan sebagai baseline karena kesederhanaan dan efisiensinya. Penelitian ini berkontribusi dalam menyajikan perbandingan komparatif kedua algoritma pada analisis sentimen metaverse di Indonesia, sekaligus membuka ruang bagi pengembangan metode yang lebih mutakhir pada studi berikutnya.
Scenario-Based Risk and Control Formulation for Electronic Banking Channels ATM and EDC in Indonesian Commercial Banks Navya Kirana Safitri; Sinung Suakanto; Basuki Rahmad
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 3 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i3.353

Abstract

Background: ATMs and EDCs remain critical infrastructure components within Indonesia’s national payment system. However, their extensive utilization exposes commercial banks to various technological, operational, human, physical, and third-party risks. Despite recurring incidents comprehensive studies that formulate ATM and EDC risk scenarios and corresponding control measures within the Indonesian banking context remain limited. Objective: This study formulates risk scenarios and corresponding control measures for ATM and EDC electronic banking channels in Indonesian commercial banks. Methods: A structured literature review was conducted using six academic databases: Google Scholar, ScienceDirect, ResearchGate, Scopus, Wiley Online Library, and MDPI. The identified risks were analyzed using the ISACA Risk IT Framework and mapped to the NIST Cybersecurity Framework (CSF) 2.0 and ISO/IEC 27001:2022 standards. The proposed model was validated by six Subject Matter Experts (SMEs) with expertise in banking operations, IT risk management, information security, and payment systems using a 1–5 Likert scale. Results: The study identified 25 risk scenarios across six categories and formulated 25 consolidated control measures. Each scenario links risk sources or threat actors, risk events, potential impacts, and corresponding controls. The controls were classified as preventive, detective, and corrective measures and mapped across the people, process, and technology dimensions. Expert validation produced an average score of 4.13/5.00 (83%), indicating a high level of acceptance. Conclusion: As a proposed model derived from a systematic literature review and expert validation, this study provides a structured reference framework for risk management practitioners and policymakers in Indonesian commercial banking institutions.
DETERMINANTS OF INFORMATION SYSTEM SUCCESS IN INDONESIA: SYSTEM REQUIREMENTS, TEAM CAPABILITIES, AND STAKEHOLDER INVOLVEMENT Faidatul Hikmah; Sinung Suakanto
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 1 (2026): Vol. 3 No. 1 Edisi Januari 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i1.2034

Abstract

Success in information system projects has a significant impact on the development of information technology. The research specifically explores the factors that can influence the success and failure of information system projects in Indonesia. The research method applied is a double linear regression analysis of a case study involving 67 information system projects that have been completed by various organizations and companies in Indonesia. Some of the major findings of this study cover critical aspects that have a significant impact on the performance of information system projects. Analysis of project information system requirements is identified as one of the key factors affecting the success of the project. Team capabilities, both in terms of technical skills and interpersonal coordination, are also important elements that correlate with project outcomes. The involvement of stakeholders throughout the project cycle has also proved to have a positive impact. It is understood that involving stakeholders actively can enhance a better understanding of user needs and minimize the risk of change of requirements in the middle of the way. By gaining in-depth insight into these key factors, this research makes valuable contributions to the planning, development, and implementation of future information system projects in Indonesia.
CLASSIFICATION OF USER REVIEW SENTIMENT TOWARD PAYLATER SERVICES ON THE KREDIVO AND AKULAKU APPS USING NAÏVE BAYES Sang Dara Parameswari; Muharman Lubis; Sinung Suakanto
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 3 (2026): Vol. 03 No. 3 Edisi Juli 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i3.2727

Abstract

PayLater services are one of the rapidly growing digital financial innovations widely utilised in fintech apps in Indonesia, including Kredivo and Akulaku. User reviews on the Google Play Store reflect a range of experiences, from satisfaction with the ease of use of the service to complaints regarding bills, interest rates, late payment fees, credit limits, and app performance. This study aims to classify the sentiment of user reviews regarding PayLater services on the Kredivo and Akulaku apps using the Multinomial Naïve Bayes algorithm. Data was collected via web scraping from the Google Play Store and automatically labelled based on user ratings, with ratings of 1-2 classified as negative sentiment and ratings of 4-5 as positive sentiment, whilst a rating of 3 was excluded as it was considered ambiguous. Following a preprocessing stage comprising cleaning, case folding, tokenisation, stopword removal, and stemming, as well as feature extraction using TF-IDF, 3,652 reviews were obtained with a training-to-test data split ratio of 80:20. The results indicate that positive sentiment dominates the dataset at 56.49%, whilst negative sentiment accounts for 43.51%. Analysis by application revealed that Kredivo was dominated by positive sentiment (68.20%), whilst Akulaku was dominated by negative sentiment (51.70%).  The Naïve Bayes multinomial model achieved an accuracy of 84.13%, with average precision, recall, and F1-score values of 0.84, demonstrating good and balanced classification performance across both sentiment classes.
Evaluating Civil Servant Selection through Machine Learning Analysis of National Insight, General Intelligence, and Personal Characteristics Test Scores Muhammad Fauzan Nur Adillah; Sinung Suakanto; Nur Ichsan Utama
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2300

Abstract

This study analyzes the score distribution of 2,490 candidates in the 2024 Ministry of Finance Public sector recruitment, focusing on the CNI, GIT, and PCT sections using machine learning classification. Models used include Logistic Regression (accuracy 0.7897), Random Forest (0.9779), and XGBoost (0.9809), all trained with default parameters (n_estimators=100, max_depth=None) and evaluated using accuracy, precision, recall, and F1-score. While ensemble models outperformed Logistic Regression, the presence of false negatives—especially in the latter—reveals structural imbalances in test design. PCT scores dominate the total, while CNI and GIT show limited variation. These patterns suggest the need to revise PCT items with more complex ethical scenarios and enhance CNI and GIT content for better discrimination. This study contributes to improving test validity and fairness using empirical, data-driven methods. The findings support broader policy reforms toward more meritocratic and competency-aligned recruitment in Indonesia's civil service.
Pengembangan Sistem Informasi Terpadu Untuk Pelaporan Emisi Gas Rumah Kaca Scope 1 Dengan Memanfaatkan Teknologi IoT Pada Bidang Pertambangan Ryan Muhammad Satria; Ahmad Musnansyah; Sinung Suakanto
Journal of Production, Enterprise, and Industrial Applications Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jpeia.v4i1.11039

Abstract

Sektor pertambangan di Indonesia merupakan kontributor signifikan emisi Gas Rumah Kaca (GRK), namun sistem pelaporannya terkendala oleh proses manual yang tidak akurat, memakan waktu, dan mahal. Penelitian ini bertujuan untuk merancang sistem informasi terpadu berbasis Internet of Things (IoT) untuk mengotomatiskan pelaporan emisi Lingkup 1, serta mengukur daya guna dan akurasi dari sistem yang dikembangkan. Metode pengembangan yang digunakan adalah Iterative and Incremental melalui tiga fase, dengan tumpukan teknologi mencakup Laravel untuk dasbor, Python untuk kalkulasi emisi otomatis, dan MySQL sebagai basis data. Evaluasi sistem dilakukan melalui User Acceptance Test (UAT) dan System Usability Scale (SUS) yang melibatkan konsultan ahli. Hasilnya adalah sebuah aplikasi web fungsional yang dilengkapi dasbor pemantauan real-time, modul manajemen sensor dan sumber emisi statis, serta fitur ekspor laporan otomatis. Berdasarkan pengujian, sistem memperoleh skor SUS sebesar 92,5 yang menunjukkan tingkat daya guna yang sangat baik (excellent). Kesimpulannya, sistem yang dikembangkan berhasil menjadi solusi pelaporan yang valid dan efisien, serta terbukti dapat diterima dengan baik oleh pengguna sebagai alat bantu pemantauan yang andal.
Co-Authors A., Simon Filippus Abdulaziz, Rifqi Abdulaziz Adillah, Muhammad Fauzan Nur Adyartama, Arya Putra Agustien, Ferry Ahmad Musnansyah Ahmad Sidik Rofiudin Alaric Rasendriya Aniko Albert, Vincentius Alfi Zahra Hafizhah Andreas Andreas Angela, Dina Anggraeni Xena Paradita Ani Kartini Anis Farihan Mat Raffei Anis Farihan Mat Raffei Anisa, Gia Annastasia, Syifa Aprilita Firsty Hazdia Arifudin, Nanang Bagastio, Shobrun Jamil BASUKI RAHMAD Bayuwindra, Anggera Christy, Aldi Cristian Richardo Anin Daniel Hadi Wijaya Dimas Jaya Kusuma Dina Angela Echo, Ruth Edi Nuryatno Edi Triono Nuryatno Ekky Novriza Alam Ema Rachmawati Evan Reswara Fa'rifah, Riska Yanu Fahrizky, Bimo Agung Faidatul Hikmah Faishal Mufied Al Anshary Fakhrurroja, Hanif Faqih Hamami Fauzi, Rokhman Febriyani, Widia Ferda Ernawan Firdaus, Taufiq Maulana Gamaliel, Yoyok Yusman Handoko, Mahardika Maulana Al Mahdi Hardiyanti, Margareta Hazdia, Aprilita Firsty Herry Imanta Sitepu Herry Sitepu Herry Sitepu Hikmah, Faidatul Hutagalung, Maclaurin Hutahaean, Bernad Robinson Ismail, Mohd Arfian Isnaeni, Rizqullah Maziyah Jan M. Pawlowski Krisna Dwi Permana Mahardika Maulana Al Mahdi Handoko Margareta Hardiyanti Mat Raffei, Anis Farihan Mifta Ardianti Mima Artamevia Muhammad Fahmi Hidayat Muhammad Fauzan Nur Adillah Muhammad Haris Sitompul Muhammad Ivan Fadilah Muharman Lubis Mulyati, Rika Munansyah, Ahmad Navya Kirana Safitri Nia Ambarsari Nugroho, Tunggul Nugroho, Tunggul Arief Nur Ichsan Utama Nuraliza, Hilda Nuryanto, Edi Priyadi, Djoko Rachmadita Andreswari Rafi Adinegoro Raharjo, Adi Rahmat Fauzi Raina, Apriani Nur Raisyah Nurul Amanah Randy Ferdiawan Revyolla Ananta Dila Rika Mulyati Rivero Novelino Roberd Saragih et al., Roberd Rofiudin, Ahmad Sidik Ryan Muhammad Satria Safara Cathasa Riverinda Rijadi Sang Dara Parameswari Sang Dara Parameswari Sang Dara Parameswari Satria , Ryan Muhammad Sayyid Taufiq Abdulhafizh Sebastian, Kelvin See, Tan Lian Seno Adi Putra SETYORINI Shaffiei, Zatul Alwani Siregar, Amril Mutoi Suhardi Suhono H. Supangkat Sulingallo, Irwansa Ryan Syfa Nur Lathifah Syfa Nur Lathifah Thaha, Taufik Kemal Tiara Rahmania Hadiningrum Tien Fabrianti Kusumasari Tjong Wan Sen Ulinuha, Zulfa Ventje Jeremias Lewi Engel Warmiyana Zairi Absi Widyadhari, Dinda Putri Widyatasya Agustika Nurtrisha Wijaksana, Syifa Nuurunnisa Wijaya, Yohanes Rico Yoga Raditya Nugraha Sukma Pradana Yoyok Gamaliel Yumna Zahran Ramadhan Zulkarnaen, Rizky Zaki