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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 International Journal of Artificial Intelligence Research SemanTIK : Teknik Informasi Syntax Literate: Jurnal Ilmiah Indonesia Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) 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 Jurnal INFOTEL Jurnal Pustaka Indonesia The Indonesian Journal of Computer Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Multidisciplinary Indonesian Center Journal Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika Proceeding of Community Service and Engagement
Sinung Suakanto
Universitas Telkom
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Neuroscience Nursing Physics Public Health Social Sciences Other

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Integration of Blockchain-Based Smart Contracts in the Resolution of Commercial Contract Disputes Warmiyana Zairi Absi; Rika Mulyati; Sinung Suakanto
Journal of Legal and Cultural Analytics Vol. 5 No. 1 (2026): February 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/jlca.v5i1.16233

Abstract

The development of blockchain technology encourages the use of smart contracts as digital contract instruments that are automatic and cannot be changed, especially in cross-sector commercial transactions. This study aims to analyze the legal status of blockchain-based smart contracts as well as evaluate the possibility of their integration in legally recognized commercial contract dispute resolution mechanisms. This research uses a normative legal research method with a conceptual and case legislation approach conducted through a literature study of laws and regulations, legal doctrine, as well as relevant decisions and cases. This study does not involve respondents or informants because it focuses on the analysis of legal norms and concepts. The data was analyzed qualitatively juridically through interpretation methods and legal arguments. The results of the study show that smart contracts can in principle be integrated in the settlement of commercial contract disputes as an instrument for the implementation and proof of contracts, but have not been able to fully replace the role of conventional dispute resolution mechanisms due to their limitations in handling legal interpretation, the application of the principle of good faith, and certain conditions such as non-technical defaults. This study concludes that the integration of smart contracts requires a hybrid model that combines technology-based automated execution with a law-based dispute resolution mechanism to ensure legal certainty and substantive justice in commercial contract practice.
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 User Experience Kesehatan Mental untuk Peningkatan Bisnis Perusahaan Start Up Handoko, Mahardika Maulana Al Mahdi; Kusumasari, Tien Fabrianti; Suakanto, Sinung
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3366

Abstract

Teknologi memainkan peran penting dalam bidang kesehatan mental, terutama dalam membantu individu mengelola stres dan gangguan mental. Startup kesehatan mental merupakan jenis usaha yang memanfaatkan teknologi dan pendekatan inovatif untuk mengatasi masalah kesehatan mental dan meningkatkan akses ke penanganan kesehatan mental. Tujuan penelitian ini adalah untuk melakukan pengembangan User Experience pada Kesehatan Mental untuk meningkatkan kepuasan pengguna sehingga dapat meningkatan bisnis Perusahaan Startup. Hasil dari pengumpulan data menunjukkan bahwa kebutuhan pengguna adalah Platform Kesehatan Mental Online yang dengan harga yang murah, durasi yang cukup, dan memiliki fleksibilitas waktu dimana pengguna dapat menggunakan platform sebagai tempat bercerita. . Hasil pengembangan diukur dengan pengujian System Usability Testing dengan 10 responden mendapatkan hasil skor 73,5 pada pengujian Usability Testing menandakan bahwa hasil pengembangan User Experience berada pada kategori Acceptable dengan Grade C.
Pengembangan Aplikasi Mobile Untuk Monitoring Kondisi Pasien Stroke Berbasis Pengenalan Wajah Dimas Jaya Kusuma; Hanif Fakhrurroja; Sinung Suakanto
eProceedings of Engineering Vol. 13 No. 1 (2026): Februari 2026
Publisher : eProceedings of Engineering

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Abstract

Stroke merupakan salah satu penyakit dengan dampak serius yang memerlukan pemantauan kondisi pasien secara berkelanjutan untuk mencegah kekambuhan dan komplikasi lanjutan. Namun, keterbatasan akses terhadap layanan medis dan minimnya keterlibatan pendamping pasien dalam proses monitoring menjadi kendala tersendiri. Penelitian ini bertujuan untuk mengembangkan aplikasi mobile yang dapat membantu proses monitoring kondisi pasien stroke menggunakan teknologi pengenalan wajah berbasis deep learning. Metode yang digunakan dalam penelitian ini adalah Design Thinking, yang terdiri dari lima tahapan: empathize, define, ideate, prototype, dan test. Aplikasi dibangun menggunakan framework Flutter serta Firebase sebagai layanan backend. Proses deteksi dilakukan melalui citra wajah pengguna, yang dianalisis oleh model deep learning untuk mengidentifikasi perubahan visual seperti asimetri wajah sebagai indikator kondisi pasien. Hasil evaluasi menunjukkan bahwa aplikasi ini mampu mempermudah proses monitoring , baik bagi pasien yang dapat menggunakan aplikasi secara mandiri maupun bagi kerabat yang mendampingi. Pengujian sistem menunjukkan bahwa fitur utama berjalan sesuai dengan fungsinya, dan mayoritas pengguna menyatakan aplikasi mudah digunakan serta bermanfaat dalam mendukung pemantauan pasien stroke. Kata kunci — stroke, monitoring, pengenalan wajah, aplikasi mobile, deep learning
Pengembangan Chatbot Berbasis Large Language Model (LLM) pada Platform E-Commerce Yoga Raditya Nugraha Sukma Pradana; Nur Ichsan Utama; Sinung Suakanto
eProceedings of Engineering Vol. 13 No. 1 (2026): Februari 2026
Publisher : eProceedings of Engineering

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Abstract

Perkembangan teknologi digital di Indonesia yang pesat telah mendorong adopsi sistem perdagangan elektronik pada berbagai institusi, termasuk platform e-commerce internal seperti TokoPoin di Koperasi Telkom University. Namun, implementasi sistem komunikasi pelanggan pada platform tersebut masih menghadapi tantangan signifikan, yaitu inefisiensi pengelolaan komunikasi customer service akibat keterbatasan akses penjual terhadap platform komunikasi dan pola komunikasi pelanggan yang tidak efisien dengan pertanyaan-pertanyaan repetitif. Penelitian ini bertujuan mengembangkan sistem chatbot berbasis Large Language Model (LLM) yang terintegrasi dengan WhatsApp untuk mengatasi masalah tersebut. Metode penelitian menggunakan pendekatan kuantitatif dengan implementasi model llama3.2:3b yang diintegrasikan melalui multi-channel antara platform WhatsApp dan antarmuka web. Hasil penelitian menunjukkan bahwa sistem berhasil mencapai akurasi pengenalan intent sebesar 93,75% dengan rata-rata waktu respons 8,4 detik. Integrasi multi-channel berfungsi optimal dengan pengiriman pesan real-time rata-rata 2,412 detik, berada di bawah standar 3 detik. Kualitas jawaban chatbot mencapai skor rata-rata 4,10 dari skala 5. Pengujian usability menggunakan System Usability Scale (SUS) terhadap 36 responden menghasilkan skor rata-rata 77,85, melampaui ambang batas standar 68 dan masuk kategori usability baik. Penelitian ini membuktikan bahwa implementasi chatbot berbasis LLM dapat secara efektif mengurangi beban komunikasi repetitif penjual, meningkatkan efisiensi operasional, dan memberikan pengalaman komunikasi yang responsif dalam konteks e-commerce internal. Kata kunci: chatbot, customer service, e-commerce, Large Language Model, multi-channel integration, TokoPoin, WhatsApp
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

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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
Enhancing Healthcare RFID Asset Tracking: A Multi-Objective Optimization Approach Considering Network Delay, False Identification, and Energy Efficiency Suakanto, Sinung; Nuryatno, Edi Triono; Fakhrurroja, Hanif; Rijadi, Safara Cathasa Riverinda
Journal of ICT Research and Applications Vol. 19 No. 3 (2026): (In Progress)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2026.19.3.1

Abstract

This paper discusses asset challenges detection in the healthcare industry, specifically delays and inaccuracies in asset monitoring caused by suboptimal RFID polling methods. The research question is how to determine an appropriate RFID polling interval that balances asset location accuracy, reader energy consumption, and network response time. Due to the increasing risk of mismanagement and equipment loss, an efficient approach is needed to improve asset-tracking accuracy. This study proposes a simulation-based multi-objective optimization approach by determining the optimal polling period to minimize network delay, reader energy consumption, and false identifications. Monte Carlo simulation models the stochastic movement of assets to evaluate system performance under different polling strategies. The results of one experiment showed that 100 assets, with an average moving rate of 2.48, reached the optimal scanning period of 1460 minutes. Additional experiments were conducted to analyze the sensitivity of the optimal polling interval to changes in asset population and movement rates. The contribution of this study is the development of a holistic model to determine the optimal scanning time to improve asset-tracking accuracy and reduce operational costs in RFID systems. Although evaluated in a healthcare context, the proposed framework is versatile and can also be used for other RFID-based asset monitoring scenarios with similar trade-offs.
A Personality-Aware Agentic AI Framework for Academic and Career Recommendation in Higher Education Andalusia, Friska; Suakanto, Sinung; Parameswari, Sang Dara
JPI: Jurnal Pustaka Indonesia Vol. 6 No. 1 (2026): April
Publisher : Yayasan Darussalam Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62159/jpi.v6i1.2084

Abstract

Although personality traits are widely recognized as important predictors of academic success and career preferences, their integration into AI-driven academic advising systems remains limited. Existing approaches predominantly rely on academic performance data and historical learning behavior, often overlooking psychological characteristics that influence students’ decision-making processes. In parallel, recent advances in artificial intelligence have enabled more sophisticated recommendation systems; however, these systems typically lack adaptive reasoning capabilities and do not incorporate personality as a core input variable. This study aims to address these gaps by examining how personality traits can support intelligent academic advising and by proposing a conceptual framework for a personality-aware agentic AI system in higher education. A systematic literature review following PRISMA 2020 guidelines was conducted using the Scopus database. From an initial set of 199 records, 21 studies were selected for qualitative synthesis after applying inclusion and exclusion criteria. The findings reveal three key limitations in existing research (1) personality traits are primarily used as explanatory variables rather than operational components in recommendation systems, (2) AI-based advising systems rely heavily on performance-driven data with limited psychological integration, and (3) there is a lack of unified frameworks that combine psychological modelling with adaptive AI architectures. To address these limitations, this study proposes a novel personality-aware agentic AI framework that integrates personality profiling, agentic AI-based reasoning, and intelligent recommendation mechanisms into a unified architecture. The framework introduces a multi-layered approach consisting of personality modelling, agentic AI processing, and recommendation delivery to support adaptive and context-aware academic and career guidance. This research contributes by bridging the gap between personality psychology and AI-driven recommendation systems while introducing agentic AI as a new paradigm for academic advising. Future research should focus on implementing and empirically validating the proposed framework in real-world higher education environments.
Python-Based Backend Architecture Design for Commercial Medical IoT Device Integration: A Case Study of Omron HEM-7142T1 Kusumasari, Tien Fabrianti; Widyatama, Yudhi; Aniko, Alaric Rasendriya; Suakanto, Sinung
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.331

Abstract

The current implementation of Remote Patient Monitoring (RPM) still faces crucial challenges related to the accuracy and integrity of medical data. Many healthcare IoT devices rely on generic sensors that require rigorous manual calibration and exhibit unstable error rates, failing to meet international clinical standards. This study aims to design and implement an integrated backend architecture that bridges certified commercial medical devices with digital health systems. The main contribution is a six-layer IoT architecture specifically designed to integrate the Omron HEM-7142T1 device to ensure data validity in remote blood pressure monitoring. Following the Design Science Research Methodology (DSRM), the system was developed using Python, the Bleak library for Bluetooth Low Energy (BLE) communication, and FastAPI to provide interoperable REST API services. Functional testing in Postman demonstrated that the system successfully extracts medical data, producing JSON output with an HTTP 200 OK status under single-access conditions. However, load testing using Apache JMeter with 10 virtual users revealed limitations in the hardware’s point-to-point BLE protocol. The /scan endpoint showed stable performance with a 0% error rate and an average response time of 5.04 seconds. In contrast, endpoints /connect-and-read and endpoint /latest-bp-records recorded error rates of 100% and 90%, respectively, with an average response time of 23.29 seconds when accessed simultaneously, due to the Omron device’s locking mechanism. This study concludes that while the six-layer architecture effectively ensures medical data integrity in single-access scenarios, it requires a database caching module in the Logic Tier to overcome parallel access constraints. The implementation provides a foundation for developing secure, standardized professional RPM systems for medical use.
LLM-Based Interview Bot for Student Big Five Assessment and Career Recommendation Parameswari, Sang Dara; Lubis, Muharman; Suakanto, Sinung; Pawlowski, Jan M.
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.
Co-Authors A. TAUPIK RAHMAN 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 Amanah, Raisyah Nurul Andalusia, Friska Andreas Andreas Angela, Dina Anggraeni Xena Paradita Ani Kartini Aniko, Alaric Rasendriya Anis Farihan Mat Raffei Anis Farihan Mat Raffei Anisa, Gia Annastasia, Syifa Aprilita Firsty Hazdia Arifudin, Nanang Bagastio, Shobrun Jamil Bayuwindra, Anggera Christy, Aldi Cristian Richardo Anin Daniel Hadi Wijaya Dila, Revyolla Ananta 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 Hadiningrum, Tiara Rahmania 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 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 Nia Ambarsari Nugroho, Tunggul Nugroho, Tunggul Arief Nur Ichsan Utama Nuraliza, Hilda Nuryanto, Edi Nuryatno, Edi Triono Parameswari, Sang Dara Pawlowski, Jan M. Priyadi, Djoko Rachmadita Andreswari Rafi Adinegoro Raharjo, Adi Rahmat Fauzi Raina, Apriani Nur Ramadhan, Yumna Zahran Randy Ferdiawan Rika Mulyati Riverinda Rijadi, Safara Cathasa Rivero Novelino Roberd Saragih Rofiudin, Ahmad Sidik S. Suhardi Safara Cathasa Riverinda Rijadi Satria , Ryan Muhammad Sayyid Taufiq Abdulhafizh Sebastian, Kelvin See, Tan Lian Seno Adi Putra SETYORINI Shaffiei, Zatul Alwani Siregar, Amril Mutoi Suhono H. Supangkat Sulingallo, Irwansa Ryan Syfa Nur Lathifah Syfa Nur Lathifah Thaha, Taufik Kemal Tien Fabrianti Kusumasari Tjong Wan Sen Ulinuha, Zulfa Ventje Jeremias Lewi Engel Warmiyana Zairi Absi Widyadhari, Dinda Putri Widyatama, Yudhi Widyatasya Agustika Nurtrisha Wijaksana, Syifa Nuurunnisa Wijaya, Yohanes Rico Yoga Raditya Nugraha Sukma Pradana Yoyok Gamaliel Zulkarnaen, Rizky Zaki