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All Journal Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Capture : Jurnal Seni Media Rekam Building of Informatics, Technology and Science TEPIAN Jurnal Teknik Informatika (JUTIF) Business Economic, Communication, and Social Sciences Journal (BECOSS) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Santoso, Handri
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Religion Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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Optimizing Software Development Through Flow Metrics Analysis in the Scaled Agile Framework (SAFe) Akbar, Achmad Fathurrazi; Indrajit, Eko; Makmur, Amelia; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14545

Abstract

To meet the ever-changing market demands, more efficient strategies are required due to the complexity of today's software development. Because it can combine Agile concepts with organizational structures to manage large-scale projects involving multiple teams and departments, the Scaled Agile Framework (SAFe) has been widely adopted. This research investigates how Flow Metrics from the SAFe framework can be used as a tool to improve productivity, efficiency and alignment of the software development process. This research examines how measurements such as flow velocity, flow efficiency, flow time, and flow load can be used to pinpoint bottlenecks, streamline processes, and improve the value delivered to clients. This research uses a qualitative methodology to examine the use of Flow Metrics in two interdependent Program Increments (PIs) by combining interviews with Agile practitioners and a literature survey. The analysis highlights how the Continuous Delivery (CD) Pipeline, backlog synchronization, and program increment planning—three critical components of SAFe—interact with each other. By highlighting the importance of metrics-based performance evaluation, collaborative planning, and continuous improvement, the findings of this research are intended to offer a useful foundation for businesses looking to implement SAFe for large-scale software development. This research advances a more comprehensive understanding of how SAFe and Flow Metrics can facilitate increasingly complex software development while guaranteeing adaptability to changing business needs.
BIG ENTERTAINMENT’S FILM AND MUSIC CREATION DESIGN: PLATFORM-BASED BUSINESS MODEL CANVAS AND ENTERPRISE ARCHITECTURE Ruddin, Isra; Santoso, Handri; Indrajit, Richardus Eko; Dazki, Erick
Capture : Jurnal Seni Media Rekam Vol. 13 No. 1 (2021)
Publisher : Seni Media Rekam ISI Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33153/capture.v13i1.3946

Abstract

BIG Entertainment is an application that offers unparalleled music and film entertainment. Previously, applications like Spotify were all about music, while Netflix, Joox, and Disney were all about movies. This entertainment combines movies and music into a single application. This research seeks to examine the Big Entertainment application's business model canvas. This study employs the literature review method, which gathers various scientific and relevant sources. The findings suggest that BIG Entertainment service providers work hard to expand their offerings in response to changing market trends and technology advancements. The business model offers both tangible and intangible assets for customers who require not only the final product (music service) but also a one-stop service. BIG Entertainment can generate profit while developing a distinctive and efficient business model if it successfully integrates tangible and intangible assets.
AI-Driven Makeup Suggestions Leveraging Mediapipe Face Landmarks For Eye Shape Detection: Rekomendasi Tampilan Riasan Mata Berbasis AI Menggunakan Landmark Wajah Mediapipe Untuk Mendeteksi Bentuk Mata Devanda, Faustin; Santoso, Handri
Technomedia Journal Vol 10 No 1 (2025): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v10i1.2316

Abstract

In the world of beauty, makeup is not only a form of self expression but also a creative skill that requires precision and an understanding of facial structures. Among all facial features, the eyes play a crucial role in defining makeup styles. Each individual has unique eye shapes such as round, monolid, upturned, almond, and downturned, which require different makeup techniques to enhance their appearance. However, many individuals struggle to identify their eye shape, leading to suboptimal makeup results. This research aims to develop an intelligent system for eye shape classification using image processing and artificial intelligence technologies. MediaPipe, a robust and lightweight framework for facial landmark detection, was employed to extract key features from the eye region, including Eye Aspect Ratio (EAR), Eye Corner (angle), and Eye Distance. A total of 1,250 images were used from various datasets including personal archives, Kaggle, and GitHub MUCT. The classification process used a Support Vector Machine (SVM) with a non-linear RBF kernel, and its performance was validated using K-Fold Cross Validation with 10 folds. The system demonstrated high accuracy for almond, downturned, monolid, and round eyes. However, classification for upturned eyes showed less optimal results, likely due to limitations in the current feature set. This study also introduces an integrated open camera interface that detects eye shape in real time and recommends suitable eye makeup styles. This research contributes to inclusive beauty technology by providing personalized makeup suggestions based on eye shape, aligning with SDG 5 (Gender Equality) and SDG 9 (Industry, Innovation, and Infrastructure). Future work will focus on improving accuracy, particularly for upturned eye classification.
Comparative Evaluation of Large Language Models for Intent Classification in Indonesian Text Karjadi, Markus; Santoso, Handri
TEPIAN Vol. 6 No. 2 (2025): June 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i2.3355

Abstract

Large Language Models (LLMs) have shown tremendous potential in intent classification tasks, yet their practical deployment in low-resource language environments remains underexplored. This study presents an informatics-based evaluation framework to compare three LLM architectures—GPT-Neo (fine-tuned), Mistral, and Phi-2.0 (zero-shot inference)—on Indonesian intent classification. The methodology integrates classic informatics approaches such as stratified sampling, label encoding, model evaluation using Scikit-learn, and a REST API-based local inference pipeline via the Ollama framework. The study also benchmarks computational efficiency by profiling execution times on consumer-grade hardware. GPT-Neo achieved 100% accuracy after fine-tuning, while Mistral and Phi-2.0 scored approximately 55% and 18%, respectively, in zero-shot settings. The hybrid architecture designed in this work demonstrates how LLMs can be systematically evaluated and deployed using lightweight, modular informatics workflows. Results suggest that fine-tuned lightweight models are viable for high-accuracy deployment, while zero-shot models enable rapid prototyping under constrained resources.
LSTM-Based Hand Gesture Recognition for Indonesian Sign Language System (SIBI) on Affix, Alphabet, Number, and Word Ho, Patricia; Santoso, Handri
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.v9i3.9607

Abstract

Sign language plays a critical role in enabling communication for the Deaf and hard-of-hearing community in Indonesia, yet there remains a significant gap in technological support for recognizing the official Indonesian sign language, Sistem Isyarat Bahasa Indonesia (SIBI). This study presents a deep learning-based hand gesture recognition system for SIBI, focusing on four primary gesture categories: affix, alphabet, number, and word. A large and diverse dataset of 21,351 videos was collected, covering 18 affix, 26 alphabet, 35 number, and 29 word classes. Hand keypoints were extracted using MediaPipe Holistic, and a bidirectional long short-term memory (BiLSTM) model was trained using 5-fold stratified cross-validation. The model achieved high recognition performance in the alphabet, number, and word categories, with mean test accuracies of 93.94%, 91.48%, and 92.41%, respectively, and slightly lower performance in the affix category at 68.17%. The affix category posed particular challenges due to subtle hand shape differences and high variability between signers, while the alphabet category consistently showed the highest accuracy due to its distinct and static handshapes. Evaluation metrics, including precision, recall, F1-score, and confusion matrix analysis, provided further insights into model strengths and limitations. Overall, the study demonstrates the effectiveness of LSTM models for sequential hand gesture recognition in SIBI and highlights areas for future improvement, such as handling non-manual features and improving generalization across signers.
Beyond Traditional QoS Management- Harnessing Machine Learning for Predictive Network Service Optimization Mareta, Arvin; Sakti, Irwin; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14664

Abstract

Quality of Service (QoS) is a fundamental aspect of modern computer networks, directly influencing performance and user experience. Key parameters such as latency, throughput, packet loss, and jitter play crucial roles in determining network efficiency. Traditional QoS management approaches, often rule-based or heuristic-driven, lack adaptability to dynamic network conditions. This study explores the application of machine learning techniques to predict QoS using historical network data, enabling proactive network optimization. We employ multiple predictive models, including linear regression, random forest, and deep learning, to analyze network performance trends and forecast QoS degradation. Experimental results demonstrate that machine learning significantly enhances prediction accuracy compared to conventional methods, allowing for more effective resource allocation and congestion control. The findings highlight the potential of data-driven approaches in real-time network management, reducing latency fluctuations and improving service reliability. Moreover, deep learning models outperform traditional statistical techniques in recognizing complex patterns within network data, making them a promising solution for next-generation network optimization. The proposed methodology not only improves predictive accuracy but also offers a scalable framework for automated QoS management in cloud computing, IoT, and 5G environments. Future work will focus on refining model generalization across diverse network conditions and integrating federated learning for privacy-preserving QoS predictions. This research underscores the transformative role of machine learning in enhancing network service quality and operational efficiency.
Data Mining Framework for EDC Terminal Repair Protocol: Combining Apriori and PrefixSpan Praharto, Suwandhy; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14759

Abstract

Electronic Data Capture (EDC) terminals are vital for financial transactions, but their repair processes often lack standardization, causing inefficiencies. Data mining techniques like Association Rule Mining (ARM) and Sequential Pattern Mining (SPM) can extract hidden patterns from service logs to inform maintenance strategies. This research addresses the limited use of these techniques within Electronic Data Capture (EDC) repair centers. Specifically, it applies Association Rule Mining (ARM) using the Apriori algorithm, and Sequential Pattern Mining (SPM) using the PrefixSpan algorithm, to optimize repair protocols based on historical repair data from PT. XYZ Indonesia. The study aimed to discover frequent fault-action-component associations and repair sequences to formulate standardized procedures. A quantitative case study analyzed 56,629 repair transactions. After data cleaning and transformation, Apriori (evaluated by support, confidence, lift) mined association rules, while PrefixSpan found frequent sequential patterns (evaluated by minimum support). Several high-confidence rules emerged: "Battery Not Charging" almost always led to "Replace Battery Pack" (≈95% confidence, lift ≈6.0), and error "2000000" (tamper indication) strongly correlated with detampering procedures and internal battery replacement (≈96% confidence, lift ≈4.9). PrefixSpan uncovered consistent repair sequences, including length-3 patterns for complex issues, with "Replace CMOS → Reinstall OS" for error "7FFFFF" being a prominent shorter sequence. Integrating these data-driven patterns into protocols and aligning inventory can improve service efficiency, reduce repair time, and enhance EDC reliability.
Sentimen Analisis Media Sosial Terhadap Isu Pagar Laut Dengan Algoritma Support Vector Machine dan Logistic Regression Perdana, Nanda; Santoso, Handri
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.888

Abstract

Penelitian ini mencakup analisis sentimen pada media sosial terkait isu pagar laut menggunakan algoritma Logistic Regression (LR) dan Support Vector Machine (SVM). Data diperoleh dari media sosial Twitter, Instagram, Facebook, dan Tiktok yang dilakukan pra pemrosesan dan labelling menggunakan VADER. Hasil dari tiga rasio yang digunakan menunjukkan rasio 0,7 atau 7:3 adalah yang terbaik, dengan akurasi SVM 0.985382 dan akurasi LR 0.988881. Secara keseluruhan kedua algoritma memberikan hasil yang sama baiknya dan seimbang melihat dari evaluasi precision, recall, dan F1-score. Penelitian ini diharapkan dapat memberikan gambaran rangkuman opini publik terhadap isu pagar laut pada media sosial.
Comparative Analysis of RAG-Based Open-Source LLMs for Indonesian Banking Customer Service Optimization Using Simulated Data Lijaya, Hendra; Ho, Patricia; Santoso, Handri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2383

Abstract

In the digital era, banks face challenges in delivering fast, accurate, and efficient customer service, especially for frequently asked simple questions. This study evaluates the effectiveness of three open-source Large Language Models (LLMs), namely Gemma2-9B-Sahabat-AI, Qwen2.5-14B-Instruct, and Mistral-Nemo-Instruct in supporting a Retrieval-Augmented Generation (RAG) question-answering system for the banking sector. Using 12,000 synthetic billing documents indexed with intfloat/multilingual-e5-large-instruct embeddings (1024 dimensions), model performance was assessed via semantic similarity metrics, LLM-as-a-Judge scores (GPT-4o-mini and Gemini 2.0 Flash), and human validation Gemma2-9B-Sahabat-AI achieved the highest semantic similarity score (0.9627), followed by Mistral (0.9614) and Qwen2.5 (0.9284). In LLM-as-a-Judge evaluations, Qwen2.5 ranked highest on GPT-4o-mini (92.2), while Gemma2 led under Gemini 2.0 Flash (88.4). Human evaluators gave perfect scores for factual questions (1–10), but all models struggled with arithmetic in question 13. Gemma2’s average response time was 41 seconds, faster than Qwen2.5’s 72 seconds and Mistral’s 48 seconds, confirming Gemma2’s balanced performance in accuracy, speed, and computational efficiency. These findings underscore the potential of locally operated open-source LLMs for banking applications, ensuring privacy and regulatory compliance. However, limitations include reliance on synthetic data, a narrow question set, and lack of user diversity. Future research should involve broader queries, real user testing, and numeric reasoning modules to ensure robust and scalable deployment in real-world banking customer service environments.
Klasterisasi Judul Berita Online Isu Pemilu Prabowo Subianto dengan Kombinasi LLMS Embedding Dengan HDBSCAN Perdana, Nanda; Santoso, Handri
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 10 (2025): : JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i10.4779

Abstract

Penelitian ini bertujuan untuk mengelompokkan judul-judul berita politik daring yang berkaitan dengan Presiden Prabowo Subianto selama Pemilu 2024 menggunakan pendekatan berbasis embedding large language models (LLMS) dan algoritma klasterisasi HDBScan. Data yang digunakan dalam penelitian ini berjumlah 24.000 judul berita yang kemudian dianalisis mellaui beberapa tahap meliputi pra-pemrosesan teks, ekstraksi embedding menggunakan model OpenAI, reduksi dimensi menggunakan UMAP, serta klasterisasi berbasis densitas adaptif dengan HDBSCAN. Hasil penelitian menunjukkan terbentuknya 85 klaster tematik dan identifikasi sekitar 27,2% data sebagai noise. Hasil temuan pada penelitian ini mengindikasikan bahwa kombinasi embedding LLM dan HDBSCAN efektif dalam, mengungkap struktur semantik wacana politik digital dari data yang digunakan, serta mampu menangani karakteristik data teks pendek yang kompleks dan heterogen. Pendekatan ini memberikan kontribusi metodologis terhadap studi analisis media berbasis data besar dan menwarakan landasan bagi penelitian lanjutan dalam pemetaan itu publik di ruang digital. Hasil penelitian ini dapat digunakan sebagai sarana untuk penelitian lebih lanjut dengan studi kasus yang berbeda namun menggunakan algoritma yang sama.
Co-Authors Akbar, Achmad Fathurrazi Akbariani, Nurhaliza Vania Amanda, Djaja Andrew Andrew, Andrew Aprilia, Novia Pramesti Dazki, Erick Devanda, Faustin Eko Hadianto, Eko Fransisca, Viviana Harlis Setiyowati HARYONO Heni Jusuf Hindarto, Djarot Ho, Patricia Indrajit, Eko ISWAHYUDI ISWAHYUDI Karjadi, Daniel Avian Karjadi, Markus Laurenso, Justin Lijaya, Hendra Lucia Sri Istiyowati Lupitha, Mariska Mareta, Arvin Maria Magdalena Mohammad, Saiful Azhari Perdana, Nanda Praharto, Suwandhy Prawira, Khairul Thamrin Priswanto, Budi Purwadi Purwadi Putri, Gladys Rahman, Ben Ramadhoni, Moehammad Richardus Eko Indrajit Rochadiani, Theresia Herlina Ruddin, Isra Sakti, Irwin Sandjaya, Julio Santoso, Billy Cahyo Saputra, Adi Dwifana Sari, Muh Masri Sentosa, Steve Suhandi, Vartin Suherman, Endang Sumargo, Ruly Sze, Edward Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa Widjaja, Herman