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All Journal TEKNIK INFORMATIKA Reaktor Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Kultivasi JOIV : International Journal on Informatics Visualization Jurnal Sistem dan Manajemen Industri JRSI (Jurnal Rekayasa Sistem dan Industri) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Mitra Manajemen Jurnal Kimia Terapan Indonesia Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Sistem Cerdas Linguistik Indonesia Teknologi Indonesia International Journal of Advances in Data and Information Systems Journal of Data Science and Its Applications Jurnal Instrumentasi INVEST : Jurnal Inovasi Bisnis dan Akuntansi Charity : Jurnal Pengabdian Masyarakat Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding SENTRI: Jurnal Riset Ilmiah Jurnal Ilmiah Teknik Elektro eProceedings of Engineering SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Polimesin AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal Journal of Production, Enterprise, and Industrial Applications IJoICT (International Journal on Information and Communication Technology)
Fakhrurroja, Hanif
Telkom University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Transportation

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Mapping IoT Applications in the Textile Industry: A Bibliometric Study using Biblioshiny and VOSviewer Kurnia, Deni; Sutanto, Agus; Fakhrurroja, Hanif; Son, Lovely
Proceedings of Universitas Muhammadiyah Yogyakarta Graduate Conference Vol. 5 No. 2 (2025): Fostering Gen Z for Sustainable Development and Renewable Energy
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/grace.v5i2.677

Abstract

The rapid advancement of technology, particularly the Internet of Things (IoT), has had a transformative impact on various industries, including the textile sector. IoT facilitates real-time data collection, monitoring, analysis, and decision-making, thereby enhancing efficiency, productivity, and resource sustainability. However, a comprehensive bibliometric study of IoT applications in the textile industry has yet to be undertaken. To address this research gap, this study employs bibliometric methods using the Biblioshiny R package and VOSviewer to examine research trends, key contributors, and emerging themes. By analyzing 177 relevant publications from 2015 to 2025, the study identifies major research directions, influential authors, leading institutions, and evolving areas of interest. The findings highlight a growing research focus on IoT-driven textile innovations, particularly the development of electronic textiles (e-textiles), which integrate electronic components into wearable devices for human use. This positioning of e-textiles at the forefront of smart wearable technology underscores their significance as a critical area of exploration within contemporary textile engineering. Furthermore, China, the United States, and India emerge as the predominant contributors to this research domain. The insights derived from this study offer valuable guidance for researchers, industry professionals, and policymakers, supporting future advancements and innovations in IoT applications within the textile industry.
A hybrid pareto–fishbone and IoT-based monitoring framework for reducing DTY yarn defects Kurnia, Deni; Fakhrurroja, Hanif; Marno, Marno; Joniko, Joniko
Jurnal Polimesin Vol 23, No 6 (2025): December
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i6.7678

Abstract

Quality Control (QC) challenges in the textile industry increasingly require data-driven and real-time solutions to reduce critical production defects. This research aims to develop a hybrid Pareto-Fishbone analysis integrated with an IoT-based monitoring framework to reduce the incidence of dominant defects in Draw Textured Yarn (DTY) yarns (X-stitch and Broken Filament). Defect data collected in 2024 (n=2,396) and early 2025 (n=1,177) were analyzed using Pareto charts, which identified X-stitch (40.15%) and Broken Filament (37.15%) as contributing 77.3% of total defects in 2024. Fishbone diagrams traced root causes to machine vibration and yarn tension anomalies. An IoT prototype was designed using ADXL345 vibration sensors (200 Hz sampling), tension monitoring, and MQTT communication to a Node-RED dashboard to enable real-time alerts. Preliminary testing achieved 95% MQTT transmission success and detected vibration anomalies correlating with 85% of X-stitch incidents. The proposed hybrid framework combines the diagnostic strength of Pareto–Fishbone analysis with the preventive capability of IoT monitoring, offering a scalable Industry 4.0-oriented solution for textile QC and predictive maintenance.
Analisis Keamanan Protokol Komunikasi Message Queuing Telemetry Transport (Studi Kasus Smart Greenhouse) Pakpahan, Andy Victor; Triwangsa, Mochamad Cory Sakti; Fakhrurroja, Hanif
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 4 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i4.4681

Abstract

Masalah keamanan pada perangkat IoT menjadi isu yang menjadi kekhawatiran pengguna. Perangkat IoT yang memiliki pemrosesan yang terbatas menjadikan perangkat ini memiliki celah keamanan. Perangkat IoT kemudian menjadi sasaran oleh penyerang untuk mengambil data-data penggunanya. Perangkat IoT yang dianalisis keamanannya adalah Smart Greenhouse. Untuk menganalisis keamanan pada Smart Greenhouse menggunakan metode penetration testing dimana terhadap tahap Reconnaissance maka perlunya penggambaran sistem yang sedang berjalan dan berdasarkan sistem yang berjalan akan dicari celah berdasarkan studi literatur yang dilakukan. lalu potensi celah dicoba diimplementasikan di Smart Greenhouse dan dibandingkan dengan protokol komunikasi MQTTS yang dianggap lebih aman Kemudian pada tahap Scanning dilakukan dengan mencari informasi seperti IP, MAC dan port pada jaringan. Tahap ketiga adalah Exploitation melakukan penetrasi menggunakan teknik sniffling, Sniffling yang digunakan adalah ARP Poisoning, pada tahap Maintaining Access dilakukan MITM Attack kemudian ditemukan celah keamanan pada bagian protokol komunikasi MQTT yang digunakan, hal yang sama dilakukan pada MQTTS sebagai pembanding. Hasil implementasi tersebut ditemukan bahwa data yang dikirim melalui protokol MQTT dapat dibaca oleh penyerang dengan melakukan ARP Poisoning dan MITM Attack dapat memodifikasi packet data sehingga packet tidak sampai ke tujuan sedangkan pada protokol MQTTS ARP Poisoning dapat dilakukan namun data terenkripsi sehingga MITM Attack tidak dapat dilakukan
Customer Engagement Transformation: A Critical Factor for Successful Digital Transformation Strategies in the Transportation Industry Ankhal, Rian Bimo; Lubis, Muharman; Fakhrurroja, Hanif
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 1 (2026): SENTRI : Jurnal Riset Ilmiah, Januari 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i1.5455

Abstract

Customer engagement transformation is a key aspect in developing digital transformation strategies in today's workplace. This paper aims to present an analysis of critical success factors (CSFs) that influence customer engagement transformation within the context of digital transformation strategies. The research methodology involves a combination of in-depth case studies of several organizations that have successfully implemented digital transformation strategies with a focus on employee engagement. Customer surveys, interviews with organizational leaders, and internal document analysis are the primary instruments for data collection. This paper will discuss the implications of using the latest technology, digital collaboration platforms, and supportive leadership approaches in achieving customer engagement transformation. We will also explore the impact of factors such as work-life balance, skills development, and organizational culture on the success of transformation strategies.
Association Analysis Between Public Sentiment and Grab Stock Performance Using SVM and Lambda Test Dita Pramesti; Hanif Fakhrurroja; Rahma Karina M.
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 1 (2025): Vol. 11 No. 1 Jun 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i1.9152

Abstract

During a period of strong economic performance in Indonesia—marked by a 5.4% growth in the second quarter of 2022—concerns about a potential downturn in the fourth quarter began to surface, as indicated by increased stock market volatility, including fluctuations in Grab’s share prices. This study aims to classify public sentiment toward Grab based on comments from the social media platform Twitter, and to analyze its relationship with the direction of the company’s stock price movement. Sentiment classification was conducted using the Support Vector Machine (SVM) algorithm through a series of steps including data preprocessing, TF-IDF weighting, imbalance data handling, and model performance evaluation. The dataset was split into 70% training data and 30% testing data. The SVM model achieved an accuracy of 87%, with a precision of 90%, recall of 91%, and F1-score of 91%. Public sentiment for each period was then aggregated using the Net Sentiment Score (NSS), which was subsequently categorized into positive or negative sentiment. These sentiment categories were analyzed in relation to stock price movements using the Goodman-Kruskal Lambda test. The result of ????(stock∣sentiment)=0.053 indicates that knowing public sentiment reduces prediction error by only 5.3%, while ????(sentimen|saham)=0.000 shows no predictive value in the opposite direction. This study contributes a novel approach by integrating machine learning-based sentiment classification with a categorical association test, specifically applied to a regional technology company in Southeast Asia, which remains underexplored in existing literature.
Multi-Output Classification of Cognitive Levels and Topics in Indonesian Questions using Deep Learning and Transformers Orvalamarva, Orvalamarva; Pratiwi, Oktariani Nurul; Fakhrurroja, Hanif
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1492

Abstract

Managing large-scale digital question banks struggles with manual metadata labeling, especially when identifying material topics and cognitive levels based on the Revised Bloom's Taxonomy. Current automated approaches usually treat these two attributes as separate tasks, which adds to the system's complexity and computational load. This study introduces a multi-output classification method using a shared encoder architecture with two task-specific heads to predict topics and cognitive levels simultaneously. We performed experiments on 685 Indonesian junior high science questions, covering 15 topic labels and four cognitive levels (C1–C4), with an imbalanced distribution in which lower cognitive levels accounted for more than 75% of the dataset. To handle this imbalance, we applied Focal Loss to taxonomy classification, and class weighting was used in the comparison model. A comparative study involved CNN, BiLSTM, DistilBERT, and IndoBERT. Our results demonstrate that IndoBERT delivered the best performance, with F1-macro scores of 0.78 for topics and 0.71 for cognitive levels and showed better performance in minority classes compared to standard cross-entropy-based models. These findings suggest that an integrated multi-output approach can boost the efficiency and accuracy of question labeling and offers potential for integration into Computer-Based Test systems and e-assessment platforms in real time.
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

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

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
Bank Mandiri Stock Performance Prediction Via SVM, LSTM, and Random Forest Rambe, Rahmat; Fakhrurroja, Hanif; Abdurrahman, Lukman
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2589

Abstract

Reliable stock price prediction is critical for effective investment decisions; however, high volatility and nonlinear dynamics continue to challenge forecasting accuracy. Despite the extensive use of machine learning in financial research, short-term comparative studies on Indonesian banking stocks remain scarce. This study evaluates the performance of Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Random Forest models in predicting Bank Mandiri’s stock prices using daily data from Yahoo Finance covering June to December 2024. The data, including price indicators and trading volume, were normalized, transformed into time-series sequences, and divided into training and testing sets. SVM was applied for directional classification, while LSTM and Random Forest were used for regression-based price prediction. Model performance was assessed using accuracy and mean squared error (MSE). The findings show that LSTM achieves the lowest prediction error (MSE = 0.0045), indicating superior ability to model temporal and nonlinear price patterns. In contrast, Random Forest records the highest classification accuracy (0.9932), demonstrating strong performance in predicting price direction. Overall, LSTM is most effective for short-term price forecasting under volatile market conditions, whereas Random Forest remains a robust option for directional classification.
FinBERT-Based Sentiment Integration in Hybrid CNN– BiLSTM Models For Stock Price Forecasting Pawitra, Mohammad Tyas; Abdurrahman, Lukman; Fakhrurroja, Hanif
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.49466

Abstract

This study investigates sentiment-aware deep learning models for short-term stock price forecasting using NVIDIA (NVDA) as a representative high-volatility technology stock. Four architectures—CNN, LSTM, BiLSTM, and a hybrid CNN–BiLSTM—are evaluated under two configurations: without sentiment and with FinBERT-based financial news sentiment integrated as a continuous contextual feature. Historical OHLV data are combined with sentiment information to enable multimodal learning under a controlled experimental setting. The results demonstrate that recurrent architectures consistently outperform convolution-only models, highlighting the importance of temporal dependency modeling in financial time series. Among all configurations, the hybrid CNN–BiLSTM with FinBERT sentiment achieves the best overall performance, yielding the highest R², the lowest MAE and RMSE, and the smallest overfitting gap. Bootstrap-based confidence intervals indicate stable generalization, while Wilcoxon signed-rank tests confirm that the observed performance improvements are statistically significant. The study also presents a near real-time deployment framework with low inference latency, demonstrating practical applicability for decision-support systems. Overall, the findings show that effective alignment between local feature extraction, bidirectional temporal modeling, and contextual sentiment integration is critical for improving stock price.  forecasting accuracy and robustness.
Co-Authors Adi Sutrisno Adi Waskito Adillah, Muhammad Fauzan Nur Agus Sutanto Agustiana, Nathifa Ahmad Musnansyah Andry Alamsyah Andy Victor Pakpahan Anindya Prameswari Putri Djakaria Ankhal, Rian Bimo Anto Tri Sugiarto Arif Abdul Aziz Aris Munandar Asriana Asriana, Asriana Azwar Farrel Wirasena Betty Natalie Fitriatin Binashir Rofi’ah Carmadi Machbub Cindy Septiani Hudaya Deden Witarsyah Deni Kurnia Denis Gresan Yubelas Deris Stiawan Dermawan, M Farhan Hussaini Derry Destian Didit Adytia Dimas Jaya Kusuma Dina Angela Dini Dwi Andayani Dita Pramesti Djakaria, Anindya Prameswari Putri Edy Tanu Elsa Melati Nurrachmat Emma Trinurani Sofyan Erlangga, Gilang Faishal Mufied Al Anshary Faishal Mufied Al-Anshary Firdaus, M Ridwan Fitri Widiantini Ghifari, Raden Faqih Hilmiy Hakim, Aqil Rahman Hans Melkisedek Simanjuntak Hariyadi , Joniko, Joniko Karina M., Rahma Kemahyanto Exaudi Lidanta, Fairuz Zahirah Lovely Son, Lovely Lukman Abdurrahman Mahardiono, Novan Agung Marno Marno Mimin Muhaemin Muharman Lubis Nopendri Nopendri Novan Agung Mahardiono Novan Agung Mahardiono Novan Agung Mahardiono Nuryatno, Edi Triono Oktariani Nurul Pratiwi Orvalamarva, Orvalamarva Pawitra, Mohammad Tyas Permatasari, Yessy Prahastiwi, Narita Ayu Prima Audina Wibowo Puspitasari, Devi Ambarwati Putra Perdana Prasetyo, Aditya Rahayu, Indah Sari Rahma Karina M. Rahman, Jodi Rizki Rahmat Budiarto Rahmat Mulyana Rahmat Rambe Rais, Muhammad Haidar Ramdhani, Fiqri Rimba Pratama Putra Rukmana, Putri Utami Sadewa, Rizki Salsabila, Syifa Aria Sarmayanta Sembiring Sendhitasari, Aulia Ferina Seno Adi Putra Setyorini Setyorini Sinung Suakanto Sudaryati Cahyaningsih Sugiono, - Sutoyo, Edi Tanu, Edy Tatang Mulyana Tien Fabrianti Kusumasari Triwangsa, Mochamad Cory Sakti Tualar Simarmata Utama, Muhammad Hasbi Juri V. Luvita Veithzal Rivai Zainal Veny Luvita Veny Luvita Wibowo, Jony Winaryo Wibowo, Nanang Roni Widianto Soekarnen Wijaya, I Made Darma Putra Yolanda, Mitra Marlina Zuhdi, Hafidh