Article Per Year (5 Year)
p-Index From 2021 - 2026
7.702
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Reaktor Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Journal of ICT Research and Applications Jurnal Agro Kultivasi JOIV : International Journal on Informatics Visualization Jurnal Sistem dan Manajemen Industri RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JRSI (Jurnal Rekayasa Sistem dan Industri) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Mitra Manajemen Indonesian Journal of Information System 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 Jurnal Teknik Informatika (JUTIF) INVEST : Jurnal Inovasi Bisnis dan Akuntansi Charity : Jurnal Pengabdian Masyarakat Mechanical Engineering for Society and Industry Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding SENTRI: Jurnal Riset Ilmiah Jurnal Ilmiah Teknik Elektro eProceedings of Engineering Eduvest - Journal of Universal Studies SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Polimesin Scientific Journal of Informatics Rekayasa Mekanika: Jurnal Ilmiah Teknik Mesin AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal Journal of Production, Enterprise, and Industrial Applications IJoICT (International Journal on Information and Communication Technology) ITEJ (Information Technology Engineering Journals)
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 Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Neuroscience Public Health Social Sciences Transportation

Published : 62 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 62 Documents
Search

 3   4   5   6   7 
Sentiment Analysis of Fintech Application Users in Indonesia Using Machine Learning Algorithms Made Marshall Vira Deva; Lukman Abdurrahman; Hanif Fakhrurroja
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.171

Abstract

This study focuses on Indonesian users' sentiments regarding 9 fintech apps based on their Google Play Store reviews. The rapid growth of the fintech industry in Indonesia makes it crucial to understand user perceptions and satisfaction. Around 2,554 reviews from users of Kredivo, ShopeePay, Dana, GoPay, LinkAja, Bareksa, Flip, Jenius, and OVO were analyzed. The user review text and data were preprocessed using text cleaning, slang normalization, stopword removal, stemming, and the Sastrawi library and were moved through the TF-IDF vectorizer (term frequency-inverse document frequency). The four algorithms were Naive Bayes, Logistic Regression, Support Vector Machine (SVM), and Random Forest. The results showed that SVM (Linear) achieved the best overall balanced performance with an accuracy of 80.23%, precision of 77.79%, recall of 80.23%, and the highest F1-score of 78.53%, outperforming Naive Bayes (accuracy 81.21%, F1-score 78.32%), Logistic Regression (accuracy 80.43%, F1-score 77.81%), and Random Forest (accuracy 78.08%, F1-score 75.81%). While Naive Bayes recorded the highest raw accuracy, SVM was selected as the best model due to its superior F1-score, which provides a more balanced evaluation across all sentiment classes. Machine learning provided a snapshot of the reviews’ sentiments, with 42.4% positive, 51.4% negative, and 6.1% neutral. Kredivo and ShopeePay had the most favorable sentiments of 72.4% and 70.9%. The most salient sentiment indicators include 'bagus' (good) and 'bantu' (help) as top positive classifiers, while 'buruk' (bad) and 'kecewa' (disappointed) emerged as the most prominent negative classifiers, with 'mudah' (easy) and 'cepat' (fast) also strongly associated with positive sentiment. The results of this study give fintech firms a better grasp of user satisfaction, and fintech user positive sentiments.
Predictive Analysis of the Impact of Exchange Rate Fluctuations on the Financial Performance of Multinational Fintech Companies in Indonesia Abdullah Ridwan; Hanif Fakhrurroja
Eduvest - Journal of Universal Studies Vol. 5 No. 7 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i7.51249

Abstract

Exchange rate fluctuations are one of the main risks affecting the financial stability of multinational companies, including the digital banking sector in Indonesia. This study aims to analyze the impact of exchange rate fluctuations on the company's financial performance by using a case study on Bank BNI Digital. The financial data used covers the period January 2015 to December 2024, including variables such as exchange rate (IDR/USD), revenue, operating expenses, net profit, equity, liabilities, and exchange rate volatility. A machine learning-based predictive approach was applied through the Random Forest and XGBoost algorithms to evaluate the relationship between exchange rate fluctuations and the company's financial performance. The results showed that exchange rate fluctuations have a weak linear relationship with financial performance, especially company revenue, with a correlation coefficient of 0.01. Nevertheless, the simulation of the impact of the exchange rate on net profit shows that the company is able to maintain financial stability in a scenario of moderate exchange rate changes (±15%). The feature importance analysis of the XGBoost model shows that revenue and operating expenses are the dominant factors affecting financial performance, while exchange rates contribute less. Based on these findings, the study recommends the implementation of forward contracts to manage exchange rate risk, natural hedging strategies to balance currency exposure, and optimization of operational efficiency as a risk mitigation measure. This research provides strategic insights for Bank BNI Digital and similar companies in designing a resilient risk management strategy against exchange rate fluctuations in the global market.
IMPLEMENTASI SMART CLASSROOM BERBASIS IOE: STUDI EVALUATIF DENGAN PENDEKATAN PDCA (Plan-Do-Check-Act) DAN MODEL TAM (Technology Acceptance Model): IMPLEMENTATION OF SMART CLASSROOM BASED ON IOE: EVALUATIVE STUDY WITH PDSA APPROACH AND TAM MODEL Bismar Fadli; Seno Adi Putra; Hanif Fakhrurroja
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6410

Abstract

Modern universities are required to transform by embracing digital technologies to support the implementation of the Tri Dharma of Higher Education. This study explores how Indonesian higher education institutions adopt the Smart Classroom concept as part of digital transformation to improve the quality of learning. One of the key initiatives is the creation of interactive and connected learning environments through technology integration. This research proposes a standardized Smart Classroom model based on the Internet of Everything (IoE), utilizing devices such as Interactive Flat Panels, PTZ cameras, smart door locks, Wireless Presentation Displays, and audio systems. The methodology includes literature review, conceptual design, prototype development, and evaluation using the Plan-Do-Check-Act (PDCA) approach. Additionally, the Technology Acceptance Model (TAM) is employed to analyze students’ perceptions and intentions to adopt Smart Classroom technologies. The results show that the application of IoE in Smart Classrooms enhances interaction between lecturers and students, operational efficiency, and provides a more adaptive and comfortable learning experience. The main contribution of this study is the development of a comprehensive implementation guide for IoE-based Smart Classrooms, incorporating technical, managerial, and pedagogical aspects to support the advancement of higher education in the digital era.
Bank Mandiri Stock Performance Prediction Via SVM, LSTM, and Random Forest Rahmat Rambe; Hanif Fakhrurroja; Lukman Abdurrahman
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 2 (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.
A hybrid pareto–fishbone and IoT-based monitoring framework for reducing DTY yarn defects Deni Kurnia; Hanif Fakhrurroja; 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.
Customer Engagement Transformation: A Critical Factor for Successful Digital Transformation Strategies in the Transportation Industry Rian Bimo Ankhal; Muharman Lubis; Hanif Fakhrurroja
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.
The Influence of IT Leadership on Business Continuity: Analysis of the Role of Digital Governance in Increasing Company Competitiveness Muhammad Fauzan Nur Adillah; Hanif Fakhrurroja
INVEST : Jurnal Inovasi Bisnis dan Akuntansi Vol. 4 No. 2 (2023): INVEST : Jurnal Inovasi Bisnis dan Akuntansi
Publisher : Lembaga Riset dan Inovasi Al-Matani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/invest.v4i2.704

Abstract

This research aims to explore and analyze the influence of leadership in Information Technology (IT) on company business continuity with a focus on the role of Digital Governance in increasing competitiveness. Using qualitative literature study methods, this research details the background to the importance of digital transformation in the modern business context, as well as investigating the key role played by IT leadership in guiding organizations towards digital success. The data and research objects involve analysis of texts and scientific literature that includes various sources and frameworks related to IT leadership and Digital Governance. It is hoped that the results of this research will provide a deeper understanding of how effective IT leaders can influence a company's business continuity through the application of Digital Governance principles, which in turn will increase the company's competitiveness in the digital era.  
FinBERT-Based Sentiment Integration in Hybrid CNN– BiLSTM Models For Stock Price Forecasting Mohammad Tyas Pawitra; Lukman Abdurrahman; Hanif Fakhrurroja
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.
Smart vibration sensing and predictive analytics for intelligent textile manufacturing: An IoT-edge and machine learning method Kurnia, Deni; Sutanto, Agus; Fakhrurroja, Hanif; Son, Lovely
Mechanical Engineering for Society and Industry Vol. 5 No. 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.13588

Abstract

This study aimed to propose an IoT-Edge method for detecting vibration abnormalities in Textile Manufacturing, specifically on Draw Texturing Yarn (DTY) machines using an ADXL345 sensor and a Machine Learning Algorithm. The proposed system incorporated wireless sensor nodes, the MQTT protocol, Fast Fourier Transform (FFT) analysis, and a tuned Random Forest (RF) classifier to enable real-time monitoring as well as predictive maintenance. During the analysis, vibration data were collected from 13 spindles, with features extracted in both time as well as frequency domains to distinguish between normal and abnormal machine conditions. The RF model, optimized through hyperparameter tuning, achieved an accuracy of 97%, significantly outperforming the Support Vector Machine (SVM) baseline, which reached 71%. Major results showed the effectiveness of energy and centroid features in fault detection, with the Z-axis vibration proving to be a good indicator of yarn defects. The system presented low latency (average 20.37 ms) in data transmission using the MQTT protocol, ensuring practical deployability. This study offered a scalable and cost-effective solution for industrial vibration monitoring, bridging gaps in real-time processing and seamless IoT incorporation to support predictive maintenance in textile manufacturing.
Comparative Analysis of Logistic Regression and Random Forest with SMOTE for Sentiment Classification on Ethanol Policy in Indonesia Nabiel Muhammad Al Ghazali; Hanif Fakhrurroja
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5717

Abstract

Public opinion plays a crucial role in the successful implementation of renewable energy policies, particularly regarding the transition to ethanol-based fuels in Indonesia. Understanding this sentiment is vital to mitigate social resistance and design effective communication strategies, as policy failure often stems from public rejection rather than technical issues. However, social media data regarding this topic is often highly imbalanced, with a dominance of non-positive sentiments (96%) compared to positive ones (4%), creating a severe bias in machine learning models known as the accuracy paradox. This study aims to classify public sentiment towards ethanol policy and evaluate the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in handling extreme class imbalance. The methods used include text preprocessing with Sastrawi, feature extraction using TF-IDF, and a comparative classification between Logistic Regression (LR) and Random Forest (RF). The novelty of this research lies in addressing the extreme imbalance in high-dimensional text data, proving that simpler linear models can outperform complex ensemble models in terms of minority class detection. The results show that the Optimized Logistic Regression model with SMOTE outperformed Random Forest, achieving a Precision of 1 and an F1-Score of 0.67 for the minority class, compared to RF which only reached an F1-Score of 0.55. This study concludes that for high-dimensional sparse text data, linear models combined with SMOTE provide superior performance in identifying minority sentiments.
Co-Authors Abdullah Ridwan Adi Sutrisno Adi Waskito Agus Sutanto Agustiana, Nathifa Ahmad Musnansyah Andry Alamsyah Andy Victor Pakpahan Anindya Prameswari Putri Djakaria Anto Tri Sugiarto Arif Abdul Aziz Aris Munandar Asriana Asriana, Asriana Azwar Farrel Wirasena Betty Natalie Fitriatin Binashir Rofi’ah Bismar Fadli 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 Dita, Limbong Agatha Dita Djakaria, Anindya Prameswari Putri Edi Triono Nuryatno Edy Tanu Elsa Melati Nurrachmat Emma Trinurani Sofyan Erlangga, Gilang Faishal Mufied Al Anshary Faishal Mufied Al-Anshary Fauziah, Nicky Oktav Firdaus, M Ridwan Fitri Widiantini Ghifari, Raden Faqih Hilmiy Hakim, Aqil Rahman Hans Melkisedek Simanjuntak Hariyadi , Hendri Hestiawan Joniko Joniko Karina M., Rahma Kemahyanto Exaudi Lidanta, Fairuz Zahirah Lovely Son, Lovely Lukman Abdurrahman Made Marshall Vira Deva Mahardiono, Novan Agung Marno Marno Mimin Muhaemin Mohammad Tyas Pawitra Muhammad Fakhrul Safitra Muhammad Fauzan Nur Adillah Muharman Lubis Nabiel Muhammad Al Ghazali Nathifa Agustiana Nopendri Nopendri Novan Agung Mahardiono Novan Agung Mahardiono Novan Agung Mahardiono Nuryatno, Edi Triono Oktariani Nurul Pratiwi Orvalamarva, Orvalamarva Permatasari, Yessy Prahastiwi, Narita Ayu Prima Audina Wibowo Puspitasari, Devi Ambarwati Putra Perdana Prasetyo, Aditya Putri Utami Rukmana Rahayu, Indah Sari Rahma Karina M. Rahman, Jodi Rizki Rahmat Budiarto Rahmat Mulyana Rahmat Rambe Rais, Muhammad Haidar Ramdhani, Fiqri Rian Bimo Ankhal Rian Bimo Ankhal Rimba Pratama Putra Riverinda Rijadi, Safara Cathasa Sadewa, Rizki Salsabila, Syifa Aria Sandy, Muhammad Dwi Hary 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 Wira Guna, Tezar Yolanda, Mitra Marlina Zuhdi, Hafidh