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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Martabe : Jurnal Pengabdian Kepada Masyarakat ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA The IJICS (International Journal of Informatics and Computer Science) Indonesian Journal of Education and Mathematical Science Journal of Applied Engineering and Technological Science (JAETS) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science INFOKUM Computer Science and Information Technologies Ihsan: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) International Journal Of Science, Technology & Management (IJSTM) LEARNING : Jurnal Inovasi Penelitian Pendidikan dan Pembelajaran Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Proceeding International Seminar of Islamic Studies Prosiding Snastikom sudo Jurnal Teknik Informatika Edu Society: Jurnal Pendidikan, Ilmu Sosial dan Pengabdian Kepada Masyarakat Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Blend Sains Jurnal Teknik Wahana TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi International Journal of Economic, Technology and Social Sciences (Injects) Jurnal Sains Student Research Jurnal Pengabdian Barelang Jurnal Komprehenshif Hanif Journal of Information Systems Electronic Integrated Computer Algorithm Journal Jurnal Sains, Teknologi dan Komputer Economic: Journal Economic and Business Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Pengabdiaan Masyarakat Larisma Al'Adzkiya International of Computer Science and Information Technology Journal AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal Tsabit
Al Khowarizmi
Universitas Muhammadiyah Sumatera Utara
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Supply chain efficiency transformation: analysis of raw material staff selection based on preference selection index Amrullah, Amrullah; Idaman, Akbar; Al-Khowarizmi, Al-Khowarizmi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2459-2470

Abstract

In the era of intense business globalization, supply chain management is becoming a vital key to improving the efficiency and competitiveness of enterprises. The selection of raw material supply staff is an important aspect of supply chain management, affecting smooth supply, efficiency and cost control. This research focuses on using the preference selection index (PSI) method in the selection of raw material supply staff. PSI is a tool that integrates data from multiple criteria in the selection process. The results show that PSI provides an effective evaluation in staff selection, identifies key variables that affect selection success and analyzes the impact of using PSI on supply chain efficiency and company productivity. This research fills the knowledge gap in the application of PSI in the context of raw material supply staff selection and contributes to the understanding of efficient and sustainable supply chain management. The results provide valuable insights for industries and organizations that depend on reliable raw material supply and demonstrate the potential to improve the overall staff selection process. The outcome of this study found that Muliyono received a PSI score of 0.9643 and was ranked first, while Ramli received a PSI score of 0.9548 and was ranked second.
Data-Driven Portfolio Optimization using K-Means and Markowitz Model: Evidence from LQ45 Stocks Nasution, Mutiara Akbar; Al-khowarizmi, Al-Khowarizmi
Economic: Journal Economic and Business Vol. 4 No. 2 (2025): ECONOMIC: Journal Economic and Business
Publisher : Lembaga Riset Mutiara Akbar (LARISMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejeb.v4i2.1155

Abstract

This study aims to optimize stock portfolio allocation through a data-driven approach by integrating the K-Means Clustering algorithm and the Markowitz Model. The dataset includes technical and fundamental indicators of LQ45 index stocks from 2019 to 2024. The process begins with data normalization and feature extraction, followed by stock clustering using the K-Means algorithm. From the four resulting clusters, the top-performing stock with the highest average return is selected from each. Portfolio weights are then optimized using the Markowitz Model under a mean-variance framework without short selling. The optimization results allocate the largest weights to ARTO, BRPT, and ISAT. Performance evaluation through a backtest simulation in 2024 shows that the portfolio experienced only an 8.02% decline, outperforming the LQ45 index which dropped by 15.60%. These findings underscore the potential of integrating data mining and quantitative optimization methods to improve diversification efficiency and strengthen portfolio resilience during market downturns.
Optimization of Feature Extraction in Images Using Variants of Decomposition Algorithms Hutagalung , Fatma Sari; Siregar, Farid Akbar; Al-Khowarizmi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.12705

Abstract

This research aims to optimize the feature extraction process in digital images using two decomposition algorithms, namely Haar and Riyad. Feature extraction is an important step in digital image processing, used to extract significant information from images for applications such as pattern recognition, medical image analysis, and surveillance systems. Haar and Riyad algorithms are tested on three types of images: grayscale, color, and texture. Results show that Haar's algorithm excels in processing speed with an average time of 121.67 ms, making it ideal for real-time applications. In contrast, the Riyad algorithm showed higher feature detection accuracy, achieving an average of 93.33% on complex images, despite requiring a longer processing time of 154 ms. This research shows that the selection of a feature extraction algorithm should consider the type of image and the application needs. Haar's algorithm is suitable for real-time surveillance applications, while Riyad is more suitable for in-depth analysis such as on medical images. The significant contribution of this research is that it provides insight into the trade-off between speed and accuracy, and opens up opportunities to develop hybrid methods that combine the advantages of both algorithms to create more efficient and effective image processing solutions.
Metode MOORA Diterapkan untuk Menentukan Promosi Karyawan PTPN 4, dengan Analisis Keputusan Berbasis Kriteria Objektif Ade Haikal; Al-Khowarizmi
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.994

Abstract

The application of the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method in employee promotion decisions at PTPN 4 aims to enhance efficiency and objectivity in decision-making. This method allows managers to evaluate employees based on multiple criteria simultaneously, such as performance, experience, contributions, and other relevant factors. By considering these various aspects, MOORA helps make promotion decisions more transparent and fair. One of the primary advantages of applying the MOORA method is its ability to reduce bias that may occur during the promotion process. Bias can arise from subjectivity or imbalance in employee assessments, which are often based on individual judgments or personal perceptions. By using MOORA, promotion decisions are based on more objective and measurable data, making the process more systematic and structured. The MOORA method can also increase employee motivation. A transparent promotion process based on clear criteria provides employees with a strong incentive to continuously improve their performance. When employees know that promotions are based on fair evaluation, they are more motivated to work harder. This, in turn, will increase overall productivity and performance at PTPN 4. The implementation of MOORA at PTPN 4 also provides advantages in better human resource management. With the MOORA-based decision support system, managers can easily identify employees who have the best potential for promotion. This process involves several steps, such as data normalization, determining criteria weights, and calculating final values that reflect overall employee performance. The end result is the selection of employees who meet the qualifications and have outstanding performance for promotion, supporting the sustainable development of the organization.
Integration of Artificial Intelligence in Management Information Systems to Improve the Effectiveness of Strategic Decision-Making in the Digital Era Wasesa, Istikha Ruchitra Hayudirga; Permatasari, Dhyta; Angkat, Fhatiya Alzahra; Al-Khowarizmi, Al-Khowarizmi
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26051

Abstract

The integration of Artificial Intelligence (AI) into Management Information Systems (MIS) has emerged as a strategic imperative for enhancing the effectiveness of organizational decision-making in the digital era. This study aims to analyze the factors influencing successful AI adoption in MIS, evaluate its impact on strategic decision-making effectiveness, and explore the mediating role of dynamic capabilities. Grounded in the Technology Acceptance Model (TAM) and Dynamic Capabilities Theory, a conceptual framework was developed and tested using a mixed-methods approach. Quantitative data were collected from 715 respondents across six industry sectors in Indonesia, while qualitative insights were derived from case studies in 25 organizations with varying levels of AI implementation maturity. Results from Structural Equation Modeling revealed that perceived usefulness, ease of use, organizational readiness, and management support significantly influence AI adoption in MIS. The integration of AI was found to improve decision quality (34.7%), speed (42.3%), predictive accuracy (28.6%), strategic alignment (31.2%), and risk assessment capabilities (36.8%). Qualitative findings highlighted key implementation challenges, including data quality, skills gaps, employee resistance, and integration complexity. This study contributes theoretically by enriching TAM with organizational and strategic dimensions, and practically by offering a comprehensive framework to guide AI integration in MIS for sustained competitive advantage.
Systematic Literature Review: Management Information Systems and Information Technology Putri, Wan Hafizah Ainun Syah; Fadhilah, Ulfa; Lubis, Mhd Muchlisin; Khowarizmi, Al
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26099

Abstract

This research aims to review articles related to Management Information Systems with Information Technology. This research method uses SLR (Systematic Literature Review) where article sources are taken from several databases in order to obtain data that matches the title of this article. Data retrieval sources come from searches of national and international journals published on Google Scholar, through the Publish or Perish application. The results of the analysis using VOSviewer on network visualization, overlay visualization, and density visualization show the relationship between various concepts related to management information systems and information technology. 
Application of Scrum Method in the Design of Water Bill Payment Report Information System at BUMdes Mbinalun Putri, Berlianda Oktariani Jelita; Al-Khowarizmi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 1 No. 2 (2024): VOLUME 1, NO 2: DECEMBER 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v1i2.50

Abstract

An information system comprises components that process data, transforming it into meaningful information and assisting in the achievement of organizational objectives. The report information system on the design and development of this system is web-based, using the scrum methodology, which has flexible properties to develop a data processing application. However, the information system for processing water bill report data carried out at BUMDes Mbinalun still uses Microsoft Excel, so that the resulting data contains many errors in its management. Thus, the Scrum method makes work or data processing neater because the development process uses sprints, which are development activities to achieve small goals (which are broken down from the main goal) that usually take 2-4 weeks, which is called TimeBox. The final result of this work is a web-based water bill payment report system to make it easier to record water bill reports such as customer data, basic tariff data, and usage data. Apart from that, the design of the water bill payment report information system also provides information in the form of reports that can be printed directly, and customers can view bill data on the web.
Application of Region of Interest (ROI) in Student Attendance Detection System in Classroom Faizi, Setyo Fahmi Noor; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.107

Abstract

Efficient classroom management is a crucial requirement in academic environments such as the Faculty of Computer Science and Information Technology to increase productivity. This study aims to design and evaluate a real-time presence detection and counting system by implementing the Region of Interest (ROI) method to improve computational efficiency and accuracy. This methodology involves the use of a Logitech C270 HD webcam, with a static ROI set at 90% of the central video frame to focus the analysis. Person detection and counting are performed using a combination of Histogram of Oriented Gradients (HOG) for the body and Haar Cascade for the face. Time series reasoning with a minimum duration of 60 seconds and a grace period of 5 seconds is implemented to validate presence and stabilize the room status, with system performance evaluated using Precision and Recall metrics. The results show that the system successfully displays the status and number of people in the room very well, but the evaluation shows a Recall value of 1.00, which means the system detects every actual human presence. However, this system has significant accuracy issues, indicated by a low Precision of 0.04 and a high number of False Positives of 710. In conclusion, although the ROI application successfully improves the computational load and the temporal logic stabilizes the output, the HOG and Haar Cascade models are inadequate to handle visual noise in the ROI, resulting in low Precision and indicating the need for more sophisticated detection models.
Implementation of Machine Learning For Indonesian Sign Language Recognition Using Convolutional Neural Network Model Salamah, Umi; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.108

Abstract

Sign language is the primary means of communication for people with hearing impairments. However, the public's limited understanding of Indonesian Sign Language (BISINDO) remains a communication barrier. This study implemented machine learning with a Convolutional Neural Network (CNN) model to automatically recognize BISINDO gestures. The dataset consists of 2,600 manually captured hand images representing the letters A–Z. The training process was carried out through data pre-processing, image augmentation, and CNN parameter optimization. Test results showed that the system was able to recognize BISINDO letters with high accuracy and could combine letters into simple words such as "HAI", "SAYA", and "UMI" in real-time. This study demonstrates that CNN is effective in supporting a computer-based sign language translation system, thus becoming an inclusive communication solution for people with hearing impairments.
Implementation of a Drowsiness Detection System in Four-Wheel Vehicle Drivers Using OpenCv Ma’ajid, Farhan Riqi; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.109

Abstract

Drowsiness while driving is one of the triggers of traffic accidents. This study proposes a non-invasive and economical computer vision-based real-time drowsiness detection system. The system combines Eye Aspect Ratio (EAR) to assess eye openness, Convolutional Neural Network (CNN) for open/closed eye classification, and MediaPipe FaceMesh for stable facial landmark extraction. The dataset is taken from Kaggle (Open and Closed classes, totaling 1,452 images) and processed through grayscale conversion, normalization, 64×64 pixel resizing, and augmentation. Drowsiness detection is triggered when EAR <0.25 and CNN classifies both eyes as closed for ±2 consecutive seconds; visual/audio alarms are automatically activated. Test results on 218 images show excellent performance with only 1 misclassification (≈99.5% accuracy), with no false alarms for the open eye class. The system is implemented as a Flask-based web application for easy cross-device access. These findings demonstrate an efficient visual approach that is feasible to be integrated as a driving safety feature.
Co-Authors Abdul Razak Nasution Ade Haikal Adidtya Perdana, Adidtya Adila Mawaddah Meuraxa Ahmad Al Qodri Ajulio Padly Sembiring Akbar Idaman Al Hamidy Albara Amrullah Amrullah Amrullah Andy Satria Angkat, Fhatiya Alzahra Anton Abdulbasah Kamil Aulia Jannah Baehaqi Bela Bela Budi Kurniawan Hutasuhut Diana, Has Dicky Apdilah Edy Rahman Syahputra Efendi, Syahril Elveny, Marischa Fadhilah, Ulfa Faizi, Setyo Fahmi Noor Faradillah, Yanty Farid Akbar Siregar Fatma Sari Hutagalung Fauzi FAUZI . Faza, Sharfina Ferry Fachrizal - Firahmi Rizky Frainskoy Rio Naibaho Gabriel Ardi Hutagalung Gaizka Pasya Dermawan Sinukaban Ginting, Nurman Habibi Ramdani Safitri Halim Maulana Hapzi Ali Harefa, Hafid Rahman Hariani, Pipit Putri Hasanuddin Hasanuddin Hasdiana Herman Mawengkang Hutagalung , Fatma Sari Hutagalung, Fatma Sari Ichsan, Aulia Ilham Ramadhan Nasution Indah Purnama Sari Indah Purnama Sari Irvan, Irvan Ismail Hanif Batubara Julham Julham Julham Julham Kamil, Idham Lubis, Arif Ridho Lubis, Mhd Muchlisin M. Iqbal Tanjung M.Pd, Akrim Mahyuddin K. M Nasution Mandra Saragih Manurung, Asrar Aspia Marah Doly Nasution Ma’ajid, Farhan Riqi MD, Pipit Putri Hariani Mhd Faris Pratama Mhd. Basri Michael J Watts Miftah Fariz Prima Putra Muhammad Basri Muhammad Furqon Muhammad Luthfi Hamzah Muhammad Revi Akbar Muhammad Rizky Pratama Siregar Muhammad Said Harahap Muharman Lubis Muhathir, Muhathir Muliawan Firdaus Mulkan Azhari Mutiara Akbar Nasution Nadeak, Nurhalimah Nasution, Tia Alfi Sahara Niken Aprilina Oris Krianto Sulaiman Permatasari, Dhyta Pipit Putri Hariani MD Pradesyah, Riyan Pradesyah, Riyan Prastyono, Reza Prayoga Sungkowo Prayudani, Santi Putri, Berlianda Oktariani Jelita Putri, Wan Hafizah Ainun Syah Rahmad B.Y Syah Rahmad Syah Rahmad Syah, Rahmad Rahmat Mushlihuddin Ramadhani, Fanny Romi Fadillah Rahmat Sarah Purnamawati Sari Hutagalung, Fatma Sibarani, Theofil Tri Saputra Simanungkalit, Ahmad Hazazi Siregar, Ananda Afifah Siregar, Muhammad Rizky Pratama Solly Aryza Suherman, Suherman Triantono, Gatot Tua Halomoan Harahap, Tua Halomoan Umi Salamah Vicky Rolanda Wasesa, Istikha Ruchitra Hayudirga Watts, Michael J. Yoshida Sary Yuyun Yusnida Lase Zhafirah, Zhahrah