Article Per Year (5 Year)
p-Index From 2021 - 2026
11.353
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) 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) Informatika 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) Jurnal Sains Teknologi dan Sistem Informasi 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 Tarbiyah bil Qalam : Jurnal Pendidikan Agama dan Sains TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi International Journal of Economic, Technology and Social Sciences (Injects) 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 Jurnal Informatika Dan Tekonologi Komputer
Al Khowarizmi
Universitas Muhammadiyah Sumatera Utara
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 89 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 89 Documents
Search

 5   6   7   8   9 
An Optimization Approach Goal Programming to Improve the Academic and Administrative Quality of Postgraduate Programs Azis, Zainal; Al-Khowarizmi, Al-Khowarizmi; Harahap, Tua Halomoan; Firdaus, Muliawan; Irvan, Irvan
Indonesian Journal of Education and Mathematical Science Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara (UMSU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/ijems.v7i1.28873

Abstract

The main objective of this article is to implement the GP approach to improve academic and administrative performance at UMSU postgraduates. In the context of expected results, this article is intended so that UMSU postgraduates can establish strategies to achieve higher academic standards and create a more conducive educational environment through achieving predetermined targets, which include improving the quality of teaching and learning, effectiveness of program administration, and student and lecturer satisfaction with the academic process. The article can make a significant contribution to the academic literature in the field of educational management and operations by offering a new perspective on the application of GP and AHP in a higher education context. Specifically, this article shows how quantitative approaches can be used to improve decision making in the management of academic programs and administration, thereby providing valuable practical and theoretical insights for the development of educational policy and managerial practice in the higher education sector. It can be seen from the results that the parameters in this paper contribute to the weight of the objective (w_1), target (T_1), and the budget constraint (B), all of which play an important role in determining the optimal solution produced by the model
Penerapan Metode Content and Language Integrated Learning (CLIL) dalam Pembelajaran untuk Mengatasi Hambatan Bahasa Indonesia di Saengsattha School, Thailand M.Rafi; Al-Khowarizmi
Tarbiyah bil Qalam : Jurnal Pendidikan Agama dan Sains Vol. 10 No. 1 (2026): Vol X Edisi I 2026 (In-Press)
Publisher : Sekolah Tinggi Ilmu Tarbiyah Al-Bukhary Labuhanbatu

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

Abstract

This study investigates the application of Content and Language Integrated Learning (CLIL) to overcome Indonesian language barriers at Saengsattha School in Southern Thailand, where multilingual students encounter academic vocabulary limitations and speaking anxiety. The objective is to evaluate CLIL's effectiveness in enhancing elementary students' language proficiency. Employing a quasi-experimental one-group pre-test post-test design, the population comprised Prathom 4-6 students, with a purposive sample of 45 (Prathom 4: 13, Prathom 5: 15, Prathom 6: 17). Written tests assessed basic vocabulary, pronouns, and simple sentences, analyzed through descriptive statistics (means, differences, percentages). Results indicate scores improved from 56.2 (pre-test) to 75.8 (post-test), a 34.9% increase. The conclusion affirms CLIL effectively supports contextual language mastery and reduces affective barriers in multilingual settings.
Perancangan Sistem Informasi Manajemen Sekolah dalam Penerapan Smart School untuk Meningkatkan Efisiensi Administrasi dan Pembelajaran Menggunakan Metode CRM (Customer Relationship Management) di Waransansart Lammai School Anggi Irana Bela; Al-Khowarizmi Al-Khowarizmi
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.9615

Abstract

Waransansart Lammai School, an Islamic school in Yala, Thailand, faces challenges in implementing Smart School due to manual administration and limited teacher digital literacy. This research aims to analyze existing conditions, design a web-based School Management Information System (SIMS) with CRM integration, measure digitalization impact over 30 days KKN, and develop system concepts. Using qualitative descriptive approach with quantitative measurements, the population comprises school principal, teachers, and staff, selected via purposive sampling. Instruments include observation guidelines, semi-structured interviews, and documentation sheets, analyzed through Miles & Huberman's model and descriptive statistics. Results show 58.31% time efficiency improvement (from 49.97 to 20.83 minutes daily) and error rate reduction to 3.75%. CRM implementation enhances operational, analytical, and collaborative processes. The study concludes that SIMS with CRM significantly boosts administrative efficiency and stakeholder communication in Smart School framework.
Explainable Data-Driven Machine Learning for Identifying MBG Program Beneficiaries in Medan City Solly Solly Aryza; Al Khowarizmi; Muhammad Furqon; Zulkarnain Lubis; Abdul Razak Nasution
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29560

Abstract

The effectiveness of social assistance programs depends heavily on the accuracy and transparency of beneficiary identification. In many urban areas, including Medan, challenges such as incomplete data, administrative bias, and inefficient targeting often lead to inclusion and exclusion errors in determining beneficiaries of the MBG (Makan Bergizi Gratis) program. This study aims to develop an explainable data-driven machine learning model to improve the accuracy and transparency of identifying eligible MBG program beneficiaries. The research employs a quantitative approach using socio-economic and demographic datasets collected from local government records, including variables such as household income, employment status, education level, household size, housing conditions, and access to public services. Several machine learning algorithms, including Random Forest, Gradient Boosting, and Logistic Regression, are implemented to classify potential beneficiaries. To enhance transparency and interpretability, the model integrates Explainable Artificial Intelligence (XAI) techniques, such as SHAP (Shapley Additive Explanations), to identify the most influential factors affecting eligibility predictions. The results demonstrate that the proposed data-driven model significantly improves the accuracy of beneficiary classification while providing interpretable insights into key socio-economic indicators influencing eligibility. The findings indicate that income level, employment status, household dependency ratio, and housing conditions are among the most critical determinants in identifying eligible recipients. The implementation of explainable machine learning models supports more transparent and accountable decision-making in social assistance programs. This research contributes to the development of data-driven governance by providing a robust analytical framework for improving the targeting efficiency of social welfare programs in urban areas. Practically, the proposed framework can assist policymakers and local government agencies in designing fairer and more efficient beneficiary identification systems for the MBG program in Medan City, ultimately supporting better resource allocation and improved social welfare outcomes.
Visual Detection of Oil Palm Maturity Leveraging Simple Evolving Connectionist System Al-Khowarizmi Al-Khowarizmi; Fatma Sari Hutagalung; Halim Maulana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/kvqm6450

Abstract

Detecting the ripeness of oil palm fruit bunches is a crucial process in the palm oil industry to ensure the quality and quantity of oil extracted. Conventional methods still rely on subjective and inefficient manual observation. This study proposes a visual detection system using the Simple Evolving Connectionist System (SECoS) algorithm to identify the ripeness of oil palm bunches based on visual images. This model utilizes color, texture, and shape characteristics extracted from images and processed through an adaptive and evolving neural network structure. The results demonstrate that SECoS is capable of high detection accuracy and adapts to new data patterns. This system has the potential to be applied in precision agriculture practices. The model achieved an average accuracy of 91.3%, with the highest accuracy of 94% in the "Ripe" category in the final test based on 300 dataset. This demonstrates that parameter optimization is crucial in improving the model's ability to adapt to variations in oil palm bunch image data. Accuracy improvements were evident in both training and validation data. However, not all categories achieved optimal results, with accuracy for the "empty bunch" labels (89%) and "unripe" labels (88%) being relatively lower than for the other categories.
Deteksi Kematanagan Buah Sawit dengan Menggunakan Algoritma Convolutional Neural Network Muhammad Rizky Pratama Siregar; Al-Khowarizmi Al-Khowarizmi
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp175-183

Abstract

This research aims to develop an automatic palm fruit ripeness detection system using the Convolutional Neural Network (CNN) algorithm. The dataset used consists of thousands of images of ripe and unripe palm fruits with varying lighting conditions and shooting angles. The CNN model used is MobileNetV2 which has been adapted for binary classification tasks. The training process is performed using data augmentation techniques to improve the generalization of the model. The evaluation results show that the developed CNN model is able to classify the ripeness of palm fruits with an accuracy of 84%. Comparison with conventional methods that rely on visual assessment shows that the CNN model provides more consistent and objective results. The implementation of this model has the potential to increase the efficiency of the harvesting and processing of palm fruits and reduce production costs.
Clustering of Crime-Prone Areas in East Medan Based on Police Data Using K-Means and DBSCAN Algorithms Gaizka Pasya Dermawan Sinukaban; Al-Khowarizmi Al-Khowarizmi
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.29498

Abstract

The Medan Timur sub-district is one of the high-crime areas in Medan City, recording 853 cases out of 1,308 criminal incidents collected by the Medan Timur Police Sector during the 2023–2025 period. The cases consist of motorcycle theft or curanmor (689 cases, 52.7%), aggravated theft or curat (448 cases, 34.3%), and robbery or curas (171 cases, 13.1%), spread across 20 sub-villages with a range of 13 to 162 cases per sub-village. This study clusters crime-prone areas using K-Means and DBSCAN algorithms and compares their performance through the Silhouette Index (SI) and Davies-Bouldin Index (DBI). The features used include total_kriminal, curanmor, curas, curat, and rata_waktu, normalized using Min-Max Normalization. The optimal number of clusters for K-Means was determined through the Elbow method yielding K=3, while DBSCAN parameters were determined through a KNN Distance Plot yielding eps=0.20 and minPts=2. Evaluation results show that K-Means yields SI=0.4105 (weak category) and DBI=1.2599, while DBSCAN yields SI=0.6788 (moderate category) and DBI=0.4986 on 8 non-noise sub-villages. DBSCAN outperforms K-Means on both metrics with an SI difference of 0.2683 and a DBI difference of 0.7613, although K-Means is superior in coverage by clustering all 20 sub-villages. These findings can be utilized by the Medan Timur Police Sector as a basis for determining priority patrol areas and allocating security resources more effectively. Keywords: Crime; Clustering; K-Means; DBSCAN; Silhouette Index; Davies-Bouldin Index 
Pengembangan Rancang Bangun Detektor Kebakaran Sprinkler Air Berbasis Internet of things (IOT) Dengan Menggunakan Sensor Multi Deteksi Ahmad Al Qodri; Al- Khowarizmi
Komprehensif Vol 4 No 1 (2026)
Publisher : CV Edu Tech Jaya

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

Abstract

Kebakaran merupakan salah satu bencana yang sering terjadi dan dapat menimbulkan kerugian besar baik dari segi material maupun keselamatan manusia. Keterlambatan dalam mendeteksi kebakaran menjadi salah satu faktor utama yang memperbesar dampak yang ditimbulkan. Penelitian ini bertujuan untuk merancang dan membangun sistem detektor kebakaran sprinkler air berbasis Internet of things (IOT) menggunakan NodeMCU ESP32 dengan sensor multi deteksi yang terdiri dari sensor gas MQ135, sensor asap MQ2, flame sensor, dan sensor suhu DS18B20. Metode penelitian yang digunakan meliputi tahap pengumpulan data, analisis kebutuhan sistem, perancangan perangkat keras dan perangkat lunak, implementasi, serta pengujian sistem. Sistem dirancang untuk mendeteksi indikasi kebakaran berdasarkan nilai ambang batas yang telah ditentukan, kemudian secara otomatis mengaktifkan buzzer dan pompa air yang terhubung dengan sprinkler. Selain itu, sistem dilengkapi dengan fitur monitoring berbasis Arduino IoT Cloud sehingga pengguna dapat memantau kondisi lingkungan secara real-time melalui smartphone maupun website. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi asap, gas, suhu, dan nyala api dengan baik serta memberikan respons otomatis sesuai kondisi yang terdeteksi. Sistem juga berhasil menampilkan data sensor secara real-time pada LCD dan dashboard monitoring. Dengan demikian, sistem detektor kebakaran berbasis IoT ini dapat menjadi solusi alternatif dalam meningkatkan keamanan dan keselamatan terhadap risiko kebakaran, khususnya pada lingkungan tertutup seperti rumah dan perkantoran.
Clustering of uninhabitable houses using the optimized apriori algorithm Al-Khowarizmi Al-Khowarizmi; Marah Doly Nasution; Yoshida Sary; Bela Bela
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p150-159

Abstract

Clustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs.
Co-Authors Abdul Razak Nasution Abdulbasah Kamil, Anton Ade Haikal Adidtya Perdana, Adidtya Adila Mawaddah Meuraxa Ahmad Al Qodri Ajulio Padly Sembiring Akbar Idaman Al Hamidy Albara Amrullah Amrullah Amrullah Andy Satria Anggi Irana Bela Angkat, Fhatiya Alzahra Aulia Jannah Azis, Zainal Baehaqi Bela Bela Budi Kurniawan Hutasuhut Chindy Yovita Sukma Dalimunthe, Yulia Agustina 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 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 Irvan, Irvan Ismail Hanif Batubara Julham Julham Julham Julham Kamil, Idham Lubis, Arif Ridho Lubis, Mhd Muchlisin M. Iqbal Tanjung M.Pd, Akrim M.Rafi 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 Rizky Pratama Siregar Muhammad Said Harahap Muharman Lubis Muhathir, Muhathir Muhathir, Muhathir 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 Prayudani, Santi Putri, Berlianda Oktariani Jelita Putri, Wan Hafizah Ainun Syah Qadri, Habib Al Rahmad B.Y Syah Rahmad Syah Rahmad Syah, Rahmad Rahmat Mushlihuddin Ramadhani, Fanny Romi Fadillah Rahmat Salma, Riza Sarah Purnamawati Sari Hutagalung, Fatma Septiana Dewi Andriana, Septiana Dewi Sibarani, Theofil Tri Saputra Simanungkalit, Ahmad Hazazi Siregar, Ananda Afifah Siregar, Muhammad Rizky Pratama Solly 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