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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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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 Akun
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.
Implementation and design of GPS tracker monitoring system on car rental vehicles based on internet of things using Nodemcu ESP-32 Indah Purnama Sari; Al-Khowarizmi Al-Khowarizmi; Asrar Aspia Manurung
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p214-223

Abstract

Internet of things (IoT) based vehicle tracking system is an effective solution to overcome various problems in the vehicle rental industry, such as asset loss, route misuse, and late returns. This study aims to design and implement a real-time vehicle position monitoring system using the NodeMCU ESP-32 module integrated with the NEO-6M GPS module and Wi-Fi connectivity to send data to a cloud-based server. This system is designed to display the vehicle position directly through a web-based digital map interface, which can be accessed by vehicle owners anytime and anywhere. The methodology used includes hardware and software design, location accuracy testing, and data integration with a web-based visualization platform using a map API. The test results show that the system is capable of sending vehicle location data with a position accuracy level of up to ±5 meters and data updates every 10 seconds under stable network conditions. In addition, the system has good power efficiency, with an average current consumption of 80–100 mA when active. All data was successfully stored and visualized in real-time using the Google Maps API, and the system was able to operate stably for 24 hours of non-stop testing. Based on these results, the IoT-based GPS tracker system with NodeMCU ESP-32 can be effectively implemented on rental vehicles as a modern monitoring solution that is cost-effective, flexible, and easily accessible. This system provides added value in fleet monitoring and supports faster and data-based decision making.
Optimization of support vector machine with cubic kernel function to detect cyberbullying in social networks Al-Khowarizmi Al-Khowarizmi; Indah Purnama Sari; Halim Maulana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25437

Abstract

Social networking is a place where humans can interact using the internet network to be able to disseminate information, discuss, exchange ideas, pour out their hearts, and share activities. Many social networks are popularly used, one of which is Twitter. Information can be received quickly using Twitter. In addition, various government agencies also use Twitter to be able to interact directly with the community so that every government policy is disseminated through this social network. Every government policy neglects to reap the pros and cons of society, both collectively and individually. As a result of the pros and cons, a trial called cyberbullying was recorded. Cyberbullying in various studies has been carried out to change a person’s raw material so that with the application of information technology, identifying cyberbullying needs to be carried out further. The problem of cyberbullying is generally detected using the support vector machine (SVM) method. Cyberbullying detection is conducted in dealing with government policy data such as “cipta kerja” by using the SVM method which is optimized using the cubic kernel function. The accuracy value achieved in SVM uses a linear kernel function of 92.3% while using a cubic linear function of 90%.
Apple Inc. (AAPL) stock price forecasting using a stacked long short-term memory model with Yahoo Finance data Prayoga Sungkowo; Al-Khowarizmi Al-Khowarizmi
Jurnal Sains, Teknologi & Komputer Vol. 3 No. 1 (2026): Jurnal Sains, Teknologi & Komputer (SAINTEK)
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/saintek.v3i1.1321

Abstract

Stock price prediction is a complex time-series problem because price movements are dynamic and exhibit temporal patterns. This study aims to implement and evaluate a Long Short-Term Memory (LSTM) model for forecasting the closing price of Apple Inc. (AAPL) stock using historical data obtained from Yahoo Finance for the 2022–2024 period. The data were preprocessed using Min-Max Scaling, transformed into sequences with a 60-day time step, and chronologically divided into 80% training data and 20% testing data. The model employed two LSTM layers with a dropout rate of 0.2, the Adam optimizer, and Mean Squared Error as the loss function. The evaluation results yielded a Root Mean Squared Error (RMSE) of 3.31 and a Mean Absolute Error (MAE) of 2.68. The visualization indicates that the predicted values generally follow the actual price trend, although deviations occur during several periods of sharper price changes. These findings indicate that the LSTM model can learn temporal patterns in AAPL closing prices and generate forecasts that approximate the actual values.
STUDI FORENSIK PERUBAHAN SISTEM AKIBAT EKSEKUSI FILE BERBAHAYA PADA WINDOWS MENGGUNAKAN EVENT LOG DAN PREFETCH Muhammad Revi Akbar; Al-Khowarizmi Al-Khowarizmi
JOURNAL SAINS STUDENT RESEARCH Vol. 4 No. 5 (2026): Oktober: Jurnal Sains Student Research
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v4i5.12658

Abstract

Forensik digital pada sistem operasi Windows memerlukan korelasi antartefak agar aktivitas eksekusi program dapat direkonstruksi secara lebih terstruktur. Penelitian ini bertujuan menganalisis perubahan sistem akibat eksekusi program dengan memanfaatkan Windows Event Log dan Windows Prefetch serta mengembangkan aplikasi berbasis web untuk mengintegrasikan proses analisis. Metode penelitian mengacu pada tahapan NIST SP 800-86 yang meliputi collection, examination, analysis, dan reporting. Pengujian dilakukan pada Windows 10 Pro 64-bit dalam lingkungan virtual terkontrol. Artefak utama berupa Security Event Log dan Prefetch diakuisisi, diverifikasi menggunakan hash MD5 dan SHA-256, kemudian diproses oleh aplikasi. Hasil pengujian menunjukkan 3.244 event berhasil diproses dari Security.evtx. Penyaringan menemukan Event ID 4688 yang mencatat pembuatan proses FORENSIC_TEST.exe pada 26 Agustus 2026 pukul 09:55:33. Artefak FORENSIC_TEST.EXE-D2028CEF.pf mencatat executable yang sama, run count 1, dan last run time yang identik. Korelasi menghasilkan selisih waktu 0 detik dengan confidence 100% (High) berdasarkan mekanisme penilaian aplikasi. Hasil ini menunjukkan bahwa Event Log memberikan konteks pembuatan proses, sedangkan Prefetch memberikan bukti pendukung eksekusi program. Integrasi kedua artefak melalui aplikasi dapat membantu investigator menyusun timeline aktivitas secara lebih sistematis.
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