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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MAHAJANA INFORMASI Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal ADAM : Jurnal Pengabdian Masyarakat Jurnal ABDIMAS MUTIARA Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) The Indonesian Journal of Computer Science
Fadhillah Azmi, Fadhillah
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Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Medicine & Pharmacology Nursing Social Sciences Other

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An Approach for Early Heart Attack Prediction Systems Using K-Means Clustering and Cosine Similarity Novita, Nanda; Saleh, Amir; Azmi, Fadhillah
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3324

Abstract

In this study, we used cosine similarity and k-means clustering to construct a system to predict heart attacks. In order to divide patient data into groups with distinct clinical profiles based on their clinical characteristics, the k-means clustering approach is used. The new patient profiles were also contrasted with predetermined risk group profiles using the cosine similarity method. Heart attack high-risk patients are those with a profile that resembles that of the high-risk category. This suggested prediction system offers numerous benefits and contributions. First, the technique helps identify individuals who are at high risk of having a heart attack, allowing for prompt intervention and treatment. Second, the technology aids in lowering the mortality and effects of a heart attack by foreseeing the possibility of one in high-risk patients. Combining the k-means clustering method and cosine similarity, this system can predict heart attacks with an accuracy and dependability of 93.71%. In order to aid medical practitioners in making wise decisions and enhancing patient care, this research offers fresh perspectives on how to understand and manage heart attacks.
RANCANG BANGUN MONITORING KETINGGIAN AIR BERBASIS IOT UNTUK DETEKSI DINI BANJIR PADA BENDUNGAN SUNGAI DELI Josep Sitepu, Muhammad; Azmi, Fadhillah
Jurnal Mahajana Informasi Vol 10 No 1 (2025): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jurnalmi.v10i1.5981

Abstract

Banjir merupakan salah satu bencana yang sering terjadi di Kota Medan, khususnya di sekitar aliran Sungai Deli, akibat tingginya curah hujan dan kurangnya sistem peringatan dini yang efektif. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem monitoring ketinggian air berbasis Internet of Things (IoT) untuk mendeteksi dini potensi banjir. Penelitian dilakukan secara prototipe di laboratorium menggunakan desain penelitian Design and Creation, tanpa melibatkan partisipan manusia. Sistem terdiri dari mikrokontroler ESP32 yang terintegrasi dengan tiga sensor utama: sensor ultrasonik SRF-05 untuk mengukur ketinggian air, water flow sensor G1/2 untuk mengukur debit aliran air, dan raindrop sensor untuk mendeteksi keberadaan hujan. Data dari sensor dikirimkan secara real-time ke platform ThingSpeak dan dapat dimonitor melalui aplikasi ThingView pada smartphone. Pengumpulan data dilakukan selama simulasi kondisi banjir dalam akuarium, dan dianalisis secara deskriptif kuantitatif. Hasil menunjukkan bahwa sistem dapat mengukur ketinggian air dengan akurasi ±1 cm dan mengirimkan data setiap 15 detik secara stabil. Selain itu, indikator LED dan buzzer aktif ketika air mencapai level siaga, menunjukkan respons sistem terhadap kondisi kritis. Rancangan ini menunjukkan potensi besar sebagai sistem deteksi banjir yang efisien, murah, dan mudah digunakan masyarakat serta instansi terkait untuk mengurangi dampak bencana banjir di wilayah Sungai Deli.
PENINGKATAN KREATIVITAS GURU MTS. AL HIJRAH NU MEDAN MELALUI PELATIHAN DESAIN MEDIA PEMBELAJARAN INTERAKTIF BERBASIS CANVA Azmi, Fadhillah; Amir Saleh; Muhammad Riki Atsauri; Nanda Novita; Mega Puspita Sari
Jurnal Abdimas Mutiara Vol. 6 No. 2 (2025): JURNAL ABDIMAS MUTIARA (IN PRESS)
Publisher : Universitas Sari Mutiara Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jam.v6i2.6163

Abstract

Transformasi digital dalam dunia pendidikan menuntut guru untuk mampu merancang media pembelajaran yang menarik, interaktif, dan relevan dengan karakteristik generasi digital. Kegiatan ini bertujuan untuk meningkatkan kreativitas guru dalam mendesain media pembelajaran interaktif berbasis Canva. Pelatihan dilaksanakan di MTs Al Hijrah NU Medan, diikuti oleh 15 guru dari berbagai mata pelajaran. Metode pelatihan meliputi pemberian materi teori tentang prinsip desain pembelajaran, demonstrasi penggunaan Canva, praktik langsung membuat media, serta presentasi dan evaluasi hasil karya peserta. Hasil kegiatan menunjukkan bahwa 82% peserta mengalami peningkatan kreativitas dan keterampilan dalam merancang media pembelajaran setelah pelatihan. Sebagian besar guru mampu menghasilkan media interaktif seperti poster, infografis, dan presentasi digital dengan tampilan visual yang menarik dan konten yang sesuai dengan tujuan pembelajaran. Respon peserta terhadap pelatihan juga sangat positif, dengan 92% menyatakan bahwa pelatihan ini relevan dan bermanfaat untuk diterapkan dalam proses pembelajaran. Pelatihan ini memberikan kontribusi nyata dalam membangun kapasitas guru menuju pembelajaran berbasis teknologi yang lebih kreatif dan inovatif.
ANALISIS LEARNING JARINGAN RBF (RADIAL BASIS FUNCTION NETWORK)PADA PENGENALAN POLA ALFANUMERIK Azmi, Fadhillah
Jurnal TIMES Vol 5 No 2 (2016)
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (384.043 KB) | DOI: 10.51351/jtm.5.2.2016554

Abstract

Learning merupakan salah satu parameter yang sangat penting dalam jaringan syaraf tiruan untuk mendapatkan hasil yang diinginkan. Namun, pencapaian hasil yang diperoleh learning rate bukan menjadi jaminan, karena salah satu algoritma jaringan syaraf tiruan seperti backpropagation kemungkinan terjebak ke dalam nilai minimum lokal (local minima), sehingga diperoleh solusi suboptimal. Tujuan utama tulisan ini adalah menganalisis pembelajaran pada algoritma radial basis function (RBF) dalam pengenalan pola alfanumerik, yang mana proses pembelajaran dengan menggunakan perhituungan matriks Gaussian.
Herbal Plant Image Retrieval Using HSV Color Histogram and Random Forest Algorithm Azmi, Fadhillah; Gibran, M Khalil; Saleh, Amir
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.v6i2.26495

Abstract

Herbal plants have significant importance in traditional medicine and are often useful in various natural health products. Visual identification of these plants is usually carried out based on the shape of the leaves and often encounters difficulties in distinguishing species due to similarities in shape and color. Therefore, a system capable of automatically and efficiently recognizing and searching for herbal plant images is needed. This study aims to implement an image search engine for herbal plants based on leaf color similarity. The method used includes color feature extraction using an HSV (Hue, Saturation, Value) histogram with an 8×8×8 bin configuration, resulting in a 512-dimensional feature vector. This histogram feature is then used as input for the Random Forest classification algorithm to group images based on the type of herbal plant. The dataset used consists of 450 herbal leaf images from 9 different classes, obtained through direct image capture using a digital camera. The test results indicates that the developed system is able to classify types of herbal plants with an accuracy of 95.56%. In addition, the computation time and system response during both training and testing processes are relatively fast and efficient. The advantage of this system lies in the simplicity of feature extraction while still being able to provide high classification performance. This system has great potential to be used as an educational tool as well as an initial component in the development of mobile applications for automatic herbal plant identification.
Analisis Rancangan Trainer Kombinasi PV Tipe Polycrystalline dan Monocrystalline pada EBT Sebayang, Youstra; Satria, Habib; Azmi, Fadhillah
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 3, No 1 (2024): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v3i1.2263

Abstract

Trainer is a medium object similar to real objects that facilitates learning. Solar energy is renewable energy.Each type of PV has its own advantages and disadvantages. In this regard, the design of a solar panel trainer, a combination of pollycrystalline and monocrystalline PV type in NRE, aims to be a learning medium for Newand Renewable Energy (EBT). Solar panels of polycrystalline and monocrystalline types have different characteristics, performance, and efficiency. To get the results of the data so that it can be analyzed, the trainer is designed using a DC Wattmeter, Solar Charge Controller, and Battery. The experiment was conducted on July 17, 2022 and in this trainer can be seen the average efficiency on polycrystalline solar panels of 12.01%, monocrystalline solar panels of 6.11%, and parallel polycrystalline and monocrystallinesolar panels of 4.20%.
Co-Authors Achmad Ridwan Adelina siregar , Pitria afnira fadilla, Marni Amir Saleh Azadin Nasution Dani, Rahma David David Dharshinni, NP Firman P.Sihombing Gibran, M Khalil Gurning, Oki Thomas Habib Satria habibah ritonga, Husni Harahap , Hardiansyah Josep Sitepu, Muhammad Klara Manalu, Julita Lahagu, Sirano Mega Puspita Sari Muhammad Riki Atsauri Muhammad Rivai Nanda Novita Nanda Novita, Nanda Ndraha, Syukurman Perangin-angin, Despaleri Rina Anugrahwaty, Rina Sari Harahap, Wilda Sarif, Muhammad Irfan Sebayang, Youstra Silitonga, Asido Simatupang, Tommy Soaloon, Adhyaksa Putra Sri Hartini sya, There Utama, Kevin Zendrato, Marselim Hengki Putra Edria