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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Zero : Jurnal Sains, Matematika, dan Terapan Jusikom: Jurnal Sistem Informasi Ilmu Komputer JURNAL MAHAJANA INFORMASI INFOKUM Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Pengabdian UNDIKMA Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal ABDIMAS MUTIARA The Indonesian Journal of Computer Science JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL
Amir Saleh
Politeknik Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Improving the Major Recommendation Systems: Analysis of Hybrid Naïve Bayes-based Collaborative Filtering and Fuzzy Logic Amir Saleh; Sitompul, Boy Arnol; Wijaya Laia, Laksana Febri; Sinaga, Nicholas Ferdinan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1797

Abstract

Major recommendation systems have been widely used to assist prospective students in choosing major that matches their interests and potential. In an effort to improve the performance of the recommendation system, this study proposed to use collaborative filtering techniques with naïve Bayes approach. In addition, this study improved the input parameters using fuzzy logic in determining the recommended majors. The methodology used started from collecting user data, including gender, academic history, interests, and other relevant attributes. The data were used to train the naïve Bayes technique by estimating the probability of feature conformity between users and students in the recommended majors. However, there were problems such as uncertainty and ambiguity in user preferences for input data. The fuzzy logic method aimed to improve the input parameters to more accurately reflect the user preferences. The results of improving the input parameters by using fuzzy logic were then used in the naïve Bayes technique to obtain recommendations for the direction that best suits the user’s preferences. The final stage of this study used evaluation metrics such as precision, recall, and f1-score to measure the performance of the recommendation system in providing accurate recommendations. The use of a hybrid of naïve Bayes and fuzzy logic algorithms obtains an accuracy value of 87.27%, a precision value of 87.33%, a recall value of 87.24%, and an f1-score value of 87.26%. These results are higher than the usual naïve Bayes model applied in major recommendation systems.
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.
Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases Saleh, Amir; Ridwan, Achmad; Gibran, M Khalil
The Indonesian Journal of Computer Science Vol. 12 No. 5 (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.v12i5.3379

Abstract

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.
Pengembangan dan Pemanfaatan Aplikasi Literasi Digital Berbasis Android untuk Meningkatkan Kompetensi Mengajar Guru Amir Saleh; Fadhillah Azmi; Achmad Ridwan; M. Khalil Gibran
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3550

Abstract

Dalam era digital, guru perlu memiliki kompetensi pedagogis, kepribadian, profesional, dan sosial, termasuk kemampuan menggunakan teknologi. Sementara itu, pembelajaran berbasis teknologi di MTs. Al-Hijrah NU Medan belum sepenuhnya dilaksanakan karena berbagai kendala, seperti belum dimanfaatkannya aplikasi literasi digital dengan maksimal. Penelitian ini mengusulkan pengembangan aplikasi literasi digital untuk membantu guru dalam meningkatkan kemampuan mengajar dengan memanfaatkan teknologi dalam pembelajaran. Beberapa kendala yang ada terkait ketersediaan perangkat dan pemahaman guru tentang literasi digital. Pembelajaran literasi digital diperlukan untuk meningkatkan kemampuan guru dalam mengoperasikan teknologi karena hampir semua pembelajaran saat ini menggunakan media digital. Berdasarkan hasil implementasi aplikasi yang telah dikembangkan memperoleh hasil yang cukup baik, dimana memperoleh tingkat kepraktisan produk sebesar 83,13%. Sementara itu, penilaian yang diperoleh dari guru menunjukkan bahwa terdapat peningkatan sebesar 75% pada pengetahuan guru mengenai literasi digital dan peningkatan sebesar 81% pada kemampuan mereka dalam menerapkan literasi digital. Dari hasil perolehan nilai-nilai tersebut menyatakan bahwa pengembangan aplikasi yang dilakukan terbukti efektif dan mampu meningkatkan kemampuan mengajar guru.
Kombinasi Jaringan Learning Vector Quantization Dan Normalized Cross Correlation Pada Pengenalan Wajah Saleh, Amir; Indra, Evta; Harahap, Mawaddah
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 3 No. 2 (2020): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jusikom.v3i2.851

Abstract

Pengenalan wajah merupakan cara yang dilakukan untuk mengidentifikasi wajah berdasarkan nilai ciri yang terdapat pada citra wajah dan dapat diterapkan di dalam berbagai sistem, seperti absensi, akses keamanan ruangan dan login aplikasi atau perangkat. Salah satu algoritma untuk pengenalan wajah adalah LVQ (learning vector quantization), tetapi dalam pemilihan bobot awal yang kurang tepat dapat berdampak pada penurunan kinerja algoritma tersebut, sehingga hasil dari pengenalan wajah kurang akurat. Permasalahan ini dapat diselesaikan dengan penentuan bobot awal yang tepat dengan metode tertentu. Bobot yang dipilih pada penelitian ini berdasarkan kemiripan citra, salah satu metode untuk mengukur kemiripan adalah NCC (Normalized Cross Correlation). Penelitian ini akan dilakukan dengan mengkombinasi jaringan LVQ dengan menggunakan NCC dalam penentuan bobot awal untuk pengenalan wajah. Hasil pengujian yang diperoleh dengan kombinasi kedua metode tersebut untuk pengenalan wajah sebesar 94%.
Intelligent Actuator Control in Smart Agriculture through Machine Learning and Sensor Data Integration Azmi, Fadhillah; Gibran, M. Khalil; Fawwaz, Insidini; Anugrahwaty, Rina; Saleh, Amir
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24421

Abstract

Smart agriculture leverages Internet of Things (IoT) technology to develop intelligent greenhouses capable of monitoring and responding to environmental changes in real time. This study proposes the use of machine learning to analyze real-time sensor data—such as temperature, humidity, water level, and soil nutrient levels (N, P, K)—to determine the optimal timing for activating actuators, including fans, irrigation systems, and water pumps. In the initial stage, the study utilized the "IoT Agriculture 2024" dataset from Kaggle, which consists of 37,922 records and 13 attributes describing crop and environmental conditions. This dataset was used to train a robust machine learning model based on gradient boosting to support intelligent actuator control decisions. The model demonstrated strong predictive accuracy, achieving 99.62%. In the final stage, the model was evaluated in a simulated IoT-based agricultural system using synthetic sensor data designed to mimic real-world readings of temperature, humidity, soil moisture, and nutrient concentrations. The model achieved a high validation accuracy of 99.55%, indicating its reliability and robustness within the simulated environment. These results demonstrate that the integration of machine learning with real-time sensor data is an effective strategy for automating actuator control in smart greenhouses. The proposed approach has the potential to reduce manual intervention, optimize resource utilization, and improve overall agricultural productivity. This study contributes to the advancement of adaptive, data-driven precision agriculture systems that support long-term food security.
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.
The Implementation of a Learning Management System for Improving Teacher Knowledge and Skills in MTs. Teladan Medan Fadhillah Azmi; Amir Saleh; N P Dharshinni; Despaleri Perangin-Angin
Jurnal Pengabdian UNDIKMA Vol. 4 No. 2 (2023): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v4i2.6611

Abstract

The implementation of community service (PKM) aims to improve teachers' ability to teach by applying the website-based learning management system (LMS) application at MTs. Teladan Medan is the location of the dedication that has been done. The application that had been developed was a contribution to PKM partners to helped the school conduct learned management, especially in implementing online learned.  PKM activities were carried out through several activities, namely: socialization, training, and assistance in the used of the website-based learned management system (LMS) application that had been developed for several 16 teachers. The instruments used in the form of questionnaires, pretests, and posttests were distributed before and after the training. Based on the results of the activities carried out, there was an increase in teacher knowledge about integrated learning technology using the learning management system application, with a score of 75.65%, and an increase in teacher skills in using or applying the learning management system application, with a score of 80.47%.
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.
PKM Pemanfaatan Aplikasi Augmented Reality Interaktif Dalam Pembelajaran Pra Literasi dan Pra Numerasi Anak Usia Dini Pada TK Amanda Sibolga Azanuddin; Sari Siregar, Yunita; Saleh, Amir; Jannah, Miftahul; Regina Puspa Sari Damanik, Almerinda
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 02 (2025): EDISI SEPTEMBER 2025
Publisher : Universitas Harapan Medan

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

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk menerapkan teknologi Augmented Reality (AR) sebagai media pembelajaran interaktif bagi anak usia dini, khususnya dalam pengenalan huruf dan angka pada tahap pra-literasi dan pra-numerasi. Mitra kegiatan, TK Amanda Sibolga di Kota Sibolga, menghadapi kendala kurangnya media pembelajaran digital yang menarik dan interaktif. Proses belajar masih bersifat konvensional sehingga anak-anak mudah bosan dan guru kesulitan menghadirkan pembelajaran yang menyenangkan. Solusi yang ditawarkan adalah pengembangan aplikasi AR berbasis Android yang menampilkan huruf dan angka dalam bentuk tiga dimensi (3D) melalui kartu bergambar (flashcard) sebagai penanda (marker). Saat dipindai dengan kamera ponsel, objek huruf dan angka akan muncul dalam bentuk animasi berwarna cerah, sehingga menarik perhatian anak dan membantu memahami konsep dasar dengan cara visual dan menyenangkan. Kegiatan dilaksanakan melalui tahapan observasi, pelatihan guru, penerapan aplikasi di kelas, serta evaluasi hasil pembelajaran. Guru diberikan pendampingan untuk mengoperasikan aplikasi dan mengintegrasikannya dalam kegiatan belajar. Hasil menunjukkan peningkatan minat dan partisipasi anak dalam mengenal huruf dan angka, serta meningkatnya kemampuan guru dalam menggunakan teknologi pembelajaran. Program ini menjadi langkah nyata dalam mendukung transformasi digital pendidikan anak usia dini dan memperkuat peran perguruan tinggi dalam pemerataan akses teknologi di daerah.
Co-Authors Achmad Ridwan Alexander F.K. Sibero Alvin Alvin, Alvin Azanuddin Azmi, Fadhillah Despaleri Perangin-Angin Dharshinni, NP Diky Suryandy Fadhillah Azmi FADHILLAH AZMI Fadhillah Azmi Fadhillah Azmi, Fadhillah Fawwaz, Insidini Fransiskus Gowasa Gibran, M Khalil Gibran, M. Khalil I Fawwaz Indra, Evta Interiesta, Diva Cahaya Jesron Nainggolan M Khalil Gibran M. Khalil Gibran Manurung, Immanuel H G Maulana, Ilham Habib MAULANA, RIZA Mawaddah Harahap, Mawaddah Mega Puspita Sari Miftahul Jannah Muhammad Khalil Gibran Muhammad Riki Atsauri N P Dharshinni N P Dharshinni N. PRIYA DHARSHINNI Nanda Novita Nanda Novita Nanda Novita, Nanda Pasaribu, Martin Josua Perangin-angin, Despaleri Regina Puspa Sari Damanik, Almerinda Rina Anugrahwaty, Rina Sarif, Muhammad Irfan Sibero, Alexander F K Simanjuntak, Englis Franata Sinaga, Monica Sari Sinaga, Nicholas Ferdinan Sipahutar, Yohannes Saputra Siregar, Ana Tince Siregar, Yunita Sari Sitompul, Boy Arnol Sofy, Annisa Kurnia Victor Daniel Sihombing Wijaya Laia, Laksana Febri Yusuf Roni Laia