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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 Ilmu Komputer dan Sistem Informasi Jurnal ABDIMAS MUTIARA Management of Information System Journal 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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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
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
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
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.
Algoritma Data Mining Menggunakan Metode Decision Tree Untuk Memprediksi Pola Penjualan Produk Springbed Mengggunakan Algoritma C4.5 Sanjaya, Donny; Saleh, Amir; Novida Sari, Sri; Rahmi Danur, Surizar
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2567

Abstract

Problems that often occur in the world of spring bed sales business are frequent ups and downs in predictions, the difficulty of detecting patterns in what can increase sales from buyers makes spring bed sales business people often experience losses, this also happens because business people don't know the strategy. Certainly in increasing sales, it is necessary to make predictions with a high level of accuracy, one of which is with the help of the application of computer science data mining using the C4.5 method. The C4.5 method used in this research is able to produce an optimal decision tree, with the ability to sort out the most relevant attributes in predicting springbed sales. The use of this data mining algorithm is expected to provide insight to springbed business players in making strategic decisions, such as stock management, production planning and more effective marketing campaigns. The experimental steps in this research include collecting springbed sales data. Experimental results show that the Decision Tree algorithm using the C4.5 method is able to provide spring bed sales predictions with an adequate level of accuracy. This model can help Springbed sales business players in planning more appropriate business strategies based on estimated market demand to increase the ups and downs of sales
A Gradient Boosting–Based Platform with Fuzzy Linguistic Representation for Cardiovascular Disease Risk Prediction Amir Saleh; Fadhillah Azmi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2699

Abstract

Cardiovascular disease (CVD) is one of the most common causes of death around the world. In order to effectively prevent and manage CVD, early detection and prediction of risk are essential. This research introduces a healthcare platform based on CVD risk prediction using advanced machine learning (ML) methods. This platform is designed to provide accurate risk assessment by integrating the gradient boosting (GB) classifier method. Additionally, other ML models are used as comparison algorithms. Initially, this research used preprocessing techniques such as data normalization and data cleaning to tackle outliers in the dataset. Recursive feature elimination (RFE) feature selection approaches are utilized to find features that affect prediction performance, hence lowering the amount of data dimensions and enhancing model performance. Then, using metrics such as accuracy, precision, recall, and F1-score, each model’s performance is evaluated. The modeling results of the suggested approach are then used to create a digital health platform that predicts new input from users. Additionally, fuzzy logic is applied to transform data into linguistic variables to help users find simpler information. Using the proposed GB model and preprocessing method, the platform can make more accurate CVD risk predictions during data validation than other ML methods. When compared to other approaches with lower accuracy, the evaluation results demonstrate that the GB method can achieve the highest prediction accuracy of 94.30%.
Use of Data Visualization Techniques in Bioinformatics for Time-Based Gene Expression Pattern Analysis M. Khalil Gibran; Mhd Ikhsan Rifki; Amir Saleh
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.373

Abstract

This study explores data visualization techniques in bioinformatics for analyzing time-series gene expression patterns. It examines how different visualization approaches support the interpretation of large-scale temporal gene expression data. A dataset comprising 4,381 genes across 24 time intervals was analyzed using heatmaps, Principal Component Analysis (PCA), volcano plots, and dendrograms. Heatmaps were used to observe expression correlations, PCA was applied to reduce dimensionality, volcano plots identified differentially expressed genes between conditions, and dendrograms grouped genes with similar expression profiles. The PCA results showed that the first two principal components accounted for 42.32% of the total variance, indicating that these components captured a substantial but not complete portion of the data structure. Volcano plot analysis detected differentially expressed genes based on log2 fold change > 1 and p-value < 0.05, while dendrogram visualization revealed several major clusters with comparable temporal expression patterns. Overall, the findings suggest that combining multiple visualization methods can improve the exploratory analysis of temporal gene expression data by clarifying patterns, highlighting potentially relevant genes, and supporting further biological interpretation. Rather than serving as standalone evidence for clinical application, these visual approaches provide a useful analytical foundation for subsequent validation, biomarker investigation, and large-scale omics research.  
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 M. Khalil Gibran Manurung, Immanuel H G Maulana, Ilham Habib MAULANA, RIZA Mawaddah Harahap, Mawaddah Mhd Ikhsan Rifki 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 Puspita Sari, Mega Rahmi Danur, Surizar Regina Puspa Sari Damanik, Almerinda Rina Anugrahwaty, Rina Sanjaya, Donny 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 Sri Novida Sari Victor Daniel Sihombing Wijaya Laia, Laksana Febri Yusuf Roni Laia