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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi PUBLIDIMAS CSRID
Soeheri, Soeheri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering

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Comparison of Drug Type Classification Performance Using KNN Algorithm Aldi, Febri; Nozomi, Irohito; Soeheri, Soeheri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11487

Abstract

The error of decommissioning is a serious problem that is often faced in medicine. In the face of these problems, information technology has a very important role. One of the information technologies that can be used is to use the machine learning classification algorithm K-Nearest Neighbor KNN. KNN is a type of machine learning algorithm that can be applied to problems with classification and regression prediction. The classification of types of drugs for patients greatly affects the health of the patient. The patient data is processed and transformed to numbers, which are then divided into training data and test data from 90:10, 80:20, 70:30 and using the Cross Validation model. KNN works through the nearest neighboring value with a value of k = 3 calculated by the calculation of Euclidean Distance, and then evaluated using the Confusion Matrix. The performance of the KNN algorithm resulted in the highest Accuracy value of 98.33%, a Precision value of 98.8%, a Recall value of 96.2%, and an F-measure value of 97.48%. The performance is obtained from the sharing of training data and 90:10 test data. The data share results in high performance compared to other data shares, including using the Cross Validation model. And the lower the k value, the higher the value of the resulting performance. The results show that the performance of the KNN algorithm is working well.
Penerapan Gray Level Co-Ocurrence Matrix Dengan Metode Self Organizing Map Pada Deteksi Kematangan Buah Pinang Setiawan, Adil; Soeheri, Soeheri; Sumijan, Sumijan
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.2.2024.174-187

Abstract

Areca nut can be seen through its fiber which plays an important role in improving digestion. Fiber helps facilitate bowel movements and prevent constipation, provides improvements in the digestive system and keeps teeth healthy. The results of this research obtained a classification model using the Gray Level Co-Occurrence Matrix. Many areca nut plantations still use manual methods to sort fruit, but this method is often inaccurate and varies, this is due to differences in the perceptions of each person. Histograms help you find images with similar color composition. Similarity is measured by calculating the distance between histograms. Color composition can be seen in the form of a histogram which represents the distribution of the number of intensity pixels for each color in an image. This research aims to detect the ripeness of areca nut fruit. This research uses a combination of RGB and HSV feature extraction techniques and GLCM extraction techniques. The resulting information is in the form of a percentage of similarity and classification of fruit maturity which includes Ripe (Hue=0.11893, saturation= 0.75727, value= 0.81813), half ripe (Hue= 0.17933, Saturation=0.20123, value= 0.44968) Unripe (Hue=0.21514, Saturation= 0.47934, Value= 0.36719) with an accuracy level of 100%, from images that have been processed.
Pemanfaatan Artificial Intelligence (AI) untuk Ide Penciptaan Karya Seni Berbahan Serbuk Kayu di CV. Rendy Hendicraft Soeheri, Soeheri; Setiawan, Adil; Nasution, M. Irfan Aldy; Akbar, Muhammad Barkah
Publikasi Pengabdian Masyarakat Vol 4 No 2 (2024): PUBLIDIMAS Vol. 4 No. 2 NOVEMBER 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/publidimas.v4i2.408

Abstract

The utilization of Artificial Intelligence (AI) in the creative industry has opened new opportunities to enhance innovation and efficiency. This study aims to explore the use of AI as a tool to generate creative ideas for wood powder-based art at CV. Rendy Hendicraft. The methodology includes experiments with generative AI tools such as Flux AI, DALL-E and MidJourney to produce initial designs, followed by a selection and adaptation process by local artists. The results demonstrate that AI can generate diverse innovative design patterns, including traditional carvings, geometric abstracts, and modern-traditional hybrid elements, which are then transformed into wood powder artworks. The collaboration between AI and humans accelerates the conceptualization phase and broadens creative perspectives. However, adapting AI-generated designs to the wood powder medium requires modifications to accommodate material characteristics, such as texture and structural durability. The impacts of this initiative include increased productivity for CV. Rendy Hendicraft, product diversification, and strengthened competitiveness in the craft market. This study concludes that integrating AI into the creative process does not replace human roles but acts as an innovation catalyst supporting the sustainability of local-based creative industries.
Rancang Bangun Aplikasi Presensi Siswa Berbasis QR Code pada SMK Swasta Bina Satria Medan Nugraha, Rendy Editya; Soeheri, Soeheri
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

Student attendance is a routine activity in schools that serves to monitor students’ presence and acts as an indicator of discipline. At SMK Swasta Bina Satria Medan, the attendance process is still carried out manually using paper-based attendance lists, which is time-consuming, prone to fraud, and at risk of data loss. To address these issues, a technology-based attendance system that is fast, accurate, and integrated is required. This study aims to design and develop a student attendance application based on QR code technology, which can be used by subject teachers through Android devices. Each student has a personal QR code displayed through a dedicated Android application for students. Teachers scan the QR code using the Android application to automatically record attendance into the database. The system is developed using Java with MySQL as the database. The test results show that the application can perform attendance quickly and accurately, minimize recording errors, and reduce the potential for fraud. Attendance data is stored digitally and can be easily recapped according to the desired period. With this application, the attendance administration process at SMK Swasta Bina Satria Medan becomes more efficient and practical and supports digital transformation in the school environment.
Rancang Bangun Aplikasi Rekam Medis pada Klinik Bidan Supini di Desa Tanjung Rejo Berbasis Android Fauziah, Nuri; Soeheri, Soeheri
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 2 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i2.1348

Abstract

Perkembangan teknologi informasi telah memberikan kemudahan dalam berbagai sektor, termasuk bidang kesehatan. Klinik Bidan Supini di Desa Tanjung Rejo masih menggunakan pencatatan manual dalam pengelolaan data rekam medis pasien, yang berpotensi menimbulkan keterlambatan, kesalahan pencatatan, dan kesulitan dalam pencarian data. Oleh karena itu, penelitian ini bertujuan untuk merancang dan membangun sebuah aplikasi rekam medis berbasis Android. Aplikasi ini dibangun menggunakan bahasa pemrograman Java dan basis data Mysql sebagai backend. Hasil dari penelitian ini adalah aplikasi yang mampu membantu pihak klinik dalam mengelola data pasien, riwayat pemeriksaan, dan proses antrian pelayanan secara efisien dan terstruktur. Dengan adanya aplikasi ini, diharapkan dapat meningkatkan efisiensi pelayanan serta meminimalisir kesalahan dalam pengelolaan data rekam medis di Klinik Bidan Supini. Dalam penelitian ini berhasil dilakukan uji coba program dengan pengujian secara black box (interface) yaitu pengujian perangkat lunak tes fungsionalitas dari aplikasi yang bertentangan dengan struktur internal atau kerja.
Optimizing Breast Cancer Classification: SVM and Random Forest with Hybrid Hyperparameter Tuning and Feature Selection Setiawan, Adil; Soeheri, Soeheri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5720

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, underscoring the urgent need for early, accurate, and reliable diagnostic support systems. This study proposes an optimized breast cancer classification framework using Support Vector Machine (SVM) and Random Forest (RF) models enhanced through hybrid hyperparameter tuning and feature selection. The Breast Cancer Wisconsin (Diagnostic) dataset, comprising 569 samples with 30 numerical features derived from Fine Needle Aspirate (FNA) examinations, was utilized in this research. Feature selection was conducted using Random Forest feature importance to identify the most relevant diagnostic attributes and reduce dimensionality. Hybrid hyperparameter tuning was implemented using GridSearchCV combined with 5-fold cross-validation to obtain optimal model configurations. Model performance was evaluated using accuracy, malignant-class recall, confusion matrix analysis, and Receiver Operating Characteristic–Area Under the Curve (ROC–AUC). Experimental results show that the optimized SVM model achieved significant improvements in accuracy, recall, and ROC–AUC compared to baseline models, indicating enhanced sensitivity and discrimination capability, while the Random Forest model maintained stable performance with marginal gains after optimization. These findings highlight the critical importance of systematic optimization strategies in improving diagnostic safety and reducing false negatives, thereby contributing to the development of more reliable and clinically applicable machine learning-based medical decision support systems.
Co-Authors Akbar, Muhammad Barkah Fauziah, Nuri Febri Aldi Nasution, M. Irfan Aldy nozomi, irohito Nugraha, Rendy Editya Setiawan, Adil Sumijan Sumijan