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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan ILKOM Jurnal Ilmiah J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Journal of Computer System and Informatics (JoSYC) Jurnal Akuntansi dan Keuangan Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Randwick International of Social Science Journal IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) JOMLAI: Journal of Machine Learning and Artificial Intelligence SWARNA JGEN : Jurnal Pengabdian Kepada Masyarakat SWARNA: Jurnal Pengabdian Kepada Masyarakat Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Rahmat Widia Sembiring -
Politeknik Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture 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 Social Sciences Other

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Pembuatan Program Game Menggunakan UNITY Pada SMK PAB 2 Helvetia Kecamatan Labuhan Deli, Kabupaten Deli Serdang, Sumatera Utara Friendly, Friendly; Harizahayu, Harizahayu; Sembiring, Zakaria; Sembiring, Rahmat Widia
JGEN : Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 1 (2025): JGEN : Jurnal Pengabdian Kepada Masyarakat, Februari 2025
Publisher : Lumbung Pare Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60126/jgen.v3i1.700

Abstract

Pengembangan Game sangat berbeda dengan pengembangan aplikasi komputer yang hanya membutuhkan algoritma, bisnis proses dan basis data. Game membutuhkan aset multimedia berupa gambar, animasi, audio, dan pemrograman yang berbasis thread. Pengembangan game dan pengembangan perangkat lunak merupakan dua bidang yang berbeda meskipun keduanya berfokus pada pengembangan aplikasi komputer. Pelatihan pembuatan game berbasis UNITY di SMK PAB 2 Helvetia Medan bertujuan untuk meningkatkan kompetensi guru dalam teknologi pengembangan game. Workshop ini diadakan pada tanggal 28 September 2024 dan bertujuan agar para guru dapat mengajarkan siswa untuk membuat game berbasis 2D dan 3D. Pelatihan ini memfokuskan pada penguasaan dasar-dasar UNITY, mulai dari antarmuka pengguna, pembuatan objek, hingga pemrograman interaksi game. Kegiatan ini diakhiri dengan evaluasi melalui hasil karya siswa yang dihasilkan setelah guru mengimplementasikan materi yang telah diajarkan. Pelatihan ini menunjukkan hasil positif dalam mengembangkan keterampilan guru, serta meningkatkan motivasi belajar siswa melalui media pembelajaran berbasis game.
Analisa Terhadap Perbandingan Algoritma Decision Tree Dengan Algoritma Random Tree Untuk Pre-Processing Data Saifullah, Saifullah; Zarlis, Muhammad; Zakaria, Zakaria; Sembiring, Rahmat Widia
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 2 (2017): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.436 KB) | DOI: 10.30645/j-sakti.v1i2.41

Abstract

Preprocessing data is needed some methods to get better results. This research is intended to process employee dataset as preprocessing input. Furthermore, model decision algorithm is used, random tree and random forest. Decision trees are used to create a model of the rule selected in the decision process. With the results of the preprocessing approach and the model rules obtained, can be a reference for decision makers to decide which variables should be considered to support employee performance improvement
Analisa Terhadap Perbandingan Algoritma Decision Tree Dengan Algoritma Random Tree Untuk Pre-Processing Data Saifullah, Saifullah; Zarlis, Muhammad; Zakaria, Zakaria; Sembiring, Rahmat Widia
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 2 (2017): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v1i2.41

Abstract

Preprocessing data is needed some methods to get better results. This research is intended to process employee dataset as preprocessing input. Furthermore, model decision algorithm is used, random tree and random forest. Decision trees are used to create a model of the rule selected in the decision process. With the results of the preprocessing approach and the model rules obtained, can be a reference for decision makers to decide which variables should be considered to support employee performance improvement
Analisis Interaksi Lingkungan dan Genetik Menggunakan Metode Komputasi Muhammad Gusti Aditya; Rahmat Widia Sembiring
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 2 (2025): Mei: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i2.763

Abstract

The interaction between genetic and environmental factors plays a crucial role in determining phenotypic traits in organisms. This study aims to analyze these interactions using computational approaches, including statistical models and machine learning algorithms. The data used include genetic factors (genotypes) and simulated environmental factors. Results indicate that machine learning models such as Random Forest can detect interaction patterns with high accuracy, as demonstrated by significant R² values. Additionally, heatmap visualizations provide deeper insights into the non-linear effects of genetic-environment interactions. This study highlights the potential of computational methods in exploring complex interactions, with broad applications in health, agriculture, and biotechnology.
Implementation of Random Forest Optimized with Ant Colony Optimization (ACO) for Breast Cancer Prediction Ht. Barat, Ade Ismiaty Ramadhona; Siregar, Sandy Putra; Poningsih, Poningsih; Windarto, Agus Perdana; Solikhun, Solikhun; Sembiring, Rahmat Widia
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.7116

Abstract

Breast cancer is a significant disease impacting women globally, highlighting the necessity for precise and dependable diagnostic models. This study aims to improve breast cancer prediction by optimizing the Random Forest algorithm using Ant Colony Optimization (ACO). This study uses datasets containing various cell characteristics to build and evaluate models. The ACO algorithm is applied to fine-tune the hyperparameters of the Random Forest model and improve its predictive performance. The experimental results showed that the optimized Random Forest model outperformed the baseline model in all evaluation metrics. The optimized model achieved an accuracy of 94.74%, precision of 97.92%, recall 90.38%, an F1 score of 92.93%, and an AUC score of 0, 9449 compared to the basic Random Forest model, with lower scores across all metrics. This improvement highlights the effectiveness of ACOs in improving model performance, especially in reducing false negatives, which are critical for medical diagnosis. This study demonstrates that ACO successfully fine-tunes Random Forest hyperparameters, achieving superior accuracy compared to baseline and outperforming previous optimization methods such as PSO. These findings confirm that the combination of Random Forest and ACO offers a powerful and effective approach to improving the accuracy of breast cancer predictions, making them a valuable tool for clinical decision-making.
Pengaruh Profesionalisme, Independensi Auditor dan Etika Profesi Terhadap Kinerja Auditor Yusuf, Fadel; Surianti, Meily; Deliana, Deliana; Sembiring, Rahmat Widia
Jurnal Akuntansi dan Keuangan Vol. 13 No. 1 (2025): Jurnal Akuntansi dan Keuangan: Maret 2025
Publisher : Department of Accounting, Faculty of Economics & Business

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jak.v13i1.21233

Abstract

This research aims to determine whether Professionalism, Auditor Independence, and Professional Ethics affect Auditor Performance. The research method employed is quantitative research. The population in this study consists of 138 auditors working at Public Accounting Firms in Medan City. The sample in this study uses a purposive sampling technique with criteria for auditors working at Public Accounting Firms in Medan, auditors with a minimum position as senior auditors, and auditors who have worked for at least one year. This research uses a survey method with primary data obtained from questionnaires. The data analysis technique used in this study is a quantitative analysis technique using the statistical software Smart PLS (Partial Least Square). The results of this study show that Professionalism has a positive effect on Auditor Performance. Auditor Independence has a positive effect on Auditor Performance. Professional Ethics has a positive effect on Auditor Performance.
Identifikasi Predikat Hasil Pengelompokan Data Kualitas Udara dengan Menggunakan Affinity Propagation dan Silhouette Coefficient Rahmah, Maulidya; Candra, Ade; Sembiring, Rahmat Widia
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 2 (2022): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i2.4670

Abstract

Penelitian ini bertujuan untuk mengidentifikasi pengelompokan data kualitas udara dan mendapatkan predikat hasil pengelompokan kualitas data udara tersebut. Data yang digunakan adalah data kualitas udara Kota Pekanbaru yang diperoleh dari pengolahan data Laboratorium Udara Pemerintah Kota Pekan Baru dengan rentang waktu tahun 2014, 2015, dan 2016. Penulis menerapkan Affinity Propagation untuk melakukan klasterisasi pada data tersebut dan menghitung jumlah klaster yang dihasilkan. Berdasarkan penerapan Affinity Propagation pada data kualitas udara Kota Pekanbaru dengan pengujian nilai damping factor dari rentang 0.5 sampai 0.95 diperoleh 5 klaster saat damping factor bernilai 0.95. Sementara jumlah klaster terbanyak adalah 156 saat damping factor bernilai 0.55. Nilai rata-rata Silhouette Coefficient dari data yang diujikan adalah 0.264, dan jika dikategorikan maka kualitas klaster yang dihasilkan berdasarkan predikat nilai Silhouette Coefficient adalah Weak Structure.
Enhancing Eye Disease Classification Accuracy Using Convolutional Neural Networks with Transfer Learning Addyna, Nazlina Izmi; Sembiring, Rahmat Widia; Windarto, Agus Perdana
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2886.195-206

Abstract

Eye diseases serve as a primary contributor to global blindness, making early detection a critical determinant in effective treatment outcomes. While retinal fundus image analysis is the diagnostic standard, conventional manual methods are often hindered by observer subjectivity and time inefficiencies. This study aims to optimize eye disease classification using a Convolutional Neural Network (CNN) approach empowered by transfer learning techniques. Utilizing a dataset of 1,200 retinal fundus images sourced from Kaggle, this research classifies four categories: normal, glaucoma, cataract, and diabetic retinopathy. To mitigate the challenge of limited labeled medical datasets, specific data augmentation strategies—including random flip, zoom, and contrast adjustments—were applied. The study conducts a comparative evaluation of three architectures: standard VGG16, baseline MobileNet, and a proposed optimized MobileNet. The proposed method utilizes Random Search to systematically optimize hyperparameters such as learning rates, dense layer units, and dropout rates. Experimental results demonstrate that the optimized MobileNet achieved superior performance with 89.17% accuracy, significantly outperforming the VGG16 baseline 82,00% and baseline MobileNet 85,00%. Notably, the model achieved perfect recall for diabetic retinopathy, although glaucoma remained the most challenging class due to subtle morphological similarities with normal eyes. These findings confirm that integrating lightweight CNNs with appropriate transfer learning yields a diagnostic system that is not only accurate but also efficient for deployment in resource-constrained environments
Co-Authors Addyna, Nazlina Izmi Ade Candra, Ade Agus Perdana Windarto Benny B. Nasution Deliana, Deliana Devanta Abraham Tarigan Erozeki Sialagan F. Friendly Friendly, Friendly Harizahayu, Harizahayu Ht. Barat, Ade Ismiaty Ramadhona Iin Parlina Ina Kusanti Purba Indra Gunawan Irawan Irawan Irwandi Irwandi Muhammad Gusti Aditya Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis, Muhammad Nasib Marbun Novitaria Manullang Opim Salim Sitompul Poningsih Poningsih, Poningsih Putra, Purwa Hasan Rahmah, Maulidya Rina Walmiaty Mardi S Suhada Saifullah Saifullah Saifullah Saifullah Sembiring, Zakaria Siregar, Sandy Putra Solikhun Solikhun, Solikhun Surianti, Meily Yusuf, Fadel Zakaria Zakaria