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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnas Nasional Teknologi dan Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Jurnal Teknologi Informasi dan Pendidikan Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies BERNAS: Jurnal Pengabdian Kepada Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Scientific Journal of Informatics
Siregar, Amril Mutoi
Teknik Informatika, Universitas Buana Perjuangan Karawang
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Mentor : Changemakers Mentorship Program, dengan Tema "Smart & Lean Marketing Strategy" Tiawan; Nur Davi Kurniawan; Eliza Ariesta; Amril Mutoi Siregar; Surjandy; Merios Gusan Putra; Timotius Victory; Nilam Atsirina Krisnaputri; Ade Kurniawan; Dani Lukman Hakim
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.17980

Abstract

Program Mentorship Changemaker adalah inisiatif pengembangan kapasitas yang dirancang untuk meningkatkan kinerja dan dampak para penggerak perubahan, khususnya inovator sosial dan pendiri bisnis. Program ini diimplementasikan melalui sesi mentoring kelompok berbasis aksi yang melibatkan praktisi berpengalaman dari berbagai bidang seperti teknologi, pendidikan, inovasi sosial, dan pengembangan masyarakat. Program ini dilaksanakan dari tanggal 19 Desember 2025 hingga 9 Januari 2026. Mentor yang terlibat dalam program ini meliputi: Tiawan (Dosen Bisnis Digital di ITSB – Sinarmas Group), Agis Nurholis (Direktur M Foundation), Aqil Wida Arief (Social Impact Engineer), Arninta Puspitasari (Pendiri Barakah Communication & Indonesia Muslim Women), Dharmaji Suradika (Pendiri LearnAlways & Kawan Pintar), Nindyta Ayu Putri Ningtyas (Pendiri & CEO Linkupcareer.id), Grenda Qomara (Peneliti Konten di Corporate Innovation Asia), dan Ahmad Baihaqy (Project Lead di Corporate Innovation Asia). Hasil program ini menunjukkan peningkatan kapasitas peserta dalam kepemimpinan, inovasi sosial, dan kemampuan untuk merancang solusi yang berdampak. Lebih lanjut, program ini berkontribusi pada pencapaian beberapa Tujuan Pembangunan Berkelanjutan (SDG), khususnya SDG 4 (Pendidikan Berkualitas), SDG 8 (Pekerjaan Layak dan Pertumbuhan Ekonomi), SDG 9 (Industri, Inovasi, dan Infrastruktur), dan SDG 17 (Kemitraan untuk Tujuan).
Lightweight YOLO Models for Robust Facial Expression Detection Achmad Indra Aulia; Albert Jofrandi Hutapea; Amril Mutoi Siregar; Surjandy
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1120

Abstract

Facial expression recognition is a fundamental component of artificial intelligence systems, particularly in human–machine interaction. However, achieving robust detection accuracy remains challenging due to variations in lighting, facial orientation, and limited training data diversity. While recent lightweight YOLO architectures—YOLOv8n, YOLOv10n, and YOLO11n—have demonstrated strong performance in general object detection, comparative studies evaluating these models specifically for facial expression detection remain limited. This study addresses this gap by systematically comparing these three nano-variant models on a dataset of 2,000 labeled facial images across four expression categories: flat face, angry, sad, and smile. The dataset was divided into training (70%), validation (20%), and test (10%) subsets. Experiments were conducted under two scenarios—with and without data augmentation—using identical training configurations. Augmentation techniques included mosaic composition, HSV variation, geometric transformations, and flipping. Results show that augmentation improved the F1 score of YOLOv10n from 0.68 to 0.72 and YOLO11n from 0.65 to 0.72, with the latter achieving the highest overall precision of 0.82. YOLOv8n exhibited stable performance with an F1 score of 0.75 under both conditions. Confidence threshold optimization revealed distinct optimal operating points for each model, ranging from 0.1 to 0.6, confirming that per-model threshold tuning is necessary to maximize detection performance. These findings provide practical guidance for selecting and configuring lightweight YOLO models for facial expression detection in resource-constrained environments.
SMART HOME BERBASIS INTERNET OF THINGS UNTUK PENGENDALIAN PERALATAN ELEKTRONIK DAN PEMANTAUAN RUMAH Sutan Faisal; Amril Mutoi Siregar; Yana Cahyana; Muhamad Ikbal Ramdani; Romlah; Fariz Umam
BUANA ILMU Vol. 10 No. 1 (2025): Buana Ilmu
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/ct2pwk97

Abstract

Dalam era digital saat ini, sistem smart home berbasis Internet of Things (IoT) menawarkan inovasi yang signifikan dalam manajemen dan kontrol lingkungan rumah. Artikel ini membahas pengembangan sistem smart home yang mengintegrasikan berbagai komponen perangkat keras, termasuk lampu, sensor DHT11, sensor magnetis, buzzer, dan motor DC, serta aplikasi Blynk sebagai antarmuka pengguna. Sistem ini dirancang untuk memantau dan mengelola parameter lingkungan seperti suhu dan kelembapan melalui sensor DHT11, serta meningkatkan keamanan dengan penggunaan sensor magnetis yang mendeteksi pembukaan pintu atau jendela. Ketika sensor magnetis mendeteksi aktivitas mencurigakan, buzzer akan memberikan peringatan kepada penghuni rumah. Motor DC diimplementasikan untuk mengontrol perangkat seperti tirai otomatis, meningkatkan kenyamanan pengguna. Melalui aplikasi Blynk, pengguna dapat mengakses dan mengontrol semua perangkat secara real-time melalui smartphone, memungkinkan pengelolaan yang lebih efisien dan responsif. Hasil penelitian menunjukkan bahwa sistem ini tidak hanya meningkatkan kenyamanan dan keamanan rumah, tetapi juga memberikan solusi inovatif untuk pengelolaan energi. Dengan demikian, pengembangan sistem smart home ini membuka peluang baru dalam menciptakan lingkungan hidup yang lebih cerdas dan responsif terhadap kebutuhan penghuninya.
Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle Amril Mutoi Siregar; Sony Hartono Wijaya; Ahmad Fauzi; Tjong Wan Sen; Sutan Faisal; Tukino Tukino; Yana Cahyana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26750

Abstract

Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition.
Pengaruh Feature Engineering terhadap Kualitas Clustering pada Segmentasi Pelanggan Ritel Daring Amril Mutoi Siregar
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10267

Abstract

Segmentasi pelanggan yang didasarkan pada data transaksi merupakan tantangan signifikan dalam ranah pemasaran digital, karena kemanjuran hasil pengelompokan sangat bergantung pada fitur yang digunakan. Ketergantungan pada fitur transaksi yang belum sempurna sering menghasilkan metrik evaluasi numerik yang meningkat; Namun, itu tidak cukup menangkap seluk-beluk perilaku pelanggan yang sebenarnya. Upaya penelitian ini berusaha untuk menyelidiki dampak rekayasa fitur pada kualitas segmentasi yang berkaitan dengan pelanggan ritel online. Metodologi yang digunakan adalah kerangka kerja kuantitatif eksperimental yang menyandingkan dua model fitur, khususnya Model 1 yang didasarkan pada fitur fundamental dan Model 2 yang berlabuh dalam fitur rekayasa perilaku pelanggan, mencakup RFM, Nilai Pesanan Rata-Rata, dan keragaman produk. Untuk tujuan ini, tiga algoritma pengelompokan tanpa pengawasan digunakan, yaitu K-Means, Model Campuran Gaussian, dan DBSCAN, dengan evaluasi dilakukan menggunakan Skor Silhouette dan Indeks Davies-Bouldin. Temuan menunjukkan bahwa sementara Model 1 menghasilkan nilai metrik yang unggul, ia menunjukkan relevansi yang berkurang dengan konteks bisnis. Sebaliknya, Model 2 menghasilkan cluster yang ditandai dengan peningkatan stabilitas, konsistensi, dan interpretabilitas untuk aplikasi pemasaran. Studi ini akhirnya menyimpulkan bahwa keberhasilan segmentasi pelanggan sebagian besar dipengaruhi oleh kaliber rekayasa fitur daripada hanya nilai yang meningkat dari metrik evaluasi internal
Evaluasi Model Pembelajaran Mesin dalam Memprediksi Kualitas Tidur Berdasarkan Pola Makan dan Aktivitas Fisik Amril Mutoi Siregar; Gunawan Witjaksono; Angga Jovansyah
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.213-221

Abstract

Sleep quality is an important indicator of health, shaped by the intricate interactions of various multidimensional elements, including eating habits, physical activity levels, and psychological well-being. The purpose of this study is to assess the efficacy of linear (Logistic Regression) and non-linear machine learning algorithms (Decision Tree and Gradient Boosting) in predicting sleep disorders. The dataset used came from a survey that included 195 participants and underwent several stages of preprocessing, including data cleansing, feature engineering (especially regarding duration), and standardization through Z-score normalization. Model validation is performed using the Stratified 5-Fold Cross-Validation technique to reduce bias and ensure the stability of performance metrics. The findings show that the Logistics Regression model, optimized through threshold adjustment (with an adjustment threshold of 0.55), shows the most favorable and consistent performance, achieving an accuracy rate of 79.5% and a drawback rate of 96.7%, thus surpassing the efficacy of non-linear models such as Gradient Boosting. These results reinforce the principle of simplicity, which states that, in limited datasets, simpler linear models often show greater resilience than their more complex counterparts, which are prone to overfitting. An interpretability assessment conducted through the Analysis of the Importance of Permutation Features showed that bedtime dietary practices and stress levels emerged as the dominant factors affecting sleep quality, surpassing the contribution of physical activity and anthropometric variables (such as height and weight). In light of these findings, this study advocates health interventions that emphasize time-oriented nutrition management (chrononutrition) and recommends the use of linear models as a basic strategy in the screening process for populations characterized by limited sample sizes
Co-Authors Abda Abda Abdul Mufti Achmad Indra Aulia Ade Kurniawan Ahmad Fauzi Ahmad Fauzi Albert Jofrandi Hutapea Alma Hidayanti Andri Juliyanto Angga Jovansyah Anton Romadoni Junior Ariesta, Eliza ARIF, SITI NOVIANTI NURAINI Baihaqi, Kiki Ahmad Basuni, Nursela Bunga Tiara, Vira Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Faisal, Sutan Fariz Duta Nugraha Fariz Umam Farkhina Dwi Utari Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Gunawan Witjaksono Hanny Hikmayanti Handayani Hilda Yulia Novita Indi Nurul Hassanah Indra Maulana` Indra Maulana Indra, Jamaludin Jaman, Jajam Haerul Jayidan, Zirji Jessika Welliana BR. Siahaan Juwita, Ayu Ratna Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Lilis Kartika Lutfiah Adeliana Maulana Abdur Rofik Maulana, Ikhsan Muhamad Ikbal Ramdani Mulya Cahya Ramadanty Murniasih nabila, putri Nahrowi Nahrowi Nahrowi Nilam Atsirina Krisnaputri Nofita Sari Nur Davi Kurniawan Permana, Tedi Pratama, Adi Rizky Priyatna, Bayu Rahmad Nahar Siregar Rahmat Rahmat Ramadhan, Naufal Cahya Rizqi Fahrozi Rohana, Tatang Romadoni, Nurul Romlah Salsa Desmalia Santi Arum Puspita Lestari Sekar Wuni Sinta Candra Dewi Sinung Suakanto SITI NURJANAH Siti Silvia Arifin Sony Hartono Wijaya Sony Hartono Wijaya Sukamto, Ika Sumiyarsi Surjandy Surjandy Sutan Faisal Sutan Faisal Sutan Faisal Tatang Rohana Tia Astiyah Hasan Tiawan Timotius Victory Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin