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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Informasi dan Pendidikan Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Infortech Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence SmartComp
Yuhandri Yuhandri, Yuhandri
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Aerospace Engineering Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Penerapan Preference Selection Index (PSI) dalam Pengangkatan Karyawan di Yayasan XYZ Kurniawan, Esa; Yuhandri, Yuhandri; Sumijan, Sumijan
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 1 (2023): April 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i1.2023.78-85

Abstract

Yayasan XYZ merupakan salah satu lembaga Pendidikan dan Kesehatan yang ada di kota pekanbaru, lembaganya terdiri dari Universitas, STEI, SMK, SMA, SMP, Rumah Sakit, dan juga mempunyai beberapa icon lainnya, maka karyawan yang menunjang operasional juga tidak sedikit yang tersebar di beberapa institusi, untuk itu perlunya dilakukan pengangkatan karyawan tetap, agar dapat mensejahterakan karyawan dan tujuan perusahaan tercapai. Merekrut calon yang tepat pada posisi yang tepat bukanlah hal yang mudah, Oleh sebab itu diperlukan suatu sistem yang membantu SDM dalam pengangkatan karyawan. Pengangkatan karyawan diperlukan penilaian oleh tim ahli dibidangnya untuk mendapatkan karyawan tepat yang mempunyai kualitas dan kesungguhan untuk mencapai visi misi dari yayasan, maka perlunya Sistem Pendukung Keputusan yang dirancang untuk mempermudah pengguna dalam mengambil keputusan yang dilakukan secara sistematis, dengan menggunakan metode Prefernce Selection Index dalam pengangkatan karyawan. Pengangkatan karyawan diperlukan penilaian oleh tim ahli dibidangnya untuk mendapatkan karyawan tepat yang mempunyai kualitas dan kesungguhan untuk mencapai visi misi dari yayasan, maka perlunya Sistem Pendukung Keputusan yang dirancang untuk mempermudah pengguna dalam mengambil keputusan yang dilakukan secara sistematis, dengan menggunakan metode Prefernce Selection Index dalam pengangkatan karyawan.Pengangkatan karyawan diperlukan penilaian oleh tim ahli dibidangnya untuk mendapatkan karyawan tepat yang mempunyai kualitas dan kesungguhan untuk mencapai visi misi dari yayasan XYZ, maka perlunya keputusan dalam pengangkatan karyawan menggunakan metode Prefernce Selection Index (PSI) yang akurasiya lebih tinggi dan lebih tepat sasaran tentunya. Hasil dari penelitian ini yaitu ada tiga alternatif dengan nilai peringkat tertinggi antara lain A9 = 0,9307, A1 = 0,9121 dan A6 = 0,9001.
Penerapan Metode Yolov10 Untuk Mendeteksi Penyakit Daun Pada Tanaman Gambir Daun Pada Tanaman Gambir Aziz, Majid Rahman; Yuhandri, Yuhandri; Veri, Jhon
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8544

Abstract

Kemajuan teknologi kecerdasan buatan (Artificial Intelligence) telah memungkinkan berbagai aplikasi dalam bidang deteksi objek dan Pengolahan Citra. Salah satu algoritma yang banyak digunakan adalah You Only Look Once (YOLO). Tujuan dari YOLOv10 diterapkan untuk mendeteksi penyakit daun pada tanaman Gambir (Uncaria Gambir Roxb). Tanaman Gambir memiliki nilai ekonomis tinggi dan merupakan komoditas ekspor utama dari Sumatera Barat, Indonesia. Produktivitas hasil dari tanaman Gambir terancam oleh serangan penyakit seperti Mati Pucuk dan Karat Coklat. Metode YOLOv10 digunakan untuk mendeteksi objek yang memiliki kemampuan dalam mengidentifikasi objek. Petani umumnya mengandalkan metode deteksi konvensional yang kurang efektif, sehingga diperlukan solusi berbasis kecerdasan buatan menggunakan untuk meningkatkan efisiensi dan akurasi dalam mendeteksi penyakit daun pada tanaman Gambir. Penelitian ini menggunakan dataset primer yang terdiri dari 198 gambar penyakit Mati Pucuk dan 186  gambar Karat Daun sehingga total keseluruhan data yaitu 384 gambar. Setelah proses augmentasi data, jumlah gambar meningkat menjadi 2.688 untuk meningkatkan performa model. Model yang dilatih mencapai nilai dengan Precision 100%, dengan Recall 98%, Precission-Recall 94%, dengan akurasi 73% Setelah mendapatkan hasil dari proses Training Data Pengujian deteksi menggunakan metode YOLO model YOLOv10 untuk mengidentifikasi penyakit pada tanaman Gambir. Penelitian ini menunjukkan bahwa YOLOv10 mampu mendeteksi penyakit daun Gambir dengan akurasi yang baik. Metode ini lebih efisien dibandingkan deteksi konvensional, membantu petani dalam identifikasi dini penyakit untuk meningkatkan produktivitas Gambir.Kata Kunci: YOLOv10, Deteksi Penyakit Daun, Kecerdasan Buatan, Pengolahan Citra, Gambir.
Penerapan Metode Profile Matching pada Penilaian Kinerja Dosen Effendy, Geraldo Revanska; Yuhandri, Yuhandri; Sovia, Rini
Jurnal PROCESSOR Vol 20 No 2 (2025): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2025.20.2.2502

Abstract

The evaluation of lecturer performance at Adzkia University faces challenges in terms of inefficient data processing. This research aims to implement the Profile Matching method to optimize the lecturer performance assessment system, evaluate its effectiveness, and develop an application based on this method. The research was conducted using a quantitative method employing Profile Matching, which includes several stages: GAP calculation, GAP mapping, core factor and secondary factor analysis, total value calculation, and ranking determination. The evaluation was conducted on 38 lecturers considering five main criteria: Adzkian Values, Education, Research, Community Service, and Supporting Activities, which are detailed in 28 sub-criteria. The implementation of the Profile Matching method proved to produce objective assessments by placing Lecturer 31 as the lecturer with the highest score (4.251), followed by Lecturer 3 and Lecturer 30 (4.092). The developed web-based application successfully integrated this method and improved the efficiency of the assessment process. This study demonstrates the effectiveness of the Profile Matching method in evaluating lecturer performance with more objective results. The implemented system helps BPSDM conduct assessments more efficiently and generate more structured reports.
Implementasi E-Commerce Untuk Memperluas Pangsa Pasar Hasil Kerajinan UMKM Komunitas Hobi Kayu Padang Hadi, Febri; Yuhandri, Yuhandri; Mayola , Liga
JDISTIRA - Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Vol. 1 No. 1 (2021)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1022.177 KB) | DOI: 10.58794/jdt.v1i1.31

Abstract

Setiap UMKM sebenarnya sudah mempunyai ciri khas dari masing-masing produknya, terlebih lagi cara pengolahan kerajinan kayu. Tetapi yang perlu dilakukan disini adalah bagaimana produk milik UMKM tersebut dapat dipasarkan secara nasional maupun internasional dengan memanfaatkan E-Commerce. Selain itu dengan sosialisasi strategi pemasaran produk ini, tentunya juga bisa membantu meningkatkan penjualan UMKM tersebut. Terlebih Kota Padang merupakan Ibukota Sumatera Barat yang mana banyak dikunjungi oleh para wisatawan dari berbagai daerah di Indonesia.
Deteksi Pelanggaran Tata Tertib Siswa Sistem Cerdas Menggunakan Face Recognition dengan Metode Convolutional Neural Network Syafril, Syafril; Yuhandri, Yuhandri; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.753

Abstract

Student disciplinary violations are a social problem increasingly common in schools and can negatively impact students' academic and moral development. This phenomenon requires an effective identification system so that prevention and mitigation efforts can be carried out quickly and accurately. This research aims to develop a student face detection system based on Digital Image Processing (DIP) technology that functions to identify and classify adolescent disciplinary violations. The designed system utilizes a camera as an image acquisition device, then processes it to detect the presence of student faces in real-time. The face detection process is carried out using the Haar Cascade Viola-Jones method, which is known to be able to recognize faces with high speed and accuracy. Once a face is detected, the system continues the analysis process using the Convolutional Neural Network (CNN) method to classify facial expressions and behavioral patterns that could potentially indicate violations. The integration between Haar Cascade and CNN allows the system to work efficiently in identifying signs of negative behavior based on visual data. System testing shows satisfactory results, with a high level of facial detection accuracy and fairly reliable behavior classification capabilities. This technology has the potential to be used as a monitoring tool in the school environment, allowing teachers and school management to quickly identify students who need special attention. With the implementation of this system, it is hoped that schools will be able to provide timely guidance, prevent the escalation of deviant behavior, and create a more conducive learning environment. The use of digital image processing-based technology for detecting and classifying student behavior is a relevant innovation in the modern education era, while also supporting efforts to prevent juvenile disciplinary violations through a systematic and measurable approach.
Analisis Metode Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit pada Ibu Hamil Yasmin, Nabilla; Yuhandri, Yuhandri; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.756

Abstract

The high number of complications that occur during pregnancy and childbirth has the potential to significantly increase the risk of morbidity and mortality in pregnant women. The Maternal Mortality Rate (MMR) reflects the condition of pregnant, delivering, and postpartum mothers, which remains relatively high and is a major concern in the health sector. Based on this, this study aims to develop and evaluate an Expert System based on the Forward Chaining and Certainty Factor methods to diagnose diseases in pregnant women at an early stage, thereby providing fast and accurate medical decision support and minimizing the risk of complications during pregnancy. The Forward Chaining and Certainty Factor methods were chosen for their ability to handle rule-based inference processes and provide certainty level calculations in the diagnosis results. Forward Chaining is used to find solutions based on the symptoms entered by users, while the Certainty Factor helps assign confidence weights to the generated diagnosis. The dataset in this study consists of 30 data samples with 30 types of symptoms experienced by patients as variables. The results show that the Forward Chaining and Certainty Factor methods are capable of producing disease diagnoses in pregnant women with an accuracy rate of 95%. The contribution of this research is to improve the quality of maternal health services through fast and accurate diagnoses by medical personnel and to assist pregnant women in obtaining an initial diagnosis of common diseases during pregnancy.
The Development of Affine Transformation Method Using Scale Invariant Feature Transform (SIFT) Hartika Zain, Ruri Hartika; Yuhandri, Yuhandri; Sovia, Rini
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3653

Abstract

Carving is a technique used to create decorative images on wood, stone, and other materials. In Indonesia, wood is a popular choice because of its durability and attractive grain. Examples of wood carvings include floral designs. The carving process can involve changes in color, texture, and scale, which may affect the carving's size and appearance and cause dimensional changes in certain materials. This study addresses the issue of quality control in wood carving on thin veneer layers. Free wood-carving data are provided as 200 flower images that can be used as input images. Affine transformation is used to determine the system behavior and the material transfer function during the production process. Additionally, we propose extending the affine transformation method to use the Scale-Invariant Feature Transform (SIFT). Affine transformations enable correlation analysis, outlier removal, and feature orientation in the affine domain. The SIFT algorithm accounts for scale, rotation, brightness, and perspective. Applications using ASIFT can efficiently process images and handle those with different pixel sizes to create new carvings. Training samples used to update the filter model are changed to the same pose. This enables the flower wood carving filter to represent objects with 98% accuracy. The model is then used to predict the class of the flower-carving data and to compute the distance between the template image's features and those of the input flower-wood-carving image. This research project has successfully developed an Affine Transformation method using SIFT features to create a new engraving application based on the ASIFT approach. 
Sistem Deteksi Kepuasan Pelanggan dengan Teknik Pengelolaan Citra Menggunakan Convolutional Neural Networks Saputra, Randy; Yuhandri, Yuhandri; Arlis , Syafri
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1219

Abstract

Advancements in computer vision and facial expression recognition provide a new, objective, and non-intrusive method for measuring customer satisfaction in real time. This study develops a customer satisfaction detection system at Rumah Diskusi ALCO Café using Convolutional Neural Networks (CNN) with a mixed-methods approach, combining quantitative and qualitative analysis. The RAF-DB dataset containing 15,339 labeled images (12,271 for training and 3,068 for testing) across seven emotion classes was processed through image acquisition, preprocessing, and ResNet50 fine-tuning. The resulting model achieved an accuracy of 80.34%, with a Precision of 83.55%, Recall of 81.78%, and F1-Score of 82.32% on the test data. Field implementation over four weeks successfully recorded and analyzed thousands of customer facial expressions in key areas such as the cashier and main seating area in real time. Results showed a customer satisfaction distribution of approximately 72% “Satisfied,” 16% “Quite Satisfied,” and 12% “Not Satisfied,” with a declining trend during peak hours in the afternoon. Cross-validation with customer surveys demonstrated a strong correlation between the system’s predictions and reported satisfaction, proving the effectiveness of this method as a real-time monitoring tool. The study contributes a practical technical and methodological framework that can be replicated in other service industries for objective and real-time customer satisfaction monitoring.
A Modified Watershed Algorithm for Rice Plant Growth Stage Analysis Putra, Teri Ade; Yuhandri, Yuhandri; Ramadhanu, Agung
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1117

Abstract

Information technology plays a crucial role in enhancing various sectors, including agriculture. In particular, technological advancements in crop monitoring are essential for sustainable food production, where accurate growth analysis is vital. Image-based approaches have emerged as a promising tool for assessing crop growth, particularly in rice plants. This study aims to enhance rice plant image segmentation using an improved Watershed algorithm, integrating the Laplacian operator and Distance Transform. This study utilizes a Support Vector Machine (SVM) classifier for segmenting and classifying rice plant growth stages, achieving accuracy, precision, recall, and F1-score metrics. The dataset consists of 1080 images of rice plants, with 74 images used for training, 31 for testing, and 975 images for validation. The image processing pipeline involves preprocessing steps such as grayscale conversion, normalization, color segmentation, Otsu thresholding, filtering, and edge detection. Following preprocessing, the Watershed algorithm is applied in two scenarios: the conventional method and the enhanced method with the Laplacian operator and Distance Transform. Performance evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that the enhanced Watershed algorithm significantly outperforms the conventional method, achieving an accuracy of 99.58%, precision of 80.55%, recall of 79.92%, and an F1-score of 81.50%. While there is a slight imbalance in precision and recall, the model demonstrates reliable performance in identifying rice plant growth. This study confirms that integrating the Laplacian operator and Distance Transform into the Watershed algorithm significantly improves segmentation accuracy, supporting the development of automated monitoring systems in smart farming. Furthermore, this approach opens avenues for application in other crops and diverse environmental conditions.
Co-Authors Abda Abda Afifah Cahayani Adha Agus Perdana Windarto Akbar Iskandar Aldi Muharsyah Aldi, Febri Andrean, Fajri Ilhami Anita Sindar Ardiyan, Destio Arif Budiman Arlis , Syafri Aulia, Allans Prima Aziz, Majid Rahman Budayawan, Khairi Chandra, Mrs Montesna Dahria, Muhammad Devita, Retno Dewi Eka Putri Dikki Handoko Dolly Indra Dwi Narulita Dwika Assrani Effendy, Geraldo Revanska Efori Buulolo Eka Praja Wiyata Mandala Esa Kurniawan Fauzan, Yuniko Febri Hadi Feri Irawan Finny Fitry Yani Firzada, Fahmi Fuad El Khair Gayatri, Satya Gemilang, Fhajri Arye Gunadi Widi Nurcahyo Hartika Zain, Ruri Hartika Hartomi, Zupri Henra Hendrick, H Idun Ariastuti Iskandar Fitri, Iskandar Jaya, Budi Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Kadrahman, Kadrahman Kurniawan, Jefdy Lidia K Simanjuntak M Ikhsan Setiawan M, Mutia Maharani Maharani, Maharani Mayola , Liga Mesran, Mesran Musli Yanto Na'am, Jufriadif Natalia Silalahi, Natalia Nelly Astuti Hasibuan Nuning Kurniasih Nurdiyanto, Heri Permana, Randy Petti Indrayati Sijabat Pohan, Yosua Ade Purnomo, Nopi Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putri, Stefani Rahayu, Rita Rahmad Dian Rakhmad Kuswandhie Ramadhanu, Agung Rio Andika Malik Ronda Deli Sianturi S Sumijan Sagala, Gamrina Salmiati, S Saputra, Randy Sarjon Defit Sarjon Defit Septiana, Vina Tri Setiawan, Adil Sisi Hendriani Siska, Ayu Prima Soraya Rahma Hayati Sovia, Rini Sri Dewi Stephano, Rivo Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Darma Nasution Sutiksno, Dian Utami Syafrika Deni Rizki, Syafrika Deni Syafril Syafril Syaiffullah, Afif Tajuddin, Muhammad Takyudin, Takyudin Teri Ade Putra Tessa Y M Sihite Tukino, Tukino Veri, Jhon Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Winanda, Teddy Yanto, Musli Yasmin, Nabilla Yendi Putra Yeni, Nasma Yenila, Firna Yolla Rahmadi Helmi Yudha Aditya Fiandra Zikir Risky, Muhammad Arif