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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding KOMMIT Bulletin of Electrical Engineering and Informatics Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Indonesian Journal of Artificial Intelligence and Data Mining Journal of Information Technology and Computer Engineering JURTEKSI Jambura Journal of Informatics Jurnal Teknologi Jurnal Sistim Informasi dan Teknologi Sainstek : Jurnal Sains dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Holistics and Health Sciences (JHHS) Jurnal Pendidikan dan Teknologi Indonesia Innovative: Journal Of Social Science Research SmartComp Jurnal Terapan Pendidikan Dasar dan Menengah CSRID
Sumijan Sumijan
Gunadarma University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Sistem Pakar Metode Backward Chaining untuk Optimalisasi Pelayanan Pemberian Informasi Obat: Studi Kasus Puskesmas Lasi Kabupaten Agam Putra, Surya Dwi; Putri, Dhena Marichy; Defit, Sarjon; Sumijan, Sumijan
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 01 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.1-7.2023

Abstract

Drug information service is an assistance service to handle the needs of pharmacists related to medicines consumed by patients at the Lasi Health Center, Agam Regency. Nowadays, most of drug information services always require pharmacists to carry out their services, although there is limited number of pharmacists for providing drug information services at the Lasi Health Center, Agam Regency. This study aims to optimize drug information services so that the services can be carried out without the direct presence of a pharmacist. The data used in this study were drug prescription data available at the Pharmacy of Lasi Health Center Agam for the last 12 months and drug information services provided by pharmacists at the Lasi Health Center Agam Regency. This study used the backward chaining method to identify the drugs prescribed to the patients. The result achieved by this study were 356 Rules that could be applied directly to drug information services, with an accuracy rate of 100%. The rules generated using the backward chaining method can be used to optimize drug information services at the Lasi Health Center in Agam Regency without having to be served directly by pharmacists.
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.
Development of watershed algorithm for identification of diabetic retinopathy based on fundus images Surmayanti, Surmayanti; Sumijan, Sumijan; Bukhori, Saiful
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2845-2856

Abstract

Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to blindness if not detected early. This research presents a novel method for the identification of DR using fundus images, employing the Watershed Algorithm for accurate image segmentation and the gray level co-occurrence matrix (GLCM) for texture feature extraction. The image processing pipeline involves several stages, including grayscale conversion, noise reduction through Gaussian and median filters, and Otsu's thresholding to isolate key features such as retinal lesions. The watershed algorithm is applied to delineate the boundaries of abnormal regions, while the GLCM method extracts texture features like contrast, correlation, energy, and homogeneity, which are essential for diagnosing retinal abnormalities. The proposed approach demonstrates a high accuracy rate of 92%, successfully identifying abnormalities in 46 out of 50 fundus images. The method shows significant potential for enhancing early detection of DR, providing accurate segmentation and texture analysis, making it a valuable tool for medical professionals in diagnosing retinal diseases.
Development of ResNet-18 architecture to lesion identification in breast ultrasound images Andini, Silfia; Sumijan, Sumijan; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1236-1248

Abstract

Breast ultrasound (USG) is widely used for early breast cancer detection, but challenges such as noise, low contrast, and resolution limitations hinder accurate lesion identification. This study proposes a modified residual network-18 (ResNet-18) architecture for breast lesion segmentation, aimed at improving detection accuracy. The methodology involves preprocessing steps including red green blue (RGB) to Grayscale conversion, contrast stretching, and median filtering to enhance image quality. The modified ResNet-18 model introduces additional convolutional layers to refine feature extraction. The proposed model was trained and validated on 30 breast ultrasound images, with evaluation metrics including accuracy, sensitivity, and specificity. Experimental results indicate that the modified architecture outperforms the baseline model, achieving an average accuracy of 0.97093, sensitivity of 0.90056, and specificity of 0.97705. Validation by a radiology specialist confirms the model’s clinical relevance. These findings suggest that the enhanced ResNet-18 model has the potential to assist radiologists in more accurately identifying breast lesions. Future research should focus on expanding the dataset, integrating multi-modal imaging, and optimizing model generalizability for real-time clinical applications. The study contributes to advancing artificial intelligence (AI)-driven breast cancer diagnostics, supporting early detection, and improving patient outcomes.
Application of Convolutional Neural Networks for Automated Iris Edge Detection in Sleepiness Monitoring during Blended Learning Tukino, Tukino; Yuhandri, Yuhandri; Sumijan, Sumijan
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.882

Abstract

This study introduces a novel lightweight Convolutional Neural Network (CNN) model, T-Net, designed for real-time drowsiness detection based on eye closure patterns. The model was developed to address the prevalent issue of student fatigue in resource-constrained environments, such as during prolonged online learning or blended learning sessions. Unlike traditional deep learning models, T-Net prioritizes efficiency while maintaining high accuracy, making it suitable for deployment on devices with limited computational resources. The model uses a 68-point facial landmark detection technique to extract the eye region and accurately classify eyelid states (open or closed). Evaluated on two benchmark datasets, Dataset-1 (342 eye images) and Dataset-2 (1,510 eye images), T-Net demonstrated superior performance, achieving classification accuracies of 99.33% and 99.27%, respectively, outperforming other pre-trained models such as VGG19, ResNet50, and MobileNetV2. Usability testing revealed a high acceptance rate, with a System Usability Scale (SUS) score of 84.5, indicating the system’s practicality for real-world use. Additionally, statistical analysis showed a significant correlation (r = 0.67, p 0.01) between prolonged screen time and the emergence of visual fatigue symptoms. This study highlights the effectiveness of a lightweight CNN approach for real-time fatigue monitoring, offering a balance between performance and computational efficiency. The results suggest that T-Net can be effectively integrated into student monitoring systems to ensure alertness during learning sessions. Future research will focus on expanding the dataset, integrating infrared imaging for low-light environments, and incorporating additional fatigue indicators such as yawning and head pose.
Image-Based Classification of Rice Field Conversion: A Comparison Between MLP and SVM Using Multispectral Features Anisya, Anisya; Sumijan, Sumijan; Syahrani, Anna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5179

Abstract

The conversion of farmland into non-agricultural purposes has emerged as a pressing concern in many urban regions, including Koto Tangah District, Padang City. In this area, agricultural land experienced a 4% shift in land use between 2022 and 2024. If this trend continues, it could lead to a notable decline in rice production and ultimately threaten food security. This research focuses on examining spatial transformations of rice fields from 2022 to 2024 by utilizing Sentinel-2 satellite imagery along with advanced classification techniques. Vegetation and moisture features were extracted using NDVI, NDWI, texture analysis through GLCM, and Principal Component Analysis (PCA). To classify land cover changes and assess model accuracy, two machine learning approaches were applied: Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The findings reveal a considerable reduction in dense vegetation, indicated by the downward shift of NDVI values in 2024. MLP achieved an accuracy of 82%, outperforming SVM, which reached 71%. Furthermore, MLP obtained a higher F1-score for non-rice field detection (0.75 vs. 0.74) and produced more realistic delineations of rice field boundaries during spatial validation. These outcomes highlight the potential of MLP in monitoring land use conversion, supporting agricultural land conservation, and guiding sustainable urban planning. Moreover, the study contributes to computer science by advancing the use of machine learning for spatio-temporal analysis and reinforcing the role of non-linear models in satellite image classification.
Penerapan Metode Monte Carlo Dalam Memprediksi Jumlah Antrian Pasien Yang Berobat Ardiyan, Destio; Yuhandri, Yuhandri; Sumijan, Sumijan
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.v14i4.8498

Abstract

Abstrak: Prediksi jumlah antrian pasien yang berobat merupakan suatu pendekatan dalam analisis data untuk memperkirakan jumlah pasien yang akan datang ke fasilitas kesehatan dalam periode tertentu. Prediksi ini dapat membantu rumah sakit atau klinik dalam mengoptimalkan sumber daya, seperti tenaga medis, ruang tunggu, dan waktu pelayanan, sehingga dapat meningkatkan efisiensi dan kualitas layanan. Jumlah kunjungan pasien yang terlalu banyak terkadang berpotensi menimbulkan rasa tidak puas menunggu menyebabkan keterlambatan pelayanan, ketidakpuasan pasien, dan beban kerja staf medis yang tidak merata. Penelitian ini bertujuan untuk memprediksi jumlah antrian pasien yang berobat kedepannya dengan lebih efesien. Metode yang digunakan dalam penelitian ini adalah Metode Monte Carlo. Metode ini memiliki tahapan, Menentukan Distribusi Probabilitas, Distribusi Probabilitas Kumulatif, Menetapkan Interval Angka Acak, Membangkitkan Angka Acak, Percobaan Simulasi Monte Carlo Antrian Kedatangan Pasien. Dataset yang diolah dalam penelitian ini bersumber dari bagian Administrasi diklit RSU Aisyiyah Padang. Dataset terdiri dari data reservasi 1 periode dengan ketentuan dataset mingguan dari minggu 1 sampai minggu 52. Hasil penelitian ini dapat menghitung presentase jumlah antrian pasien yang berobat dengan Tingkat akurasi 83%. Penelitian ini dapat menjadi acuan dalam memprediksi jumlah antrian pasien yang berobat untuk mencegah rasa tidak puas menunggu dan keterlambatan pelayanan dan beban kerja staf medis yang tidak merata.Kata Kunci – Teknologi Informasi; Simulasi; Prediksi; Antrian, Monte Carlo
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.
Sistem Pakar Menggunakan Metode Forward Chaining Untuk Mendeteksi Kerusakan Jaringan Internet (Studi Kasus : Di Layanan Internet Diskominfotik Sumatera Barat) Zaki, Ahmad; Defit, Sarjon; Sumijan, Sumijan; Fauzana, Rahmi
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 3 (2023): Desember 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.227-236

Abstract

Sistem informasi yang interaktif dapat membantu kinerja pegawai dalam mendukung program SPBE (Sistem Pemerintah Berbasis Elektronik. Dinas Kominfotik Sumatera Barat berperan penting dalam memberikan layanan internet kepada OPD-OPD di bawah lingkup Pemerintahan Provinsi Sumatera Barat. Pembangunan sistem jaringan internet yang sudah baik tidak dapat dijamin bahwa jaringan tersebut terbebas dari gangguan dan kerusakan. Gangguan terhadap akses internet akan berdampak terhadap produktifitas bekerja pegawai dan pelayanan kepada masyarakat. Kurangnya pemahaman PIC OPD dan pengguna dalam menangani permasalahan gangguan jaringan internet, maka dibutuhkan keahlian pakar dalam melakukan identifikasi kerusakan pada jaringan internet berdasarkan gejala-gejala yang terjadi, serta diberikan solusi perbaikan pada gangguan yang ada. Pengumpulan data dilakukan melalui wawancara dan observasi lapangan. Metode yang digunakan untuk pengolahan data pada Sistem Pakar ini yaitu metode forward chaining. Forward Chaining adalah sebuah strategi pencarian dalam system pakar yang dimulai dari sekumpulan data atau fakta, dari data-data tersebut, system akan mencari suatu kesimpulan yang menjadi solusi dari permasalahan yang dihadapi. Berdasarkan hasil pengujian Sistem Pakar menggunakan metode forward chaining untuk mendeteksi gangguan jaringan internet menghasilkan tingkat akurasi sebesar 100 % menggunakan 29 data uji. Berdasarkan hasil yang didapatkan dari Sistem Pakar dengan metode forward chaining, system tersebut dapat digunakan untuk mendeteksi kerusakan jaringan internet di Layanan Internet Diskominfotik Sumatera Barat.
Peningkatan Hasil Belajar Matematika Materi Operasi Hitung Pecahan melalui Model Pembelajaran Kooperatif Tipe Jigsaw pada Peserta Didik Kelas V Semester 1 Tahun Pelajaran 2018/2019 di SD Negeri 3 Pule Kecamatan Pule Kabupaten Trenggalek Sumijan, Sumijan
Jurnal Terapan Pendidikan Dasar dan Menengah Vol 3 No 2 (2023): Volume 3 Nomor 2, Juni 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jtpdm.v3i2.906

Abstract

Kurikulum SD Negeri 3 Pule mengamanatkan pada mata pelajaran Matematika Kelas V materi operasi hitung pecahan, peserta didik memiliki kompetensi mencapai Kriteria Ketuntasan Minimal (KKM) yaitu 60 dengan persentase ketuntasan klasikal adalah 60%. Akan tetapi, kenyataannya pada tahun pelajaran 2016/2017 dari 14 peserta didik, hanya 6 peserta didik (42,9%) yang memiliki nilai mencapai KKM. Pada tahun pelajaran 2017/2018 dari 10 peserta didik, hanya 5 peserta didik (50%) yang memiliki nilai mencapai KKM. Hal ini terjadi karena guru masih menggunakan metode ceramah dan hanya meminta peserta didik mengerjakan soal di buku paket. Akibatnya, peserta didik kurang antusias dalam mengikuti pembelajaran. Peneliti mencari solusi untuk mendapatkan hasil terbaik pada pembelajaran melalui model pembelajaran Kooperatif Tipe Jigsaw. Adapun tujuan penelitian ini adalah untuk mendapatkan gambaran objektif tentang peningkatan hasil belajar Matematika materi operasi hitung pecahan melalui model pembelajaran Kooperatif Tipe Jigsaw pada peserta didik kelas V Semester 1 Tahun Pelajaran 2018/2019 di SD Negeri 3 Pule Kecamatan Pule Kabupaten Trenggalek. Penelitian ini menggunakan pendekatan kualitatif dan merupakan penelitian tindakan kelas yang dilaksanakan di kelas V SD Negeri 3 Pule pada materi pengumpulan dan penyajian data. Penelitian ini dilakukan dalam dua siklus yang masing-masing melalui empat tahapan, yaitu: (1) tahap perencanaan, (2) tahap pelaksanaan, (3) tahap observasi, (4) tahap refleksi. Hasil penelitian menunjukkan bahwa nilai rata-rata tes peserta didik yang mengalami peningkatan. Pada pra siklus yaitu 57,1 dan 56, siklus 1 yaitu 60 dan siklus 2 yaitu 70,6. Selain itu, ketuntasan belajar pada pra siklus, siklus 1, dan siklus 2 juga meningkat yaitu 42,9% dan 50% (tidak tuntas), 58,8% (tidak tuntas) serta 70,6% (tuntas). Berdasarkan hasil penelitian tersebut disimpulkan bahwa model pembelajaran Kooperatif Tipe Jigsaw dapat meningkatkan hasil belajar Matematika materi operasi hitung pecahan pada peserta didik kelas V Semester 1 Tahun Pelajaran 2018/2019 di SD Negeri 3 Pule Kecamatan Pule Kabupaten Trenggalek.
Co-Authors Achmad Noerkhaerin Putra Adi Gunawan, Adi Agasi, Andre Ahmad Zaki Am, Andri Nofiar Anam, M Khairul Anisya Anisya Ardiani, Novia Sutra Ardiyan, Destio Boediarsih Devita, Retno Esa Kurniawan Fajrul Islami Fauzana, Rahmi Febriani, Widya Firdaus, Muhammad Bambang Galih Pramuja Inngam Fanani Gunadi Widi Nurcahyo Hidayad, Asri Husni Thamrin Ilham Roni Yansyah Iskandar Fitri, Iskandar Jaya, Budi Juansen, Monsya Julius Santony Kadrahman, Kadrahman Karfindo, Karfindo Mardian, Zurni Mardison Mardison Masparudin, Masparudin Mifthahul Rahmi Negoro, Wahyu Saptha Pohan, Yosua Ade Putra, Ondra Eka Putra, Surya Dwi Putra, Yoga Ananda Putri, Adek Putri, Dhena Marichy Rahmad Dian Rahman, Surya Aulia Saiful Bukhori Sarjon Defit Setiawan, Adil Silfia Andini, Silfia Siska, Ayu Prima Soeheri, Soeheri Suhaidir, Lc Granadi Sunaryo, Nandra Surmayanti, Surmayanti Tajuddin, Muhammad Tamaza, Muhammad Abyanda Tukino, Tukino Yamin, Abdul Yamin Yendi Putra Yuhandri Yuhandri, Yuhandri Yunus, Yuhandri