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All Journal Jurnal Ilmu Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) SENTIA 2015 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Journal of Information Systems and Informatics TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Manajemen Informatika Jayakarta ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) International Journal of Electronics and Communications Systems Journal Of Information System And Artificial Intelligence Jurnal Sistem Informasi dan Teknologi (SINTEK) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Journal of Management, Entrepreneurship and Tourism Jurnal Pengabdian kepada Masyarakat IPTEKS Jurnal Pengabdian Pada Masyarakat IPTEKS Sintek Kuwera
Triwijoyo, Bambang Krismono
Universitas Bumigora
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EKSPLORASI MULTISPECTRAL TERHADAP CITRA FOTO UDARA MENGGUNAKAN KAMERA BERSENSOR APS-C 3100 Pribadi, Agus; Krismono, Bambang
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 5, No 2 (2025): JEIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol5no2.822

Abstract

Aerial photo imagery usage has expanded significantly, driven by the need to calculate regional potential, provide pre-planning views, support tourism, advertising, events, surveillance, and entertainment. Visually, obtaining aerial photo imagery using a DSLR camera can meet spatial information needs. A study is needed to investigate the potential for spectrum diversity (multispectral) in aerial photo imagery captured by the APS-C 3100 sensor camera, to determine the availability of spatial information based on its multispectral capabilities. Exploration of APS-C 3100 sensor camera imagery with stages ( or method): aerial photo capture using APS-C 3100 sensor camera, image correction and sharpening, and multispectral observation. The stage of the method involves exploring the imagery produced by the APS-C 3100 sensor camera to determine the multispectral capabilities of the image. Based on the exploration result of 4 (four) aerial photo images acquired using an APS-C 3100 sensor camera, it was found that all of the aerial photo images did not have multispectral capabilities.Pemanfaatan citra aerialphoto untuk data spasial telah berkembang lebih luas, mulai untuk keperluan perhitungan potensi wilayah, view pra-perencanaan, pariwisata, pelaksanaan event, surveillance, hiburan dan periklanan. Secara visual, perolehan citra aerialphoto menggunakan kamera DSLR dapat memenuhi kebutuhan informasi secara spasial. Kajian potensi keragaman spektrum pada citra aerialphoto hasil kamera bersensor APS-C 3100 diperlukan untuk mengetahui ketersediaan informasi spasial berdasar kapabilitas multispectral. Eksplorasi citra hasil kamera bersensor APS-C 3100 dengan tahapan (/ metode), pemotretan udara menggunakan kamera bersensor APS-C 3100, koreksi dan penajaman citra, observasi multispectral. Tahapan tersebut merupakan proses eksplorasi terhadap citra hasil kamera bersensor APS-C 3100 untuk mengetahui kapabilitas multispectral citra. Berdasar hasil eksplorasi terhadap 4 (empat) citra aerialphoto hasil kamera bersensor APS-C 3100, diperoleh bahwa keseluruhan citra aerialphoto tersebut tidak memiliki kapabilitas multispectral. Dengan demikian, citra aerialphoto hasil kamera bersensor APS-C 3100 tidak dapat digunakan untuk pemrosesan maupun analisa multispectral.
Implementasi Perangkat Lunak Deteksi Penyakit Retinopati Hipertensi Di Polimata Rumah Sakit Umum Provinsi Nusa Tenggara Barat Triwijoyo, Bambang Krismono; Adil, Ahmat; Zulfikri, Muhammad; Widyawati, Lilik; Miswaty, Titik Ceriyani; Patty, Elyakim Nova Supriyedi
Jurnal Pengabdian Pada Masyarakat IPTEKS Vol. 2 No. 1: Jurnal Pengabdian Pada Masyarakat IPTEKS, Desember 2024
Publisher : CV. Global Cendekia Inti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71094/jppmi.v2i1.71

Abstract

Hypertensive retinopathy is a type of eye disease where microvascular changes occur in the retina experienced by high blood pressure sufferers. The arterial and venous ratio (AVR) in the retina of the eye is an indicator used to determine the presence of high blood pressure, which is measured by the ratio of the width of the retinal arteries and veins. Traditionally, ophthalmologists use fundus images or retinal images of the eye to diagnose hypertensive retinopathy's physical symptoms and determine the phase of evolution. Still, traditional methods have limitations because, in the case of borderline stages, the early symptoms of hypertensive retinopathy will be difficult to identify manually, so they are often ignored. Referring to these problems, early diagnosis is needed for accurate prevention and treatment of hypertensive retinopathy. Based on the abovementioned issues, this service activity aims to implement a hypertensive retinopathy disease detection model using a local dataset from a regional general hospital in West Nusa Tenggara (NTB). It will compare the model detection results with those of three eye disease experts. Classification model testing results using the Messidor training and NTB Regional Hospital datasets. In models using the Messidor training dataset, the highest accuracy is a comparison with the results of the most senior expert's observations. The results of the classification model are only a tool to assist ophthalmologists in diagnosing hypertensive retinopathy, while the final decision remains with the expert or ophthalmologist.
Hybrid Deep Learning Untuk Prediksi Kunjungan Tamu Hotel Satrani, Azral; Krismono, Bambang; Hidjah, Khasnur
Jurnal Sistem Informasi dan Teknologi Vol 5 No 2 (2025): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v5i2.173

Abstract

Prediksi jumlah kunjungan tamu hotel adalah aspek penting dalam pengelolaan operasional dan perencanaan strategis, terutama pasca pandemi Covid-19 yang menyebabkan fluktuasi tinggi dalam kunjungan. Holiday Resort Lombok, resort bintang empat di Senggigi, mencatat pertumbuhan kunjungan 35,20% dari 2022 hingga 2023, menunjukkan pemulihan pariwisata. Penelitian ini mengembangkan model prediksi menggunakan hybrid deep learning yang mengintegrasikan Convolutional Neural Network (CNN) untuk mengekstraksi pola spasial dan Long Short-Term Memory (LSTM) untuk menangani aspek temporal. Dataset terdiri dari 730 catatan harian kunjungan dari Januari 2022 hingga Desember 2023, dengan pelatihan model pada variasi epoch (50, 100, 150, dan 200). Hasil terbaik diperoleh pada 150 epoch, dengan Root Mean Sequare Error (RMSE) 29,55 untuk data pelatihan dan 32,23 untuk data pengujian, menunjukkan akurasi yang lebih baik dibandingkan metode tradisional. Namun, model menunjukkan potensi overfitting, memerlukan optimalisasi lebih lanjut. Model ini dapat mendukung pengambilan keputusan terkait alokasi sumber daya dan strategi pemasaran. Penelitian selanjutnya disarankan untuk mengeksplorasi ensemble learning dan integrasi variabel eksternal untuk meningkatkan ketepatan model.
Optic Disk Segmentation Using Histogram Analysis Triwijoyo, Bambang Krismono
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1799

Abstract

In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR database
Visualization of Gastric Acid Reflux Using Mobile-Based Augmented Reality Adil, Ahmat; Triwijoyo, Bambang Krismono; Madani, Miftahul; Damar, Lalu Riyandi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 2 (2022): September 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i2.2370

Abstract

Interactive information media has many benefits in the process of conveying information, one of which is by visualizing objects in real time. Socialization activities at the Rensing Health Center in conveying information on gastric diseases still do not use visual aids as a medium for delivering counseling. Therefore, this study aims to develop information media in the form of an android application by utilizing Augmented reality (AR) technology to show the stomach acid process in real time which is visualized in the form of 3D animation. The method used in application development is Multimedia Development Life Cycle (MDLC). Where this method consists of 6 (six) stages of development, namely concept, design, Material Collecting, Assembly, testing and distribution. The results of this study are a visualization application of gastric acid reflux using mobile-based augmented reality built using Unity 2017.3.1f1 and the Vuforia Software Development Kit (SDK), with several stages of the process, namely entering the database and all assets into unity, lighting, creating a user interface, scripting and finally build the application to the android platform Based on the results of application trials that have been carried out at the Rensing Health Center, it shows that mobile-based augmented reality has succeeded in assisting officers in visualizing gastric acid reflux. The satisfaction of health officers in using the application can be seen from the results of the questionnaire to the respondents, where the results of the questionnaire 32% stated strongly agree, 59% agreed, 6% disagreed and 2% disagreed.
Image Classification of Medicinal Plants Using Inception V3 and CNN: A Novel Implementation Kartarina, Kartarina; Islamiah, Nuratun; Supatmiwati, Diah; Zulfiqri, Muhammad; Triwijoyo, Bambang Krismono; Amrullah, Rahayun
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042//ijecs.v5i2.27930

Abstract

Indonesia is recognized as one of the world's biodiversity hotspots, with around 30,000 of the 40,000 global medicinal plant species found in its territory. This biological wealth is a strategic asset for health innovation and digital preservation. In areas with limited access to healthcare services, medicinal plants are the primary source of treatment, but their use is still hampered by the lack of a technology-based identification and documentation system. This study aims to develop and test a classification model for medicinal plants using a Convolutional Neural Network with Inception V3 architecture. The study uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, which ensures systematic stages of business understanding, data preparation, modeling, and evaluation. The dataset used consists of 2,750 leaf images in 25 classes, compiled from previous research and independent collections. The data was divided into 1,921 images for training and 823 images for testing using a 70:30 ratio. The model was evaluated using accuracy, precision, recall, and F1 score. The results showed that the Inception V3-based CNN achieved a training accuracy of 96%, which increased to 97% with optimized weights, while maintaining strong precision, recall, and F1 scores. This proves that the Inception V3-based approach is capable of providing high and stable classification performance for the identification of Indonesian medicinal plants. These findings highlight the effectiveness of the model in identifying Indonesian medicinal plants from leaf images, providing a promising foundation for the development of knowledge and potential real-world applications
Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN): Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN) Resmiranta, Dading Oktaviadi; Krismono, Bambang; Hidjah, Khasnur
Journal Of Information System And Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.271

Abstract

The face is an important object in the biometric identification system. However, low image quality due to uneven lighting, noise, and variations in facial expressions can interfere with the accuracy of the recognition system. The study investigated the use of Convolutional Artificial Neural Network (CNN) combined with hybrid screening techniques to improve image quality, thereby improving the accuracy of facial recognition systems. Filters used include weight mean filtering, median filtering, Contrast-Limited Adaptive Histogram Equalization and gaussian filtering, wavelet filtering. The pre-processed image was then trained using image denoising measurements of the Structural Similarity Index, Mean Squared Error, and Peak Signal to Noise Ratio. The main objective of this study is to evaluate the best filtration combination to produce high accuracy in face classification. The datasets used were 55 classes and 100 images per class. The inceptionV3 architecture model is used for classifications with a number of epochs of 10. Evaluation was carried out on a facial data set with an 80%:20% scheme. The results of the experiment showed that the hybrid method produced the best performance with 94.5% validation accuracy, 94.2% precision, and 94.6% recall, an increase of +1.4% compared to baseline. The (original) baseline itself recorded 93.1% validation accuracy, 92.8% precision, and 93.2% recall. In addition, the loss graph shows that the pre-process model has faster and more stable convergence than the non-pre-processing model. These results confirm that the application of preprocessing, especially the hybrid approach, is able to improve the accuracy and stability of the model in image classification tasks.
Prediksi Gender Berdasarkan Nama Menggunakan Kombinasi Model IndoBERT, Convolutional Neural Network (CNN) dan Bidirectional Long Short-Term Memory (BiLSTM) Abi Mas'ud; Bambang Krismono Triwijoyo; Dadang Priyanto
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.736

Abstract

This study proposes a name-based gender prediction model in the Indonesian language by combining the architectures of Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). The non-standardized and diverse structure of Indonesian names presents a significant challenge for text-based gender classification tasks. To address this, a hybrid approach was developed to leverage the contextual representation power of IndoBERT, the local pattern extraction capability of CNN, and the sequential dependency modeling strength of BiLSTM. The dataset consists of 4,796 student names from Universitas Bumigora, collected between 2018 and 2023. The preprocessing steps include lowercasing, punctuation removal, label encoding, and train-test splitting. Evaluation results based on accuracy, precision, recall, and F1-score indicate that the IndoBERT-CNN-BiLSTM model achieved the best performance, with an accuracy of 90.94%, F1-score of 91.03%, and training stability without signs of overfitting. This model demonstrates high effectiveness in name-based gender classification and holds strong potential for applications such as population information systems, service personalization, and name-based demographic analysis.
SEGMENTASI CITRA PEMBULUH DARAH RETINA MENGGUNAKAN METODE DETEKSI GARIS MULTI SKALA Bambang Krismono Triwijoyo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 15 No. 1 (2015)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i1.28

Abstract

Changes in retinal blood vessels feature a sign of serious illnesses such as heart disease and stroke. Therefore, the analysis of retinal vascular features can help in detecting these changes and allow patients to take preventive measures at an early stage of this disease. Automation of this process will help reduce the costs associated with the specialist and eliminate inconsistencies that occur in manual detection system. Among the retinal image analysis, image extraction retinal blood vessels is a crucial step before measurement. In this paper, we use an effective method of automatically extracting the blood vessels of the color images of the retina using a length detector line in several different scales, in order to maintain the strength and eliminates the weaknesses of each detector individual lines, the result of the detection lines on various scales combined to produce a segmentation of each image of the retina. The performance of the method is evaluated quantitatively using DRIVE dataset. Test results show that this method achieve high accuracy is 0.9407 approaching measurement results manually by experts, and this method produces accurate segmentation in detecting retinal blood vessels with effciency by quickly segmenting time is 2.5 seconds per image.
SEGMENTASI CITRA MRI MENGGUNAKAN DETEKSI TEPI UNTUK IDENTIFIKASI KANKER PAYUDARA Ervina Farijki; Bambang Krismono Triwijoyo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 15 No. 2 (2016)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.38

Abstract

One type of cancer that is capable identifed using MRI technology is breast cancer. Breast cancer is still the leading cause of death world. therefore early detection of this disease is needed. In identifying breast cancer, a doctor or radiologist analyzing the results of magnetic resonance image that is stored in the format of the Digital Imaging Communication In Medicine (DICOM). It takes skill and experience suffcient for diagnosis is appropriate, and accurate, so it is necessary to create a digital image processing applications by utilizing the process of object segmentation and edge detection to assist the physician or radiologist in identifying breast cancer. MRI image segmentation using edge detection to identifcation of breast cancer using a method stages gryascale change the image format, then the binary image thresholding and edge detection process using the latest Robert operator. Of the 20 tested the input image to produce images with the appearance of the boundary line of each region or object that is visible and there are no edges are cut off, with the average computation time less than one minute.
Co-Authors Abi Mas'ud Agus Pribadi Aguswandi, Lalu Heri Ahmat Adil Amrullah, Rahayun Andi Sofyan Anas Anthony Anggrawan Ayu Dasriani, Ni Gusti Azhar, Raisul Cahaya Ardi, L. Nanda Christopher Michael Lauw Dadang Priyanto Dadang Priyanto Damar, Lalu Riyandi Deny Jollyta Diah Supatmiwati Dian Syafitri Chani Saputri Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Ervina Farijki Fahly, Moh. Rizky Galih Hendro Martono Hairani Hairani Hariri, Muhammad Husain Husain Islamiah, Nuratun Kartarina, Kartarina Khasnur Hidjah Lalu Riyandi Damar Lalu Yuda Rahmani Karnaen Lilik Widyawati Martono, Gallih Hendro Mayadi Mayadi Miftahul Madani Miswaty, Titik Ceriyani Muhammad Haris Nasri MUHAMMAD TAUFIK Neny Sulistianingsih Ni Wayan Yuli Ariyanti Noviansyah Noviansyah, Noviansyah Resmiranta, Dading Oktaviadi Risca Anggraeni Dwiputri Santoso, Heroe Satrani, Azral Sirojul Hadi Tajuddin, Muhammad Tomi Tri Sujaka Zulfikri, Muhammad Zulfiqri, Muhammad