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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) PROSIDING CSGTEIS 2013 Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan EMITTER International Journal of Engineering Technology Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains Dan Teknologi (SAINTEKBU) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi dan Informasi INTEK: Jurnal Penelitian J-Dinamika: Jurnal Pengabdian Kepada Masyarakat International Journal of Applied Sciences and Smart Technologies Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang GUYUB: Journal of Community Engagement Journal of Applied Computer Science and Technology (JACOST) International Conference on Industrial Revolution for Polytechnic Education Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science Jurnal ilmiah teknologi informasi Asia
Arna Fariza
Politeknik Elektronika Negeri Surabaya
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Mitigation and Emergency Management System of Landslide in Ponorogo District, Indonesia Arif Basofi; Arna Fariza; Imam Mustafa Kamal
JOIV : International Journal on Informatics Visualization Vol 3, No 2 (2019)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2526.134 KB) | DOI: 10.30630/joiv.3.2.222

Abstract

Ponorogo district, in East Java Province, is one area that is often hit by landslides.  In addition to threatening the safety of residents, this landslide caused dozens of houses and public infrastructure to be damaged. The losses caused by this landslide disaster also reached billions of rupiah each year. Integrated of emergency mitigation and response systems for landslides are needed to provide information accurately and widely to the community. This paper proposes a new framework of mitigation and emergency system for landslide in Ponorogo district. The system can analyze, display, explore and store vulnerability data based on web GIS application. The information generated from the mitigation system is a landslide susceptibility map using the analytical hierarchy process (AHP) - Natural break method. The landslide susceptibility index is produced based on 4 factors that cause landslides including slope, soil type, land use and rainfall.  The map displays 4 levels of vulnerability including very low, low, moderate and high. The mitigation system is equipped with information features for the community about handling landslides.  Pearson's Chi-squared test showed the classification class of vulnerability was declared very significant result. Emergency system information in the form of alternative route information and nearest evacuation sites.
Spatial Disaster Risk Assesment of Kelud Eruption, Indonesia, using Fuzzy Titis Octary Satrio; Arna Fariza; Mu'arifin Mu'arifin
JOIV : International Journal on Informatics Visualization Vol 3, No 2-2 (2019): Internet of Things and Smart Environments
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1161.24 KB) | DOI: 10.30630/joiv.3.2-2.262

Abstract

Indonesia is one of the countries included in the area of the Ring of Fire or the Ring of the Pacific. This fact can be seen that in Indonesia there are 129 active volcanoes and 10 of them are the most active volcanoes. Mount Kelud is the most active volcano in the province of East Java, Indonesia. This mountain is recorded as actively erupting with a relatively short span of time (9-25 years), making it a volcano that is dangerous for humans. Readiness of citizens is very necessary as an effort to prevent and anticipate the eruption of Mount Kelud in the future. Disaster risk level assessments are needed to provide information for citizen and government preparedness in the face of volcanic eruptions. In this paper a new approach is proposed to assess the level of disaster risk of Kelud eruption using Fuzzy methods in each village in the disaster-prone area (KRB). Fuzzy methods classify disaster risk levels based on criteria of hazards, vulnerabilities and index of capacities. The level of disaster risk is divided into low, medium, and high which are spatially mapped. The result of calculations and spatial visualization show that the approach used produces a level of disaster risk that is fairer than only based on hazard.
Spatial-Temporal Visualization of Dengue Fever Vulnerability in Kediri Using Hierarchy Clustering Based on Mobile Devices Hamida, Silfiana Nur; Fariza, Arna; Basofi, Arif
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2195

Abstract

In Indonesia, Dengue Fever (DF) is a contagious disease that is a significant issue in public health. The Kediri Regency in East Java, as reported by the Ministry of Health in 2019, had the highest number of DF cases. If not addressed promptly, DF can lead to outbreaks, creating a health emergency. The lack of a thorough investigation into the diversity of risk within a spatial and temporal region exacerbates this issue. Therefore, spatial-temporal analysis is crucial in developing a warning system to prevent and control DF. This paper proposes a method that combines the Euclidean Distance calculation with the Hierarchical Clustering method. We collected data from the Kediri Regency health department and conducted pre-processing and classification processes, considering the number of DF victims, death rate, population, rainfall, and public facilities. The hierarchical clustering algorithm was used to categorize the 344 village analyses into low, medium, and high vulnerability categories. This method allows for a comparison of yearly single, average, complete, and centroid linkage in DF vulnerability levels. We also employed spatial-temporal visualization based on cellular applications to create a clear picture of areas vulnerable to DF. The experimental results in clustering showed a satisfactory level of matching, with variant values calculated using the hierarchical clustering method. The variants for single linkages were 0.113; for average linkages, they were 0.120; for complete linkages, they were 0.178; and for centroid linkages, they were 0.106. The grouping validation results indicated that the centroid linkage method produced the best variant level. We suggest further enhancing the methods with better process steps using other pre-processing methods to improve the validation quality.
Human Bone Age Estimation of Carpal Bone X-Ray Using Residual Network with Batch Normalization Classification Nabilah, Anisah; Sigit, Riyanto; Fariza, Arna; Madyono, Madyono
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1024

Abstract

Bone age is an index used by pediatric radiology and endocrinology departments worldwide to define skeletal maturity for medical and non-medical purposes. In general, the clinical method for bone age assessment (BAA) is based on examining the visual ossification of individual bones in the left hand and then comparing it with a standard radiographic atlas of the hand. However, this method is highly dependent on the experience and conditions of the forensic expert. This paper proposes a new approach to age estimation of human bone based on the carpal bones in the hand and using a residual network architecture. The classification layer was modified with batch normalization to optimize the training process. Before carrying out the training process, we performed an image augmentation technique to make the dataset more varied. The following augmentation techniques were used: resizing; random affine transformation; horizontal flipping; adjusting brightness, contrast, saturation, and hue; and image inversion. The output is the classification of bone age in the range of 1 to 19 years. The results obtained when using a VGG16 model were an MAE value of 5.19 and an R2 value of 0.56 while using the newly developed ResNeXt50(32x4d) model produced an MAE value of 4.75 and an R2 value of 0.63. The research results indicate that the proposed modification of the residual training model improved classification compared to using the VGG16 model, as indicated by an MAE value of 4.75 and an R2 value of 0.63.
CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification Fatihia, Wifda Muna; Fariza, Arna; Karlita, Tita
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1443

Abstract

Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural adaptation model with a normalization batch number added, to obtain a CNN model optimization with high accuracy and less training time for offline hand-written signature verification. We compare CNN with our proposed model in the experiments. The research method in this study is data collection, pre-processing, and testing using our private signature dataset (collected by capturing signature images using a smartphone), which becomes the difficulties of our study because of the different lighting, media, and pen used to sign. Experiment results show that our model ranks first, with a training accuracy of 88.89%, an accuracy validation of 75.93%, and a testing accuracy of 84.84%—also, the result of 2638.63 s for the training time consumed with CPU usage. The model evaluation results show that our model has a smaller EER value; 2.583, with FAR = 0.333 and FRR = 4.833. Although the results of our proposed model are better than basic CNN, it is still low and overfitted. It has to be enhanced by better pre-processing steps using another augmentation method required to improve dataset quality. 
Co-Authors Achmad Basuki Aditama, Darmawan Afifah, Izza Nur Afrida Helen Afrida Helen Agus Prayudi Agus Wibowo Ahsan, Ahmad Syauqi Al Falah, Adam Ghazy Alfaqih, Wildan Maulana Akbar Ali Ridho Barakbah Amadea Permana Sanusi Andhik Ampuh Yunanto Andy Soeseno Annisa Rasyid Ardinur Mahyuzar Ardiyanto Happy Susilo Arif Basofi Arif Basofi Arif Basofi Asmara, Rengga Aziz, Adam Shidqul Basofi, Arif Basofi, Arif Bima Sena Bayu Dewantara Dadet Pramadihanto Dadet Pramadihanto Damastuti, Fardani Annisa Darmawan Aditama Desy Intan Permatasari, Desy Intan Deyana Kusuma Wardani Edelani, Renovita Entin Martiana Kusumaningtyas Fardani Annisa Damastuti Faris Abdi El Hakim Faris Abdi El Hakim Fatihia, Wifda Muna Febrianti, Erita Cicilia Ferry Astika Saputra Fikriyah, Masnatul Firman Arifin Hamida, Silfiana Nur Harun, Ahmad Hestiasari Rante Hestiasari Rante Hidayah, Nadila Wirdatul Huda, Achmad Thorikul I Made Akira Ivandio Agusta I.G. Puja Astawa Idris Winarno Idris Winarno Ikawati, Yunia Ilham Iskandariansyah Imam Mustafa Kamal Istiqomah, Galuh Nurul Iwan Syarif iwan Syarif Jamilatul Badriyah Jauari Akhmad Nur Hasim Kanza, Rafly Arief Khasanah, A’at Khoirunnisa, Asy Syaffa Kholid Fathoni Kindarya, Fabyan Kirana Hanifati Kusuma, Oskar Galih Wira Kusuma, Selvia Ferdiana M Udin Harun Al Rasyid, M Udin Harun Madyono, Madyono Majid, Nur Syaela Marcell Bintang Setiawan Maulana, Rifqi Affan Mayangsari, Mustika Kurnia Mochammad Rizki Hidayat Mohammad Robihul Mufid Mu'arifin Mu'arifin Much Chafid Mufid, Mohammad Robihul Muhammad Turmudzi Nabilah, Anisah Nana Ramadijanti, Nana Nindy Ilhami Ninik Purwati Novita Putri Lestari Nur Rosyid Mubtadai, Nur Rosyid Nurhidayah - Nurhidayah - Oktavia Citra Resmi Rachmawati Pratama Eskaluspita Pratama, Chrysna Ardy Putra Primajaya, Grezio Arifiyan Puspasari Susanti Rachmawati, Oktavia Citra Resmi Rahmad Santosa Rahmana, Rizal Rante, Hestiasari Rengga Asmara Riyanto Sigit, Riyanto Rosiyah Faradisa Rossi Arisdiawan Rudi Kurniawan Sa'adah, Umi Safrudana, Maulyd Ahdan Saniyatul Mawaddah Sasmita, Rizka Rahayu Sesulihatien, Wahjoe Tjatur Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana, Setiawardhana Sumarsono, Irwan Susanti, Puspasari Tessy Badriyah Tessy Badriyah, Tessy Tita Karlita Titis Octary Satrio Tri Harsono Tri Harsono Wahjoe Tjatur Sesulihatien Walujo, Ivana Yudith Wifda Muna Fatihia Wiratmoko Yuwono Yesta Medya Mahardhika Yoedy Moegiharto Yufi Eko Firmansyah Yunia Ikawati Zulfian Nafis