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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Sunardi Sunardi
Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Computer Science & IT

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Flood Vulnerability Mapping in Cepu Subdistrict Using Mamdani Fuzzy Inference System for Disaster Risk Reduction Joko Handoyo; Anton Yudhana; Sunardi Sunardi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5390

Abstract

Floods pose a persistent and serious threat to Cepu Subdistrict, frequently causing significant economic loss, resident displacement, and damage to critical infrastructure. In response to this issue, and aligned with the National Disaster Management Agency's (BNPB) efforts to enhance landscape monitoring, a comprehensive analytical study was conducted. The purpose of this research was to assess and map the flood vulnerability levels across 17 villages in Cepu Subdistrict, categorizing them to facilitate more effective disaster response planning and resource allocation. The research method uses the Mamdani Fuzzy Inference System, an advanced computational approach adept at handling the non-linear relationships between environmental variables. This system allowed for a detailed analysis of the complex interactions among key flood-influencing factors, including rainfall intensity, watershed area, elevation, slope, and population density. The results of the quantitative research obtained from 17 villages in the Cepu Subdistrict show that Ngelo Village has the highest score of 65.16, categorized as a "high" risk level. In contrast, most other villages, such as Ngroto, Karangboyo, and Cabean, fell into the "medium" risk category with varying scores between 55.0 and 63.93. The model's accuracy was validated by evaluation metrics, with a Mean Absolute Error (MAE) of 8.67 and a Root Mean Squared Error (RMSE) of 10.29, indicating satisfactory predictive performance. The conclusion of this study emphasizes the urgent need for comprehensive and adaptive mitigation strategies, including early warning systems and community preparedness programs, to protect Cepu Subdistrict from future flood threats.
Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image Miftahuddin Fahmi; Anton Yudhana; Sunardi Sunardi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2819

Abstract

Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies.
Mobile Forensic for Body Shaming Investigation Using Association of Chief Police Officers Framework Yana Safitri; Imam Riadi; Sunardi Sunardi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2987

Abstract

Body shaming is the act of making fun of or embarrassing someone because of their appearance, including the shape or form of their body. Body shaming can occur directly or indirectly. MOBILEdit Forensic Express and Forensic ToolKit (FTK) Imager are used to perform testing of evidence gathered through Chat, User ID, Data Deletion, and Groups based on digital data obtained on IMO Messenger tokens on Android smartphones. This study aimed to collect evidence of conversations in body shaming cases using the Association of Chiefs of Police (ACPO) framework with MOBILedit Forensic Express and FTK Imager as a tool for testing. Based on the research findings, MOBILedit Forensic Express got an extraction yield of 0.75%. In contrast, using the FTK Imager got an extraction yield of 0.25%. The ACPO framework can be used to investigate cases of body shaming using mobile forensics tools so that the extraction results can be found. The results of this study contributed to forensic mobile knowledge in cases of body shaming or cyberbullying ACPO framework as well as for the investigators.
Co-Authors Anton Yudhana Imam Riadi Joko Handoyo Miftahuddin Fahmi Yana Safitri, Yana