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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal F. Teknik : RESULTAN Paradigma : Journal of Science, Religion and Culture Studies TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JREC (Journal of Electrical and Electronics) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Cakrawala : Jurnal Humaniora Bina Sarana Informatika Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) BENTANG : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil Jurnal Komtika (Komputasi dan Informatika) Jurnal Kajian Ilmiah Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat DEVOSI Journal of Students‘ Research in Computer Science (JSRCS) Journal of Computer Science Contributions (Jucosco) Jurnal Komtika (Komputasi dan Informatika)
Rahmadya Trias Handayanto
Program Studi Teknik Komputer Fakultas Teknik, Universitas Islam 45
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Transportation Other

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Land Cover Segmentation of Multispectral Images Using U-Net and DeeplabV3+ Architecture Herlawati; Handayanto, Rahmadya Trias
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i1.1206

Abstract

The application of Deep Learning has now extended to various fields, including land cover classification. Land cover classification is highly beneficial for urban planning. However, the current methods heavily rely on statistical-based applications, and generating land cover classifications requires advanced skills due to their manual nature. It takes several hours to produce a classification for a province-level area. Therefore, this research proposes the application of semantic segmentation using Deep Learning techniques, specifically U-Net and DeepLabV3+, to achieve fast land cover segmentation. This research utilizes two scenarios, namely scenario 1 with three land classes, including urban, vegetation, and water, and scenario 2 with five land classes, including agriculture, wetland, urban, forest, and water. Experimental results demonstrate that DeepLabV3+ outperforms U-Net in terms of both speed and accuracy. As a test case, Landsat satellite images were used for the Karawang and Bekasi Regency areas.
Machine Learning Berbasis Desktop dan Web dengan Metode Jaringan Syaraf Tiruan Untuk Sistem Pendukung Keputusan Handayanto, Rahmadya Trias; Herlawati, Herlawati
Jurnal Komtika (Komputasi dan Informatika) Vol 4 No 1 (2020)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v4i1.3698

Abstract

Machine learning application demand is increased massively because it provides good ability in the classification that is needed by decision makers. Machine learning application uses a programming language with strong characteristics in computing, usually the back-end programming language, such as Matlab, Python, R, etc. The obstacle faced by the decision support system developer is preparing an interface that makes it easy for the user. Some back-end programming languages have provided a good interface. Therefore, in this study they were compared by taking the case of a scholarship decision support system. The language used is Python with two web-based applications including Google Interactive Notebook and Flask framework. Both devices have their respective advantages and are worthy of being the first choice in the design of decision support systems.Python has advantages with framework Flask support and Matlab is easy in interface design.
Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM) Herlawati, Herlawati; Nidaul Khasanah, Fata; Dina Atika, Prima; Sari, Rafika; Handayanto, Rahmadya Trias
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5139

Abstract

Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry
Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine Handayanto, Rahmadya Trias; Herlawati, Herlawati; Atika, Prima Dina; Khasanah, Fata Nidaul; Yusuf, Ajif Yunizar Pratama; Septia, Dwi Yoga
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 2 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i2.6280

Abstract

Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. Keywords:
VISUAL HISTORICAL DATA-BASED TRAFFIC MOVEMENT AND DENSITY PATTERN EXTRACTION FOR ADAPTIVE PATTERN DETECTION BASE ON VEHICLE TYPE Angellia, Filda; Merlina, Nita; Subekti, Agus; Handayanto, Rahmadya Trias
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1570

Abstract

Traffic congestion in urban areas has become a crucial issue, impacting time efficiency, energy consumption, and quality of life. One of the main causes of difficulties in traffic management is the lack of optimal predictive systems capable of detecting and adaptively responding to vehicle movement patterns. This study proposes a historical digital image-based approach to extract traffic movement patterns and density based on vehicle type and dimensions. The developed model utilizes historical traffic video footage from CCTV systems as a visual data source, which is then processed using the YOLOv5 algorithm to detect the number, size, and type of vehicles. After the detection process, vehicle information is converted into a sequential format that reflects vehicle movement in the temporal dimension. This data is then analyzed using a Long Short-Term Memory (LSTM) model to generate traffic density prediction patterns. This study also compares the performance of LSTM with other algorithms such as Random Forest and XGBoost in terms of prediction accuracy. Model evaluation is conducted using MSE and RMSE metrics to measure accuracy against actual data.The research results show that the integration of dimension-based vehicle detection with a visual historical data-driven prediction approach can improve the accuracy and flexibility of modeling future traffic conditions. This approach significantly contributes to the development of intelligent transportation systems that can adapt to dynamic environmental conditions and traffic patterns
VISUAL HISTORICAL DATA-BASED TRAFFIC MOVEMENT AND DENSITY PATTERN EXTRACTION FOR ADAPTIVE PATTERN DETECTION BASE ON VEHICLE TYPE Angellia, Filda; Merlina, Nita; Subekti, Agus; Handayanto, Rahmadya Trias
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1715

Abstract

Traffic congestion in urban areas has become a crucial issue, impacting time efficiency, energy consumption, and quality of life. One of the main causes of difficulties in traffic management is the lack of optimal predictive systems capable of detecting and adaptively responding to vehicle movement patterns. This study proposes a historical digital image-based approach to extract traffic movement patterns and density based on vehicle type and dimensions. The developed model utilizes historical traffic video footage from CCTV systems as a visual data source, which is then processed using the YOLOv5 algorithm to detect the number, size, and type of vehicles. After the detection process, vehicle information is converted into a sequential format that reflects vehicle movement in the temporal dimension. This data is then analyzed using a Long Short-Term Memory (LSTM) model to generate traffic density prediction patterns. This study also compares the performance of LSTM with other algorithms such as Random Forest and XGBoost in terms of prediction accuracy. Model evaluation is conducted using MSE and RMSE metrics to measure accuracy against actual data.The research results show that the integration of dimension-based vehicle detection with a visual historical data-driven prediction approach can improve the accuracy and flexibility of modeling future traffic conditions. This approach significantly contributes to the development of intelligent transportation systems that can adapt to dynamic environmental conditions and traffic patterns
OPTIMIZING GAS STATION LOCATION USING GENETIC ALGORITHMS Rahmadya Trias Handayanto; Soedarmin Soenyoto; Yopi Handoyo
Bentang : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil Vol 1 No 1 (2013): BENTANG Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil (Januari 2013)
Publisher : Universitas Islam 45

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/bentang.v1i1.398

Abstract

Gas station location is not only based on financial but also environment aspects because it will give negative impact if there are some accidents such as fire, tank leakage, etc. Helping customer in supplying fuels should not sacrifice other needs such as health, food, education, living circumstance, and so on. Therefore we propose the system that can help someone deciding and analyzing the location of gas station, especially when doing proper analysis. By using genetic algorithms our system can find optimum location of gas station after considering other important location that must be far away from it. The distance from important place was counted by normal euclidean after converting road and the river at map into nonlinear equation using interpolation. Population was generated from converting road on the map into equation. The important places that must be far away from gas station are collected and with that equation then give an objective function. Testing result showed the system could find optimum gas stations location at Bekasi regency. Keywords : GAS STATION, GENETIC, ALGORITHMS
Analyzing Land Suitability for Housing in Bekasi Regency: Managing Farmland Conversion During Urban Growth Herlawati Herlawati; Rahmadya Trias Handayanto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9968

Abstract

Bekasi Regency is a buffer area for Jakarta and is part of the Jakarta Metropolitan Region (JMR). Along with Karawang, this region is a major rice producer, but it is currently experiencing significant land conversion from agriculture to residential and industrial uses. Preventing this conversion is very challenging due to the high population growth in the area. To address this issue, this study aims to conduct a suitability analysis to identify areas that are unsuitable for agricultural land but still suitable for residential purposes. The factors considered in the suitability analysis include slope, distance to roads, distance to residential areas, distance to facilities and infrastructure, and distance to rivers and irrigation systems. The results of the study identify areas that are unsuitable for agriculture, allowing local governments to focus on these areas as potential sites for new residential developments. The location is situated in the southern part of Bekasi Regency, specifically in the Cikarang area and its surroundings.
Comparative Study of PCA, t-SNE, and UMAP for CNN Feature Representation of Image Classification Herlawati Herlawati; Rahmadya Trias Handayanto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11634

Abstract

Currently, the use of Deep Learning is widespread across various domains, with Convolutional Neural Networks (CNNs) as one of its main pioneers due to the principle of convolution. Recent methods continue to emerge with steadily increasing accuracy, in some cases approaching perfection. However, their implementation is often limited by the lack of sufficient computational resources in many environments. Moreover, the growing demand for explainable AI compels researchers to explore approaches that reveal the inner workings of deep learning models rather than treating them as mere black boxes. In this study, a simple CNN model is employed as a testbed for examining the feature extraction process through convolution, which is subsequently transformed into a user-friendly two-dimensional representation. The dataset used in this study is the Cats and Dogs dataset from Kaggle, which contains 25,000 labeled images equally distributed between the two classes. The dimensionality reduction methods utilized include Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The results demonstrate that UMAP achieves superior performance compared to PCA and t-SNE, with the highest silhouette score and a lower Davies–Bouldin index, indicating more compact and well-separated feature clusters.
Co-Authors A.A. Ketut Agung Cahyawan W Aeri Sujatmiko Agus Subekti Ahmad Liyas Sani Ahmad Wafiq Amrillah Aji Trisnantoro Andi Hasad Angga Fahreja Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anussara Hirunpongchai, Anussara Atika , Prima Dina Bagus Suryasa Majanasastra Ben Rahman Benrahman Boravin Teng, Boravin Dadan Irwan Dadan Irwan Dadan Irwan Dadan Irwan Dede Rosadi Ekawati, Inna Endang Retnoningsih Faisal Adi Saputra Fata Nidaul Khasanah Fikri, Muhammad Ramadan Filda Angellia Galih Apriansha Pradana HARYONO Haryono Haryono Haryono . Haryono . Haryono Haryono Haryono Haryono Hendharsetiawan, Andy Achmad Heri Setiawan Herlawati Herlawati Intan Juwita Irwan Raharja Jaelani, M Khanittha Saengmanee, Khanittha Maimunah Maimunah Maimunah Maimunah Maimunah Maimunah Maimunah Maimunah, Maimunah Malikus Sumadyo Malikus Sumadyo Malikus Sumadyo Muhammad Aqil Emeraldi Muhammad Arifin Muhammad Ilham Muhammad Irvan Muhammad Ramadan Fikri Muhammad Ramadhan Fikri Nita Merlina, Nita Nitin Kumar Tripathi Nove Anggara Syah Sejati Nutthapong Khangkhun, Nutthapong Pradana , Galih Apriansha Priatna , Wowon Prima Dina Atika RAFIKA SARI Rafika Sari Randika Purwadhana Rejeki , Sri Retno Nugroho Whidhiasih Retno Nugroho Whidhiasih Retno Whidhiasih Reyvan Karani Rika Sylviana Samsiana , Seta Sani, Ahmad Liyas Saputra , Faisal Adi Sella Alaida Syifa1 Sella Alayda Syifa Septia, Dwi Yoga Seta Samsiana Seta Samsiana Seta Samsiana Seta Samsiana Seta Samsiana Setiaji Setiaji Setiawan, Ramdhani Setyo Supratno Setyowati Srie Gunarti, Anita Soedarmin Soenyoto Soedarmin Soenyoto Sohee Minsun Kim Sri Marini Sri Rejeki Sugeng Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sumarlin Syahbaniar Rofiah Taufiqur Rakhman Tyastuti Sri Lestari Yopi Handoyo Yopi Handoyo Yusuf, Ajif Yunizar Pratama