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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Primary: Jurnal Pendidikan Guru Sekolah Dasar Al Fikra : Jurnal Ilmiah Keislaman Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Natural Science and Integration MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Indonesian Physical Review JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Information Systems and Informatics Scientific Journal of Informatics G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Insearch: Information System Research Journal Journal Automation Computer Information System (JACIS) J-Intech (Journal of Information and Technology) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Information Technology and Cyber Security
Agung Teguh Wibowo Almais
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Aerospace Engineering Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Physics Social Sciences Other

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Memanfaatkan Kekuatan Komunitas: Edukasi Penanggulangan Berita Hoaks dan Propaganda Radikalisme di Komunitas Pendidikan berbasis Sosial Media di Indragiri Hulu Budiawan, Afiq; Almais, Agung Teguh Wibowo; Thahir, Musa
AL-FIKRA Vol 23, No 2 (2024): Al-Fikra : Jurnal Ilmiah Keislaman
Publisher : Program Pascasarjana Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/af.v23i2.35114

Abstract

Pengabdian masyarakat ini bertujuan untuk meningkatkan kesadaran dan pemahaman mengenai hoaks serta propaganda radikalisasi di kalangan komunitas pendidikan Indragiri Hulu. Mengusung pendekatan Asset-Based Community Development (ABCD), kegiatan ini memanfaatkan sumber daya lokal, khususnya keahlian pendidik dan dukungan lembaga pendidikan, sebagai strategi utama dalam penanggulangan masalah sosial ini. Hasil pengabdian mencakup peningkatan kesadaran masyarakat, kolaborasi yang kuat antar-stakeholder, pengembangan program edukasi sesuai kebutuhan lokal, serta penyebarluasan hasil melalui berbagai saluran. Proses evaluasi berkelanjutan juga diterapkan untuk memastikan keberlanjutan dan peningkatan program. Model pendekatan ABCD dan program edukasi yang dihasilkan diharapkan dapat menjadi panduan replikabel bagi komunitas pendidikan lain yang menghadapi tantangan serupa.
Optimizing Goods Placement in Logistics Transportation using Machine Learning Algorithms based on Delivery Data Syawab, Moh Husnus; Arief, Yunifa Miftachul; Nugroho, Fresy; Kusumawati, Ririen; Crysdian, Cahyo; Almais, Agung Teguh Wibowo
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1321

Abstract

This study addresses the challenge of predicting the optimal placement of goods for expeditionary transportation. Efficient placement is crucial to ensure that goods are transported in a manner that maximizes space and minimizes the risk of damage. This study aims to develop a prediction system using the K-Nearest Neighbor (KNN) method, which is based on expert data from expedition vehicles. To evaluate the effectiveness of the KNN method, the researcher compared it with the Support Vector Machine (SVM) method. By doing so, they sought to determine which method delivers more accurate predictions for the optimal placement of goods. The test results revealed that the KNN method outperformed SVM, achieving a higher accuracy of 95.97% compared to SVM's 92.85%. Additionally, KNN demonstrated a lower Root Mean Square Error (RMSE) of 0.18, indicating more precise predictions, while SVM had an RMSE of 0.271. These findings suggest that KNN is the more effective method for predicting the optimal placement of goods in expeditionary transportation.
Developing Programming Learning Media Using Scratch on the Concept of Buoyancy to Improve Computational Thinking in Primary School Hermita, Neni; Alim, Jesi Alexander; Almais, Agung Teguh Wibowo; Pizaini, Pizaini; Vebrianto, Rian; Thahir, Musa; Mandiro, Mulia Anton
Journal of Natural Science and Integration Vol 7, No 2 (2024): Journal of Natural Science and Integration
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jnsi.v7i2.32554

Abstract

The research focuses on the development of educational media using Scratch, a visual programming platform, to teach the concept of buoyancy and enhance computational thinking (CT) skills in primary school students. By adopting the 4D development model (Define, Design, Development, Dissemination), the study identifies challenges in traditional teaching methods, particularly the abstract nature of buoyancy, which often leaves students unengaged. The Scratch-based media addresses this by providing interactive simulations, allowing students to visualize and experiment with floating and sinking objects, thus making the learning process more engaging. The study involves designing a storyboard and flow of the media, followed by the development of simulations where students instruct sprites (characters) to test buoyancy. The media's effectiveness is validated by experts, who rate it based on display design, navigation, content relevance, interactivity, and technical suitability, with the overall results indicating that the media is valid and practical for use in educational settings. This approach not only helps students grasp scientific concepts but also builds their CT skills by integrating programming with science learning. The findings imply that such interdisciplinary tools can transform science learning by making abstract concepts more accessible and engaging, and encourage the development of both scientific and computational competencies in young learners.Keywords: buoyancy; computational thinking (ct); educational media; primary education; scratch programming
Clustering of Post-Disaster Building Damage Levels Using Discrete Wavelet Transform and Principal Component Analysis Purnamasari, Putri; Imamudin, Mochamad; Zaman, Syahiduz; Syauqi, A’la; Almais, Agung Teguh Wibowo
Journal of Information Technology and Cyber Security Vol. 3 No. 1 (2025): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.12270

Abstract

Damage assessment of buildings after natural disasters is generally performed manually by a team of experts at the disaster site, making it prone to human error and resulting in low accuracy in classifying the level of damage. This research aims to develop a more efficient and accurate method in post-disaster building damage assessment by integrating Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) techniques. The main contribution of this research is the use of DWT as well as the application of this method on more than one image to improve the accuracy of damage level classification. A total of nine unlabelled images of post-disaster buildings were used in this study, which were obtained from the Regional Disaster Management Agency or Badan Penanggulangan Bencana Daerah (BPBD) of Malang City, Indonesia. The methods applied include data pre-processing, DWT decomposition for image analysis to identify features, and clustering using PCA to cluster the level of building damage into light, medium, and heavy categories, which are then evaluated based on accuracy. The results showed that the method yielded 100% accuracy with validation results from surveyors, as evidenced through 2D and 3D visualisations based on principal components (PC1-PC3). These findings confirm that the integration of DWT and PCA can be an effective alternative in improving the accuracy of post-disaster building damage assessment, as well as supporting decision-making in rehabilitation and reconstruction after natural disasters.
Evaluating primary students’ motivation and computational thinking in scratch-based learning: a confusion matrix analysis Neni Hermita; Jesi Alexander Alim; Agung Teguh Wibowo Almais; Pizaini; Rian Vebrianto; Musa Thahir; Tommy Tanu Wijaya; Mulia Anton Mandiro
Primary: Jurnal Pendidikan Guru Sekolah Dasar Vol. 13 No. 6 (2024): December
Publisher : Laboratorium Program Studi Pendidikan Guru Sekolah Dasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33578/jpfkip-v13i6.p264-273

Abstract

This study examined the relationship between student motivation and computational thinking (CT) skills within a Scratch-based learning environment for primary school students. Utilizing a quantitative research design with a pretest-posttest framework, the research involved 28 primary school students engaged in a computational learning program centered on the Jumping Bean concept. A confusion matrix analysis was employed to assess the predictive relationship between motivation levels and improvements in CT skills. The results showed that motivation is a reliable predictor of CT gains, with high precision indicating that highly motivated students are very likely to demonstrate measurable progress. However, the recall score suggests motivation alone is not a conclusive factor, as some motivated students did not achieve the expected CT improvements. This implies that other instructional elements, such as prior knowledge, cognitive differences, teaching methods, and learning design, also significantly impact outcomes. The implications of this research suggest that educators should cultivate motivating learning environments to foster students’ CT skills effectively. Recommendations include integrating gamified elements and personalized feedback to enhance student engagement and motivation in computational learning contexts.
Analisis dan Sistem Perancangan Software Pemetaan Model Proses Bisnis dengan Web Service Artimordika, Firgy Aulia; Sa’adah Rahmaningtyas, Nilmadiana Nur; Ningtias, Nadila Oktavia; Ainul Yaqin, Muhammad; Wibowo Almais, Agung Teguh
Journal Automation Computer Information System Vol. 4 No. 1 (2024): Mei
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v4i1.71

Abstract

Studi ini mengkaji tentang pemetaan model proses bisnis dengan web service. Tujuan studi ini adalah untuk mengidentifikasi kebutuhan sistem sehingga membutuhkan penentuan model proses bisnis dengan menggunakan BPMN. Proses pengerjaan dilakukan dengan cara mengumpulkan tugas atau aktivitas terstruktur yang digunakan untuk menggambarkan langkah-langkah yang harus diambil untuk mencapai tujuan. Setelah mengumpulkan aktivitas, dilakukan pengerjaan pada web service sehingga memudahkan pembangunan sebuah sistem baru, web service juga berkomunikasi melalui XML dan protokol SOAP. Proses terakhir dilakukan penghitungan TF-IDF dengan menggunakan Algoritma Nazief dan  Adriani untuk menghitung kemiripan antar aktivitas dan web service yang paling mirip.
Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks Rahmatmulya, Revaldi; Almais, Agung Teguh Wibowo; Amin Hariyadi, Mokhamad
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1919

Abstract

Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. The data utilized consists of aerial images sourced from xView2: Assess Building Damage, comprising 50 aerial images with 5 classes: no-damage, minor-damage, major-damage, destroyed, and unlabeled. The steps undertaken in this research include data preprocessing using patchify and data augmentation. Subsequently, feature extraction is performed using convolution, followed by the training process using a neural network with the proposed architecture. This study proposes an architecture with 27 hidden layers, with feature extraction utilizing average pooling. The model evaluation process will employ Mean Intersection over Union (MIoU) to assess how closely the segmentation prediction results resemble the original data. The proposed architecture demonstrates the best MIoU result with a value of 0.31 and an accuracy of 0.9577.
LANDSLIDE AREA MAPPING IN DAMPIT SUBDISTRICT, MALANG DISTRICT, EAST JAVA PROVINCE USING SATELLITE IMAGERY OF GRAVITY DATA FOR DISASTER MITIGATION Sutasoma, Muwardi; Susilo, Adi; Maryanto, Sukir; Aprilia, Faridha; Bunga Puspita, Mayang; Habibiy Idmi, Mohammad; Hasan, Muhammad Fathur Rouf; Almais, Agung Teguh Wibowo; Herwiningsih, Sri
Indonesian Physical Review Vol. 8 No. 3 (2025)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v8i3.487

Abstract

Research using satellite imagery of gravity data has been conducted in the Dampit District, Malang Regency, East Java Province. This research was conducted to identify areas vulnerable to landslides. The results of this research can serve as a basis for the government to develop effective landslide disaster mitigation policies, thereby minimizing the losses incurred. The data used is TOPEX satellite gravity data in the form of Free Air Correction data, and supported by landslide vulnerable areas data from the InaRisk satellite. The research area is 23 km x 16 km with 2 km spacing between points and 184 measurement points. Furthermore, the research area is divided into four areas: Area A1, Area A2, Area A3, and Area A4.  The residual anomaly value in the study area is between 82.7 mGal to 142.4 mGal. The residual anomalies are more variable due to the local nature of the anomalies. The correlation between the residual anomaly value and InaRisk satellite image data shows that Area A4 is the most vulnerable to landslides, especially if there is a trigger such as an earthquake.  This is because Area A4 has a low-density value, a large fault, and is the contact area between the Mandalika Formation and Wuni Formation.
Spatial Decision Support System to Determine the Feasibility of Evacuation Posts in Natural Disasters Alviola, Nuril Afni; Almais, Agung Teguh Wibowo; Syauqi, A’la; Chamidy, Totok; A Basid, Puspa Miladin Nuraida Safitri; Anisa, Anisa; Wardana, M. Dafa
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.307-318

Abstract

This study aimed to improve the accuracy of determining the feasibility of evacuation posts after natural disasters using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within a Spatial Decision Support System (SDSS). A dataset of 50 evacuation posts from the 2021 Mount Semeru eruption was analyzed. The Rank Order Centroid (ROC) method was applied for criteria weighting, and TOPSIS was used to process the data. Results showed 72% accuracy, confirming that TOPSIS is a passable method for assessing post-feasibility based on accessibility, sanitation, and refugee facilities. Although the focus is on evaluating post-disaster evacuation posts, the system can be adapted for use in various other types of disasters. However, it is still dependent on historical data and lacks real-time adaptability. Future research can integrate Artificial Intelligence (AI) and Machine Learning (ML) with real-time data to improve decision-making in disaster management.
Optimasi Extreme Gradient Boosting dengan Particle Swarm Optimization untuk Estimasi Software Effort: Optimized Extreme Gradient Boosting using Particle Swarm Optimization for Software Effort Estimation Alif Pahlevi, Achmad Fahreza; Hariyadi, Mokhammad Amin; Almais, Agung Teguh Wibowo
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2055

Abstract

Estimasi upaya perangkat lunak (SEE) sangat penting dalam manajemen proyek, namun akurasi sering terganggu oleh kompleksitas proyek. Untuk mengatasinya, studi ini mengusulkan metode hibrida inovatif Particle Swarm Optimization (PSO) - Extreme Gradient Boosting (XGBoost) untuk SEE. Algoritma PSO mengoptimalkan hiperparameter XGBoost, meningkatkan kemampuannya memodelkan hubungan nonlinier dalam data proyek perangkat lunak, sehingga mengurangi kesalahan estimasi. Hasil eksperimen pada kumpulan data China dan Nasa93 menunjukkan bahwa PSO-XGBoost secara signifikan mengungguli metode tradisional dan model pembelajaran mesin mandiri. Metode yang diusulkan mencapai Root Mean Square Error (RMSE) yang lebih rendah sebesar 0,024 untuk China dan 0,0653 untuk Nasa93 menunjukkan efektivitasnya dalam memberikan estimasi upaya yang presisi. Meskipun memiliki kompleksitas komputasi dan bergantung pada data berkualitas, studi ini berkontribusi pada bidang SEE dengan menyajikan solusi praktis dan andal, membantu manajer perangkat lunak dalam perencanaan sumber daya dan pengambilan keputusan.
Co-Authors A Basid, Puspa Miladin Nuraida Safitri A'la Syauqi AA Sudharmawan, AA Abd. Rouf Abdurrosyid, R. Adi Susilo Adinda Dhea Pramitha Afiq Budiawan Agus Naba Ainafatul Nur Muslikah Ainul Yaqin Akbar Roihan Akkad, Muhammad Iqbal Alif Pahlevi, Achmad Fahreza Alviola, Nuril Afni Anis Fatul Fu'adah Anisa Anisa Aniss Fatul Fu'adah Aprilia, Faridha Arief, Yunifa Miftachul Artimordika, Firgy Aulia A’la Syauqi Brawijaya, Fanny Bunga Puspita, Mayang Cahyo Crysdian Dyah Ayu Wiranti Dyah Febriantina Istiqomah Dyah Wardani Fajrin, Rahma Annisa Farhanah, Nisrina Darin Fresy Nugroho Habibiy Idmi, Mohammad Halimahtus Mukminna, Halimahtus Hariyadi, Mokhammad Amin Jesi Alexander Alim Jesi Alexander Alim Juhari Juhari, Juhari Khadijah Fahmi Hayati Holle Kurnia Siwi Kinasih Kurniawan, Puan Maharani Kusuma, Selvia Ferdiana Laela Nurul Qomariyah Mandiro, Mulia Anton Mochamad Imamudin Moechammad Sarosa Mokhamad Amin Hariyadi Muhammad Aji Pangestu Muhammad Aziz Muslim Muhammad Fathur Rouf Hasan Mulia Anton Mandiro Musa Thahir Muwardi Sutasoma Neni Hermita Ningtias, Nadila Oktavia Pizaini Pizaini Putri Purnamasari Rahmatmulya, Revaldi Ramadan, Afrijal Rizqi Ramadhan, Rizal Furqan Ririen Kusumawati Roro Inda Melani Safitri, Annisa Heparyanti Sa’adah Rahmaningtyas, Nilmadiana Nur Shinta Rizki Firdina Sugiono Sri Herwiningsih Suhartono Sukir Maryanto Syahiduz Zaman Syauqi, A'la Syauqi, A’la Syawab, Moh Husnus Tanti Rismawati Thahir, Musa Tommy Tanu Wijaya Totok Chamidy Vebrianto, Rian Wardana, M. Dafa