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All Journal Jurnal Reka Lingkungan MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Edukasi dan Penelitian Informatika (JEPIN) REKAYASA 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 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 Jurnal ilmiah teknologi informasi Asia Teknologi : Jurnal Ilmiah Sistem Informasi
Agung Teguh Wibowo Almais
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Physics Social Sciences Other

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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.
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.
Implementation and Evaluation of Artificial Neural Networks for Product Sales Prediction at Basmalah Stores Akkad, Muhammad Iqbal; Hariyadi, Mokhamad Amin; Almais, Agung Teguh Wibowo
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15341

Abstract

This study aims to develop a product sales prediction system for Toko Basmalah located in the Malang Regency area by utilizing the Artificial Neural Network (ANN) algorithm. A quantitative approach was employed, using time series sales data obtained from the Marketing Division of PT. Sidogiri Pandu Utama for the period of January 1, 2023, to December 31, 2024. The research stages included data collection and preprocessing, normalization using the min-max scaling technique, data splitting into training and testing sets, ANN model experimentation with various data compositions, and performance evaluation based on the Mean Squared Error (MSE) metric. The experiments were conducted five times using the Kaggle Editor platform. The results showed that the ANN-E model with a specific architecture achieved the lowest MSE value of 34.38%, making it the most optimal model for sales prediction. These findings are expected to assist in making better decisions regarding stock management, sales planning, and business strategies in the retail environment.
Manajemen Perangkat Lunak Aplikasi Sistem Informasi Berbasis Android Farhanah, Nisrina Darin; Almais, Agung Teguh Wibowo
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 5 No. 2 (2022): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v5i2.268

Abstract

Perangkat lunak ialah istilah khusus yang digunakan untuk penyebutan data yang disimpan dan diformat secara digital. Dalam proses pembuatannya, perangkat lunak membutuhkan pengetahuan (teknik) khusus dikarenakan perangkat lunak yang tak berwujud. Manajemen perangkat lunak dapat dinyatakan sebagai metode pembangunan perangkat lunak yang paling tepat. Metode penelitian yang digunakan adalah studi pustaka dari beberapa jurnal karya pendahulu, wawancara dengan ahli, dan observasi. Hasil analisis dari manajemen perangkat lunak yang tepat akan menghasilkan konsep manajememen yang terbaik pada sebuah sistem. Dapat disimpulkan bahwa manajemen perangkat lunak dalam pembuatan sistem aplikasi terdiri dari rencana pengelolaan, pembangunan desain, dan evaluasi manajemen melalui pengelolaan sumber daya dan pembuatan kerangka kerja pengelolaan yang tepat sesuai kebutuhan aplikasi tanpa melupakan komponen-komponen penting penyusun sistem informasi berbasis android.
Deteksi Dini Diabetes menggunakan Machine Learning dengan Metode PCA dan XGBoost Abdurrosyid, R.; Almais, Agung Teguh Wibowo
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 1 (2025): Volume 11 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i1.87780

Abstract

Diabetes melitus merupakan masalah kesehatan global yang terus meningkat, dengan dampak signifikan terhadap kualitas hidup individu dan ekonomi masyarakat. Deteksi dini diabetes memainkan peran penting dalam mencegah komplikasi serius, tetapi metode konvensional sering kali terbatas oleh waktu, biaya, dan akurasi. Penelitian ini mengusulkan kombinasi Principal Component Analysis (PCA) dan algoritma XGBoost untuk meningkatkan akurasi dan efisiensi deteksi dini diabetes. PCA digunakan untuk mereduksi dimensi data, sementara XGBoost diterapkan sebagai algoritma klasifikasi. Dataset Pima Indians Diabetes Database digunakan sebagai objek penelitian, dengan tahapan meliputi preprocessing data, penerapan PCA, dan pelatihan model menggunakan XGBoost. Evaluasi model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kombinasi PCA dan XGBoost meningkatkan performa model dibandingkan dengan XGBoost tanpa PCA, dengan peningkatan akurasi hingga 5.4% dan F1-score sebesar 6.45%. Namun, terdapat tantangan berupa sedikit penurunan recall, yang memerlukan optimasi lebih lanjut. Penelitian ini menunjukkan potensi besar teknologi machine learning dalam mendukung deteksi dini diabetes secara lebih cepat, akurat, dan efisien, serta membuka peluang implementasi di sistem kesehatan berbasis data.
Co-Authors A Basid, Puspa Miladin Nuraida Safitri A'la Syauqi AA Sudharmawan, AA Abd. Rouf Abdurrosyid, R. Adi Susilo Adinda Dhea Pramitha Agus Naba Ainafatul Nur Muslikah Ainul Yaqin Akbar Roihan Akkad, Muhammad Iqbal Alfina Taurida Alaydrus Alif Pahlevi, Achmad Fahreza Alviola, Nuril Afni Amani, Holidiyatul Anis Fatul Fu'adah Anisa Anisa Aniss Fatul Fu'adah Anton Prasetyo 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 Fachrizal Fazza Ashari Fachrul Kurniawan Fajar Rohman Hariri Fajrin, Rahma Annisa Farhanah, Nisrina Darin Fresy Nugroho Habibiy Idmi, Mohammad Halimahtus Mukminna, Halimahtus Hariyadi, Mokhammad Amin 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 Muwardi Sutasoma Neni Hermita Ningtias, Nadila Oktavia Pizaini Pizaini Putri Purnamasari Rahmatmulya, Revaldi Ramadan, Afrijal Rizqi Rif'atul Mahmudah Ririen Kusumawati Rizal Furqan Ramadhan Roro Inda Melani Safitri, Annisa Heparyanti Sa’adah Rahmaningtyas, Nilmadiana Nur Shinta Rizki Firdina Sugiono Sri Herwiningsih Sugiharto , Tomy Ivan Syahiduz Zaman Syauqi, A'la Syauqi, A’la Syawab, Moh Husnus Tanti Rismawati Thahir, Musa Totok Chamidy Tri Harningsih Tri Kustono Adi Usman Pagalay Vebrianto, Rian Wardana, M. Dafa Wiyono, Masdar Zainal Abidin Zarkoni, Ahmad