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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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Forcasting Analysis of Drug Use in Hospitals Based on Multivariate Long Short-Term Memory Networks Brawijaya, Fanny; Almais, Agung Teguh Wibowo; Chamidy, Totok
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8244

Abstract

Effective drug inventory management is crucial for maintaining service quality and cost efficiency in hospitals. Inaccurate procurement planning can cause stockouts or overstock conditions, disrupting healthcare operations. This study presents a predictive model for outpatient drug consumption using a Multivariate Long Short-Term Memory (LSTM) network. The dataset comprises historical records from the general, pediatric, and maternity polyclinics at RSIA Fatimah Hospital, Probolinggo Regency, East Java, Indonesia, collected in January 2023. The variables include timestamp, polyclinic name, drug name, and quantity used. Data preprocessing involved cleaning, one-hot encoding for categorical features, min-max normalization, and time-based train-test splitting to avoid data leakage. The multivariate LSTM model was trained for 500 epochs under various configurations, evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Three model groups (A, B, C) with distinct neuron counts and batch sizes were tested to assess performance variations. Model B1 achieved the best results, with the lowest MAE (10.239), MAPE (1.979%), and highest R² (0.199). Although the R² value indicates limited variance explanation, Nonetheless, the model remains useful for operational forecasting, the model effectively captures temporal patterns in drug consumption, demonstrating its potential as a decision-support tool for optimizing hospital pharmaceutical inventory management.
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.
Assessment of Post-Disaster Building Damage Levels Using Back-Propagation Neural Network Prediction Techniques Wibowo Almais, Agung Teguh; Fajrin, Rahma Annisa; Naba, Agus; Sarosa, Moechammad; Juhari, Juhari; Susilo, Adi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2711

Abstract

Indonesia is susceptible to natural disasters, with its geographical location being one of the contributing factors. To mitigate the harmful effects of natural catastrophes, a disaster emergency response must be undertaken, consisting of steps taken immediately following the event. These operations include rescuing and evacuating victims and property, addressing basic needs, providing protection, and restoring buildings and infrastructure. Accurate data is required for adequate recovery after a disaster. The Badan Penanggulangan Bencana Daerah (BPBD) oversaw disaster relief efforts, but faulty damage assessments slowed restoration. Surveyor subjectivity and differing criteria result in discrepancies between reported damage and reality, generating issues during the post-disaster reconstruction. The objective of this study is to develop a prediction system to measure the extent of damage caused by natural disasters to buildings. The five criteria that decide the level of building damage after a disaster are building conditions, building structure condition, physical condition of severely damaged buildings, building function, and other supporting conditions. The data used are from the BPBD of Malang city from 2019 to 2023. This system would allow surveyors to make speedy and objective evaluations. Five different models were tested using the Neural Network Backpropagation approach. Model A2 produces the highest accuracy of 93.81%. A2 uses a 40-38-36-34 hidden layer pattern, 1000 epochs, and a learning rate 0.1. These findings can lay the groundwork for advanced prediction models in post-disaster building damage evaluation research.
Multivariate LSTM for Drug Purchase Prediction in Pharmaceutical Management Brawijaya, Fanny; Almais, Agung Teguh Wibowo; Chamidy, Totok
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1313

Abstract

This study aims to develop a structured approach to predict the number of hospital drug purchases using deep learning techniques. The Multivariate Long Short-Term Memory (LSTM) model is designed to capture temporal and contextual patterns including transaction time, polyclinic type, and drug type to improve the efficiency of pharmaceutical management. The model was tested using outpatient transaction data at RSIA Fatimah Probolinggo hospital in East Java, Indonesia, through three configurations (A, B, and C) to determine the optimal parameters. The best model, the Model B1, produces a Mean Absolute Error (MAE) value of 10.239, Mean Absolute Percentage Error (MAPE) of 1.976%, and the Coefficient of Determination (R²) of 0.199, which indicates a high degree of accuracy. The results of the study prove that multivariate LSTM is able to model complex intervariable dependencies and provide superior results than conventional forecasting methods. In practical terms, this model can be used as a decision-making tool for hospital management in planning drug procurement, optimizing inventory, and preventing shortages and overstocks. The application of this model contributes to data-driven pharmaceutical supply chain planning in smart hospital management systems.
XGBoost Model Optimization Using PCA for Classification of Cyber Attacks on The Internet of Things Ramadan, Afrijal Rizqi; Hariyadi, Mokhamad Amin; Almais, Agung Teguh Wibowo
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid expansion of the Internet of Things (IoT) ecosystem has increased its susceptibility to cyberattacks, creating a critical need for reliable Intrusion Detection Systems (IDS). However, IDS performance is often hindered by severe class imbalance, high-dimensional features, and similarities among attack behaviors. This study proposes an optimized XGBoost model enhanced with the Synthetic Minority Over-sampling Technique (SMOTE) and Principal Component Analysis (PCA) to address these challenges. A systematic grid-search procedure was employed to ensure transparency, reproducibility, and optimal hyperparameter selection. The original imbalance ratio of approximately 1:27 was successfully normalized to nearly 1:1 through SMOTE. The Gotham dataset used in this study consists of roughly 350,000 IoT traffic records across eight attack categories. Five data-splitting scenarios (50:50 to 90:10) were evaluated using stratified hold-out validation supported by k-fold cross-validation. The optimized model achieved 99.68% accuracy, while extremely high AUC values approaching 1.0 were carefully validated to eliminate potential data leakage. Naive Bayes, Logistic Regression, Support Vector Machine, and Deep Neural Network were included as baseline comparisons. The results demonstrate that combining SMOTE and PCA significantly improves model stability and generalization on imbalanced IoT traffic, confirming the effectiveness of the proposed XGBSP method.
Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method Kurniawan, Puan Maharani; Almais, Agung Teguh Wibowo; Hariyadi, M. Amin; Yaqin, M. Ainul; Suhartono, Suhartono
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1698

Abstract

Performance allowance is a form of appreciation given by an agency to its human resources. The Office of the Ministry of Religion of Batu City provides performance allowances to civil servants who work in the agency. Several things that affect the provision of performance allowances, such as grade, deduction, taxable income, income tax, and total tax, are used in this study to produce the total gross performance allowances and total performance allowances received. Based on the data obtained, there are some missing data from the parameters of taxable income, income tax, and total tax. This study aims to predict performance allowance when there is missing data. The method used is Neural Network Backpropagation. This study uses 480 data with split data ratios of 50:50, 60:40, 70:30, and 80:20, with epochs 40,000 and a learning rate 0,9. Four types of models used in this study are distinguished based on the number of hidden layers and epochs used. Model A uses two hidden layers to produce the highest accuracy with a 50:50 data split ratio of 65,16%. Model B uses four hidden layers to produce the highest accuracy with a 50:50 data split ratio of 69,34%. Model C uses six hidden layers to produce the highest accuracy with a 50:50 data split ratio of 68,18%. Model D uses eight hidden layers to produce the highest accuracy with a 50:50 data split ratio of 70,90%.
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