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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ilmiah Biosaintropis (Bioscience-Tropic) Fountain of Informatics Journal Journal of Development Research JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA EnviroScienteae JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information Systems and Informatics Jurnal Mnemonic G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Cahaya Mandalika Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Janitra Informatika dan Sistem Informasi J-Intech (Journal of Information and Technology) Internet of Things and Artificial Intelligence Journal Ta'dib Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal ilmiah teknologi informasi Asia
Mokhamad Amin Hariyadi
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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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.
Analytic Predictive of Crescent Sighting Using Astronomical Data-Based Multinomial Logistic Regression in Indonesia Sugiharto, Tomy Ivan; Hariyadi, Mokhamad Amin; Chamidy, Totok; Santoso, Irwan Budi; Crysdian, Cahyo; Zarkoni, Ahmad; Ma'muri, Ma'muri; Syahreni, Syahreni
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.v9i4.8246

Abstract

This research aims to develop and validate a sophisticated crescent visibility classification model in Indonesia. Multinomial Logistic Regression (MLR) was chosen for its capability to provide clear model interpretation through coefficient analysis. Utilizing comprehensive observational data (2021-2025) from Indonesia's Meteorology, Climatology, and Geophysics Agency (BMKG), the study comprised 2210 data points. The model classifies visibility into three categories (Dark, Faint, and Bright) based on defined elongation thresholds. The final predictor variables used were azimuth difference, moon altitude, and elongation. Analysis of the optimal model's (Model A3) coefficients revealed azimuth difference and elongation as the most dominant predictors, marked by exceptionally large positive coefficients (12.050 and 12.018, respectively) for classifying the 'Faint' category. After data preprocessing and systematic optimization ('saga' solver, L2 penalty), the optimal model (A3, C=100) demonstrated exceptional performance with an outstanding F1-Score of 99.10%. These findings strongly validate MLR's effectiveness for elongation-based crescent visibility classification and highlight its substantial potential as a reliable foundation for objective decision-making.
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.
Clustering Gempabumi di Wilayah Regional VII Menggunakan Pendekatan DBSCAN Arafat, Ihsan Bagus Fahad; Hariyadi, Mokhamad Amin; Santoso, Irwan Budi; Crysdian, Cahyo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106918

Abstract

Wilayah Regional VII meliputi Jawa Tengah, Yogyakarta, dan Jawa Timur merupakan wilayah tektonik yang aktif karena terletak di wilayah zona subduksi lempeng Indo-Australia dan Eurasia serta terdapat beberapa patahan aktif di daratan. Oleh karena itu, perlu dilakukan klasifikasi gempabumi untuk memetakan zona rawan gempabumi berdasarkan sumbernya di wilayah Regional VII berdasarkan kesamaan atribut salah satunya adalah berdasarkan karakteristik gempabumi dari sumber yang sama. Pada penelitian ini digunakan pendekatan algoritma Unsupervised Learning Clustering berbasis kepadatan yaitu, Density Based Spatial Clustering of Application with Noise atau DBSCAN, algoritma ini membutuhkan parameter input epsilon (ε) dan MinPts. Data yang digunakan pada penelitian ini adalah data gempabumi wilayah Regional VII tahun 2017 hingga 2021 yang diperoleh dari BMKG. Selanjutnya, proses clustering dilakukan dengan membagi data gempabumi berdasarkan periode yaitu periode tahunan dan periode lima tahun dengan tujuan untuk mengetahui pola cluster berdasarkan periode waktu. Hasil yang terbentuk selanjutnya dievaluasi menggunakan Silhouette Coefficient serta dibandingkan dengan peta Seismisitas Jawa yang telah ada dari katalog PuSGeN 2017. Hasil clustering menggunakan DBSCAN diperoleh jumlah cluster sebanyak 2 hingga 6 cluster dengan nilai Silhouette Coefficient terendah sebesar 0.270 untuk periode T5_2017-2021 dan tertinggi sebesar 0.499 untuk periode T1_2020. AbstractRegional VII area covering Central Java, Yogyakarta and East Java is an active tectonic region because it is located in the subduction zone of the Indo-Australian and Eurasian plates and there are several active faults on land. Therefore, it is necessary to classify earthquakes to map earthquake-prone zones based on their sources in Regional VII area based on the similarity of attibutes, based on the characteristics of earthquakes from the same source. In this study, a density-based Unsupervised Learning Clustering algorithm approach was used namely, Density Based Spatial Clustering of Application with Noise or DBSCAN, this algorithm requires the input parameters epsilon (ε) and MinPts. The data used in this study are earthquake data for Regional VII from 2017 to 2021 obtained from the BMKG. Then, the clustering process is carried out by dividing earthquake data based on the period, namely the annual period and the five-year period with the aim of knowing the pattern of cluster based on the time period. The results are then evaluated using the Sillhouette Coefficient and compared with the existing Java Seismicity map from the 2017 PuSGeN catalog. Clustering results using DBSCAN obtained a number of clusters of 2 to 6 clusters with the lowest Silhouette Coefficient value is 0.270 for the T5_2017-2021 period and the highest is 0.499 for the T1_2020 period.  
Klasifikasi Prestasi Akademik Siswa Berdasarkan Nilai Rapor dan Kedisiplinan dengan Metode K-Nearest Neighbor Muhaimin, Afif; Amin Hariyadi, Mokhamad; Imamudin, M. Imamudin
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 1 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v7i1.2865

Abstract

Sekolah melaksanakan kegiatan pembelajaran melalui tahapan-tahapan dan proses, agar peserta didik mencapai prestasi belajar yang baik. Prestasi belajar adalah hasil yang diperoleh berupa kesan-kesan yang mengakibatkan perubahan dalam diri individu sebagai hasil dari aktivitas. Sebagai pedoman penentuan siswa yang berprestasi adalah dengan memiliki nilai akhir setiap semester yang baik serta nilai kedisiplinan selama mengikuti pembelajaran disekolah, seperti tidak memiliki poin pelanggaran yang tinggi. Belum adanya metode khusus yang digunakan untuk mengklasifikasikan siswa berdasarkan prestasinya dan banyak kemiripan data, dibutuhkan metode klasifikasi yang tepat dan akurat, salah satunya menggunakan ilmu di bidang data mining. Dalam artikel ini peneliti ingin mengklasifikasi prestasi siswa dengan metode K-Nearest Neighbor (K-NN) berdasarkan nilai akademik dan nilai kedisplinan siswa dengan menggunakan data berjumlah 348 siswa di SMA Negeri 2 Batu Jawa Timur. Hasil eksperimen dan evaluasi model yang dilakukan, dengan pembagian data training dan data testing secara acak dengan beberapa percobaan diperoleh nilai akurasi tertinggi sebesar 91.39 %.
A VIKOR-Based Decision Support System for Prioritizing Public Facility Improvements in Malang City with Geotagging Integration Hariyadi, Mokhamad Amin; Fadila, Juniardi Nur; Harini, Sri; Saputra, Muhammad Andryan Wahyu
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.4237

Abstract

Public facilities play a crucial role in driving economic growth and development. Nevertheless, the dearth of public information concerning facility enhancements fosters a sense of public distrust towards the government. Additionally, numerous facilities, which should be prioritized for improvement, have not received adequate attention. In contrast to several prior studies, the present study encompasses a broader scope and incorporates geotagging techniques to precisely identify the location of complaints and determine the optimal route to reach them. Moreover, an analysis process utilizing the VIKOR method has been devised to assess the priority of public facility improvements. This method yielded an accuracy rate of 89,7%, signifying a commendable level of precision and a 16% increase in accuracy based on confusion matrix method compared to previous studies. Through user usability testing, it was determined that the majority of users agreed that this system can facilitate public reporting, enable progress monitoring of public facility improvements, and aid in prioritizing such improvements.
Perbandingan Feature extraction TF-IDF dan BOW Untuk Analisis Sentimen Berbasis SVM Putra, Kurniawan Tri; Hariyadi, Mokhamad Amin; Crysdian, Cahyo
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 3 No. 2 (2022)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

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

Abstract

Dengan adanya transformasi society 5.0 pegaruh paling besar yang bisa dirasakan saat ini adalah berkembang pesatnya jumlah data yang ada di seluruh dunia baik yang bermanfaat secara langsung maupun data yang tidak bermanfaat secara langsung atau dikenal dengan istilah big data, dengan adanya sumber big data tersebut banyak peneliti-peneliti yang memanfaatkanya menjadi suatu data yang berharga dan berguna jika diproses dan diolah dengan cara yang baik dan benar salah satunya adalah dengan tujuan analisis sentimen. Pada permasalahan yang ada penelitian ini bertujuan untuk mencari dan mendapatkan alur dan teknik yang benar serta memiliki hasil optimal pada pengolahan data teks dengan tujuan analisis sentimen dengan membandingakan penerapan TF-IDF dan BOW yang menggunakan metode SVM. Pada penelitian analisis sentimen menggunakan data teks bersumber dari aplikasi media social twitter hasil yang didapatkan adalah pada penerapan teknik TF-IDF yang dipadukan dengan metode SVM memiliki hasil yang lebih baik dengan nilai Accuracy 86%, Precission 85%, Recall 85% dan F1-Score 85% sedangkan penerapan teknik BOW yang dipadukan metode SVM hanya unggul pada nilai Recall sebesar 89%.
Utilizing Random Forest Method for Predicting Student Dropout Risk in Madrasah Environments Mahsun, Muhammad; Hariyadi, M. Amin; Harini, Sri
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.v7i4.1364

Abstract

The phenomenon of school dropout represents a crucial issue with negative impacts on educational institution performance, social stability, and national development. Consequently, the early detection of high-risk students constitutes a strategic preventive measure. This research aims to develop an accurate predictive model using a Machine Learning approach. The study employed a comparative evaluation using classification algorithms, with the primary focus being the performance analysis of the Random Forest Classifier. The dataset utilized, comprising 1,763 student records, underwent a rigorous data pre-processing phase, including data cleaning, variable transformation, and class imbalance handling, to ensure high-quality input. The model was trained using a Random Seed configuration of 75 to guarantee experimental reproducibility and consistency in evaluation results. Experimental findings indicate that the Random Forest algorithm provided the best performance, achieving an accuracy of 82.0% and a precision of 83.8%. This superior performance confirms the model's effectiveness in identifying the key determinants of dropout, stemming from both students' internal and external factors. Based on these results, the research recommends the application of Random Forest as a Decision Support System instrument to facilitate targeted interventions, including medical support, economic assistance, and academic counseling. Future research is advised to integrate historical counseling data to further enhance the prediction sensitivity of the model.
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 Agung Sedayu Agung Teguh Wibowo Almais Ainul Yaqin Ajib Hanani Akbar, Galuh Muhammad Iman Akkad, Muhammad Iqbal Arafat, Ihsan Bagus Fahad Badruz Zamanil Charis Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Eva Yustina Fathir Fathir Fathir Fresy Nugroho Hamid, Abdulhalim Hamid Salih Imam Rofiki Imamudin, M. Imamudin Irwan Budi Santoso Juniardi Nur Fadila Kurniawan, Andang Kurniawan, Puan Maharani Ma'muri, Ma'muri Mahmudiah, Rikha Rizki Mahsun, Muhammad Muhaimin, Afif Muhammad Andryan Wahyu Saputra Muhammad Farid Wadjdi Muhammad Isa Ansori Nur Elyta Febriyanty Nur Hidayah Hanifah Putra, Kurniawan Tri Rahmatmulya, Revaldi Ramadan, Afrijal Rizqi Ririen Kusumawati Setiyaris Setiyaris Setiyawan, Niko Heri Sifaulloh, Hafizzudin Sri Harini Sugiharto, Tomy Ivan Suhartono Syahreni, Syahreni Syaifudin Ramadhani Totok Chamidy Usman Pagalay Yuliawan, Audi Bayu Yunifa Miftachul Arif Yustina, Eva Zarkoni, Ahmad