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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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Recommendation of Prospective Construction Service Providers in Government Procurement Using Decision Tree Eva Yustina; Mokhamad Amin Hariyadi; Cahyo Crysdian
International Journal of Advances in Data and Information Systems Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i1.1316

Abstract

The determination of prospective construction service providers using the direct procurement method is the authority of the Goods/ Services Procurement Officer. Administrative requirements are an important factor in selecting prospective construction service providers. The use of the decision tree method in this study is to find out, determine, and analyse the variables that influence the assessment of the feasibility of prospective construction service providers, and get an accuracy value in providing an assessment of the feasibility of prospective construction service providers. The data used in this study are 153 datasets consisting of 13 variables. The existing variables are divided into basic variables and additional variables. The basic variables consist of 5 variables, namely experts, work experience, quality of work, winning tenders and contract value. While the additional variables consist of 8 variables namely business entity status, business entity form, business entity NPWP, business entity domicile, business entity qualification, type of business licence, percentage of work and construction services business licence. By using the decision tree method, the accuracy on the basic variable is 84.84%. The addition of additional variables to the basic variables resulted in an accuracy of 90.91%. This shows that by adding additional variables the accuracy results are higher than using only the basic variables.
PENILAIAN KELAYAKAN CALON PENYEDIA JASA KONSTRUKSI PENGADAAN BARANG/JASA PEMERINTAH MENGGUNAKAN MACHINE LEARNING Yustina, Eva; Amin Hariyadi, Mokhamad; Crysdian, Cahyo
Jurnal Mnemonic Vol 7 No 1 (2024): Mnemonic Vol. 7 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i1.7996

Abstract

Pengadaan Barang/Jasa Pemerintah bidang konstruksi menjadi kunci terlaksananya pembangunan infrastruktur pada pemerintah pusat maupun daerah. Dalam menentukan calon penyedia jasa konstruksi dengan metode pengadaan langsung merupakan tugas dari Pejabat Pengadaaan Barang/Jasa. Dengan banyaknya calon penyedia jasa konstruksi yang ada maka perlu dilakukan penilaian kelayakan calon penyedia jasa konstruksi. Penelitian ini menggunakan metode Decision Tree dan Random Forest untuk penilaian calon penyedia jasa konstruksi berupa perseroan terbatas maupun commanditare vennootschap dengan menggunakan dataset terdiri dari 154 record yang terdiri dari 5 variabel antara lain: tenaga ahli, pengalaman kerja, kualitas hasil pekerjaan, menang tender, dan nilai kontrak. Hasil Akurasi metode Random Forest lebih tinggi dibandingkan dengan dengan metode Decision Tree. Metode Random Forest menghasilkan akurasi sebesar 90,91% dengan nilai Area Under Curve (AUC) sebesar 0,471, sedangkan metode Decision Tree menghasilkan akurasi sebesar 84,85%, dengan nilai Area Under Curve (AUC) sebesar 0,693
ANALISIS SENTIMEN ARTIKEL BERITA PEMILU BERBASIS METODE KLASIFIKASI Fathir; Hariyadi, M. Amin; Miftachul A, Yunifa
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 2 (2023): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i2.220

Abstract

The distribution of information in the form of online news is so massive in the wider community, that it is difficult to distinguish between haox news and positive news. So that a classification is needed regarding public sentiment about the implementation of elections using mainstream media news article data using 1064 dataset test data. The methods used in this study are the naive Bayes algorithm, the random forest algorithm, and the support vector machine algorithm. The test model uses smote where the performance results are carried out by the algorithm used using smote and not using smote, where random forest produces an accuracy of 91.88%, while without using a smote support vector machine it produces an accuracy of 92.05%.
AUTOMATED MULTI-MODEL PREDICTION AND EVALUATION FOR CONNECTING RAINFALL PREDICTION INFORMATION AND SINGLE-YEAR OPERATIONAL PLAN OF LAHOR-SUTAMI DAM Mahmudiah, Rikha Rizki; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Kurniawan, Andang
EnviroScienteae Vol 20, No 4 (2024): ENVIROSCIENTEAE VOLUME 20 NOMOR 4, NOVEMBER 2024
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v20i4.21054

Abstract

There is a gap between existing climate information and the needs of annual dam operational planning. This study aims to demonstrate that the percentile approach currently used for planning is not optimal, especially now that automation has become more accessible. The purpose of this study is to design an automated forecasting and evaluation system based on 36 10-days rainfall projections using a multi-model approach. This approach comprises a percentile, ARIMA, ECMWF+ARIMA, IOD DMI regression, ERSST regression, and ensemble methods models. Additionally, this study aims to demonstrate how a verified, multi-model-based rainfall forecast can provide more reliable assurance for the annual operational planning of Lahor-SutamiDam, simulated operationally in November 2022 for the 2022/2023 planning cycle. Data utilized include historical 10-days rainfall data from 1991 to 2023, ECMWF raw and corrected model outputs, Nino-Dipole index, and global sea surface temperature. The verification method employs four criteria based on MAE and fit index. An operational simulation approach is used for training-testing period segmentation, while a 10-year window is applied to account for possible climate-change-induced shifts in relationships. Single linear regression is used to avoid overfitting. The automation system was developed using R-Statistics. Results indicate that the current approach is only optimal for 58% of locations. Superior methods identified include ECMWFcorrected, ERSST regression, and Ensemble models. A case study for 2022/2023 demonstrates that the forecast results outperform the existing plan for at least 78% of the projected periods.
Smart Home Berbasis IoT Menggunakan Suara Pada Google Assistant Ajib Hanani; Mokhamad Amin Hariyadi
Jurnal Ilmiah Teknologi Informasi Asia Vol 14 No 1 (2020): Volume 14 Nomor 1 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v14i1.456

Abstract

This research builds an IoT based Smart Home using voice command on Google Assistant. It is a solution for sick people who are in wheelchairs / beds or people with disabilities but can speak or elderly people who cannot reach the switch in order to turn on / turn off home devices. In addition, we able to control home devices from any where. The system is built using voice command on the Google Assistant application. Google Assistant converts the voice command to the text command. And then, the text command is forwarded from Google Assistant to Webhooks by IFTTT. Webhooks makes a request to the HTTP RESTful API. The text command is published to MQTT Broker by phpMQTT library available on the HTTP RESTful API. ESP32 Dev Kit as an internet connected microcontroller receives text command from MQTT Broker to turn on or turn off the lights in home. The system testing has succeeded in turning on and turning off the lights with voice commands using Google Assistant.
433Mhz based Robot using PID (Proportional Integral Derivative) for Precise Facing Direction Hariyadi, Mokhamad Amin; Fadila, Juniardi Nur; Sifaulloh, Hafizzudin
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.7.3.1841

Abstract

This research endeavor aims to evaluate the effectiveness of the robot's direction control system by employing PID (Proportional Integral Derivative) output and utilizing wireless communication LoRa E32 433MHz. The experimental robot used in this study was a tank model robot equipped with 4 channels of control. LoRa was implemented in the robot control system, in conjunction with an Android control application, through serial data communication. The LoRa E32 module system was selected based on its established reliability in long-range communication applications. However, encountered challenges included the sluggishness of data transmission when using LoRa for transferring control data and the decreased performance of the robot under Non-Line of Sight conditions. To overcome these challenges, the PID method was employed to generate control data for the robot, thereby minimizing the error associated with controlling its movements. The PID system utilized feedback from a compass sensor (HMC5883L) to evaluate the setpoint data transmitted by the user, employing Kp, Ki, and Kd in calculations to enable smooth movements toward the setpoint. The findings of this study regarding the direct control of the robot using wireless LoRa E32 communication demonstrated an error range of 0.6% to 13.34%. A trial-and-error approach for control variables determined the optimal values for Kp, Ki, and Kd as 10, 0.1, and 1.5, respectively. Future investigations can integrate additional methodologies to precisely and accurately determine the PID constants (Kp, Ki, and Kd) mathematically.
Employing Multi-Layer Perceptron Models for Heart Failure Disease Prediction Hamid, Abdulhalim Hamid Salih; Arif, Yunifa Miftachul; Hariyadi, M. Amin
Journal of Development Research Vol. 9 No. 1 (2025): Volume 9, Number 1, May 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v9i1.430

Abstract

This study aims to develop a predictive model for heart failure using a multilayer perceptron (MLP) as part of the application of deep learning techniques in medical data analysis. Given the increasing prevalence of heart failure and its significant impact on patients' quality of life and healthcare costs, early detection is of paramount importance. The dataset, obtained from Kaggle, consists of 918 medical records containing 12 key health variables, including age, blood pressure, cholesterol level, and fasting blood sugar. The model underwent extensive training and testing, and its performance was evaluated using statistical measures such as precision, recall, accuracy, and AUC-ROC curve. The results showed that the proposed model achieved a prediction accuracy of 91.1%, with a sensitivity of 90.3% and a specificity of 92%, indicating its effectiveness in predicting heart failure compared to traditional models. Further analysis identified ST-segment depression, resting blood pressure, and cholesterol level as the most influential factors in determining the risk of heart failure. Based on these results, the MLP model can be considered an effective tool to assist physicians in the early diagnosis of heart failure. Optimization techniques such as particle swarm optimization (PSO) can be used to improve prediction accuracy. Furthermore, combining the model with advanced analytical methods may enhance its predictive performance. This study highlights the importance of using artificial neural networks in the medical field, emphasizing their role in improving early diagnosis systems, reducing heart failure complications, and improving the overall quality of healthcare services.
Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm Akbar, Galuh Muhammad Iman; Hariyadi , Mokhamad Amin; Hanani, Ajib
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i3.630

Abstract

Bruteforce is a hacking technique that launches an attack by guessing the username and password of the system that is the target of the attack. The Bruteforce attack on the MQTT protocol is an attack that often occurs on the IoT, so it is necessary to detect attacks on the MQTT protocol to find out normal traffic and brute force traffic. Random Forest was chosen because this method can classify a lot of data in a relatively short time, and the results from Random Forest can improve accuracy and prevent overfitting in the data classification process. This study uses two types of data: primary data from the hacking environment lab and secondary data from the IEEE Data Port MQTT-IOT-IDS2020 dataset. Trials on primary data and the results obtained are accuracy of 99.55%, precision of 100%, recall of 99.54%, and f-measure of 99.77%, the duration needed to get these results with 1796 data lines, i.e., for 0 seconds. As for the secondary data, the researcher obtained an accuracy of 99.77%, a precision of 100%, a recall of 99.43%, and an f-measure of 98.71%, the duration required to obtain these results with 85002 data lines, i.e., for 62 seconds.
Sistem Pengawasan CCTV Pada ATM Secara Real-Time Berbasis Internet of Things Setiyawan, Niko Heri; Hariyadi, Mokhamad Amin; Arif, Yunifa Miftachul
Jurnal Janitra Informatika dan Sistem Informasi Vol. 5 No. 1 (2025): April - Jurnal Janitra Informatika dan Sistem Informasi
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/k0e60m33

Abstract

Efisiensi laporan kriminalitas terhadap CCTV merujuk pada kemampuan sistem pengawasan menggunakan kamera CCTV dalam mempercepat, mempermudah, dan meningkatkan akurasi proses pelaporan tindakan kriminal. Dengan kata lain, efisiensi ini mencakup bagaimana penggunaan CCTV, terutama yang berbasis Internet of Things (IoT), dapat mengurangi waktu dan tenaga dalam mendeteksi, merekam, serta menyampaikan informasi tentang kejadian kriminal kepada pihak yang berwenang (Lutviansyah, 2025). Penelitian ini termasuk dalam kategori penelitian eksperimen kuantitatif yang bertujuan untuk mengukur efisiensi pelaporan tindakan kriminal melalui penerapan sistem CCTV berbasis IoT pada mesin ATM. Penelitian ini juga menguji pengaruh protokol MQTT terhadap kecepatan transmisi data dan efisiensi waktu pelaporan. sistem pengawasan CCTV berbasis Internet of Things (IoT) dengan protokol MQTT memberikan efisiensi yang signifikan dalam pengawasan keamanan ATM secara real-time. Sistem ini mampu mempercepat waktu deteksi kejadian kriminalitas, dari sebelumnya rata-rata 120 menit menjadi 15 menit. Mempercepat waktu pelaporan ke pusat keamanan, dari 90 menit menjadi 10 menit, melalui notifikasi otomatis tanpa intervensi manual. Menghemat konsumsi bandwidth, dari 1024 Kbps pada sistem konvensional menjadi hanya 256 Kbps dengan pendekatan event-based MQTT. Mempercepat waktu respon keamanan, dari 30 menit menjadi 5 menit, karena petugas menerima laporan secara langsung dan instan. Meningkatkan keberhasilan pemantauan real-time, dari 60% pada sistem lama menjadi 100% dalam mendeteksi kejadian saat berlangsung.
PENILAIAN KINERJA PEGAWAI DENGAN METODE TOPSIS DAN BACKPROPAGATION NEURAL NETWORK Yuliawan, Audi Bayu; Hariyadi, M. Amin; Kusumawati, Ririen; Crysdian, Cahyo; Nugroho, Fresy
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7826

Abstract

Transformasi digital melalui penerapan Industri 4.0 dan e-Government telah mengubah paradigma administrasi publik, sehingga menuntut sistem evaluasi kinerja pegawai yang lebih adaptif dan objektif. Penelitian ini bertujuan untuk mengklasifikasikan kinerja pegawai ke dalam lima kategori, yaitu "sangat baik", "baik", "cukup", "buruk", dan "sangat buruk", dengan menggunakan pendekatan Neural Network Backpropagation. Metodologi yang digunakan mencakup beberapa tahapan utama, dimulai dari proses preprocessing data yang menge-lompokkan kriteria penilaian ke dalam empat aspek: kualifikasi, kom-petensi, kinerja, dan disiplin. Selanjutnya, dilakukan seleksi fitur menggunakan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), dan hasilnya digunakan sebagai data pelatihan pada model Neural Network Backpropagation. Hasil pelati-han menunjukkan performa model yang cukup baik, dengan nilai loss dan Mean Squared Error (MSE) sebesar 0,000465, Mean Absolute Per-centage Error (MAPE) sebesar 19,59%, dan akurasi mencapai 80,41%. Sementara itu, hasil eksperimen dengan metode TOPSIS secara terpisah mencatat akurasi sebesar 81% dan nilai loss sebesar 0,377. Kombinasi metode TOPSIS dan Neural Network Backpropagation ter-bukti efektif dalam mengklasifikasikan kinerja pegawai secara konsis-ten. Temuan ini memberikan kontribusi terhadap pengembangan sis-tem evaluasi kinerja berbasis kecerdasan buatan yang lebih akurat dan adaptif terhadap tantangan administrasi publik modern.
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