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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Gamatika Prosiding Seminar Matematika dan Pendidikan Matematik JURNAL SOSIAL HUMANIORA (JSH) Semesta Teknika Elkawnie Register: Jurnal Ilmiah Teknologi Sistem Informasi Science and Technology Indonesia Journal of Environment and Sustainability Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan JAIM (Jurnal Abdi Masyarakat) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Majalah Ilmiah Matematika dan Statistika (MIMS) Airlangga Journal of Innovation Management Journal of Applied Data Sciences Journal of Research in Social Science and Humanities COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Sewagati International Journal of Computing Science and Applied Mathematics-IJCSAM
Mohammad Isa Irawan
Departemen Matematika Institut Teknologi Sepuluh Nopember Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Advancing machine learning for identifying cardiovascular disease via granular computing Ku Khalif, Ku Muhammad Naim; Muhammad, Noryanti; Mohd Aziz, Mohd Khairul Bazli; Irawan, Mohammad Isa; Iqbal, Mohammad; Setiawan, Muhammad Nanda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2433-2440

Abstract

Machine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in CVD detection.
Hydrophobicity signal analysis for robust SARS-CoV-2 classification Jamhuri, Mohammad; Irawan, Mohammad Isa; Mukhlash, Imam; Tri Puspaningsih, Ni Nyoman
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1294-1305

Abstract

Rapid and accurate classification of viral pathogens is critical for effective public health interventions. This study introduces a novel approach using convolutional neural networks (CNN) to classify SARS-CoV-2 and non-SARS-CoV-2 viruses via hydrophobicity signal derived from DNA sequences. Conventional machine learning methods grapple with the variability of viral genetic material, requiring fixed-length sequences and extensive preprocessing. The proposed method transforms genetic sequences into image-based representations, enabling CNNs to handle complexity and variability without these constraints. The dataset includes 8,143 DNA sequences from seven coronaviruses, translated into amino acid sequences and evaluated for hydrophobicity. Experimental results demonstrate that the CNN model achieves superior performance, with an accuracy of over 99.84% in the classification task. The model also performs well with extended sequence lengths, showcasing robustness and adaptability. Compared to previous studies, this method offers higher accuracy and computational efficiency, providing a reliable solution for rapid virus detection with potential applications in bioinformatics and clinical settings.
Analisa Efektivitas sistem Adaptive Defense Schemes (ADS) dengan teknologi 4.0 dalam Meningkatkan Keandalan Sistem Transmisi Tenaga Listrik Kalimantan Nugraha, Arma Perwira; Irawan, Mohammad Isa
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 1 (2025): Januari 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i1.1767

Abstract

The disruption of the electricity transmission system can lead to a total blackout, resulting in losses for both customers and PT PLN (Persero) UIP3B Kalimantan company. The vulnerability map of the Kalimantan interconnection system indicates that the Barikin - Tanjung Line is particularly vulnerable due to this transmission line is a crucial junction connecting major power plants to the transmission lines of South Kalimantan, Central Kalimantan, and East Kalimantan. Adaptive Defense Schemes (ADS) is an adaptive protection system that dynamically implements load shedding to reduce the risk of blackouts. ADS employs a Technology 4.0 system that integrates SCADA, protection, and IT systems, necessitating substantial costs. The effectiveness of ADS is assessed using ARIMA forecasting. Research result shows that ADS implementation reducing total disturbance as much as 77%, disturbance duration 68%, and ENS 65%. The result of ADS implementation impact in financial sector shows the reduction of 65% of company potential loss in effect of disturbance in transmission system, and also in the period of 2023 – 2026 where the ADS is still implemented, then the company could reduce 60% of its potential loss because of the disturbance in the transmission system
Upaya Peningkatan Omzet Melalui Pemasaran Digital Berbasis Website dan Media Sosial pada Industri Rumah Tangga (IRT) Minyak Kelapa Kediri Irawan, Mohammad Isa; Mukhlash, Imam; Rukmi, Alvida Mustika; Hendy; Iqbal, Mohammad; Probojati, Rasyadan Taufiq; Hidayat, Nurul; Rukmini, Meme
Sewagati Vol 8 No 6 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i6.2237

Abstract

Industri rumah tangga minyak kelapa “d’Hayfa Sukses Berkah” Kediri berdiri sejak tahun 2017. Usaha ini terletak di Jl. Kaliombo Perum Bumi Asri E-10 Kediri. Kualitas dari produk minyak kelapa Abila yang merupakan produk utama dari industri ini tidak kalah dibandingkan dengan produk sejenis yang ada di pasaran. Industri rumah tangga ini mampu menghasilkan beberapa produk turunan dari minyak kelapa, seperti sabun herbal dengan kandungan minyak kelapa dan kombinasi ekstrak tumbuhan berkhasiat, serum rambut, obat kutu, hand sanitizer, hand soap, cairan pencuci piring, serta pengharum ruangan. Dari hasil observasi dirasakan kurangnya pengetahuan masyarakat mengenai produk-produk yang dihasilkan serta kurangnya pengetahuan mengenai khasiat dan kegunaan dari produk mitra. Tujuan utama dari kegiatan abmas ini adalah untuk mengupayakan perluasan pangsa pasar melalui pemasaran digital sehingga produk minyak kelapa ini bisa dikenal luas dan meningkatkan pesanan. Sebuah website abilaindonesia.com dirancang tim abmas untuk mitra serta pengaturan unggahan media sosial yang berisi konten iklan maupun pengetahuan produk abila. Pembuatan website dan konten media sosial telah signifikan meningkatkan visibilitas online dari produk “d’Hayfa Sukses Berkah”. Penyediaan konten media sosial yang konsisten telah membantu membangun brand awareness dan keterlibatan pelanggan. Kegiatan ini mendukung pertumbuhan ekonomi berkelanjutan dan menciptakan pekerjaan yang layak (SDG 8), serta mendorong pola konsumsi dan produksi berkelanjutan (SDG 12).
The Model of Carbon Price Risk Prediction in European Markets Using Long Short-Term Memory- Geometric Brownian Motion Pradana, Yan Aditya; Mukhlash, Imam; Irawan, Mohammad Isa; Putri, Endah Rokhmati Merdika; Iqbal, Mohammad
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.536

Abstract

Accurate carbon market price prediction is one of the fundamentals in assessing the risks associated with carbon trading. Related studies on carbon price prediction were mainly focused on two major approaches: mathematical and/or machine learning models. Geometric Brownian Motion (GBM) is one of the mathematical models that can represent carbon price movements but requires modifying the sample size and the number of parameters for compiling the simulation numerically. Moreover, two critical parameters: (μ) mu and (σ) sigma need to be estimated to simulate the carbon price movements. In this study, the parameters μ and σ estimation are based on the average return value and standard deviation. However, if the carbon price movement is very volatile, we need to recognize its trend and characteristics by estimating the parameters precisely until there is no significant change (or stable) patterns. That is very expensive and may be intractable on high-dimensional data with less precise prediction. Therefore, we propose a hybrid model for carbon price prediction based on GBM with the parameter estimation using the Long Short-Term Memory (LSTM) model. The LSTM model was chosen because it has high accuracy in parameter estimation without losing the characteristics of the GBM stochastic model. Furthermore, Value at Risk (VaR) is utilized to measure the risk of carbon price volatility predictions. The simulation results showed the proposed model has higher prediction accuracy with a not-too-significant time difference, and the model is proven reliable in measuring future risks.
Efforts to Increase Electricity Sales at Pln Ulp Belawan Customer Service Zone Using AHP-ANP Method Implementation Sepriadi, Robby; Irawan, Mohammad Isa
Journal of Research in Social Science and Humanities Vol 5, No 1 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i1.281

Abstract

Abstract Electricity sales represent a crucial aspect of the business operations of the Perusahaan Listrik Negara (PLN). PLN is required to continuously improve its electricity sales strategies and enhance its services to meet the growing demands of its customers. PLN’s Customer Service Unit (ULP) Belawan is located in the northern part of Medan City, North Sumatra Province. PLN ULP Belawan serves a total of 75,931 customers with a total installed capacity of 220.28 MVA. In 2023, electricity sales reached 484.86 GWh, which is 105.63% of the target. The sales target for 2024 has been set at 524.46 GWh. This thesis aims to present the results of customer zoning prioritization that can be used to improve service delivery more effectively within PLN ULP Belawan. Through the weighting process using Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP), as multi-criteria decision-making methods, were to optimize criteria and contribute to enhancing electricity sales performance. Each customer zone was evaluated based on several criteria, including system reliability, electricity usage regulation (P2TL), improvement of disturbance and complaint services, replacement of electricity meters, enforcement of the 720-hour usage regulation, connection criteria, cash-in performance, and accurate meter reading. The research successfully identified customer zones based on priority criteria. For the criterion of Electricity Usage Regulation (P2TL), the total energy recorded was 341,841 kWh across 173 kWh meters. Of this total, Zone 12 contributed the most with 277,019 kWh, followed by Zone 14 with 43,098 kWh, and Zone 6 with 20,914 kWh. In the Accurate Meter Reading criterion, the total energy achieved was 157,311 kWh from 815 kWh meters. Zone 14 contributed the highest amount at 67,564 kWh, followed by Zone 12 with 48,580 kWh, and Zone 6 with 41,167 kWh. Meanwhile, for the criterion of Electricity Meter Replacement, the total energy gained was 65,559 kWh from 505 kWh meters, with the largest contribution coming from Zone 6 (26,910 kWh), followed by Zone 12 (26,688 kWh), and Zone 14 (11,961 kWh). 
Carbon Price Prediction by Incorporating Fossil Fuel Prices Using Long Short-Term Memory with Temporal Pattern Attention (TPA-LSTM) Mujiono, Edo Priyo Utomo Putro; Mukhlash, Imam; Pradana, Yan Aditya; Putri, Endah R.M.; Irawan, Mohammad Isa
Science and Technology Indonesia Vol. 10 No. 3 (2025): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.3.856-865

Abstract

Reliable carbon price prediction can help to stabilize the carbon market, minimize financial risks for investors, and encourage innovation in green industries. The forecasts have a crucial role in reaching advanced goals for reducing emissions, aiding the shift toward an economy with reduced carbon emissions, and lessening the adverse effects of climate change overall. This paper proposes applications of LSTM with Temporal Pattern Attention (TPA-LSTM) to predict carbon price fluctuations. The prediction of carbon price fluctuations not only draws on its own historical information but also from its main predictors, including fossil fuel prices from 2018 to 2023. The TPA-LSTM method is a combined method that uses the LSTM layer as the initial input of the model. Furthermore, the output generated by the LSTM layer serves as the input to the TPA layer, which is then used to predict the carbon price for the following day. The model is tested by predicting the test data and calculating the evaluation results. The experimental results indicate that TPA-LSTM has surpassed other models in accuracy by showing better performance based on MSE, RMSE, MAE, and MAPE metrics.
Penentuan Prioritas Pemeliharaan Trafo Distribusi dengan Integrasi Metode Delphi dan AHP Haloho, Freddi; Irawan, Mohammad Isa
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 5 No. 3 (2025): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v5i3.3252

Abstract

Distribution transformer maintenance plays a critical role in ensuring the continuity and reliability of PLN’s electricity supply. Currently, PLN employs a Condition-Based Maintenance (CBM) approach, relying on a Health Index (HI) derived from Tier 1 and Tier 2 inspection outcomes. However, in practice, determining maintenance priorities remains largely dependent on the intuition and technical experience of decision-makers, leading to potential inconsistencies in decision-making. Additionally, the absence of a structured weighting mechanism among HI parameters and unclear relevance of indicators used further hampers valid and reliable decision-making aligned with current operational conditions. This research aims to propose a structured and adaptive Health Index evaluation method through the integration of the Delphi and Analytical Hierarchy Process (AHP) methods. The Delphi method was employed to identify the most relevant parameters based on expert consensus, while the AHP method provided logical and consistent weighting among these parameters. Although AHP does not entirely eliminate subjectivity, as it still depends on expert judgments, it offers a systematic and transparent way to manage conflicting criteria effectively. The Delphi application yielded seven sub-criteria parameters grouped into three main criteria: oil leakage, transformer body temperature, load percentage, grounding value, physical condition, transformer age, and neutral current percentage. Based on AHP analysis, the oil leakage parameter was identified as having the highest weighting (0.23), indicating its significant impact on transformer performance. Applying this methodology to 253 distribution transformers resulted in prioritizing maintenance into three categories: Priority I (4 transformers), Priority II (54 transformers), and Priority III (195 transformers). The findings and validation from this study demonstrate that a structured, weighting-based approach significantly enhances the accuracy and transparency of decision-making processes in asset maintenance management.
APPLICATION OF CLASSIFICATION BASED ASSOCIATION (CBA) FOR MONKEYPOX DISEASE DETECTION Pratama, Qoria Yudi; Irawan, Mohammad Isa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp595-602

Abstract

Monkeypox is a zoonotic disease that can be transmitted from animals to humans. The monkeypox virus is the cause of monkeypox disease, which belongs to the orthopoxvirus family. Although the mortality rate from monkeypox is not as high as COVID-19, this virus can be the cause of the next global pandemic if the epidemic worsens. Therefore, it is very important to carry out proper surveillance and prevention to prevent the spread of this disease. In this study, researchers developed another method to detect monkeypox disease based on its symptoms using the classification by association (CBA) method. CBA integrates the advantages of classification and association analysis, allowing the classification process and a deeper understanding of the strength of the relationship between features in the dataset through the analysis of metrics such as support and confidence. Based on the results of the experiments in this study, an accuracy of 68.64%, a precision of 92.21%, and a sensitivity of 71.09% were obtained. In this case, the accuracy obtained is still low, but the results of other metrics show that the CBA model performs fairly well in predicting the positive class with high precision.
DIGITALISASI PEMASARAN DAN SISTEM KEUANGAN PADA INDUSTRI RUMAH TANGGA COCONUT OIL KEDIRI Irawan, Mohammad Isa; Mukhlash, Imam; Rukmini, Meme; Hendy; Hidayat, Nurul; Rukmi, Alvida Mustika; Iqbal, Mohammad; Probojati, Rasyadan Taufiq
Jurnal Abdi Masyarakat Vol. 8 No. 1 (2024): Jurnal Abdi Masyarakat November 2024
Publisher : Universitas Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/jaim.v8i1.6045

Abstract

Industri rumah tangga (IRT) minyak kelapa d’Hayfa Sukses Berkah memproduksi minyak kelapa murni Abila. Minyak kelapa yang dihasilkan memiliki kualitas yang tidak kalah baik dengan minyak kelapa yang dihasilkan industri besar. Beberapa kendala usaha yang dihadapi diantaranya sistem keuangan usaha yang belum tertata dan minimnya pemahaman pemilik dan staf dalam membuat laporan keuangan membuat usaha ini sulit berkembang. Tercampurnya keuangan usaha dan rumah tangga juga menjadi kendala internal. Selain memproduksi minya kelapa murni, dHayfa Sukses Berkah juga memiliki produk-produk turunan berbahan dasar minyak kelapa yang belum banyak diketahui masyarakat. Kegiatan pengabdian masyarakat ini dikembangkan sebuah sistem keuangan terkomputerisasi dan pemasaran digital bagi produk minyak kelapa Abila dan produk turunannya. Perkembangan sistem keuangan terkomputerisasi menunjukkan dampak positif dalam pengelolaan mitra. Pemanfaatan marketplace menunjukkan potensi dalam memperluas jangkauan pasar dan meningkatkan penjualan.
Co-Authors AA. Masroeri Abduh Riski, Abduh Adrianus Bagas Tantyo Dananjaya Akhmad Arif Junaidi Alan Catur Nugraha Alexander Setiawan Alvida Mustika Rukmi Amira, Siti Azza Andreas Handojo Anindita Sharkar Antonio Galileo Tando Ari Kusumastuti Ari Kusumastuti Arie Dipareza Syafei Arifah, Enny Durratul Auliya Rahmayani Baiq Findiarin Billyan Chyntia Kumalasari Puteri Danang Wahyu Wicaksono Daniel Happy Putra Darmaji, Darmaji Darmawan, Didiet Edi Satriyanto Ekky Hidma Octia Rahmah Elly Matul Imah Elnora Oktaviyani Gultom Elsen Ronando Erna Apriliani Fahim, Kistosil Fendhy Ongko Giandi, Oxsy Ginardi, Raden Venantius Hari Hadi Prasetiya Haloho, Freddi Hartanto Setiawan Hendy Hendy Hendy Hozairi Imam Mukhlash Imam Mukhlash Ira Puspitasari Juhari Juhari, Juhari Ketut Buda Artana Khilmy, Akhmad Ku Khalif, Ku Muhammad Naim Mahardika, Kadek Eri Mahdiyah, Umi Mardlijah - Maulana, Muhammad Agung Adi Mey Lista Tauryawati Mohamad Muhtaromi Mohammad Hamim Zajuli Al Faroby Mohammad Iqbal Mohammad Jamhuri Mohd Aziz, Mohd Khairul Bazli Mondal, Kartick Chandra Muchamad Jati Nugroho Muhammad Ahnaf Amrullah Muhammad Athoillah, Muhammad Muhammad Fakhrur Rozi Muhammad Hajarul Aswad Muhammad, Noryanti Muhammad, Noryanti binti Mujiono, Edo Priyo Utomo Putro Ni Nyoman Tri Puspaningsih Notopramono, Hanna Nugraha, Arma Perwira Nurul Anggraeni Hidayati NURUL HIDAYAT Nurul Hidayat Pratama, Qoria Yudi Putri, Endah R.M. Putri, Endah Rokhmati Merdika Putris , Nadhifa Afrinia Dwi Rasyadan Taufiq Probojati Resi Arumin Sani Rita Ambarwati Rita Ambarwati Sukmono Robin Wijaya, Robin Rohwana, Ulir Ronando, Elsen Rukmini, Meme Santoso Santoso Santoso Santoso Sepriadi, Robby Setiawan, Muhammad Nanda Setumin, Samsul Shahab, Muhammad Luthfi Siti Maghfiroh Soetrisno Soetrisno Sulastri Sulastri Titin J. Ambarwati Victory Tyas Pambudi Swindiarto YAN ADITYA PRADANA Yongky Ujianto Yuda Dian Harja Zulfa Afiq Fikriya