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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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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.
A Bibliometric Analysis of Metaheuristic Research and Its Applications Hendy, Hendy; Irawan, Mohammad Isa; Mukhlash, Imam; Setumin, Samsul
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.2675

Abstract

Metaheuristic algorithms are generic optimization tools to solve complex problems with extensive search spaces. This algorithm minimizes the size of the search space by using effective search strategies. Research on metaheuristic algorithms continues to grow and is widely applied to solve big data problems. This study aims to provide an analysis of the performance of metaheuristic research and to map a description of the themes of the metaheuristic research method. Using bibliometric analysis, we examined the performance of scientific articles and described the available opportunities for metaheuristic research methods. This study presents the performance analysis and bibliometric review of metaheuristic research documents indexed in the Scopus database between the period of 2016-2021. The overall number of papers published at the global level was 3846. At global optimization, heuristic methods, scheduling, genetic algorithms, evolutionary algorithms, and benchmarking dominate metaheuristic research. Meanwhile, the discussion on adaptive neuro-fuzzy inference, forecasting, feature selection, biomimetics, exploration, and exploitation, are growing hot issues for research in this field. The current research reveals a unique overview of metaheuristic research at the global level from 2016-2021, and this could be valuable for conducting future research.
Customer Churn Prediction Using the RFM Approach and Extreme Gradient Boosting for Company Strategy Recommendation Irawan, Mohammad Isa; Putris , Nadhifa Afrinia Dwi; Muhammad, Noryanti binti
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.v10i2.4004

Abstract

Customers are vital assets in the growth and sustainability of business  organizations. However, customers may discontinue their engagement with a company and switch to competitors’ products or services for various reasons. This event referred to as customer churn. Losing customers significantly impacts a company's revenue, often resulting in financial decline. Churn events, which are subject to dynamic monthly changes, are further influenced by intense competition and rapid technological advancements. Analyzing customer characteristics is crucial to understanding customer behavior, with metrics such as recency, frequency, monetary (RFM) serving as key indicators of subscription and transaction patterns. The Extreme Gradient Boosting method is applied to address the challenge of classifying churn and non-churn customers. The prescriptive analytics process is carried out to identify the features most influential in prediction outcomes, enabling the formulation of strategic recommendations to mitigate churn problems. The integration of RFM analysis with the XGBoost method provides optimal results, particularly in the third segmentation, achieving an accuracy of = 0.98833, precession = 0.98768, recall = 0.98899, and f1-score = 0.98833. The prescriptive analytics process highlights three critical features, namely city factor, GMV generation, and total customer transaction generation. This findings demonstrate that the segmentation characteristics, data representation, and behavioral approach with RFM analysis have an effect on improving the performance of the model in churn prediction.
PERANCANGAN STARTUP PLATFORM MANAJEMEN PELAPORAN SEKOLAH KEPADA ORANG TUA/WALI SISWA DENGAN PENDEKATAN DESIGN THINKING Rohwana, Ulir; Irawan, Mohammad Isa
Airlangga Journal of Innovation Management Vol. 5 No. 2 (2024): Airlangga Journal of Innovation Management
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/ajim.v5i2.52742

Abstract

This research aimed to develop a startup for a school-to-parent reporting platform, inspired by the vast potential of Indonesia's education business market. By adopting a design thinking approach, this research developed suitable business models. The design thinking process involves five stages: empathize, define, ideate, prototype, and test. The empathize stage involved observations and interviews to understand user perspectives, namely school administrators and parents. In the define stage, user problems were formulated using Point of View (POV) techniques, followed by SWOT and competitor analyses to identify solutions and opportunities. Next, in the ideate stage, ideas were gathered through brainstorming sessions and structured into a Business Model Canvas. In the prototyping phase, UI/UX designs were visualized and business models were drafted. Finally, platform testing was conducted in a school to evaluate its suitability for user needs and obtain feedback before scaling up. The testing showed that the design thinking approach was well-received by users in designing the school reporting platform startup in terms of technological solutions and business models. This research contributes to designing a user-centric startup with viable business models, increasing market acceptance, and gaining substantial market share in Indonesia's education industry.
Mathematical Model and Simulation for the Mechanism of Glucose Uptake in the Cells Kusumastuti, Ari; Irawan, Mohammad Isa; Fahim, Kistosil
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.29292

Abstract

Understanding the mechanism of Glucose Transporter 4 (GLUT4) translocation to the cell membrane is essential for describing daily glucose uptake. A normal mechanism maintains glucose homeostasis and reduces the occurrence of Type 2 Diabetes Mellitus (T2DM) and its complications. Kinetic reactions are crucial for revealing the interactions involving proteins, enzymes, and complexes within the system. We propose a system of ordinary differential equations (ODEs) to elucidate the underlying mechanism under the assumption of non-conservative complexes . The insulin signaling pathway, which includes the GLUT4 mechanism, serves as the basis for reconstructing the necessary kinetic reactions. Investigating the behaviour of the model through numerical simulations and dynamics within parameters and initial conditions from relevant researchs .
Inovasi Model Bisnis dan Pengukuran Tingkat Kesiapan Teknologi dalam Mengadopsi Omnichannel (Studi Kasus Informa) Maulana, Muhammad Agung Adi; Irawan, Mohammad Isa
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i11.11758

Abstract

Perkembangan teknologi dan transformasi dalam transaksi barang dan jasa telah meningkatkan persaingan industri retail furniture terutama di antara perusahaan dengan jenis produk yang serupa. Meski PT Home Center Indonesia (Informa) telah mengalami peningkatan jumlah anggota (member) yang signifikan pada tahun 2023, hal ini belum diimbangi dengan pencapaian penjualan online yang optimal. Kontribusi penjualan online masih rendah meskipun ada peningkatan dalam jumlah pelanggan. Situasi ini menunjukkan perlunya strategi yang lebih efektif dalam memanfaatkan potensi digital. Penelitian ini menganalisis tingkat kesiapan teknologi di Informa dalam menerapkan omnichannel menggunakan Business Model Canvas (BMC) dan Technology Acceptance Model (TAM). Metode penelitian menggunakan SEM-PLS dengan data primer dari kuesioner kepada operator IT dan konsumen Informa. Hasil penelitian menunjukkan bahwa kesiapan teknologi di Informa merupakan faktor kunci dalam transformasi bisnis ini. Studi ini menyimpulkan bahwa investasi Informa dalam infrastruktur digital dan penerimaan teknologi sangat penting untuk meningkatkan kehadiran omnichannel serta menyarankan strategi digital lebih lanjut untuk memaksimalkan jangkauan pasar dan keterlibatan pelanggan.
Hybrid AHP Topsis Method for Selection and the Four Disciplines of Execution (4DX) for Monitoring the Animal Disturbance Reduction Program (Case Study: PLN UP3 East Bali) Mahardika, Kadek Eri; Irawan, Mohammad Isa
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i4.58615

Abstract

PLN (Persero) East Bali Customer Service Implementation Unit is responsible for supplying electricity in Gianyar, Klungkung, Bangli, and Karangasem regencies. The high number of feeder disturbances, especially those caused by animals, significantly impacts the reliability and continuity of electricity supply, accounting for 115 incidents (37.33%) out of a total of 308 trips in the first semester of 2023. This study aims to determine the priority program for reducing animal-related disturbances using the hybrid Analytical Hierarchy Process (AHP) - Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and to monitor its implementation using The Four Disciplines of Execution (4DX). The Delphi method established four evaluation criteria: ease of implementation, material cost, effectiveness, and power outage (KWH lost). Four alternatives were evaluated: installing palm fiber barriers, animal shields, heat shrink tubes, and tree pruning. The results indicated that tree pruning was the priority program. Implementation was monitored during the first semester of 2024 using the 4DX method, which showed a significant reduction in animal-related disturbances, decreasing from 115 incidents in the first semester of 2023 to 55 incidents in the first semester of 2024, representing a 52.21% year-over-year reduction. The implications of this study demonstrate that the hybrid AHP-TOPSIS and 4DX approaches effectively determine priorities and monitor program execution, significantly improving electricity supply reliability.
On the Approximation Capabilities of Deep Neural Networks for Multivariate Time Series Modeling Jamhuri, Mohammad; Irawan, Mohammad Isa; Kusumastuti, Ari; Mondal, Kartick Chandra; Juhari, Juhari
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.32760

Abstract

Multivariate time series forecasting plays a crucial role in various domains, including finance, where accurate stock price prediction supports strategic decision-making. Traditional methods such as Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and Vector Autoregression (VAR) often fall short when dealing with complex, non-linear data—particularly those exhibiting long-term temporal dependencies. This study evaluates deep learning approaches, namely Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), using daily AAPL stock price data from January 2020 to November 2024. The results show that the MLP model with a 10-day time window achieves the best accuracy, yielding lower values in Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) compared to CNN, LSTM, and VAR. The findings suggest that MLP is particularly effective in capturing complex patterns in multivariate time series forecasting.
Exploring the Evolution of Electric Vehicle Charging Infrastructure: A Bibliometric Perspective on Public Electric Vehicle Charging Station Location Planning Notopramono, Hanna; Irawan, Mohammad Isa
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2375

Abstract

The rapid expansion of electric vehicles (EVs) is a crucial factor in achieving low-carbon mobility and promoting sustainable regional development. The attainment of this objective depends on technological advancement, institutional readiness, spatial equity, and governance capabilities. The establishment of Stasiun Pengisian Kendaraan Listrik Umum (SPKLU) in Indonesia must be understood within a framework that connects infrastructure planning to social, economic, and political factors.   This research analyzes the evolution of international scientific dialogue regarding EV charging infrastructure through a bibliometric lens, focusing on publications from 2010 to 2025.  This study utilizes Scopus data and analytical tools, including VOSviewer and Publish or Perish, to identify leading authors, key journals, and emerging subject trends.   The results suggest that future SPKLU planning in Indonesia requires an integrated framework that aligns technological precision with public governance principles.   This approach should account for decentralization dynamics, regional disparities, and institutional capacity, ensuring that improvements to EV infrastructure contribute to carbon-reduction goals while fostering equitable and sustainable regional development.
Classification of Poverty Levels Using k-Nearest Neighbor And Learning Vector Quantization Methods Santoso Santoso; Mohammad Isa Irawan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 2 No. 1 (2016)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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

Abstract

Poverty is the inability of individuals to fulfill the minimum basic needs for a decent life. The problem of poverty is one of the fundamental problems that become the central attention of the local government. One of the government efforts to overcome poverty is using the alleviation programs. Government often faces some difficulties to sort out of the poverty levels in the society. Therefore it is necessary to conduct a study that helps the government to identify the poverty level so that the aid did not miss the targets. In order to tackle this problem, this paper leverages two classification methods: k-nearest neighbor (k-NN) and learning vector quantization (LVQ). The purpose of this study is to compare the accuracy of the value of both methods for classifying poverty levels. The data attributes that are used to characterize poverty among others include: aspects of housing, health, education, economics and income. From the testing results using both methods, the accuracy of k-NN is 93.52%, and the accuracy of LVQ is 75.93%. It can be concluded that the classification of poverty levels using k-NN method gives better performance than using LVQ method.
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