Article Per Year (5 Year)
p-Index From 2021 - 2026
6.051
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Sarjana Teknik Informatika Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Fountain of Informatics Journal SemanTIK : Teknik Informasi JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal ULTIMATICS KOMPUTA : Jurnal Ilmiah Komputer dan Informatika METIK JURNAL Journal of Electronics, Electromedical Engineering, and Medical Informatics Sains, Aplikasi, Komputasi dan Teknologi Informasi Scientific Journal of Informatics Journal of Innovation Information Technology and Application (JINITA) International Journal of Engineering, Science and Information Technology Jurnal Manajemen Informatika Jayakarta Djtechno: Jurnal Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Algoritme Jurnal Mahasiswa Teknik Informatika Journal of Advanced Sciences and Mathematics Education Adopsi Teknologi dan Sistem Informasi Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi Kreatif Teknologi dan SIstem Informasi
Irsyad, Akhmad
Universitas Mulawarman
Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 49 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 49 Documents
Search

 1   2   3   4   5 
Development of a rule-based adaptive four-tier diagnostic quiz system for identifying misconceptions in geometrical optics Muhammad Luqman; Akhmad Irsyad; Muhammad Ibadurrahman Arrasyid Supriyanto; Riskan Qadar
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 1 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i1.1056

Abstract

Background: Misconceptions in geometric optics remain persistent, including among prospective physics teachers, and are often difficult to detect using conventional assessments that focus only on final answers. Although four-tier diagnostic tests provide deeper conceptual information, their implementation is generally static and may reduce assessment efficiency. Aims: This study aimed to develop and evaluate a rule-based adaptive four-tier diagnostic quiz system capable of identifying misconceptions more efficiently while maintaining stable diagnostic classifications. Method: The study employed a research and development approach using the ADDIE model. A web-based adaptive diagnostic was developed by integrating a four-tier scoring scheme with transparent rule-based adaptive decisions. The platform was tested on 36 prospective physics teacher students. Data were analyzed descriptively to examine adaptive test length, diagnostic pathways, and classification stability. Results: The adaptive system reduced the average test length from 15 static items to 11.5 items, representing an efficiency gain of 23.3%. A total of 77.8% of participants reached diagnostic stability before the maximum item limit, and the classification consistency rate reached 83.3%. The system also revealed variations in misconception patterns across topics, with concave mirror concepts showing the highest proportion of strong misconceptions. Conclusion: The rule-based adaptive four-tier system improved diagnostic efficiency while maintaining stable classification outcomes. The transparent adaptive mechanism makes the system suitable for formative diagnostic assessment in physics education, although further studies with larger samples are recommended.
Penerapan AI Agent Berbasis n8n untuk Otomatisasi  Operasional Pertanian Muhammad Rofiif Taqiyyuddin Nabiil; Hario Jati Setyadi; Akhmad Irsyad
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/2z461g10

Abstract

This study aims to develop an artificial intelligence–based agricultural automation system by integrating an AI Agent with the n8n workflow automation platform. The development addresses common challenges in agricultural operations that remain manual, such as recording daily activities, scheduling reminders for fertilization and harvesting, and limited access to real-time weather information. The system was developed using the Waterfall model, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The proposed system integrates several components, including WhatsApp as the main user interface, n8n as the workflow controller, WAHA as the communication bridge, Google Sheets as the digital database, and an AI Agent for user query processing and recommendation generation. The Black Box Testing results show that all core functions operate as expected, covering activity recording, automatic reminders, recommendation delivery, and weekly report generation. The User Acceptance Test achieved a satisfaction score of 85 percent, categorized as very good, indicating that the system is user-friendly, responsive, and beneficial for improving agricultural efficiency. This research contributes to the advancement of AI-based information systems in the agricultural sector and demonstrates that integrating an AI Agent with n8n is an effective solution for supporting agricultural digitalization and implementing smart farming at the village level.
PERBANDINGAN METODE TRANSFER LEARNING DALAM KLASIFIKASI PENYAKIT DAUN PADI Aldi Daffa Arisyi; Muhammad Aidil Saputra; Muhammad Rafif Hanif; Anindita Septiarini; Akhmad Irsyad
Jurnal Ilmiah Informatika Vol. 11 No. 1 (2026): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/.v11i1.9611

Abstract

This study compares four transfer learning-based CNN models, namely VGG19, ResNet152, MobileNetV2, and DenseNet121, for the classification of 10 classes of rice leaf diseases. Evaluation results on the test dataset show that ResNet152 achieves the best performance with an accuracy of 0.9553, precision of 0.9589, recall of 0.9553, and F1-score of 0.9558, followed by DenseNet121 (accuracy 0.9433), MobileNetV2 (0.9353), and VGG19 (0.9247). ResNet152 excels in recognizing complex features through its skip connection mechanism, while DenseNet121 is more efficient with the lowest validation loss. MobileNetV2 is the lightest and fastest model, making it suitable for resource-limited devices. Based on the confusion matrix analysis, all models are able to classify the neck blast class perfectly; however, misclassifications still occur among visually similar classes such as brown spot, narrow brown spot, and leaf blast. Overall, transfer learning is proven effective for rice leaf disease classification, with ResNet152 and DenseNet121 being the most recommended models.
Deep Learning Methods for Pneumonia Detection Using ConvNeXt Architecture Akhmad Irsyad; Muhammad Bambang Firdaus; Gubtha Mahendra Putra; Putut Pamilih Widagdo; Hario Jati Setiady; Muhammad Fawaz Saputra; Muhammad Abdillah Rahmat
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1797

Abstract

Pneumonia remains a major respiratory infection with high mortality rates, especially in regions with limited access to medical specialists. Early and accurate diagnosis plays a critical role in reducing fatal outcomes and improving patient management. Chest X ray imaging is widely used as a primary diagnostic modality, yet interpretation relies heavily on experienced radiologists, whose availability is often insufficient to meet clinical demand. This condition motivates the development of automated pneumonia detection systems based on artificial intelligence. This study investigates the application of deep learning for pneumonia classification using chest X ray images, with a focus on the ConvNeXt architecture. ConvNeXt represents a modern neural network design that integrates structural advantages from Vision Transformers with the efficiency of traditional Convolutional Neural Networks, enabling strong feature extraction while maintaining computational efficiency. The research evaluates multiple ConvNeXt variants, including Tiny, Small, Base, and Large, to analyze the relationship between model complexity and classification performance. ResNet50 is employed as a baseline model to provide a fair comparative assessment against a widely used convolutional architecture. Model evaluation uses accuracy, precision, recall, and F measure to ensure balanced measurement across different classification outcomes and class distributions. Experimental results indicate that ConvNeXt Tiny achieves the highest overall performance, reaching an accuracy of 97.69 percent while using a relatively low number of parameters. This outcome highlights the efficiency of lightweight architectures for medical image analysis tasks. The findings demonstrate that ConvNeXt Tiny delivers strong discriminative capability with reduced computational requirements, making it suitable for deployment in resource constrained clinical environments. This study contributes evidence supporting the effectiveness of modern deep learning architectures for automated pneumonia detection and provides insight into model selection for practical medical imaging applications.
Application Of Double Exponential Smoothing Holt’s Method For Poverty Line Forecasting (Study Case: East Kalimantan Province) Ariantika Putri Maharani; Akhmad Irsyad; Muhammad Rivani Ibrahim
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4349

Abstract

Poverty is a multidimensional problem that remains a challenge in Indonesia. The poverty line is used as an indicator to determine whether someone is poor based on the average expenditure per capita per month. In East Kalimantan Province, the poverty line has increased from Rp796,193 in 2023 to Rp853,997 in 2024. This study aims to forecast the poverty line for the next ten periods using Holt's Double Exponential Smoothing method. This method was chosen because the historical data shows an increasing trend from 2011 to 2024. The forecasting results show that this method is effective with a Mean Absolute Percentage Error (MAPE) value of 4.56%, and optimal parameters α = 0.98 and β = 0.01. The findings are expected to serve as a reference in decision-making regarding poverty alleviation policies in the future.
ANALISIS SENTIMEN PADA ULASAN PENGGUNA APLIKASI JAMSOSTEK MOBILE DENGAN MENGGUNAKAN NAÏVE BAYES DAN LOGISTIC REGRETION O’neal Efrata Madao; Akhmad Irsyad; Muhammad Rivani Ibrahim
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.6775

Abstract

Jamsostek Mobile (JMO) merupakan inovasi digital dari BPJS Ketenagakerjaan yang diluncurkan pada September 2021 untuk mempermudah pelayanan kepada pengguna. Meskipun dirancang untuk meningkatkan akses dan kualitas layanan, masih ditemukan ulasan negatif dari pengguna terkait fitur dan kinerja aplikasi. Ulasan tersebut menjadi sumber informasi penting untuk mengevaluasi persepsi pengguna. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi JMO menggunakan algoritma Naïve Bayes dan Logistic Regression. Data diambil dari Google Playstore, terdiri dari 1.500 ulasan berbahasa Indonesia yang telah diberi label secara manual sebagai positif atau negatif. Proses analisis dilakukan dengan Python melalui platform Google Colab. Evaluasi performa model menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa kedua algoritma memiliki akurasi yang sama, yaitu 92,67%. Naïve Bayes unggul dalam presisi sebesar 97,58%, sedangkan Logistic Regression lebih baik dalam recall (93,30%) dan F1-score (93,82%). Meskipun keduanya menunjukkan kinerja yang baik, Logistic Regression dinilai lebih seimbang dalam mengklasifikasikan data. Pemilihan algoritma terbaik tetap disesuaikan dengan prioritas analisis, apakah mengutamakan ketepatan klasifikasi atau kelengkapan dalam mendeteksi sentimen.
Pelatihan dan Bimtek Media Digital untuk Siswa SMP Wahidiyah Tanah Merah Samarinda Amalia Kartika Sari; Sandrina Aulia; Nazwa Tri Ananda; Dinnuhoni Trahutomo; Uswatun Khasanah; Angelia Cristin; Aristy Avrianti; Najwa Caesa Putri Ramadhania Suharizman Poerwo; Namira Risjayanti; Alya Rizqi Ramadhani; Amin Padmo Azam Masa; Akhmad Irsyad; Muhammad Zulfariansyah; Muhammad Rivani Ibrahim
Pengabdian kepada Masyarakat Bidang Teknologi dan Sistem Informasi (PETISI) Vol. 4 No. 1 (2026): Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/petisi.v4i1.2261

Abstract

Kegiatan pengabdian masyarakat yang dilaksanakan oleh INFORSA di SMP Wahidiyah bertujuan untuk meningkatkan keterampilan digital siswa, khususnya anggota OSIS, dalam menciptakan konten visual yang menarik menggunakan aplikasi Canva. Permasalahan utama yang dihadapi adalah kurangnya kemampuan siswa dalam membuat desain grafis yang dapat mendukung promosi kegiatan sekolah dan membangun citra positif di media sosial. Metode yang digunakan dalam pelatihan ini meliputi penjelasan teori, diskusi, dan praktik langsung selama dua hari. Pada hari pertama, peserta diperkenalkan pada dasar-dasar desain grafis dan fitur-fitur utama dalam Canva, diikuti dengan sesi praktik membuat id card dan instastory. Hari kedua difokuskan pada pembuatan desain yang lebih kompleks seperti banner dan poster. Hasil evaluasi menunjukkan peningkatan signifikan dalam pemahaman peserta mengenai penggunaan Canva. Peserta mampu membuat desain yang menarik dan relevan, serta aktif terlibat dalam diskusi dan praktik. Pemberian pre-test dan post-test mengindikasikan perkembangan keterampilan yang jelas. Dampak dari pelatihan ini tidak hanya meningkatkan kreativitas siswa, tetapi juga memperkuat citra sekolah di media sosial melalui konten promosi yang lebih menarik. Kesimpulannya, pelatihan ini berhasil mencapai tujuannya dengan pendekatan interaktif dan pendampingan mentor yang efektif. Rekomendasi untuk pengembangan lebih lanjut termasuk pengenalan fitur Canva lanjutan dan penyediaan fasilitas pendukung agar lebih banyak siswa dapat berpartisipasi. Program serupa diharapkan dapat diterapkan di sekolah lain dengan penyesuaian sesuai kebutuhan masing-masing.
Analisis Buffer Dalam Sistem Informasi Geografis Untuk Optimalisasi Jangkauan Fasilitas Puskesmas Makassar Biko Harianto; Gilang Firmansyah; Akhmad Irsyad; Amin Padmo Azam Masa; Muhammad Zulfariansyah; Muhammad Rivani Ibrahim
Kreatif Teknologi dan Sistem Informasi (KRETISI) Vol. 4 No. 1 (2026): Kreatif Teknologi dan Sistem Informasi (KRETISI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/kretisi.v4i1.360

Abstract

Penelitian ini bertujuan untuk menganalisis persebaran dan jangkauan pelayanan fasilitas puskesmas di Kabupaten Makassar dengan memanfaatkan metode buffer dan Multiple Ring Buffer dalam Sistem Informasi Geografis (SIG). Latar belakang penelitian ini didasarkan pada pentingnya pemerataan fasilitas kesehatan tingkat pertama agar masyarakat di wilayah permukiman dapat memperoleh akses pelayanan kesehatan secara lebih mudah dan merata. Dalam penelitian ini digunakan data sekunder yang meliputi data lokasi fasilitas puskesmas, batas administrasi wilayah, serta data kawasan permukiman. Seluruh data kemudian diolah menggunakan perangkat lunak QGIS versi 3.26.3 melalui tahapan penyesuaian sistem koordinat, pembuatan buffer dengan radius tertentu, serta analisis Multiple Ring Buffer untuk melihat variasi jangkauan pelayanan dalam beberapa lapisan radius.Hasil analisis menunjukkan bahwa fasilitas puskesmas di Kabupaten Makassar secara umum telah tersebar pada berbagai wilayah permukiman. Peta buffer memperlihatkan bahwa sebagian besar kawasan permukiman telah berada dalam cakupan radius pelayanan fasilitas kesehatan. Sementara itu, hasil Multiple Ring Buffer menunjukkan adanya beberapa wilayah yang memiliki tumpang tindih jangkauan pelayanan antar fasilitas kesehatan, yang menandakan bahwa sejumlah puskesmas berada pada lokasi yang relatif berdekatan. Di sisi lain, analisis ini juga memberikan gambaran spasial yang lebih jelas mengenai pola persebaran fasilitas kesehatan dari berbagai arah wilayah, baik utara selatan maupun timur barat.Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode buffer dan Multiple Ring Buffer efektif digunakan untuk mengevaluasi persebaran serta keterjangkauan pelayanan puskesmas di Kabupaten Makassar. Penelitian ini diharapkan dapat memberikan informasi spasial yang bermanfaat sebagai bahan pertimbangan dalam perencanaan pemerataan fasilitas kesehatan, khususnya dalam upaya meningkatkan akses layanan kesehatan masyarakat secara lebih optimal  
Analisis Sentimen Terhadap Ulasan Film Menggunakan Algoritma Naive Bayes dan Random Forest Nur Shafa Azizah; Nurul Indriani; Dera Kayla Khairani; Akhmad Irsyad; Islamiyah Islamiyah; Muhammad Rivani Ibrahim; Septya Maharani
Kreatif Teknologi dan Sistem Informasi (KRETISI) Vol. 4 No. 1 (2026): Kreatif Teknologi dan Sistem Informasi (KRETISI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/kretisi.v4i1.1346

Abstract

Analisis sentimen pada ulasan film penting dilakukan untuk membantu memahami respons audiens terhadap sebuah film. Penelitian ini bertujuan untuk mengklasifikasikan ulasan film ke dalam kategori sentimen positif dan negatif menggunakan algoritma Naive Bayes dan Random Forest. Data yang digunakan berjumlah 10.000 ulasan yang diperoleh dari beberapa platform ulasan film dan telah diberi label sentimen. Tahapan penelitian meliputi pengumpulan data, pra-pemrosesan teks, pembobotan TF-IDF, serta seleksi fitur menggunakan Information Gain sebelum dilakukan proses klasifikasi. Hasil pengujian menunjukkan bahwa Naive Bayes menghasilkan kinerja yang lebih baik dengan akurasi 88%, precision 85%, recall 86%, dan F1 score 85,5%, sedangkan Random Forest memperoleh akurasi 85%, precision 82%, recall 84%, dan F1 score 83%. Temuan ini menunjukkan bahwa pemilihan algoritma yang tepat sangat berpengaruh terhadap kualitas hasil klasifikasi sentimen pada data teks. Berdasarkan hasil tersebut, Naive Bayes lebih efektif digunakan untuk analisis sentimen ulasan film pada penelitian ini
Co-Authors -, Irvan Apdila Agnestia Ahmad Choirun Najib Aini, Dinda Nur Al Hidayat, Muhammad Restu Al'Aqsa, Muhammad Ramadhan Aldi Daffa Arisyi Alivia Amin, Dhestyara Alviana, Kurnia Alya Rizqi Ramadhani Amal, Fakhmul Amalia Kartika Sari Amin Padmo Azam Masa Amin Padmo Azam Masa Amin Padmo Azam, Masa Anam, M Khairul Angela, Jeroline Betsy Angelia Cristin Anindita Septiarini, Anindita Apdila, Irvan Aprilia, Trisna Arba, Muhammad Hendra Ari Pradhana, Alvin Ariantika Putri Maharani Arif, Afdinal Aristy Avrianti Astuti, Eka Desi Puji Avivah, Nur Balan, Nicola Fernando Bernikusti Mentik, Sulpisius Biko Harianto Caesar Ananta, Firzian Dengen, Helen Amalia Dera Kayla Khairani Dinda Nur Aini Dinnuhoni Trahutomo Enjellita, Rissa Fariz Aisyar Dafin, Ahmad Fathul, Maulidhan Firdaus, Muhammad Bambang Geralda, Raihan Daiva Ghiffari Assamar Qandi Gilang Firmansyah Ginting, Stephanie Elfriede Gubtha Mahendra Putra Handoko, Heldi Harianto, Biko Hario Jati Setiady Hario, Jati Setiyadi Harsyal Kila, Hiskya Ibrahim, Muhammad Rivani Ikram, Adli Dzil Imelda Putri Irvan Apdila Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah, Islamiyah Isnawaty Isnawaty Kamila, Vina Zahrotun Karinda, Siti Solikah Yosi Kartika Sari, Amalia Labib Jundillah, Muhammad Listiana Dewi Milasari Lumbantobing, Lisweni Masa, Amin Padmo Azam Mentik, Sulpisius Bernikusti Mohammad Ibnu, Praditya Muhamad Ali Muhammad Abdillah Rahmat Muhammad Aidil Saputra Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Dwi Refansyah Muhammad Fawaz Saputra Muhammad Fawaz, Saputra Muhammad Fa’iz Muhammad Hisyam Nugroho Muhammad Ibadurrahman Arrasyid Supriyanto Muhammad Ibadurrahman Arrasyid, Supriyanto Muhammad Indra Buana Muhammad Irvan, Al-Wari Muhammad Labib Jundillah Muhammad Luqman Muhammad Rafif Hanif Muhammad Rivani Ibrahim Muhammad Rizky Setiawan Muhammad Rofiif Taqiyyuddin Nabiil Muhammad Zulfariansyah Muhammad, Zulfariansyah Najla Nayla Putri Najwa Caesa Putri Ramadhania Suharizman Poerwo Namira Risjayanti Nazwa Tri Ananda Nazwa Tri Ananda Nifansa, Albygael Rifal Nur Aini Rakhmawati Nur Shafa Azizah Nuritha, Ifrina Nurul Indriani Nurwahyu, Ferryza O’neal Efrata Madao Pardosi, Josia Pardosi, Josia Giribosar Praditya, Mohammad Ibnu Prafanto, Anton Prasetyo, Afrila Zahra Putra, Gubtha Mahendra Putri, Juventia Adelia Putut Pamilih Widagdo Putut Pamilih Widagdo Putut Pamilih Widagdo, Putut Pamilih Rapiq, Rayhan Abdilah Refansyah, Muhammad Dwi Rinto Rinto Riskan Qadar Riswanti, Nita Riyandi, Selamat Rizal Adi Saputra Rizawanti, Riftika Rizky, Avinka Rosmasari Rosmasari, Rosmasari Ruswantomo, Ruswantomo Sandrina Aulia Sandrina Aulia Saputra, Muhammad Fawaz Saragi, Bertha Joy Rodo Sari, Amalia Kartika Sativa, Alisya Nisrina Septya Maharani, Septya Septya, Maharani Setyadi, Hario Jati Sholawati, Anisa Sidabutar, Erni Veronica Sogen, Valentina Febrizah Peni Stephanie Elfriede Ginting Surachkaryadi, Angelina Syaputra, Karlen Tarigan, Phascalis Chevin Taruk, Medi Tejawati, Andi Tobing, Christina Febriyanti Uswatun Khasanah Vina Zahrotun Kamila Vina Zahrotun Kamila Waksito, Alan Zulfikar Wardhana, Reza Zamani, Harrys Qomarul