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All Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika JISTech (Journal of Islamic Science and Technology) JURNAL TEKNOLOGI DAN OPEN SOURCE JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Technologia: Jurnal Ilmiah Jurnal Ilmu Komputer dan Bisnis Health Information : Jurnal Penelitian Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) INFOKUM Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) El-Qist : Journal of Islamic Economics and Business (JIEB) Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Walisongo Journal of Information Technology Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Instal : Jurnal Komputer Jurnal Teknisi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Sains dan Teknologi JOMLAI: Journal of Machine Learning and Artificial Intelligence Data Sciences Indonesia (DSI) Internet of Things and Artificial Intelligence Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Ilmu Komputer dan Sistem Informasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Nasional Komputasi dan Teknologi Informasi
Mhd Furqan
Universitas Islam Negeri Sumatera Utara
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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PENERAPAN ALGORITMA BRUTE FORCE PADA APLIKASI PENERJEMAH BAHASA INDONESIA - BAHASA MANDAILING BERBASIS MOBILE Dalimunthe, Ayu Sahriani; Furqan, Mhd.; Hasugian, Abdul Halim
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3128

Abstract

Abstract: Language is a means to communicate. Knowledge of language is very important because in a conversation or conversation requires a language. In Indonesia, there are many various regional languages, including the Mandailing language. Mandailing language is one of the regional languages in South Tapanuli, North Sumatra. The use of regional languages has experienced a lot of decline in use in the language of everyday communication. Preserving regional languages is very necessary in the midst of increasingly rapid technological developments. Dictionary media can be a solution to introduce various regional languages in Indonesia. In this study, the design and development of the Indonesian-Mandailing Translator Application was carried out with the application of the Mobile-based Brute Force Algorithm. This application was built using Android Studio software using the Java programming language and XML. The database used to store data for the Batak Mandailing-Indonesian translator is a SQLite database so that the application can be used offline. Applications that are designed in a user friendly manner can perform the search function for Indonesian-Mandailing and Mandailing-Indonesian Vocabulary, making it easier for users to operate them. Keywords: Mandailing language, dictionary, Brute Force Algorithm, Android                  Application Abstrak: Bahasa merupakan sarana untuk berkomunikasi. Pengetahuan bahasa sangatlah penting karena dalam sebuah percakapan atau pembicaraan memerlukan sebuah bahasa. Di Indonesia ada banyak beragam bahasa daerah diantaranya adalah Bahasa Mandailing. Bahasa Mandailing merupakan salah satu bahasa daerah bagian Tapanuli Selatan, Sumatera Utara. Penggunaan bahasa daerah telah mengalami banyak penurunan penggunaan dalam bahasa komunikasi sehari-hari. Melestarikan bahasa daerah sangat perlu ditengah perkembangan teknologi yang semakin pesat. Media kamus dapat menjadi solusi untuk mengenalkan beragam bahasa daerah yang ada di Indonesia. Dalam penelitian ini dilakukan perancangan dan membangun Aplikasi Penerjemah Bahasa Indonesia-Mandailing dengan penerapan Algoritma Brute Force berbasis mobile. Aplikasi ini dibangun menggunakan perangkat lunak Android Studio menggunakan bahasa pemrograman Java dan XML. Database yang digunakan untuk menyimpan data penerjemah bahasa Batak Mandailing-Indonesia adalah SQLite database sehingga aplikasi dapat digunakan secara offline. Aplikasi yang dirancang secara user friendly dapat melakukan fungsi pencarian Kosa kata Bahasa Indonesia - Mandailing dan Mandailing - Indonesia sehingga memudahkan para pengguna dalam mengoperasikannya. Kata kunci: Bahasa Mandailing, Kamus, Aplikasi Android 
Perancangan Sistem Kontrol Pendingin Udara Otomatis Berbasis Suhu Ruangan Menggunakan Arduino Mhd Galih Khairi; Muhammad Irfan Gurning; Mhd. Furqan
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 1 (2024): Januari 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i1.96

Abstract

Air conditioning has become essential in the modern era, with nearly everyone worldwide owning an air conditioner in their homes. Its presence is crucial given the increasingly hot weather conditions due to global warming. However, we often forget to turn off the air conditioner, resulting in energy waste and potential damage to the device. Therefore, this writing aims to address these issues through the development of an automatic system using Arduino Nano, programmed in the C language, to regulate the operation of the air conditioner based on room temperature. This way, users do not need to bother manually turning the air conditioner on or off using a remote control or buttons.
SEGMENTASI KEAKTIFAN MAHASISWA UNIVERSITAS ISLAM NEGERI SUMATERA UTARA DALAM KEGIATAN KAMPUS MENGGUNAKAN K-MEANS CLUSTERING Nazwa Aliya Muthmainnah Hasibuan; Dodyk Fahlome; Putri Salsa Nabila; Said Arrahman; Mhd. Furqan
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 7, No 1 (2026): Juni 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v7i1.8981

Abstract

Kegiatan kemahasiswaan berperan penting dalam pengembangan kompetensi mahasiswa, namun tingkat keaktifan pada berbagai aktivitas seperti organisasi, seminar, kepanitiaan, lomba, dan pengembangan diri menunjukkan variasi yang signifikan sehingga diperlukan pendekatan berbasis data untuk mengidentifikasi pola keterlibatan secara lebih objektif. Penelitian ini menerapkan K-Means Clustering pada data 100 responden mahasiswa UINSU yang diperoleh melalui Google Forms, melalui tahapan preprocessing, konversi skala ordinal, serta analisis menggunakan Python (Google Colab). Jumlah cluster optimal ditentukan menggunakan metode Elbow berbasis Within Cluster Sum of Squares (WCSS). Hasil penelitian menunjukkan terbentuk tiga cluster (k=3), yaitu C0 (30 mahasiswa) dengan karakteristik aktif organisasi dan kepanitiaan yang ditandai skor panitia 2.43 dan organisasi 1.63, C1 (38 mahasiswa) sebagai kelompok sangat aktif/multitalenta dengan dominasi pengembangan diri 2.03 dan lomba 1.76, serta C2 (32 mahasiswa) sebagai kelompok kurang aktif dengan skor terendah pada organisasi 0.31 dan lomba 0.47. Visualisasi PCA memperkuat pemisahan cluster yang terbentuk, sehingga menunjukkan bahwa K-Means efektif dalam mengungkap heterogenitas tingkat keaktifan mahasiswa dan dapat digunakan sebagai dasar pengambilan keputusan berbasis data dalam pengelolaan program kemahasiswaan.Kata Kunci— Kegiatan Kampus, Keaktifan Mahasiswa, Klasterisasi; K-Means, Segmentasi ABSTRACT Student activities play a crucial role in developing students’ competencies; however, participation levels in various activities—such as student organizations, seminars, event committees, competitions, and personal development—show significant variation, necessitating a data-driven approach to identify patterns of engagement more objectively. This study applied K-Means Clustering to data from 100 UINSU student respondents collected via Google Forms, through stages of preprocessing, ordinal scale conversion, and analysis using Python (Google Colab). The optimal number of clusters was determined using the Elbow method based on the Within Cluster Sum of Squares (WCSS). The results indicate the formation of three clusters (k=3): C0 (30 students) characterized by active involvement in organizations and committees, marked by a committee score of 2.43 and an organizational score of 1.63; C1 (38 students) as a highly active/multitalented group dominated by personal development (2.03) and competitions (1.76), and C2 (32 students) as a less active group with the lowest scores in organizational activities (0.31) and competitions (0.47). PCA visualization reinforces the separation of the formed clusters, indicating that K-Means is effective in revealing the heterogeneity of student activity levels and can serve as a basis for data-driven decision-making in the management of student programs. Keywords— Campus Activities, Clustering, K-Means, Segmentation, Student Activity 
PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK SEGMENTASI PENGGUNA DISCORD BERDASARKAN POLA PENGGUNAAN DAN TINGKAT KEPUASAN Dea Alya; Tiara Bela Harahap; Salsabila Mahfuza; Naina Nazwa Hasibuan; Mhd. Furqan
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 7, No 1 (2026): Juni 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v7i1.9019

Abstract

Abstrak—Discord merupakan platform komunikasi digital yang digunakan untuk berbagai kebutuhan seperti komunitas, hiburan, pembelajaran, dan komunikasi daring. Perbedaan pola penggunaan Discord menyebabkan munculnya karakteristik pengguna dan tingkat kepuasan yang berbeda sehingga diperlukan proses segmentasi pengguna. Penelitian ini bertujuan untuk melakukan segmentasi pengguna Discord menggunakan metode K-Means Clustering berdasarkan pola penggunaan dan tingkat kepuasan pengguna. Dataset penelitian diperoleh melalui penyebaran kuesioner daring kepada 200 responden. Proses penelitian meliputi preprocessing data, pengujian reliabilitas menggunakan Cronbach Alpha, transformasi data, normalisasi menggunakan StandardScaler, penentuan jumlah cluster menggunakan Elbow Method, serta evaluasi model menggunakan Silhouette Score. Seluruh proses pengolahan data dilakukan menggunakan Google Colab berbasis Python. Hasil pengujian reliabilitas memperoleh nilai Cronbach Alpha sebesar 0,861 yang menunjukkan bahwa data penelitian memiliki tingkat konsistensi yang baik. Hasil penelitian menunjukkan bahwa jumlah cluster optimal diperoleh pada K=2 dengan nilai Silhouette Score sebesar 0,33. Hasil clustering berhasil membagi pengguna Discord ke dalam dua kelompok utama, yaitu kelompok pengguna aktif dengan frekuensi penggunaan, interaksi sosial, dan tingkat kepuasan yang tinggi serta kelompok pengguna moderat dengan frekuensi penggunaan dan tingkat kepuasan yang relatif lebih rendah. Visualisasi menggunakan Principal Component Analysis (PCA) menunjukkan persebaran cluster yang cukup baik.Kata Kunci— Discord, K-Means Clustering, Segmentasi Pengguna, Silhouette ScoreAbstract—Discord is a digital communication platform used for various purposes such as community activities, entertainment, learning, and online communication. Differences in Discord usage patterns lead to varying user characteristics and satisfaction levels, making user segmentation necessary. This study aims to segment Discord users using the K-Means Clustering method based on usage patterns and user satisfaction levels. The research dataset was obtained through an online questionnaire distributed to 200 Discord users. The research process included data preprocessing, reliability testing using Cronbach Alpha, data transformation, normalization using StandardScaler, determining the optimal number of clusters using the Elbow Method, and model evaluation using the Silhouette Score. All data processing was conducted using Python-based Google Colab. The reliability test obtained a Cronbach Alpha value of 0.861, indicating that the research data had good consistency. The results showed that the optimal number of clusters was obtained at K=2 with a Silhouette Score of 0.33. The clustering process successfully divided Discord users into two main groups, namely active users with high usage frequency, social interaction, and satisfaction levels, and moderate users with relatively lower usage frequency and satisfaction levels. Visualization using Principal Component Analysis (PCA) showed a fairly good distribution of the clusters.Keywords— Discord, K-Means Clustering, User Segmentation, Silhouette Score
Aplikasi Mobile Media Pembelajaran Dasar Algoritma dan Pemrograman Berbasis Android Yusuf Ramadhan Nasution; Mhd Furqan
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 1, No 1 (2020): Juni 2020
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v1i1.791

Abstract

This research is a type of development research. The product development model adopts a software development model consisting of (1) Analysis of software requirements, (2) design, (3) writing code and (4) testing. Data collection techniques are done by observation, interviews and questionnaires. The testing phase is carried out with product validation by experts, testing on the first user (lecturer) and testing on the end user (student).Keywords : Learning Media, Mobile Applications, Algorithms and Programming.
KLASIFIKASI PENYAKIT PADA DAUN CABAI MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX DAN K-NEAREST NEIGHBOR Miftahul Rizky Pulungan; Mhd Furqan; Mhd Ikhsan Rifki
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 5, No 2 (2024): Desember 2024
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v5i2.5386

Abstract

Penyakit tanaman cabai dapat menyebabkan penurunan produksi yang signifikan, sehingga membuat keberlanjutan pertanian dan pangan. Penelitian ini mengembangkan sistem untuk mengkategorikan daun cabai menggunakan Gray Level Co-occurrence Matrix (GLCM) untuk ekstraksi tekstur dan K-Nearest Neighbors (KNN) untuk klasifikasi. Data citra daun cabai yang digunakan meliputi jenis penyakit virus mosaik cabai, layu fusarium, virus kuning, dan bercak daun. Proses tersebut meliputi pemilihan citra, ekstraksi fitur menggunakan GLCM, dan klasifikasi menggunakan KNN. Hasil penelitian menunjukkan bahwa rasio tersebut dapat mencapai hingga 90%, tergantung pada parameter K. Temuan ini penting bagi dunia pertanian, karena dapat menjadi dasar pengembangan sistem deteksi dini berbasis teknologi, sehingga petani dapat mengambil tindakan lebih cepat dan efektif dalam mengendalikan penyebaran penyakit. Implementasi metode ini memiliki potensi besar untuk meningkatkan efisiensi pengelolaan tanaman, mengurangi kerugian ekonomi, dan mendukung pertanian berkelanjutan.Kata kunci: Penyakit daun cabai, K-Nearest Neighbor, GLCM, Klasifikasi. ABSTRACT Chili plant diseases can cause significant production declines, thus making the sustainability of agriculture and food. This study develops a system to categorize chili leaves using Gray Level Co-occurrence Matrix (GLCM) for texture extraction and K-Nearest Neighbors (KNN) for classification. The chili leaf image data used includes types of chili mosaic virus diseases, fusarium wilt, yellow virus, and leaf spots. The process includes image selection, feature extraction using GLCM, and classification using KNN. The results of the study show that the ratio can reach up to 90%, depending on the K parameter. This finding is important for the world of agriculture, because it can be the basis for the development of a technology-based early detection system, so that farmers can take faster and more effective action in controlling the spread of disease. The implementation of this method has great potential to improve the efficiency of crop management, reduce economic losses, and support sustainable agriculture. Keywords: Chile leaf disease, K-Nearest Neighbor, GLCM, Classification.
Classification Of Rice Plant Diseases Using K-Nearest Neighbor Algorithm Based On Hue Saturation Value Color Extraction And Gray Level Co-Occurrence Matrix Features Siti Saniah; Mhd. Furqan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3972

Abstract

This research aims to classify diseases in rice plants using the K-Nearest Neighbor (K-NN) algorithm based on Hue Saturation Value (HSV) color feature extraction and Gray Level Co-Occurrence Matrix (GLCM) texture. The main problem faced is how to identify the type of disease in rice plants automatically using digital images. Diseases such as Blight, Tungro, and Crackle often attack rice plants and require an accurate early detection system. Lack of understanding in recognizing disease symptoms manually often leads to errors in handling. For this reason, this research develops an image processing-based classification system that can detect diseases such as Blight, Tungro, and Crackle. The method used in this research is image processing which includes RGB to HSV color space conversion, texture feature extraction using GLCM, and classification using K-NN algorithm. The dataset consists of 240 images, divided into training data and testing data, namely 192 training data and 48 testing data. Tests were conducted by calculating accuracy at various values of the K parameter, namely K = 1, K = 3, and K = 5, to determine the effectiveness of the model in classifying plant diseases. The purpose of this study was to evaluate the accuracy of the system in identifying rice diseases and test the combination of HSV and GLCM features in improving classification performance. The results showed that using HSV and GLCM features together resulted in the highest accuracy at K=3 with an accuracy value of 75%. The system is expected to assist farmers in detecting plant diseases quickly and effectively, thus minimizing production losses and supporting agricultural sustainability
Analisis Sentimen Pengguna X terhadap Kebijakan PPN 12% Menggunakan Naive Bayes Panggabean, Alwi Andika; Kartikasari, Diah Putri; Aulia, Rafif Risdi; Tambak, Tiara Ayu Triarta; Nabila, Siti Fadiyah; Furqan, Mhd
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1002

Abstract

Kebijakan kenaikan Pajak Pertambahan Nilai (PPN) dari 11% menjadi 12% yang direncanakan berlaku pada tahun 2025 telah menimbulkan berbagai reaksi publik, terutama di media sosial. Penelitian ini bertujuan untuk menganalisis sentimen pengguna media sosial X (sebelumnya Twitter) terhadap kebijakan tersebut menggunakan metode Naive Bayes yang diimplementasikan dalam bahasa pemrograman R. Data diperoleh dari tweet yang relevan dengan topik PPN 12%, kemudian diproses melalui tahapan pra-pemrosesan dan pelabelan manual. Hasil analisis menunjukkan bahwa sentimen negatif mendominasi dengan proporsi 39%, diikuti sentimen netral 32%, dan sentimen positif 29%. Evaluasi performa model Naive Bayes menunjukkan akurasi sebesar 50%, dengan ketepatan klasifikasi tertinggi pada kategori negatif. Analisis lebih lanjut terhadap istilah kunci dan topik diskusi mengungkapkan bahwa kekhawatiran terhadap beban ekonomi dan dampak terhadap UMKM menjadi sumber utama sentimen negatif, sementara sentimen positif dikaitkan dengan harapan terhadap perbaikan layanan publik dan pembangunan. Penelitian ini memberikan wawasan penting bagi pembuat kebijakan untuk memahami persepsi publik terhadap kebijakan fiskal secara lebih mendalam dan berbasis data.
Analisis Data Biologis dalam Mengidentifikasi Gen atau Protein yang Memiliki Pola Ekspresi Serupa Akmal, Muhammad Haikal; Pangestu, Dimas; Siregar, Dzilhulaifa; Harahap, Khaila Mukti; Furqan, Mhd.
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1008

Abstract

Ekspresi protein dalam data biologis umumnya memiliki kompleksitas tinggi dan dimensi besar, sehingga menyulitkan pengenalan pola secara langsung. Studi ini memanfaatkan algoritma Spectral Clustering untuk mengeksplorasi struktur tersembunyi dalam kumpulan data ekspresi protein. Langkah awal mencakup pembersihan data dengan imputasi nilai hilang menggunakan metode rata-rata serta normalisasi fitur numerik menggunakan StandardScaler. Dataset terdiri dari 1.080 observasi dan 77 atribut numerik hasil percobaan pada tikus. Proses pengelompokan dilakukan dengan pendekatan berbasis graf, menggunakan parameter empat klaster dan afinitas nearest neighbors. Selanjutnya, dilakukan reduksi dimensi melalui teknik Principal Component Analysis (PCA) untuk menghasilkan representasi dua dimensi yang mudah divisualisasikan. Hasil pengelompokan memperlihatkan pemisahan yang mencerminkan perbedaan biologis antar sampel. Hal ini menunjukkan bahwa metode tak terawasi seperti Spectral Clustering efektif dalam mengungkap struktur laten pada data ekspresi protein dan dapat menjadi dasar bagi analisis klasifikasi berbasis karakteristik biologis.
An Interpretable Deep Learning Framework for Multi-Class Lung Disease Diagnosis Using ConvNeXt Architecture Basyir, Muhammad Khalidin; Furqan, Mhd; Fadlan, Aulia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5404

Abstract

Lung diseases remain a major global health challenge, requiring accurate and interpretable diagnostic systems to support timely detection and treatment. This study proposes a high-fidelity deep learning approach using the ConvNeXt architecture for automated multi-class classification of chest X-ray (CXR) images into five categories: Bacterial Pneumonia, Viral Pneumonia, COVID-19, Tuberculosis, and Normal. The methodology involved preprocessing 10.095 Kaggle-sourced images (normalization, CLAHE, augmentation, resizing) and training a ConvNeXt model for 70 epochs with the Adam optimizer. The model achieved strong performance with 92.66% validation accuracy, 86.32% test accuracy, a macro-average F1-score of 0.86, and a macro-average AUC of 0.99. Grad-CAM visualizations demonstrated the model's consistent focus on clinically relevant lung regions, significantly improving interpretability and clinical applicability. This study contributes to advancing interpretable AI methods for clinical decision support in medical imaging, offering a reliable and transparent framework for automated lung disease diagnosis.
Co-Authors ., Zulpadli Abdul Halim Hasugian Adha, Rifki Mahsyaf Agpina, Pipi Agung Nugroho Ahmad Fakhri Ab. Nasir Ahmad Fauzi Aidil Halim Lubis Aisyah Nurrahmah Siregar Akmal, Muhammad Haikal Andita Utami Anggraini, Delia Anwar, Mufti Husain Apriansyah, Yuda Ardyanti, Tiwy Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah, A Aulia, Atiqah Aulia, Muhammad Arief Aulia, Muhammad Fathir Aulia, Rafif Risdi Badria, Lailatul Bagus Ageng Alfahri Basyir, Muhammad Khalidin Bintang Kurniawan Herman Bob Subhan Riza, Bob Subhan Br Rambe, Indri Gusmita Cahyadi, Bhagaskara Dalimunthe, Ayu Sahriani Daulay, Ikhsan Agus Martua Dea Alya Dewi Aulia Tanjung Diah Putri Kartikasari Dodyk Fahlome Elce, Furkan Fadil, Ulfi Muzayyanah Fadillah, Rini Fadlan, Aulia Fahrul Azis Nasution Faiza, Nayla fandi, Fandi Ahmad Farhan Amar Pramudya Farhan Sadli Siregar Farhan Sadly Siregar Fikri Haikal FIKRI HAIKAL Fredy Kusuma Ramadhani Gunawan, Irwan Hapisfatly Sir Harahap, Khaila Mukti Harahap, Raihan Rizieq Harahap, Rosa Linda Hasrul Hasibuan, Mhd Fikri Heri Santoso Hervilla Amanda R. Siregar Himawan Hasibuan, Riswanda Ichsan HP, Kiki Iranda Hsb, Dinda Umami Hsb, Munawir Siddik Hutagalung, Muhammad Wandisyah R Ilham Fuadi Nasution Imam Zaki Husein Nst Iskandar, Rozai Ismail Pulungan Januar, Bagus Jundi Haqqoni K Khairunnisa Khairi, Nouval Khairunnisa Khairunnisa Khairunnisa, K Kurniawan, Riski Askia Laila Nurzannah Lailatul Badria Lely Sahrani Lubis, Akbar Maulana M. Alfatoni Muarrip M. Fakhriza Mahendra, Rifandi Manza, Yuke Matondang, Toibatur Rahma Maulana Ihsan, Maulana Mey Hendra Putra Sirait Mhd Fadil Ramadhana Mhd Fikri Hasrul Hasibuan Mhd Galih Khairi Mhd Ikhsan Rifki Mhd Reza Alfani Miftahul Rizky Pulungan Muhammad Akbar Ramadhan Tanjung Muhammad Fadil Ramadhana Muhammad Farhan Muhammad Fathir Aulia Muhammad Ikhsan Muhammad Irfan Gurning Muhammad Luthfi Muhammad Naufal Shidqi Muhammad Ridzki Hasibuan Muhammad Rizki Munadi Munadi Nabawy, Putri Nabila, Siti Fadiyah Naina Nazwa Hasibuan Nasution, Afri Yunda Nasution, Irma Yunita Nasution, Romaito Nasution, Zulia Lestari Nayla Faiza Nazwa Aliya Muthmainnah Hasibuan Ningsih, Siti Alus Nur Bainatun Nisa Nur Shafwa Aulia Sitorus Nurhasanah Nurhasanah Nurul Hadi Muliani Hariadi Saputra Nurzannah, Laila Pane, Putri Pratiwi Pangestu, Dimas Panggabean, Alwi Andika Pratama, Haris Prayoga Elfanda Fachmi Hasibuan Putra, Suan Ekie Nanda Putri Salsa Nabila Putri, Alma Irawanti Radhifan Mardhi Raissa Amanda Putri Rakhmat Kurniawan R Ramadani, Wily Supi Ramadhan Nasution, Yusuf Ramadhani, Fredy Kusuma Razzaq H. Nur Wijaya Reza Muhammad Rifnandy, Muhammad Fauzan Rika Rosnelly, Rika Riswanda Ichsan Himawan Hasibuan Rivaldi Prima Nanda Rizka Rizki Ananda Rizki Siregar, Awal Rizqi Hidayat Tanjung RR. Ella Evrita Hestiandari Said Arrahman Salsabila Mahfuza Saparuddin Siregar Sembiring, Yogasurya Pranantha Shafa, Dafa Ikhwanu Sigit Muslim Anggoro Pratono Sinaga, Meri Siregar, Dzilhulaifa Siregar, Hervilla Amanda R. Siregar, Kalfida Eka Wati Sitepu, Anggi Jelita Siti Saniah Siti Sarah Harahap Siti Sumita Harahap Sitorus, Nur Shafwa Aulia Solly Aryza Sri Rahmadani Sri Wahyuni Sriani Sriani Sriani Sriani Sriani, S Suci Syahputri Suci Wulandari Suhardi, S Suhardi, Suhardi Susan Mayang Sari Syamia, Nanda Tambak, Tiara Ayu Triarta Tanjung, Tegar Haryahya Tiara Bela Harahap Tria Elisa Wahyudin, Rahmat Wan Fadilla Rischa Wati, Putri Kurni Wicaksana, Agum Widiya Yuli Kartika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zabni, Nur Hera Zahra Humaira Kudadiri Zaki Musyaffa Ziqra Addilah Zulnun, M. Ridho Azmuddin