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All Journal Jurnal Ilmu Komputer dan Informasi Buletin Udayana Mengabdi Jurnal Harpodon Borneo Jurnal Buana Informatika MUSTEK ANIM HA Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Informatika Upgris Elementary: Jurnal Ilmiah Pendidikan Dasar Jurnal Teknik Komputer AMIK BSI JIKO (Jurnal Informatika dan Komputer) INOVTEK POLBENG JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Inotera Jurnal Technopreneur (JTech) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi JISKa (Jurnal Informatika Sunan Kalijaga) Mobile and Forensics Indonesian Journal of Electrical Engineering and Computer Science International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Jurnal Pengabdian Masyarakat Multidisiplin Elektrika Borneo JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Borneo Informatika dan Teknik Komputer (JBIT) JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Informatika Polinema (JIP) TANJAK : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika, Komputasi, Aplikasi & Bisnis (INFOKABIN)
Arif Fadllullah
Jurusan Teknik Komputer, Fakultas Teknik Universitas Borneo Tarakan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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THE APPLICATION OF THE MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF SIMPLE RATIO ANALYSIS METHOD IN A DECISION SUPPORT SYSTEM FOR PROSPECTIVE UBT STUDENT ASSOCIATION CHAIR CANDIDATES Pradana, Awang; Fadllullah, Arif; Prasetya, Agung; Fadliansyah, Fadliansyah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.8227

Abstract

Decision Support Systems (DSS) have become essential tools in the de-cision-making process across various fields. In the context of selecting the chairman of the Computer Engineering Student Association at Uni-versitas Borneo Tarakan, the use of DSS is also highly relevant and beneficial. The MOOSRA (Multi-Objective Optimization on the basis of Ratio Analysis) method has been chosen as the approach to implement this decision support system. This study aims to apply the MOOSRA method in the implementation of a web-based decision support system for the selection of prospective chairpersons of the Computer Engineer-ing Student Association at Universitas Borneo Tarakan. The MOOSRA method is utilized to consider several criteria, such as leadership skills, communication abilities, dedication, and organizational experience. In this research, the use of MOOSRA is combined with web technology to enhance the efficiency and quality of the candidate selection process. The MOOSRA method offers a structured and objective approach to evaluating candidates for the chairmanship. This approach involves ratio analysis and multi-objective optimization to produce better out-comes. The results of this study are expected to facilitate a fairer and more objective selection process, as well as to improve student satisfac-tion within the Computer Engineering Student Association at Universi-tas Borneo Tarakan.
Sistem Deteksi Otomatis Penggunaan Senjata Tajam Menggunakan YOLOv8n Deep Learning Nadziah Fitriani; Arif Fadllullah
Jurnal Borneo Informatika dan Teknik Komputer Vol 6, No 1 (2026): Edisi April - September
Publisher : Jurusan Teknik Komputer, Fakultas Teknik, Universitas Borneo Tarakan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35334/jbit.v6i1.7316

Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan sistem deteksi objek senjata tajam berbasis deep learning menggunakan algoritma YOLOv8n. Sistem yang dikembangkan khusus pada pendeteksian tiga jenis senjata tajam, yaitu pisau, sabit, dan parang. Sebanyak 1.200 citra dikumpulkan, terdiri dari 600 citra primer dan 600 citra sekunder. Pada tahap konstruksi dataset, seluruh citra melalui proses anotasi dan augmentasi data yang meliputi penyesuaian saturasi, kecerahan, dan resize, sehingga diperoleh total 2.640 dataset. Dataset tersebut kemudian dibagi menjadi 2.160 data latih, 240 data validasi, dan 240 data uji. Model YOLOv8n dibor selama 100 epoch menggunakan 2.160 data training dan menghasilkan nilai box Precision sebesar 0,865, recall 0,815, mAP50 0,895, serta mAP50-95 sebesar 0,649. Hasil evaluasi pada pengujian data menunjukkan nilai precision 0,837, recall 0,819, dan F1-score 0,827. Kelas sabit memperoleh performa terbaik dengan precision 0,937, recall 0,913, dan F1-score 0,924. Pengujian sistem secara real-time pada jarak 1–4 meter menunjukkan deteksi yang responsif dengan rata-rata precision 0,824, recall 0,760, dan F1-score 0,790. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi senjata tajam secara efektif dan menampilkan hasil deteksi secara langsung sebagai referensi pengembangan sistem di berbagai lingkungan.
Sistem Klasifikasi Citra Daun Obat Tradisional Dayak Kenyah Menggunakan Metode Convolutional Neural Network Dengan Arsitektur MobileNetV2 Fadllullah, Arif; Natalia, Tia
INFOMATEK Vol 28 No 1 (2026): Juni 2026 (In Progress)
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v28i1.36796

Abstract

Masyarakat Suku Dayak Kenyah mewarisi pengetahuan tradisional dalam pemanfaatan tumbuhan sebagai obat. Namun, kemiripan morfologi antarspesies, terutama pada bagian daun, sering menjadi hambatan dalam proses identifikasi yang akurat. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi citra daun obat tradisional Suku Dayak Kenyah menggunakan Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Tiga jenis daun yang menjadi fokus penelitian adalah daun senggani (Melastoma malabathricum L.), daun jarak (Jatropha curcas), dan daun sengkubak (Pycnarrhena cauliflora). Dataset penelitian terdiri dari 900 citra utama yang telah melalui tahap preprocessing dan augmentation, menghasilkan total 2.340 citra yang dibagi menjadi 80% data latih, 10% data validasi, dan 10% data uji. Pelatihan model dilakukan hingga 50 epoch, menghasilkan akurasi pelatihan dan validasi sebesar 100%, serta validation loss sebesar 0,0565. Evaluasi menggunakan data uji menunjukkan nilai precision, recall, dan F1-score rata-rata sebesar 100%. Namun, pengujian dengan 90 data uji eksternal (di luar dataset) menunjukkan penurunan performa dengan nilai precision 88%, recall 84%, dan F1-score 83% yang mengindikasikan adanya keterbatasan dalam generalisasi model terhadap data di luar distribusi pelatihan. Meskipun begitu, model MobileNetV2 mampu memberikan performa tinggi dalam klasifikasi citra daun pada dataset terkontrol serta mengidentifikasi tantangan generalisasi pada data eksternal. Oleh karena itu, model ini berpotensi dikembangkan lebih lanjut sebagai dasar sistem identifikasi tanaman obat berbasis citra daun yang adaptif dan aplikatif di lapangan.
Implementasi Sistem Pakar Untuk Diagnosis Hama Tanaman Kelapa Sawit Dengan Metode Certainty Factor Felisia Silalahi; Kharis Hudaiby Hanif; Arif Fadllullah
INFOKABIN (Informatika, Komputasi, Aplikasi dan Bisnis) Vol 1 No 1 (2026): Januari 2026
Publisher : Universitas Al-Irsyad Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36760/ifkb.v1i1.719

Abstract

This study proposes the development of an expert system based on the Certainty Factor method for rapid and accurate diagnosis of oil palm pests. The system is designed to help farmers identify four main types of pests (rats, termites, caterpillars, and bagworms) based on 18 specific symptoms and provide control recommendations. Data were obtained from interviews with agricultural experts, literature studies, and field observations in a 2.5-hectare oil palm plantation in Tanjung Selor. Users expressed their confidence level using a five-point scale (none, slightly confident, fairly confident, confident, and very confident). The Certainty Factor method calculates the confidence level of the diagnosis by combining the observed symptoms. The system was implemented as an offline desktop application based on Python and PyQt5. Functional testing (Black Box Testing) on ​​14 scenarios showed that all features functioned according to specifications. Performance evaluation included diagnostic accuracy (confusion matrix), Certainty Factor validation, and usability (System Usability Scale). The results showed 100% accuracy, precision, recall, and F1-Score in 20 sample cases. An average SUS score of 85.125 (Acceptable category approaching Excellent) from 40 respondents indicated ease of use. The system has proven effective in supporting decision-making for farmers and extension workers. Further development is recommended for secondary pests, plant diseases, and web and mobile platforms. Penelitian ini mengusulkan pengembangan sistem pakar berbasis metode Certainty Factor untuk diagnosis hama tanaman kelapa sawit secara cepat dan akurat. Sistem dirancang membantu petani mengidentifikasi empat jenis hama utama (tikus, rayap, ulat api, ulat kantong) berdasarkan 18 gejala spesifik serta memberikan rekomendasi pengendalian. Data diperoleh dari wawancara pakar pertanian, studi literatur, dan observasi lapangan di perkebunan kelapa sawit seluas 2,5 hektar di Tanjung Selor. Pengguna menyatakan tingkat keyakinan melalui lima skala (tidak ada, sedikit yakin, cukup yakin, yakin, sangat yakin). Metode Certainty Factor menghitung tingkat kepercayaan diagnosis dengan mengombinasikan gejala yang diamati. Sistem diimplementasikan sebagai aplikasi desktop offline berbasis Python dan PyQt5. Pengujian fungsional (Black Box Testing) terhadap 14 skenario menunjukkan semua fitur berfungsi sesuai spesifikasi. Evaluasi kinerja mencakup akurasi diagnosis (confusion matrix), validasi Certainty Factor, dan usabilitas (System Usability Scale). Hasil menunjukkan akurasi, presisi, recall, dan F1-Score 100% pada 20 kasus sampel. Skor SUS rata-rata 85,125 (kategori Acceptable mendekati Excellent) dari 40 responden mengindikasikan kemudahan penggunaan. Sistem terbukti efektif sebagai pendukung keputusan petani dan penyuluh. Pengembangan lanjutan disarankan untuk hama sekunder, penyakit tanaman, serta platform web dan mobile.
Identifikasi Penyakit Bercak Daun Kelapa Sawit Menggunakan Algoritma CNN dengan Arsitektur VGG19 Berbasis Citra Digital Arif Fadllullah; Sri Erdina
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8391

Abstract

This study aims to develop a digital image-based system for identifying leaf spot diseases in oil palm plants using the CNN (Convolutional Neural Network) algorithm with the VGG19 architecture. The dataset consists of 330 primary oil palm leaf images categorized into three classes: leaves infected with leaf rust, healthy leaves, and leaves infected with curvularia. The dataset was divided into 64% training data, 16% validation data, and 20% testing data. The system development process includes preprocessing, data augmentation, data splitting, model training using the VGG19 architecture, and model evaluation. The training results over 200 epochs achieved an accuracy of 0.93 on the training data and 0.98 on the validation data. Model evaluation on the test data produced precision, recall, and F1-score values of 0.94, 0.81, and 0.87 for the “Leaf Rust” class; 0.84, 0.95, and 0.89 for the “Healthy Leaf” class; and 1.00 for the “Curvularia” class. The testing results indicate consistent performance, suggesting that the proposed system is effective in classifying oil palm leaf spot diseases. The developed system has the potential to be used as an early detection tool for leaf spot diseases to support the improvement of oil palm productivity.
PENINGKATAN KETERAMPILAN BRANDING UMKM TARAKAN MELALUI PEMANFAATAN TEKNOLOGI DIGITAL DI ERA 4.0 Arif Fadllullah; Moch. Gilang Elang Perkasa; Alpa Rizky Lubis Triyansyah; Putri Natasya Lisandra; Asriandi Asriandi; Rega Rizkan Azizan
Jurnal Pengabdian Masyarakat Multidisiplin Vol 8 No 2 (2025): Februari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/jpm.v8i2.5522

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Tarakan City often face challenges in utilizing branding and technology in the digital era to enhance the competitiveness of their products, including competing with national brands from outside the city. Limited knowledge of effective branding strategies and a lack of understanding in using technology, such as graphic design and social media, are the main obstacles in expanding their marketing reach. Therefore, based on these issues, a community service program was proposed in the form of seminars and workshops focused on improving branding skills through the uses of digital technology. The implementation method was based on the Project Action Plan (PAP), involving several stages: site survey, preparation, seminar and workshop execution, evaluation and implementation, as well as reporting and publication preparation. The results of this activity showed a positive impact, as the capability of MSME actors in Tarakan City increased in utilizing digital technology to create and manage their branding more effectively, as well as gaining skills in using editing software and social media to support their product promotion. The benefits obtained from this activity not only helped Tarakan MSMEs adapt to the challenges and opportunities of the digital era but also expanded their product marketing reach and enhanced their competitiveness in both local and national markets. Overall, this activity contributed to improving the knowledge, skills, and competitiveness of MSMEs in Tarakan City, while supporting sustainable local economic growth.
Sistem Deteksi Otomatis Penggunaan Senjata Tajam Menggunakan YOLOv8n Deep Learning Nadziah Fitriani; Arif Fadllullah
Jurnal Borneo Informatika dan Teknik Komputer Vol 6, No 1 (2026): Edisi April - September
Publisher : Jurusan Teknik Komputer, Fakultas Teknik, Universitas Borneo Tarakan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35334/jbit.v6i1.7316

Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan sistem deteksi objek senjata tajam berbasis deep learning menggunakan algoritma YOLOv8n. Sistem yang dikembangkan khusus pada pendeteksian tiga jenis senjata tajam, yaitu pisau, sabit, dan parang. Sebanyak 1.200 citra dikumpulkan, terdiri dari 600 citra primer dan 600 citra sekunder. Pada tahap konstruksi dataset, seluruh citra melalui proses anotasi dan augmentasi data yang meliputi penyesuaian saturasi, kecerahan, dan resize, sehingga diperoleh total 2.640 dataset. Dataset tersebut kemudian dibagi menjadi 2.160 data latih, 240 data validasi, dan 240 data uji. Model YOLOv8n dibor selama 100 epoch menggunakan 2.160 data training dan menghasilkan nilai box Precision sebesar 0,865, recall 0,815, mAP50 0,895, serta mAP50-95 sebesar 0,649. Hasil evaluasi pada pengujian data menunjukkan nilai precision 0,837, recall 0,819, dan F1-score 0,827. Kelas sabit memperoleh performa terbaik dengan precision 0,937, recall 0,913, dan F1-score 0,924. Pengujian sistem secara real-time pada jarak 1–4 meter menunjukkan deteksi yang responsif dengan rata-rata precision 0,824, recall 0,760, dan F1-score 0,790. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi senjata tajam secara efektif dan menampilkan hasil deteksi secara langsung sebagai referensi pengembangan sistem di berbagai lingkungan.
Sistem Deep-Learning Yolov8 untuk Deteksi Penggunaan APD Secara Real-Time Nelson Mandela Rande Langi; Arif Fadllullah
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 1 (2026): January 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5051

Abstract

Although workplace safety regulations in construction are clear, many workers are still reluctant to use Personal Protective Equipment (PPE) due to a lack of awareness, work pressure, and limited facilities. As a result, the risk of serious accidents increases. Conventional approaches such as verbal warnings or CCTV monitoring are considered less effective for early detection and prevention of violations. This study proposes an automatic detection system for PPE usage in construction areas using YOLOv8. The model was trained on a secondary dataset of 3,569 images for 100 epochs, with a 60% training, 20% validation, and 20% test split. Testing on 90 real-time frames showed good performance in detecting 8 PPE classes, with an average precision of 0.935, recall of 0.806, and F1-measure of 0.862. The results indicate that the system can classify PPE usage with high accuracy. However, a recall below 1 suggests that some objects, particularly "not wearing glasses" and "not wearing shoes," failed to be detected. The F1-measure of 0.862 reflects a good balance between precision and recall.
Co-Authors A. Pradana Adiasti, Nindya Agung Prasetya Agus Zainal Arifin Agus Zainal Arifin Aji Bijaksana Abadi AK, Muh Surtaji Akbar, Muhammad Adhiya Yudhistira Allenidekania Allenidekania Alpa Rizky Lubis Triyansyah Amelia Manda Sari Amelia Manda Sari Ani Kurniawati Ani Kurniawati Antonius Rajagukguk, Antonius Ariessanto, Prayudi Aris Triwiyatno Asriandi Asriandi Audrey Nauffal Juniar Awang Pradana, Awang Azman, Muhammad cahyadi, jimmy D. Harto Dasrit Debora Kamudi Dedy Harto Devi Sarmilah Chomariah Devi Sarmilah Chomariah Diana Purwitasari Dini Adni Navastara Dini Adni Navastara, Dini Adni Edy Utomo Fadliansyah, Fadliansyah Farhan Muhammad Nabil Farhan Muhammad Nabil Felisia Silalahi Gazali Salim Hanif, Kharis Hudaiby Hasriana Hasriana Irgi Ahmad Fahrezi Kambu, Megawati Langi, Nelson Mandela Rande Mado, Ismit Maulana, Ikhsan Meilindy, Laura Nur Moch. Gilang Elang Perkasa Muhamad Nasir muhammad irfan Muhammad Irfan Mulyadi , Mulyadi Mulyadi Muntiari, Novita Ranti Nadziah Fitriani Natalia, Tia Nelson Mandela Rande Langi Noer, Roby Zulkarnain Nur Ainun Perangin Angin, Nur Hasanah Putri Natasya Lisandra R. W. Arsianti Rahman, Mukti Dika Refanti Rega Rizkan Azizan Rochaniati Rochaniati Rudy Rudy . Rudy Rudy Sri Erdina Sri Widodo Sudirman Sugeng Riyanto Sultan Mahdi Sultan Mahdi Sumardi Sumardi Tegar Palyus Fiqar Wahdana Wahdana Widya Ambarwati Widya Ambarwati Yudhistira Akbar, Muhammad Adhiya Zulfia, Rahmatuz