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All Journal Riau Journal of Computer Science INOVTEK Polbeng - Seri Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar JMM (Jurnal Masyarakat Mandiri) Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL PENDIDIKAN TAMBUSAI Journal on Education JOURNAL OF SPORT EDUCATION (JOPE) Jurnal PGSD International Journal of Informatics and Computation Jurnal Pendidikan Teknologi Informasi dan Vokasional Jurnal Prakarsa Paedagogia Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Journal of Engineering, Science and Information Technology JURNAL MASYARAKAT NEGERI ROKANIA DECODE: Jurnal Pendidikan Teknologi Informasi JOURNAL OF ICT APLICATIONS AND SYSTEM Innovative: Journal Of Social Science Research RJOCS (Riau Journal of Computer Science)
Adyanata Lubis
Universitas Rokania, Indonesia
Religion Humanities 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 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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Classification Of Palm Oil Maturity Using CNN (Convolution Neural Network) Modelling RestNet 50 Prasiwiningrum, Elyandri; Adyanata Lubis
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.822

Abstract

Accurate classification of palm fruit maturity levels is very important to optimize harvest time and increase production efficiency in the palm oil industry. Traditional methods that rely on visual assessment of factors such as fruit shedding and skin discoloration are prone to human error. To overcome this limitation, this research applies deep learning techniques, specifically using Convolutional Neural Network (CNN) with ResNet-50 architecture, to classify Fresh Fruit Bunches (FFB) into two stages of maturity: unripe and ripe. The model is trained and validated using a combination of data augmentation techniques to improve model performance. Various configurations were tested, including variations in data sharing, optimizer, and learning rate. The optimal configuration—90/10 training and validation data split, Adam optimizer, and learning rate of 0.0001—resulted in excellent model performance. The ResNet-50 model achieved 97% accuracy, with 96% precision, 98% recall, and an F1 score of 97%. This metric reflects the high reliability of the model in classifying palm fruit maturity levels, significantly reducing classification errors compared to traditional methods. This research highlights the transformational potential of deep learning to improve maturity classification in the palm oil industry, by offering a more efficient, accurate and automated approach. Further research should focus on expanding the dataset to increase model robustness as well as exploring real-time implementation to further improve decision making in palm oil production. This approach promises to increase agricultural efficiency by ensuring optimal harvest timing and better resource management.
Detection of Oil Palm Seedling Disease Based on Leaf Images Using the MobileNetV2-CNN Architecture Ego Oktafanda; Adyanata Lubis; Elyandri Prasiwiningrum
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.71

Abstract

This study aims to develop and implement a plant disease detection system for oil palm seedlings based on leaf images using the MobileNetV2 architecture, which is based on Convolutional Neural Networks (CNN). The model was trained using a dataset of oil palm leaf images to detect several types of plant diseases. In the experiments, the applied model showed excellent results, with training accuracy increasing from 79% in the first epoch to 96% in the 15 epoch, and validation accuracy also increasing from 89% to 97%. These results demonstrate that the model can effectively detect plant diseases with good generalization ability on unseen data. With stable loss reduction and continuously improving accuracy, this study proves that the MobileNetV2 architecture can be efficiently used for plant disease detection. The research also highlights the potential integration of the model into an application to provide a practical solution in oil palm plantation management and to support decision-making and improve agricultural outcomes.
Using Artificial Neural Networks and the Kohonen Method, an Image Pattern Recognition System for Khat Art Types Rasna, Rasna; Lubis, Adyanata; Suryadi, Dikky; Bani, Alexius Ulan; Nugroho, Fifto
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.851

Abstract

Arabic letter writing is known as khat art. Khat is classified into many categories and can be identified into three types: Khat Naskhi, Khat Qufi, and Khat Farisi, per the rules established in the art of Khat. Arabic letters, the subjects of khat art, evolved following the region where it first appeared. As a result, the Qufi style, for instance, marked the start of the evolution of Khat in the tenth century. Previously somewhat rigid, Khat became more fluid and beautiful, although it remained angular. Subsequently, the art of Sulus, Naskhi, Raiham, Riqa, and Tauqi evolved and exhibited the form of Khat, cursive (italic)—artificial neural network-based khat art type recognition by selecting the Kohonen
Penerapan Metode Forward Chaining Pada Sakit Gusi Handayani, Meli; Hayadi, B.Herawan; Lubis, Adyanata
Journal of ICT Applications System Vol 1 No 1 (2022): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (290.79 KB) | DOI: 10.56313/jictas.v1i1.128

Abstract

Sampai saat ini, perkembangan teknologi informasi telah merambah ke berbagai sektor termasuk di sektor kesehatan yang mampu membantu dalam mendiagnosa penyakit melalui gejala yang diberikan serta dapat memberikan solusi penanganan penyakit. Salah satu penyakit yang dapat dilakukan diagnosa dengan adanya perkembangan teknologi komputer adalah penyakit gusi. Penyakit gusi adalah penyakit infeksi yang menyerang pada jaringan di sekitar gigi. Kondisi ini merupakan penyebab utama dari gigi yang lepas pada orang dewasa. Perawatan gigi merupakan salah satu usaha penjagaan untuk mencegah kerusakan gigi dan penyakit gusi. Penyakit gusi dapat menyerang siapa saja baik menyerang bayi, balita, remaja bahkan menyerang orang dewasa. Menurut jurnal penelitian dari Mubasyiroh, dan Andayasari (2017:141) berpendapat bahwa Penyakit gigi dapat berupa kerusakan gigi (karies) dan penyakit gusi. Penyakit gigi dan mulut (termasuk karies dan penyakit periodontal) merupakan masalah yang cukup tinggi yang dikeluhkan oleh masyarakat. Adapun cabang ilmu komputer yang dapat melakukan diagnosa untuk mengetahui penyakit gusi adalah sistem pakar.Berdasarkan hasil pembahasan diatas, maka didapatkan jawaban B1, B2, B3 sampai dengan B9 gejala penyakit berdasarkan pilihan pertanyaan yang dipilih, itu menandakan, bahwa sistem pakar dengan metode forward chaining dapat mengatasi penyakit gusi dengan tingkat kepercayaan 89%
ADAPTIVE LEARNING DALAM DESAIN INSTRUKSIONAL: PENDEKATAN STRATEGIS MENINGKATKAN KETERLIBATAN MAHASISWA DI E-LEARNING PERGURUAN TINGGI Rani; Jihan; Adyanata Lubis; Agung Setiawan
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 2 (2025): Volume 10 Nomor2, Juni 2025
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i2.27204

Abstract

This study aims to examine the integration of adaptive learning in instructional design and its impact on student engagement in the context of online learning in higher education. Using a systematic literature review approach, this study analyzes ten scientific articles through thematic analysis and source triangulation methods to obtain credible and comprehensive findings. The results of the analysis reveal that adaptive learning supports the personalization of the learning process through the use of learning analytics, learning style mapping, and adaptive content arrangement. Adaptive instructional design serves as an implementation framework that allows for learning flexibility through formative assessment, scaffolding, and material modularization. The integration of the two components has a positive impact on student engagement, both in the affective, cognitive, and behavioral dimensions. This study produces a conceptual model that explains the logical relationship between adaptive learning, instructional design, and student engagement. The implications of these findings encourage the development of a more personalized e-learning system and learning that is oriented to individual student needs.
PENGEMBANGAN APLIKASI E-LEARNING BERBASIS ANDROID UNTUK MENINGKATKAN AKSESIBILITAS PEMBELAJARAN Nur Aisyah; Cossy Maychandra; Adyanata Lubis; Agung Setiawan
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 2 (2025): Volume 10 Nomor2, Juni 2025
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i2.27242

Abstract

Abstract contains a brief description of the research objectives, methods used, instruments, data analysis techniques, and research results. The emphasis of writing abstracts is mainly on research results. Abstracts are written in Indonesian and English. Abstract typing is done single-spaced with margins that are narrower than the right and left margins of the main text. Keywords need to be included to describe the realm of the problem under study and the main terms that underlie the implementation of the research. Key words can be single words or a combination of words. The number of key words 3-5 words. These key words are necessary for computerization. Searching for research titles and abstracts is made easier with these key words.
Penerapan ARCS Terhadap Mata Kuliah Kewirausahaan untuk Mendukung MBKM Akhyar, Ahmad; Salsabila, Bela; Setiawan, Agung; Lubis, Adyanata
Jurnal Pendidikan Teknologi Informasi dan Vokasional Vol 5, No 1 (2023): Jurnal Pendidikan Teknologi Informasi dan Vokasional
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jptiv.v5i1.27554

Abstract

Abstrak - Jurnal ini bertujuan untuk mengeksplorasi penerapan pendekatan ARCS (Attention, Relevance, Confidence, Satisfaction) dalam mata kuliah kewirausahaan sebagai upaya untuk mendukung Program Merdeka Belajar Kampus Merdeka (MBKM). ARCS merupakan pendekatan yang berfokus pada empat aspek penting dalam pembelajaran yaitu perhatian, relevansi, keyakinan dan kepuasan. Penggunaan teknologi augmented reality (AR) dapat meningkatkan minat dan keterlibatan mahasiswa dalam pembelajaran. Penelitian selanjutnya dapat melibatkan lebih banyak kelompok mahasiswa dan institusi pendidikan yang berbeda untuk memperluas generalisabilitas temuan. Penelitian juga dapat menggali lebih dalam tentang dampak jangka panjang dari penerapan pendekatan ARCS terhadap pengembangan kewirausahaan mahasiswaKata Kunci: Penerapan ARCS, Kewirausahaan, MBKM  
Implementation of KNN Methods And GLCM Extraction For Classification Of Road Damage Level Lubis, Adyanata; Iskandar, Isdaryanto; W Panjaitan, MM Lanny
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 4 No 1 (2022): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v4i1.564

Abstract

Road damage that occurs on several road surfaces causes huge losses, especially for road users such as travel time, congestion, accidents and others , so it is necessary to assess the level of road damage. At this time, problems in determining the level of road damage such as detecting cracks, potholes, calculating the width of cracks, the percentage of cracks and generating the level of road damage are still carried out by slow manual calculations using the Surface method. Distress Index (SDI). In this study, the KNN and GLCM methods will be used to detect road damage. Based on the results of the tests carried out, the accuracy of the results of disease detection with the KNN method and GLCM extraction depends on the number of datasets contained in the system. The process of measuring the level of road damage to get the results of the level of damage to the road can be done quickly, namely by entering a road damage image into the application.
Co-Authors Acep Solihin agung setiawan Agung Setiawan AGUNG SETIAWAN Agung Setiawan Agung Setiawan Ahmad Akhyar Aini, Yulfita Akhyar, Ahmad Alexius Ulan Bani Alvin, Muhammad Amelia Chandra, Detri Andri Febriansyah Angriamilleni Angriamilleni Anik Supriani Arfandi, Zirhan Arief Hidayat Afendi, Arief Hidayat Asih Ria Ningsih Asmiati B. Herawan Hayadi Basorudin Basorudin Bayu Kusuma Bela Salsabila Budi Yanto Budi Yanto, Budi Chandra, Detri Amelia Cossy Maychandra Detri Amelia Candra Detri Amelia Chandra EFENDI Ego Oktafanda Elyandri Prasiwinigrum, Elyandri Elyandri Prasiwiningrum Erliyen Nofrianda Erna Armita, NST Fadzilatul Mutmainah Fauzi Erwis Fifto Nugroho Firman Santosa Gina Sonia Amelya Handayani, Meli Hasrijal Hasrijal Hendrisman Hera Deswita Hommy Dorthy Ellyany Sinaga Irwan Hidayat Isdaryanto Iskandar ISKANDAR Jihan Jufri Jufri Jufri Jufri Jufri Junadhi, Junadhi Karmi Karmi Khardianti Alviani Ishak Maulana, Sabda Miftahul Jannah Mila Karmelia Novica Irawati Nur Aisyah Nur Azizah Nurafidah, Annisa Nurhidayati Sholihah Nurliani Pariang Sonang Siregar Prasiwiningrum, Elyandri Rani Rasna, Rasna Rika Delima Rina Wati Rinda Gustari Rita Arianti Sabri, Khairul Safinaz Sahira Saiful Anwar Salsabila, Bela Sarjon Defit Sasnita Riyani Saudah Septi Nadia Putri Seri Mulia Reski Sri Mures Walef Sriwahyudi Sulis Wulandari Suryadi, Dikky tofikin tofikin Torkis Nasution Ulfi, Meitra W Panjaitan, MM Lanny Wahyudi, Sri Yuda Irawan Yuhasnil Yuli Asnita Nasution