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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Jurnal Informatika Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah EXPLORE Jurnal Mantik JTIM : Jurnal Teknologi Informasi dan Multimedia International Journal of Informatics and Computation INFOKUM Infotek : Jurnal Informatika dan Teknologi Jurnal Kompilasi Hukum Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi EXPLORE Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology Explore Jurnal Karya untuk Masyarakat (JKuM)
Imran, Bahtiar
Universitas Teknologi Mataram
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary

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Crack Detection of Concrete Surfaces with A Combination of Feature Extraction and Image-Based Backpropagation Artificial Neural Networks Wahyudi, Erfan; Imran, Bahtiar; Subki, Ahmad; Zaeniah, Zaeniah; Samsumar, Lalu Delsi; Salman, Salman
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2249.228-235

Abstract

Concrete surface imperfections can signify a structure undergoing severe degradation. It deteriorates when concrete is exposed to elemental reactions such as fire, chemicals, physical damage, and calcium leaching. Due to its structural degradation, concrete deterioration poses a risk to the surrounding environment. Structural buildings can collapse due to severe concrete decline. To prevent concrete cracks early, it is imperative to identify the concrete surface. This requires the development of a technique for detecting the image-based concrete surface. One way to detect concrete surfaces is to create artificial neural networks. The purpose of this study is to combine feature extraction and artificial neural networks to detect cracks in concrete surfaces. The data used is concrete surface image data divided into two classes, namely cracked class and uncracked class. The total data is 600 data points, 300 data points, and 300 data points. The technique used is feature extraction from GLCM and Backpropagation Artificial Neural Network. Test results using epoch five show 95% accuracy, epoch 10 shows 95% results, epoch 100 shows 83% accuracy, and epoch 250 shows 73% results. The test results that have been carried out show a decrease in lower accuracy results when the epoch is determined to be higher. The results of this study epoch that shows the highest accuracy results are epoch 5 with 95% accuracy and epoch 10 with 95% accuracy.
Pre-Crime dan Teknologi: Mengantisipasi Terorisme Sebelum Terjadi di Indonesia Nirmala, Atika Zahra; Nunung Rahmania; Bahtiar Imran
Jurnal Kompilasi Hukum Vol. 9 No. 2 (2024): Jurnal Kompilasi Hukum
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jkh.v9i2.183

Abstract

Permasalahan terorisme menjadi permasalahan yang kompleks dengan berkembangnya teknologi, oleh sebab itu dibutuhkan upaya pencegahan terorisme dengan pendekatan yang komprehensif, seperti pendekatan pre-crime dengan pemanfaatan teknologi. Penelitian ini merupakan penelitian normatif dengan pendekatan konsep. Hasil penelitian menunjukkan pendekatan pre-crime memungkinkan untuk mengidentifikasi dan mencegah tindakan terorisme sebelum terjadi, dalam pencegahan terorisme menekankan pentingnya strategi proaktif dan kolaboratif dengan memanfaatkan teknologi, seperti penggunaan big data, kecerdasan buatan, machine learning, data mining dan analisis situasional untuk mendeteksi dan mencegah potensi ancaman sebelum terjadi kejahatan terorisme
SISTEM PAKAR DIAGNOSIS PENYAKIT BAWANG MERAH BERBASIS WEB MENGGUNAKAN METODE FORWARD CHAINING Muttaqin, Athaur; Multazam, Muhammad; Imran, Bahtiar
Journal Computer and Technology Vol. 2 No. 2 (2024): Desember 2024
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v2i2.213

Abstract

Bawang merah merupakan salah satu tanaman hortikultura atau tanaman musiman yang paling populer dan berharga secara ekonomi di Indonesia. Namun, tanaman ini rentan terhadap penyakit selama masa budidaya. Sering kali, ketika bawang merah terserang penyakit, petani langsung menggunakan pestisida atau pengobatan yang terkadang tidak tepat sasaran terhadap jenis hama atau penyakit yang menyerang. Hal ini menyebabkan pengolahan yang dilakukan menjadi kurang optimal dan bahkan berpotensi memicu munculnya hama atau penyakit baru. Dengan memanfaatkan teknologi sistem pakar, penelitian ini bertujuan untuk membantu petani mendiagnosis penyakit bawang merah berdasarkan gejalanya. Data yang diolah mencakup 7 jenis penyakit dan 20 gejala, serta menggunakan metode forward chaining untuk menarik kesimpulan dari fakta-fakta yang tersedia.
Sentiment Analysis of a 271 Trillion Rupiahs Corruption Case Using LSTM Selamet Riadi; Rudi Muslim; Emi Suryadi; Karina Nurwijayanti; M. Zulpahmi; Muhamad Masjun Efendi; Bahtiar Imran
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.v7i1.104

Abstract

Corruption is one of the most pressing issues in Indonesia, significantly affecting public trust in governance and the nation’s development. Among the many corruption cases that have surfaced, the recent 271 trillion rupiah corruption case has drawn widespread attention and public discourse. Understanding the public's perception and sentiment regarding such cases can provide valuable insights into how these issues impact society. Researchers identified an opportunity to leverage sentiment analysis as a method to capture and analyze public sentiment in this context. The dataset for this study was collected from the social media platform Twitter (X) using a data crawling technique. Prior to analysis, preprocessing was performed to clean and prepare the data. After preprocessing, the data was categorized into three sentiment labels: negative, positive, and neutral. To perform sentiment classification, this study utilized the LSTM (Long Short-Term Memory) algorithm, a deep learning method particularly suited for sequential data analysis. The model was trained over a total of 10 epochs. The classification results demonstrated that the LSTM algorithm achieved an accuracy of 0.9365 at the 10th epoch, showcasing its effectiveness in analyzing public sentiment regarding 271 trillion rupiah corruption issues.
FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK Wahyudi, Erfan; Imran, Bahtiar; Zaeniah; Erniwati, Surni; Karim, Muh Nasirudin; Muahidin, Zumratul
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6485

Abstract

This study aims to develop a classification system for traditional Lombok songket fabric patterns using the ResNet50V2 architecture, optimized through fine-tuning and the AdamW optimizer. The data were collected directly from songket artisans in Lombok and categorized into three groups based on the origin of the patterns: Sade, Sukarara, and Pringgasela. The model was trained with data augmentation techniques, including rotation, shifting, and zooming, to increase data diversity. During the training process, fine-tuning was applied to the last layer of ResNet50V2, and optimization was performed using AdamW with a learning rate of 0.0001. The model was evaluated using a confusion matrix, classification report, and analysis of accuracy and loss. The experimental results showed that the model achieved 100% accuracy at the 15th epoch. Furthermore, experiments with different parameters (epochs, batch size, and learning rate) demonstrated that the 15th epoch provided the best results with 100% accuracy, while using higher epochs (30 and 40) did not necessarily yield better outcomes. This model is effective in identifying songket fabric patterns with good classification results for each class. Although the results are excellent, increasing the dataset size and exploring more complex model architectures could further enhance performance. Overall, this study demonstrates the significant potential of deep learning technology in classifying songket patterns with reliable accuracy in real-world applications.
Implementasi Machine Learning untuk Mendeteksi Penyakit Katarak menggunakan Kombinasi Ekstraksi Fitur dan Neural Network Berdasarkan Citra Maspaeni, Maspaeni; Imran, Bahtiar; Hidayat, Alfian; Erniwati, Surni
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 2 (2025): May
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i2.621

Abstract

According to data from the World Health Organization (WHO), more than 1.3 billion people worldwide experience visual impairments, with Cataracts being one of the main causes. Cataracts are an eye condition characterized by clouding of the lens, which can lead to blindness if left untreated. This study aims to accurately detect Cataracts using a combination of feature extraction and neural networks, utilizing digital fundus images. The Dataset used consists of 600 fundus images divided into 80% for training and 20% for testing. The feature extraction process is performed to identify distinctive characteristics of the images relevant to Cataract diagnosis. These features are then analyzed by a neural network to recognize patterns indicative of Cataracts. To optimize performance, this study implements a hypertuning process. Before tuning, the initial model achieved an accuracy of 0.83, with precision, recall, F1-score of 0.83, and an AUC of 0.92. After four stages of hypertuning, the model’s performance improved progressively. The first tuning achieved an accuracy of 0.85, with precision, recall, and F1-score of 0.85, and an AUC of 0.93. In the second tuning, accuracy increased to 0.88, with precision of 0.87, recall of 0.88, F1-score of 0.87, and an AUC of 0.93. The third tuning maintained an accuracy of 0.88, with precision improving to 0.90, recall at 0.87, F1-score of 0.88, and an AUC of 0.94. The fourth tuning delivered the best results, with an accuracy of 0.90, precision of 0.92, recall of 0.89, F1-score of 0.90, and an AUC of 0.94. These results demonstrate that the hypertuning process plays a significant role in improving model performance.
OBJECT DETECTION UNTUK MENDETEKSI CITRA BUAH-BUAHAN MENGGUNAKAN METODE YOLO Saputra, Dede Haris; Imran, Bahtiar; Juhartini
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 2 No. 2 (2023): May 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v2i2.18

Abstract

Perkembangan ilmu pengetahuan dan teknologi dalam Artificial Intelligence yang sangat pesat saat ini, telah membawa perubahan yang sangat pesat pula dalam berbagai aspek kehidupan. Terutama kecerdasan buatan merupakan sebuah teknologi komputer atau mesin yang memiliki kecerdasan layaknya manusia. Sederhananya sebuah instruksi pintar yang diberikan kepada program maupun mesin, salah satunya yaitu Object Detection untuk mendeteksi citra buah menggunakan You Only Look Once (YOLO). Metode yang dapat digunakan untuk pengenalan objek pada citra buah adalah Deep Learning. You Only Look Once (YOLO) merupakan salah satu model Deep Learning yang dapat digunakan untuk pengenalan objek. Penelitian ini bertujuan untuk pengenalan objek pada citra buah menggunakan YOLO. Pada penelitian menggunakan sebanyak 300 gambar data dengan tiga kelas yaitu apel, jeruk dan pisang. Hasil penelitian menunjukan algoritma (YOLO) dapat mengenali objek pada citra buah dengan menggunakan pre-trained weights yang telah dilatih dengan nilai akurasi mAP sebesar 91%.
RANCANG BANGUN SISTEM PAKAR DIAGNOSA PENYAKIT PADA TANAMAN CABAI DENGAN METODE CERTAINTY FACTOR Ndang, Rijalul Mujahidin; Zaeniah; Imran, Bahtiar
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 2 No. 1 (2023): January 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v2i1.22

Abstract

Teknologi telah menjadi kebutuhan yang sangat penting bagi keberlangsungan hidup setiap manusia. Mulai dari hal-hal yang sederhana hingga hal-hal rumit teknologi selalu memegang peranan penting, tak terkecuali pada bidang pertanian. Munculnya berbagai masalah yang dialami oleh para petani khususnya petani cabai seperti masalah penyakit tanaman dan hama tanaman membuat para petani kesulitan dalam mengatasinya. Tak jarang masalah-masalah tersebut mengakibatkan para petani mengalami kerugian besar karena gagal panen. Penelitian ini bertujuan untuk membuat suatu Sistem Pakar Diagnosa Penyakit pada Tanaman Cabai dengan Metode Certainty Factor. Dimana sistem pakar ini merupakan aplikasi berbasis website yang berisi pengetahuan pakar ahli tanaman cabai. Aplikasi ini dapat diakses oleh para petani untuk mendiagnosa dan mengetahui penyakit pada tanaman cabai mereka. Pada aplikasi ini sistem mampu mengidentifikasi 7 jenis penyakit berdasarkan pengetahuan pakar serta terdapat solusi mengenai penyakit pada tanaman cabai, sehingga dapat dilakukan penanganan yang sesuai untuk mengatasi permasalahan tersebut. Selain itu diagnosa penyakit dengan aplikasi ini memilki tingkat akurasi yang cukup baik dan sesuai dengan pengetahuan pakar ahli.
SISTEM PAKAR DIAGNOSA PENYAKIT PADA AYAM MENGGUNAKAN METODE CERTAINTY FACTOR Pratama, Rifqy Hamdani; Juhartini; Imran, Bahtiar
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 2 No. 2 (2023): May 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v2i2.25

Abstract

Ayam adalah hewan yang diternakkan baik ditingkat pengusaha besar sampai perorangan yang ada di desa-desa dan kampung-kampung. Beternak ayam bukanlah hal yang mudah karena banyak hal yang harus diperhatikan seperti penyakit yang diderita ayam. Penyakit ini jika tidak segera diberikan tindakan pengobatan maka dapat berakibat tidak baik bagi ayam dan berarti kerugian bagi peternak. Salah satu faktor yang menyebabkan kerugian adalah kurangnya pengetahuan para peternak tentang penyakit yang menyerang ayam dan bagaimana cara pengobatannya. Oleh karena itu, pada penelitian ini akan dibuat aplikasi Sistem pakar berbasis web untuk memberikan informasi mengenai penyakit dan gejala-gejala pada ayam, sekaligus memberikan solusi dan penanganannya menggunakan metode Certainty Factor. Digunakannya metode certainty factor ini untuk menggambarkan tingkat keyakinan terhadap masalah. Data yang dibutuhkan pada perancangan aplikasi sistem pakar ini adalah data penyakit, data gejala, dan data solusi cara penanganannya.
MACHINE LEARNING-BASED CLASSIFICATION OF SPACE TRAVEL ELIGIBILITY USING SUPPORT VECTOR MACHINE, RANDOM FOREST, AND XGBOOST Zahroni, Teguh Rizali; Imran, Bahtiar; Tahrir, Muhammad; Muh. Akshar; Marroh, Zahrotul Isti’anah
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 2 (2025): May 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i2.310

Abstract

This study applies machine learning classification techniques to predict passenger displacement events based on corrupted data retrieved from a hypothetical interstellar spacecraft mission. Using a cleaned and preprocessed dataset containing demographic, behavioral, and exposure-related features, we compare the performance of three classification models: Random Forest, Support Vector Machine (SVM), and XGBoost. Each model is trained on 80% of the data and evaluated on the remaining 20% using precision, recall, f1-score, and accuracy metrics. The SVM model shows the most notable improvement after feature selection, achieving a balanced performance across metrics. Meanwhile, Random Forest and XGBoost models maintain consistent and robust accuracy above 80% on both training and testing sets. Feature importance analysis also supports the interpretability of the models, particularly in Random Forest and XGBoost. The comparative analysis demonstrates that ensemble-based methods such as Random Forest and XGBoost are more effective in handling the complexity of the dataset, making them suitable for predictive tasks in high-dimensional, partially incomplete data scenarios.
Co-Authors AA Sudharmawan, AA Abba Suganda Girsang, Abba Suganda Ahmad Yani ahmad yani Akbar, Ardiyallah Akhmad Muzakka Alfian Hidayat Amirudin Kalbuadi Anak Agung Istri Sri Wiadnyani Andre Satriawan Atika Zahra Nirmala Baihaki, Makmun Baiq Nonik Ria Riska Baiq Nonik Ria Riska Diki Hananta Firdaus Efendi, Muhamad Masjun Erfan Wahyudi Erniwati, Surni Fachrul Kurniawan Febri, Elin Febriani Giardi, Muh Hamzah Andung Hambali Hambali Hambali Hambali Hambali, H Hamim, Lutfi Hanis Purnamasidi Hasan Basri Hendri Ramdan Hidayatullah, Beni Ari Karim, Muh Nasirudin Karina Nurwijayanti Karya Gunawan Karya Gunawan Lalu Darmawan Bakti Lalu Darmawan Bakti, Lalu Darmawan Lalu Delsi Samsumar, M.Eng. M Zulpahmi M. Zulpahmi M. Zulpahmi Mahayadi, Mahayadi Makmun Baihaki Marroh, Zahrotul Isti’anah Maspaeni Maspaeni Moch Arief Soeleman Moh. Arief Soeleman Muahidin, Zumratul Muh. Akshar Muhammad Rijal Alfian Muhammad Zohri Mutaqin, Zaenul Muttaqin, Athaur Muzakka, Akhmad Ndang, Rijalul Mujahidin Nining Putri Ningsih Nunung Rahmania Nurkholis, Lalu Moh. Pratama, Rifqy Hamdani Purwanto Purwanto Ricardus Anggi Pramunendar Riska, Baiq Nonik Ria Rosida, Sri Rudi Muslim Rudi Muslim Salman Salman Salman Salman Saputra, Dede Haris Selamet Riadi Selamet Riadi Sriasih, Sriasih Subektiningsih Subektiningsih Subki, Ahmad Suharjito Suharjito, Suharjito Suhartono Surni Erniwati Surni Erniwati Suryadi, Emi Tahrir, Muhammad Zaeniah Zaeniah Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zahroni, Teguh Rizali Zenuddin, Z Zulpahmi, M Zulpahmi, M. Zulpan Hadi