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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin
Rohana, Tatang
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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DETECTION OF THE SIZE OF PLASTIC MINERAL WATER BOTTLE WASTE USING THE YOLOV5 METHOD Karyanto, Dony Dwi; Indra, Jamaludin; Pratama, Adi Rizky; Rohana, Tatang
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8535

Abstract

The use of plastic bottles for various needs is increasingly massive, especially in consumption needs such as mineral water bottles. The use of plastic bottles is used to reduce costs and be effective in maintaining the quality of mineral water, but its impact can affect natural conditions if not managed properly. Plastic bottle waste if left buried in the ground will have difficulty expanding, which can cause environmental pollution. Therefore, we can take advantage of technology to sort plastic bottle waste using a camera based on the size of plastic bottles. Differentiating the size of bottles aims to distinguish the economic value when exchanged at the waste bank. This technology utilizes object detection and recognition functions such as the YOLO (You Only Look Once) method. YOLO is a detection method that is a development of the CNN (Convolutional Neural Network) algorithm. By using YOLOv5, we can detect objects in the form of plastic bottle waste of various different sizes. To maximize object detection according to size, data annotation is done by creating a Bounding Box on each dataset according to its size. The test was carried out with several different distance configurations including 40cm, 80cm and 1m. Detection results using YOLOv5 produce up to 84% accuracy in real-time.
Implementasi Algoritma Convolutional Neural Network dan YOLOV8 Untuk Klasifikasi Ras Kucing Adinata, Abdul Rohim; Rohana, Tatang; Baihaqi, Kiki Ahmad; Faisal, Sutan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5913

Abstract

The cat with the scientific name Felis catus is a very popular pet and is often kept in various parts of the world. There are many types or breeds of cats, each of which has its own characteristics and characteristics, such as style, body shape, fur and color. However, because of the many breeds and the uniqueness of each breed, it is often difficult for ordinary people to differentiate between the types of cat breeds that exist. Therefore, technology is needed to identify and differentiate cat breeds. By comparing the Convolutional Neural Network (CNN) and YOLOV8 methods, this research aims to develop a cat breed classification model. This research uses data from six different cat breeds, namely Bengal, Bombay, Himalayan, Local, Persian and Sphynx. There are 1,200 images in all, with 200 images for each race. Before the data is used for training with the CNN and YOLOV8 methods, a preprocessing stage is carried out which includes resize and rescale for the CNN method, while for YOLOV8 a data labeling process is carried out. There are two parts to the dataset: 20% validation data and 80% training data. The training process is carried out with the same parameters for each model, namely a learning rate of 0.001, batch size of 15, and 100 epochs. From the test results with the confusion matrix, the YOLOV8 model shows the best performance with an accuracy value of 99%, precision 96.1%, recall 98.4%, and F1-score 97.2%.
IMPLEMENTASI ALAT UKUR SUHU DAN PH AIR UNTUK BUDIDAYA LOBSTER DENGAN ALGORITMA FUZZY LOGIC BERBASIS IoT Susilo, Adi; Cahyana, Yana; Lestari, Santi Arum Puspita; Rohana, Tatang
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.8749

Abstract

Budidaya lobster air tawar memiliki prospek usaha yang cukup bagus, namun pertumbuhan usaha budidaya lobster yang ada belum selaras dengan permintaan, perihal ini teramati dari rendahnya tingkat produksi serta kualitas produk budidaya. Kualitas suhu dan pH air bisa mempengaruhi aktivitas dalam budidaya lobster air tawar, karena satu di antara faktor yang memberi pengaruh tingkat frekuensi molting serta kanibalisme menjadi rendahnya tingkat produksi budidaya. Agar mengatasi masalah tersebut maka dilakukan penelitian yang membuat alat dengan memanfaatkan algoritma fuzzy logic dan dipadukan teknologi Internet of Things. Berdasarkan capaian dengan melaksanakan uji sebanyak 10 kali, diketahui nilai error rata-rata pada sensor suhu yaitu 0,40% dan nilai error rerata pada sensor pH yaitu 0,22%.
Implementasi Fuzzy Logic Dalam Monitoring Infus Berbasis Internet of Things (IoT) Maulana, Moch Sigit Rizky; Rohana, Tatang; Mudzakir, Tohirin Al
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.699

Abstract

The main function of the infusion is to provide fluids to the patient, the availability of the patient's infusion fluids must always be considered periodically. The increasing number of patients and the limited number of medical personnel is causing uncontrollable delays in changing IV fluids. Blockage in the infusion line can cause air embolism in the blood vessels, which can result in death. By using an automatic infusion monitoring system, the risk of delays in replacing patient infusion fluids can be reduced. To calculate the ambiguity of sensor values, the fuzzy mamdani method was used in this study. Load Cell, HX711 and IR HC-89 are the sensors used. The value generated by the sensor is in the form of NodeMCU ESP32 input which is used by the mamdani method to determine the value in the form of output. The command to turn on the buzzer is the value of the output. Maximizing the effectiveness of the infusion monitoring system is designed with the Mamdani calculation method. The difference in value with an average weight of 5.9% infusion and 5.54 Vo drops is obtained from the results of a comparison of sensor testing with manual tools. Infusion monitoring obtains an accuracy rate of 92% from the test results on system performance.
Kajian Model Jaringan Syaraf Tiruan Untuk Memprediksi Secara Dini Tingkat Kelulusan Mahasiswa Rohana, Tatang; Nurlaelasari, Euis; Awal, Elsa Elvira; Novita, Hilda Yulia
Technologia : Jurnal Ilmiah Vol 15, No 4 (2024): Technologia (Oktober)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v15i4.15583

Abstract

ABSTRAKMasalah: Penelitian ini terkait dengan kajian algoritma jaringan syaraf tiruan untuk memprediksi secara dini tingkat kelulusan mahasiswa. Tujuan: Tujuan penelitian untuk mendeteksi atau memprediksi tingkat kelulusan mahasiswa yang lulus  tepat waktu, sehingga hasilnya diharapkan bisa memberikan kontribusi bagi progam studi dalam menganalisa tingkat kelulusan mahasiswa.Metode: Algoritma yang dipakai meliputi Multilayer Perceptron, Support Vector Machine, dan Decision Tree. Kemudian akan dibandingkan algoritma mana yang memiliki tingkat akurasi yang terbaik dalam memprediksi tingkat kelulusan mahasiswa.Hasil: Berdasarkan hasil penelitian, model Decision Tree memiliki tingkat error rate yang paling baik yaitu 0, model Support Vector Machine sebesar 0.011, dan Multilayer Perceptron 0.029. Berdasarkan  hasil uji performansi dengan Confusion Matrix, model Multilayer Perceptron  memiliki akurasi sebesar 97,1%, Support Vector  Machine 98,9%, dan Decision Tree memiliki akurasi 100%.Kesimpulan: Model Decision Tree memiliki tingkat akurasi terbaik, sehingga algoritma tersebut bisa digunakan dalam membuat sistem  prediksi kelulusan mahasiswa tepat waktu. Untuk penelitian selanjutnya, disarankan untuk menambahkan lebih banyak data mahasiswa agar hasil penelitian bisa lebih baik. Variabel data set juga bisa diperluas tidak hanya dari aspek akademik mahasiswa, tetapi juga dari aspek non-akademik dan latar belakang ekonomi keluarga, seperti pendapatan orang tua, status pekerjaan mahasiswa, dan variabel lainnya.Kata kunci: Jaringan Syaraf, Prediksi, Multilayer Perceptron, Support Vector Machine, Decision Tree  
Implementasi Fuzzy Logic Dalam Monitoring Infus Berbasis Internet of Things (IoT) Maulana, Moch Sigit Rizky; Rohana, Tatang; Mudzakir, Tohirin Al
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.699

Abstract

The main function of the infusion is to provide fluids to the patient, the availability of the patient's infusion fluids must always be considered periodically. The increasing number of patients and the limited number of medical personnel is causing uncontrollable delays in changing IV fluids. Blockage in the infusion line can cause air embolism in the blood vessels, which can result in death. By using an automatic infusion monitoring system, the risk of delays in replacing patient infusion fluids can be reduced. To calculate the ambiguity of sensor values, the fuzzy mamdani method was used in this study. Load Cell, HX711 and IR HC-89 are the sensors used. The value generated by the sensor is in the form of NodeMCU ESP32 input which is used by the mamdani method to determine the value in the form of output. The command to turn on the buzzer is the value of the output. Maximizing the effectiveness of the infusion monitoring system is designed with the Mamdani calculation method. The difference in value with an average weight of 5.9% infusion and 5.54 Vo drops is obtained from the results of a comparison of sensor testing with manual tools. Infusion monitoring obtains an accuracy rate of 92% from the test results on system performance.
Bank Customer Segmentation Model Using Machine Learning Bunga Tiara, Vira; Siregar, Amril Mutoi; Kusumaningrum, Dwi Sulistya Kusumaningrum; Rohana, Tatang
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.75233

Abstract

Banks generally carry out marketing strategies by offering deposit products directly to customers. However, this method is less effective because it requires individualized communication without considering the customer's interest in the product offered. Therefore, this research aims to categorize the classification of bank customers into Yes and No. This research uses a dataset of bank deposits taken from KTM. This research uses a bank deposit dataset taken from Kaggle, the data consists of 11162 rows with 17 attributes.  PCA technique was used for feature selection which was optimized by reducing the dimensionality of the dataset before modeling. It was found that the best model accuracy was SVM RBF kernel with C parameters achieving 80.51% accuracy and ANN 80.78%, but ANN showed a higher ROC graph than SVM because ANN performance results were faster than SVM. Thus, the overall performance measurement of ANN is much better.
PEMODELAN INSPEKSI PAINTING DEFECT PADA MOBIL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Ramadhan, Muchamad Fachrul; Fauzi, Ahmad; Wahiddin, Deden; Rohana, Tatang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1519

Abstract

Quality control is an important process carried out at the last stage of the production process, this activity is carried out by checking a product. Painting defects on cars are a problem that must be considered in the car production process at car companies. The perfection of a product is important to increase the level of customer satisfaction. These checking activities are still carried out manually with human power, which can still cause defective products to be missed in a production process that occurs as a result of human error. The use of artificial intelligence can be used to detect image and video objects, used to overcome the problem of human error in carrying out checks. Convolutional Neural Networks (CNN) is an algorithm that can be used in product defect inspection, image recognition, and image classification. The study focuses on modeling the inspection and detection of painting defects in cars using CNN, emphasizing the importance of quality control in ensuring product quality. The CNN model is trained with image data of normal car paint and defective car paint, and evaluated using a confusion matrix for optimal parameters. The results show quite high accuracy in detecting car paint defects of 98% with the help of the ResNet50 transfer learning CNN architecture.
PENERAPAN ALGORITMA SUPPORT VECTOR MACHINES DAN RANDOM FOREST DALAM ANALISIS SENTIMEN ULASAN APLIKASI IDENTITAS KEPENDUDUKAN DIGITAL Ramadhan, Rizky Agung; Rohana, Tatang; Mudzakir, Tohirin Al; Wahiddin, Deden
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1595

Abstract

The Digital Population Identity (IKD) application, developed by the Directorate General of Population and Civil Registration, aims to streamline access to digital documents and reduce reliance on printed KTPs. Despite its benefits, user reviews from the Play Store highlight significant issues. This research aims to analyze user sentiment towards the IKD application using Support Vector Machines (SVM) and Random Forest algorithms. The study employed these models to classify sentiment in user reviews and used word cloud analysis to further understand the feedback. Results indicate that both the Random Forest and SVM models struggled with accuracy, achieving only 19.25% and 18% respectively. The word cloud analysis revealed a high prevalence of negative reviews, reflecting the app's low rating. These findings suggest that the current sentiment analysis methods are insufficient for capturing the public's opinion on the IKD application, providing crucial insights for improving future digital population identity management strategies.
Analisis Prediksi Banjir di Indonesia Menggunakan Algoritma Support Vector Machine dan Random Forest Purnomo, Indarto Aditya; Indra, Jamaludin; Awal, Elsa Elvira; Rohana, Tatang
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5958

Abstract

Natural disasters frequently occur in Indonesia, such as floods, landslides, and volcanic eruptions. Geological factors, such as the convergence of four major tectonic plates, make Indonesia vulnerable to natural disasters. Statistical data from the National Disaster Management Agency show an increase in flood occurrences each year, peaking in 2021 with 1,794 incidents. Early anticipation is necessary to minimize the impact of natural disasters, and predictive patterns are becoming new knowledge for preventing and managing these disasters. This study applies the Support Vector Machine and Random Forest algorithms. The results of this study predict that the largest number of floods from 2024 to 2026 in Indonesia will occur in Aceh with 240 floods, North Sumatra with 215 floods, West Java with 210 floods, and Central Java with 160 floods. The best algorithm comparison results were achieved with Random Forest, which had an accuracy of 99.6% and an average RMSE value of 3.834.
Co-Authors Abigael, Rakha Adi Susilo Adinata, Abdul Rohim Agustin, Rachmayanti Tri Antoni, Alan Awal, Elsa Elvira Azhari, Febrian Akbar Baihaqi, Kiki Ahmad Bayu Prasetyo, Bayu Bunga Tiara, Vira Butar Butar, Naomi Nova Meylica Damaiarta Tejayanda, Rigger Faisal, Muhamad Agus Faisal, Sultan Faisal, Sutan Farah Maulida, Oxana Fauzi Ahmad Muda Fauzi, Ahmad Fauzi, Rifqi Arul Hakim, Mirwan Abdurrahman Hartini, Dina Indra, Jamaludin Irawan, Muhamad Anggi Jamal, Malikil Juwita, Ayu Ratna Kamilah, Nur Azizah Karyanto, Dony Dwi Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Madani, Puja Milenia Sriwildan Maulana, Moch Sigit Rizky Mudzakir, Tohirin Al Muhammad Khaerudin Niendha Biell Binna Novita, Hilda Yulia Nurlaelasari, Euis Nurmayanti, Trisya Nursyawalni, Reva NURUL AZIZAH Pratama, Adi Rizky Pratama, Irfan Nugraha Purnomo, Indarto Aditya Rahmat Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Rizky Agung Ratna Juwita, Ayu Siregar, Amril Mutoi Sukmawati, Cici Emilia tukino, tukino Wahiddin, Deden Winarni Yana Cahyana Yulia Novita, Hilda