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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Education and Learning (EduLearn) Journal of Telematics and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal Sains Teknologi dan Lingkungan (JSTL) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Computer Science and Informatics Engineering (J-Cosine) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Multimedia Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) TIERS Information Technology Journal Jurnal Begawe Teknologi Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi

I Gede Pasek Suta Wijaya

Informatics Engineering Dept., University Of Mataram, Jl. Majapahit 62 Mataram, Mataram INDONESIA

Author-ID : 417288

Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Sistem Pakar Diagnosis Penyakit pada Ayam dengan Menggunakan Metode Dempster Shafer Salsabila Putri Rajani Said; I Gede Pasek Suta Wijaya; Fitri Bimantoro
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 4 No 1 (2020): June 2020
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Chicken is one type of poultry that has many benefits, so the chicken can be an option for livestock. This research was conducted to create an expert system that helps provide information to farmers about poultry diseases, especially broilers. This expert system is built on the Android platform and uses the Dempster Shafer calculation method to get the diagnosis of chicken disease. The data used in this study consisted of 38 symptoms and 10 diseases data which were limited to diseases caused by bacteria and viruses. Each symptom has the value of belief given by 3 veterinarians. This study used four types of testing in the form of black-box testing, questionnaire testing, theoretical testing, and accuracy testing. The results of the accuracy testing of the 30 cases given are 92.22% and the system accuracy is 93.33% if the system diagnosis results are assumed to be valid because it is a subsection of expert diagnosis. For questionnaire testing using the MOS, parameters obtained 4.58 results from a scale of 5, as well as theoretical calculation tests that get the same calculation results between the results of expert diagnoses and system diagnoses. Based on the test results, the system built is good and appropriate.

Klasifikasi Jenis dan Tingkat Kesegaran Daging Berdasarkan Warna, Tekstur dan Invariant Moment Menggunakan Klasifikasi LDA: Classification of Type and Freshness Level of Meat Based on Color, Texture and Invariant Moment Using LDA Classification Siti Faria Astari; I Gede Pasek Suta Wijaya; Ida Bagus Ketut Widiartha
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 5 No 1 (2021): June 2021
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v5i1.289

Distribution process that takes a long time along with improper treatment, can cause meat become not fresh and decrease the quality of the meat. Therefore, unscrupulous meat sellers cheating on the non fresh meat by mixing the non fresh meat with the fresh one. A system that can classify the type and freshness level of meat automatically is needed. In this research, that system was developed based on texture, color and shape features using Linear Discriminant Analysis (LDA) classification. The methods used in the feature extraction process are statistical approach, GLCM and the HU's invariant moment. The total of data used in this research was 960 images of 3 different meat types which are chicken meat, goat meat, and beef. The highest accuracy obtained from the testing process was 90% on the combination features of HSI and invariant moment for the meat type in refrigerator.

Expert System of Diagnosing Building Damage due to Earthquake using Backpropagation Artificial Neural Network Method Topan Khrisnanda; Ida Bagus Ketut Widiartha; I Gede Pasek Suta Wijaya
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 4 No 1 (2020): June 2020
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Earthquake is one of the most destructive natural disasters. After the earthquake, experts were deployed to survey the damage that occurred. One of the main objectives of the assessment task carried out by experts is to evaluate and classify buildings into several categories based on the level of damage that occurs. In this study, an expert system that could facilitate the assessment of building damage due to the earthquake was made using Backpropagation neural network method. The testing techniques used in this system are blackbox, accuracy and Mean Opinion Score (MOS) testing. MOS testing conducted by 30 respondents produced an MOS value of 4.54 from a scale of 5. While the average accuracy of the system obtained is 82.22% of the 30 case cases tested by 3 building damage experts.

Analisis Pengenalan Pola Daun Berdasarkan Fitur Canny Edge Detection dan Fitur GLCM Menggunakan Metode Klasifikasi k-Nearest Neighbor (kNN): Leaf Pattern Recognition using Canny Edge Detection and GLCM with k-Nearest Neighbor (kNN) Azizah Arif Paturrahman; I Gede Pasek Suta Wijaya
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 5 No 1 (2021): June 2021
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v5i1.388

Leaves are one of the parts of plants that can be applied in the process of identifying species images of leaves. The process of classification of leaf imagery can be done by identifying the image of the leaf shape that can be done by identifying the pattern of the leaf by recognizing the structural characteristics of the leaf such as the shape and texture of the leaf. The classification of this leaf image is based on the canny edge detection and gray-level co-occurrence matrix (GLCM) features using the k-Nearest Neighbor (kNN) classification method. The data used is as many as 350 leaf imagery with seven different species. The results show that from the two extraction features used, the Canny feature gets an accuracy of 80% and, the GLCM features gets an accuracy of 93.3%. And the merging of the two features resulted in an increased accuracy of 98%. It can be concluded that this research has produced good accuracy in identifying leaf imagery based on canny edge detection features and Gray-Level Co-occurrence Matrix (GLCM) features and k-Nearest Neighbor classifier method. Key words -- Leaf, canny edge detection, gray-level co-occurrence matrix (GLCM), the k-nearest neighbor (kNN)

Ekstraksi Fitur Citra Radiografi Thorax Menggunakan DWT dan Moment Invariant: Feature Extraction of Thorax Radiography Image Using DWT and Moment Invariant I Gede Pasek Suta Wijaya; Ditha Nurcahya Avianty; Fitri Bimantoro; Rina Lestari
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 5 No 2 (2021): December 2021
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v5i2.423

COVID-19 is an infectious disease caused by the coronavirus family, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The fastest method to identify the presence of this virus is a rapid antibody or antigen test, but confirming the positive status of a COVID-19 patient requires further examination. Lung examination using chest X-ray images taken through X-rays of COVID-19 patients can be one way to confirm the patient's condition before/after the rapid test. This paper proposes a feature extraction model to detect COVID-19 through chest radiography using a combination of Discrete Wavelet Transform (DWT) and Moment Invariant features. In this case, haar wavelet transform and seven Hu moments were used to extract image features in order to find unique features that represent chest radiographic images as suspected COVID-19, pneumonia, or normal. To find out the uniqueness of the proposed features, it is coupled with the kNN and generic ANN classification techniques. Based on the performance parameters assessed, it turns out that the wavelet-based and moment invariant thorax radiographic image feature model can be used as a unique feature associated with three categories: Normal, Pneumonia, and Covid-19. This is indicated by the accuracy value of 82.7% in the kNN classification technique and the accuracy, precision, and recall of 86%, 87%, and 86% respectively with the ANN classification technique.

Rancang Bangun Purwarupa Simulasi Sistem Keamanan Rumah Berbasis Internet of Things: Design and Implement of IOT-based Home Security Simulation Prototype Saputra, Muhammad Harpan Teguh; Arimbawa, I Wayan Agus; Wijaya, I Gede Pasek Suta
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 8 No 1 (2024): Juni 2024
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v8i1.217

The use of information technology has spread to all fields, for example in the field of security using the Internet of Things technology. One of the places that need to be given security is the home, so that in this study prototypes of home security systems using the Internet of Things have been made with the aim of providing information to homeowners in the event of infiltration through windows or doors, gas leaks and fires. The tools used are Arduino Mega 2560 as a microcontroller, gas sensor, fire sensor, magnetic switch sensor, RFID, keypad, and selenoid. The home security system is made so that homeowners can control the condition of the home. The results of this study are that the home security systems that are made can run well and can be integrated with home security information systems. Sensors can detect gas leaks, fires, and doors or windows open when the security system is active. Homeowners can only turn off the home security system and unlock the door by using RFID tags that have registered IDs and correct passwords. Home security systems can also turn on alarms and send notifications to homeowners, fire stations and police stations in the event of a fire, gas leak or window or door that is opened when the security system is active.

Pengenalan Pola Tulisan Tangan Suku Kata Aksara Sasak Menggunakan Metode Integral Projection dan Neural Network Dina Juliani U M, Eka; Wijaya, I Gede Pasek Suta; Bimantoro, Fitri
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 3 No 1 (2019): June 2019
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

This paper presents sasak ancient scripts using integral projection and neural network. The purpose of using these two methods is to find out how to work this methods and how much accuracy is obtained in pattern recognition of sasak ancient scripts. The data used is 1260 handwritten image data. Testing is done by knowing the effect of the number of nodes in a hidden layer and the effect of the number of hidden layers in a network. The highest accuracy on average is in the use of 2 hidden layers where 21 nodes for the first hidden layer and 14 nodes for the second hidden layer. The experiment resulted in the obtained accuracy rate of 41.38%.

Klasifikasi Citra Lubang pada Permukaan Jalan Beraspal dengan Metode Convolutional Neural Networks (CNN): Image Classification of Potholes on Paved Road Surfaces with the Convolutional Neural Networks (CNN) Method Ni Nyoman Citariani Sumartha; I Gede Pasek Suta Wijaya; Fitri Bimantoro; Gibran Satya Nugraha
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 8 No 1 (2024): Juni 2024
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v8i1.557

A pothole is a bowl-shaped indentation in the road surface, less than 1 meter in diameter. The presence of potholes on the highway can endanger the safety of road users, so repairs need to be done as soon as possible. Images of potholed roads have high complexity, variations consisting of color contrast, hole size, presence of puddles or not, lighting when taking pictures, background and others. For this reason, an approach is needed that can classify images with a high degree of variation by extracting the important information contained in them. Judging from the potential success of using the Convolutional Neural Networks (CNN) approach in identifying images of potholes that will be reported for entry into the Public Works Service's road improvement record, the authors propose the idea of "Pothole Image Classification on Asphalt Road Surfaces with the Convolutional Neural Networks (CNN) Method”.

Perancangan Mesin Klasifikasi Menggunakan Particle Swarm Optimization: Designing A Classification Machine Using Particle Swarm Optimization Made Agus Dwiputra; I Gede Pasek Suta Wijaya; Ramaditia Dwiyansaputra
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 8 No 2 (2024): Desember 2024
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v8i2.614

Designing an effective classification engine is very important in various pattern recognition and machine learning applications. In this research, the Particle Swarm Optimization (PSO) algorithm is applied for the development of classification engines on various datasets. PSO is a population-based optimization method inspired by the behavior of flocks of birds or fish, which is effectively used to find optimal solutions in large search spaces. This research aims to develop a classification model by using Particle Swarm Optimization (PSO) as a training element to determine weights and biases. To test the performance on several different datasets, namely on a dummy multi-class dataset, Sasak Aksara image dataset, and the well-known Iris dataset. In the Sasak Aksara data, Discrete Cosine Trasnform (DCT) is used as feature extraction with the aim of reducing computation time. The results show that PSO can be used in the implementation of several datasets used, in the classification of dummy data, iris data, and Sasak Aksara image data. The model achieved 100% accuracy, precision, recall, and F1-Score on dummy data and iris data. However, on the Sasak Aksara image dataset, the performance of the model decreased with accuracy only reaching 65%, precision 50%, recall 32%, and F1-Score 39%. This research contributes in demonstrating the effectiveness of PSO in optimizing Perceptron models on simpler datasets and highlights the need for further development to handle more complex datasets.

Multitask Aspect-Based Sentiment Analysis of Indonesian Tweets on Mandalika Circuit using CNN and IndoBERTweet Embeddings Salsabila, Raissa Calista; Dwiyansaputra, Ramaditia; I Gede Pasek Suta Wijaya
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 9 No 2 (2025): December 2025
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v9i2.658

This study proposes a multitask Aspect-Based Sentiment Analysis (ABSA) model for Indonesian tweets related to the Mandalika Circuit, using IndoBERTweet embeddings and Convolutional Neural Networks (CNN). The model simultaneously predicts aspect categories and sentiment polarities. Two experimental setups were evaluated: one using raw tweets (Scenario 1) and another with preprocessed text (Scenario 2). The results show that Scenario 1 consistently outperforms Scenario 2, highlighting the ability of IndoBERTweet to handle informal tweet structures without requiring standard text cleaning. A paired t-test was conducted to evaluate statistical differences in performance between scenarios. While Scenario 1 showed higher average F1-scores, the p-value (0.7178) suggests no statistically significant improvement across all classes. Further analysis reveals that certain classes, primarily neutral and positive sentiments, tend to perform worse than negative sentiments. Data augmentation was shown to improve recall and help the model handle underrepresented classes, particularly for “Ekonomi-Negative” and “Fasilitas-Negative” labels. The study highlights the importance of preserving informal language structures and utilizing data augmentation to enhance ABSA performance on real-world tweet data.

Co-Authors Adi Sugita Pandey Afwani, Royana Agitha, Nadiyasari Ahmad Musnansyah Ahmad Zafrullah Mardiansyah Albar, Moh. Ali Aldian Wahyu Septiadi Aliyah Fajriyani Andy Hidayat Jatmika Anita Rosana MZ Annisa Mujahidah Robbani Anugrah, Febrian Rizky Aprilla, Diah Mitha Aranta, Arik Ariessaputra, Suthami Arik Aranta Arik Aranta Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo, Ario Yudo Ariyan Zubaidi Ariyan Zubaidi Awaluddin Ayu Rezki Azizah Arif Paturrahman Belmiro Razak Setiawan Budi Irmawati Budi Irmawati Bulkis Kanata Chaerus Sulton Chandra Adiguna Chandra Adiguna Cipta Ramadhani Darmawan, Riski David Arizaldi Muhammad Dedi Ermansyah Dina Juliani U M, Eka Ditha Nurcahya Avianty Dwitama, Aditya Perwira Joan Dwiyansaputra, Ramaditia Eet Widarini Fa'rifah, Riska Yanu Fachry Abda El Rahman Fadilah . Fahmi Syuhada Faqih Hamami Farhan Yakub Bawazir Fiena Efliana Alfian Firdaus, Asno Azzawagaam Fitrah, Muhammad Dinul Fitri Bimantoro Gibran Satria Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gou Koutaki Gunawan Haidra Rahman Halil Akhyar Hamidi, Mohammad Zaenuddin Hendy Marcellino Heri Wijayanto Heri Wijayanto Heri Wijayanto Hidayat, Lalu Ramdoni I B K Widiartha I Gde Putu Wirarama Wedaswhara W. I Made Budii i Suksmadana I Made Subiantara Putra I Putu Teguh Putrawan I Wayan Agus Arimbawa I Wayan Agus Arimbawa I Wayan Agus Arimbawa, I Wayan Agus Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Nyoman Tegeh Adnyana Imam Arief Putrajaya Jayusman, Dirga Jo, Minho Kadriyan, Hamsu Kansha, Lyudza Aprilia Keeichi Uchimura Keiichi Uchimura Keiichi Uchimura L. A. Syamsul Irfan Lalu Sweta Arif Lalu Zulfikar Muslim Lidia Ardhia Wardani Made Agus Dwiputra Mayzar Anas Maz Isa Ansyori Mega Laely Moh Ali Albar Moh. Ali Albar Muhamad Nizam Azmi Muhamad Syamsu Iqbal Muhammad Daden Kasandi Putra Wesa Muhammad Husnul Ramdani Muhammad Khaidar Rahman Muhammad Mukaddam Alaydrus Muhammad Naufal Rizqullah Muhammad Syulhan Al Ghofany Mulyana, Heru Murpratiwi, Santi Ika Mustiari, Mustiari Ni Nyoman Citariani Sumartha Ni Nyoman Kencanawati Nisa, Aisyah Khairun Novian Maududi Novita Nurul Fakhriyah Nugraha, Gibran Satya Nurhalimah Nurhalimah Obenu, Juanri Priskila Pahrul Irfan Pahrul Irfan Pandu Deski Prasetyo Putra, Chairul Fatikhin Rahmatin, Baiq Anggita Arsya Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramdhani, Ghina Kamilah Ramlah Nurlaeli Rani Farinda Reza Rismawandi Rina Lestari Riska Yulianti Ristirianto Adi Romi Saefudin Rosalina Rosalina Salsabila Putri Rajani Said Salsabila, Raissa Calista Santi Ika Murpratiwi Saputra, Muhammad Harpan Teguh Satya Nugraha, Gibran Selvira Anandia Intan Maulidya Setiawan, Lalu Rudi Siti Faria Astari Sri Endang Anjarwani Sri Endang Anjarwani Sri Endang Arjarwani Suhada, Destia Suksmadana, I Made Budi Sulfan Akbar Syaifullah Syaifullah Topan Khrisnanda Tri Erna Suharningsih Ulandari, Alisyia Kornelia Wahyu Alfandi Widodo, Agung Mulyo Wirarama Wedashwara Wisnujati, Andika Yogi Permana Yudo Husodo, Ario Zafrullah, Ahmad Zakiyah Rahmiati Zubaidi, Ariyan Zuhraini, Marlia Zul Rijan Firmansyah

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