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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro Lontar Komputer: Jurnal Ilmiah Teknologi Informasi 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) 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
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 Informasi Kepuasan Layanan Administrasi Akademik Berbasis IPA (Importance Performance Analysis) Studi Kasus Fakultas Teknik Universitas Mataram Syaifullah Syaifullah; I Gede Pasek Suta Wijaya; Ario Yudo Husodo
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 2 No 1 (2018): Juni 2018
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1253.86 KB) | DOI: 10.29303/jcosine.v2i1.50

Abstract

This paper presents a Satisfaction Information Systems of Academic Administration Services which is a tool to provide quality assessment of Academic Administration Services of Engineering Faculty, Mataram University. The quality assessment is determined by Importance Performance Analysis (IPA) method of survey data. This information system is designed and built by using CodeIgniter framework with PHP and HTML. The experimental results show that the proposed system has been running properly, which are indicated by a “fairly satisfied” achievement of Academic Administration Services of Engineering Faculty, Civil, Electrical, Mechanical and Informatics Engineering Dept. Based on MOS parameter on students (MOS = 4.45) and admin (MOS = 4.00), show that the system has been running properly.
Pengenalan Wajah Untuk Sistem Kehadiran Menggunakan Metode Eigenface dan Euclidean Distance Fahmi Syuhada; I Gede Pasek Suta Wijaya; Fitri Bimantoro
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 2 No 1 (2018): Juni 2018
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1113.637 KB) | DOI: 10.29303/jcosine.v2i1.74

Abstract

he development of Information Technology allows faces that are one part of identity that has been widely be used as biometric based security system. This research is a process of designing and making of identification system which is able to detect of human attended. This system implements: viola-jones, eigenface and euclidean distance methods. Viola-jones method is employed as face detection. Eigenface method is used to reduce the face image and to get the weight value. The euclidean distance is impemented as a classification method. The accuracy of this system against 30 register subjects and 15 unregistered subjects is 84%. This accuracy is obtained after the addition of preprocessing process and adaptive training model on the system. Therefore, this research concluded that viola-jones method is very good as face detection, however need to be added face correction process. Meanwhile, eigenface and euclidean distance methods provide good results to recognize face when many training faces are given. The more variations faces of subject are, the higher accuracy is given.
Predict A Person's Personality Based On The Shape of Handwriting of The Letters "i", "o", and "t" Using The Levenberg Marquardt Backpropagation Method Annisa Mujahidah Robbani; I Gede Pasek Suta Wijaya; Fitri Bimantoro; Heri Wijayanto
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.v5i2.178

Abstract

The literature shows that Graphology is common and relatively useful in our life. For example, as one of the job requirements. Professional organizations hire a professional handwriting analyst called Graphologist to analyze the characteristic traits of the candidates by identified their handwriting. However, the accuracy of handwriting analysis depends on how skilled the graphologist is, two graphologists which predict the same handwriting may give us a different result of the prediction. To improve the accuracy, we develop a system that can automatically predict a person’s personality based on the shape of the handwriting of the letters "i", "o", and "t" using the Levenberg Marquardt Backpropagation method. Based on this research we got the maximum accuracy by using 2 hidden layers. We got 71,42% of accuracy for the letter “i”, 76,92% of accuracy for the letter “o”, and 60% of accuracy for letter the “t”.
Pengenalan Pola Tulisan Tangan Suku Kata Aksara Sasak Menggunakan Metode Moment Invariant dan Support Vector Machine Riska Yulianti; I Gede Pasek Suta Wijaya; Fitri Bimantoro
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 3 No 2 (2019): December 2019
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1285.209 KB) | DOI: 10.29303/jcosine.v3i2.181

Abstract

The research of Javanese and Balinese ancient script have been done by some researches. However, the researches still have problems, such as image scaling, noise reduction and image transformation. This research implemented moment invariant and support vector machine to solve these problems especially on Sasak ancient script. The input data used in this research was 2700 handwritten Sasak ancient script. The testing was done to know the effect of thinning and the number of feature by using zoning on the classification performance. Accuracy is used as performance indicator. The highest average accuracy is 89.76%, on the second scenario, the average accuracy obtained is 92.52%.
Jaringan Syaraf Tiruan Model Backpropagation untuk Peramalan Suhu Minimum dan Maksimum, Kelembaban, Tekanan Udara, Jumlah Hari Hujan, dan Curah Hujan Bulanan di Kota Mataram Ida Nyoman Tegeh Adnyana; I Gede Pasek Suta Wijaya; Mohammad Ali Albar
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 3 No 2 (2019): December 2019
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1133.333 KB) | DOI: 10.29303/jcosine.v3i2.259

Abstract

Climate conditions are very influential on various sectors of human life, such as agriculture. Extreme climate can affect the planting and harvest period which can eventually lead to crop failure. Climate forecasting can be done to help mapping the planting period so that agricultural productivity is optimal. Artificial neural network is a method that can be used to forecast climate in the future. In this research, a backpropagation model was used to forecast monthly data on minimum and maximum temperatures, humidity, air pressure, number of rainy days, and rainfall in Mataram city. The accuracy of forecasting result is measured based on MSE (mean square error). Based on the research conducted, the MSE of testing phase are minimum temperature 3.59 x 10-3, maximum temperature 1.97 x 10-4, humidity 2.11 x 10-3, air pressure 2.50 x 10-3, number of rainy days 7.97 x 10-4, and rainfall 1.00 x 10-3.
Sistem Pakar Diagnosa Penyakit Kulit pada Manusia dengan Metode Dempster Shafer Anita Rosana MZ; I Gede Pasek Suta Wijaya; Fitri Bimantoro
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 4 No 2 (2020): December 2020
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v4i2.285

Abstract

Skin is the broadest organ in the human body that covers the entire surface of the body and acts as a support for human life. Because of its outermost location, the skin is often attacked by various diseases. This research aims to build an expert system to diagnose 10 types of skin diseases caused by viruses, fungi, bacteria, and parasites based on the knowledge of 3 experts using the Dempster Shafer method to obtain conclusions of skin diseases. Each symptom of skin disease has a value of belief that is used to calculate conclusions in the Dempster Shafer method. Expert system applications that are built can run Android-based smartphones. Testing techniques used in this research are black-box testing, theoretical calculations, system accuracy and MOS (Mean Opinion Score). Comparison of the calculation on the system has been appropriate based on the theoretical calculations. System accuracy testing in 30 sample cases resulted in an accuracy of 90%, but if it was seen as a subsection of expert diagnosis, it resulted in a system accuracy of 92.22%. MOS testing on 30 respondents results in a MOS value of 4.24 from a scale of 5 which shows that the system is proper to use and categorized into a good system.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.903 KB) | DOI: 10.29303/jcosine.v4i1.286

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.865 KB) | DOI: 10.29303/jcosine.v4i1.302

Abstract

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

Abstract

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)
Co-Authors Adi Sugita Pandey Afwani, Royana Agitha, Nadiyasari Ahmad Musnansyah Ahmad Zafrullah Mardiansyah Akhyar, Halil Albar, Moh. Ali Aldian Wahyu Septiadi 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 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 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