Journal of Computer Science and Informatics Engineering (J-Cosine) 2021-07-05T16:24:29+08:00 I Wayan Agus Arimbawa Open Journal Systems <p>J-COSINE (Journal of Computer Science and Informatics Engineering) merupakan jurnal dibidang Ilmu Komputer dan Teknik Informatika yang dipublikasi oleh Program Studi Teknik Infroamtika Fakultas Teknik Universitas Mataran dengan <em><strong>online ISSN 2541-0806</strong></em> dan <em><strong>print ISSN &nbsp;2540-8895.&nbsp;</strong></em>J-Cosine juga merupakan&nbsp;&nbsp;<em>blind</em>&nbsp;&nbsp;dan&nbsp;<em>peer-review jurnal</em> yang proses reviewenya dilakukan oleh sekurang-kurangnya &nbsp;2 orang reviewer yang ditujuk oleh&nbsp;associate editor.&nbsp;Jumlah terbitaan dari J-Cosine sebanyak 2 kali dalam setahun.</p> <p>Tujuan&nbsp;&nbsp;utama dari&nbsp;J-Cosine adalah untuk mempublikasikan paper hasil penelitian, inovasi aplikasi, studi perbandingan &nbsp;yang&nbsp;berkualitas baik&nbsp;dan&nbsp;mengikuti&nbsp;perkembangan dan tren teknologi baru dibidang&nbsp;Ilmu Komputer dan Teknik Informatika. Paper yang dipublikasikan pada J-Cosine dapat ditulis dalam bahasa Indonesia atau bahasa Inggris.</p> Implementasi IoT Untuk Early Warning System(EWS) Pada Tambak Udang Vaname 2021-07-01T06:55:01+08:00 Muhamad Wisnu Alfiansyah I Gede Putu Wirarama Wedashwara W Ahmad Zafrullah Mardiansyah <p>Vaname shrimp is a type of aquaculture that has a high economic value and has relatively fast growth. Good water quality is key in the cultivation of vaname shrimp on ponds. However, monitoring of pond water conditions is still done manually and not done intensively. Internet of Things (IoT) and Early Warning System (EWS) as the latest technology can be a solution in periodic pond monitoring activities and give early warning to farm owners. Double Exponential Smoothing Holt is one method for forecasting in time series. In this study, the system predicts the potential for endangering the pond conditions, when there are indications of danger, it will inform the user via Telegram chat. The accuracy test value of the forecasting method used for water temperature is the MSE value of 0.01954 and MAPE of 9.0859%. Whereas for the water pH, the MSE value is 0.000155 and MAPE of 0.828%.</p> 2021-06-25T22:09:16+08:00 Copyright (c) 2021 Muhamad Wisnu Alfiansyah, I Gede Putu Wirarama Wedashwara W, Ahmad Zafrullah Mardiansyah Sistem Informasi Geografis Pemantauan Status Gizi Balita di Kabupaten Lombok Barat untuk Pemeringkatan Daerah Rawan Gizi dengan Menggunakan Metode Analytical Hierarchy Process 2021-07-05T16:24:29+08:00 Sri Endang Arjarwani qori amalia fitrasani Ida Bagus Ketut Widiartha <p><strong><em>This research was triggered by the increasing number of malnutrition cases in West Lombok Regency that caused the mortality rate of children under five years old is also increasing. According to West Lombok Public Health Office, there were 49 malnutrition cases in West Lombok Regency in 2015. This number increased to 98 cases in 2016. It shows that West Lombok Public Health Office did not control and monitor these cases optimally.</em></strong></p> <p><strong><em>This research is aimed to provide the malnutrition status information and other related nutritional information, also to let the citizen send their feedbacks to the Public Health Office and Community Health Clinic. This system was built with Codeigniter framework with PHP and HTML as its programming languages. It is also integrated with the Google Maps API to show the maps of nutritional problems, health facilities, and poverties. This system was built with waterfall model as its System Development Method and Analytical Hierarchy Process (AHP) method to determine the nutritional areas. </em></strong></p> <p><strong><em>This system was tested with blackbox, whitebox and MOS. Whitebox was used to test the Analytical Hierarchy Process and the result was corresponding to the manually done calculation. Meanwhile the testing using blackbox showed that the system has been running well. The testing using MOS showed that the average assessment of public respondents to the system stated strongly agree, agree and fair were 39.04%, 55.24%, and 5.72% respectively. </em></strong></p> <p><strong><em>Keywords: Geographical Information System, Toddler’s nutritional status, Nutritional Areas, AHP</em></strong></p> 2021-06-25T18:50:35+08:00 Copyright (c) 2021 qori amalia fitrasani Accuracy Analysis of Predictive Value in Transaction Data of Service Company Using Combination of K-Means Clustering and Time Series Methods 2021-07-01T06:54:19+08:00 Santi Ika Murpratiwi Dewa Ayu Indah Cahya Dewi Arik Aranta <p>Profit decline is a frightening problem for service companies. The solution to prevent this is by analyzing data transactions using data mining and forecasting. K-Means used to cluster the level of car damage based on the number of panels repaired and the duration of repaired. The results of K-Means used as material for analysis the best time-series method for transaction data. The methods analyzed include the moving average, single exponential smoothing, double exponential smoothing, and winter's method. Single exponential smoothing is the most suitable forecasting method with transaction data. Based on the MAPE value obtained for minor damage of 12.58%, forecasting for moderate damage of 16.83%, forecasting for major damage of 17.31%, and forecasting for overall data of 8.0975%. It concluded that single exponential smoothing can apply with K-Means clustering and the company can use it to make strategies to prepare the number of workers and production materials required.</p> 2021-06-25T22:10:35+08:00 Copyright (c) 2021 Santi Ika Murpratiwi, Dewa Ayu Indah Cahya Dewi, Arik Aranta Klasifikasi Jenis Dan Tingkat Kesegaran Daging Berdasarkan Warna, Tekstur Dan Invariant Moment Menggunakan Klasifikasi LDA 2021-07-01T06:55:39+08:00 Siti Faria Astari I Gede Pasek Suta Wijaya Ida Bagus Ketut Widiartha <p>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.</p> 2021-06-25T21:59:32+08:00 Copyright (c) 2021 Siti Faria Astari, I Gede Pasek Suta Wijaya, Ida Bagus Ketut Widiartha Optimasi Penjadwalan Ujian Tugas Akhir Dengan Menggunakan Algoritma Genetika 2021-07-05T16:16:33+08:00 Adi Panca Saputra Iskandar <div>Final Project is one of the requirements for STMIK STIKOM Indonesia students to complete their studies. The final project has two stages, namely the proposal seminar process and the final project session, to complete these stages, of course, the study program must make a schedule for the stages. The problem that often occurs in scheduling activities is the occurrence of clashes between one schedule and another, the schedule clashing with the teaching activities of lecturers as supervisors and examiners. and there is a request for lecturer prohibition time to test One method to solve this problem is by using a genetic algorithm that works through natural and genetic selection. There are 8 genetic algorithm procedures, coding technique procedures, initial population and chromosome random (random), fitness function to minimize the number of clashes between schedules, roulette-wheel selection method, crossing over, genetic mutation, elitism and the condition is complete when the maximum iteration has been reached . The output of the system is in the form of lecture scheduling arrangements and final semester exams in PDF file format</div> 2021-06-25T22:11:21+08:00 Copyright (c) 2021 Adi Panca Saputra Iskandar Sistem Pakar Diagnosa Kelainan Sistem Ortopedi pada Manusia dengan Metode Forward Chaining dan Dempster Shafer 2021-07-05T16:21:46+08:00 Nurhaini Rahmawati Fitri Bimantoro <p><em>The bones and skeleton are very important parts of orthopedics and are the most vulnerable parts of the body. One of the obstacles in the diagnosis of orthopedic disorders is the distance from the hospital and the few orthopedic doctors. This study developed an expert system that runs on an Android-based smartphone to diagnosa 13 types of abnormalities in the orthopedic system with 92 symptom input based on the knowledge of 3 experts using the forward chaining and dempster shafer methods to obtain conclusions about the type of orthopedic disorder suffered. Based on the test results with theoretical calculations, it is found that the system calculation results are in accordance with the results of manual calculations. In testing the accuracy of the system, from 30 examples of cases tested on 3 experts, the accuracy value was obtained based on the average expert weight of 81.11%, the weight of each expert in sequence is 80.00% for expert 1, 83.33% for expert 2, and 73.33% for expert 3, where this accuracy value shows that the performance of the dempster shafer method in diagnosing orthopedic disorders is good and it can be said that the dempster shafer method suitable to be applied in cases of orthopedic disorders. The MOS (Mean Opinion Score) test on 30 respondents resulted in an MOS value of 4.45 from a scale of 5 which indicates that the system is feasible to use and is categorized into a good system</em></p> 2021-06-25T22:15:34+08:00 Copyright (c) 2021 Nurhaini Rahmawati, Fitri Bimantoro Pengenalan Pola Tulisan Tangan Aksara Bima menggunakan Ciri Tekstur dan KNN 2021-07-05T16:21:33+08:00 Fitri Bimantoro Arik Aranta Gibran Satya Nugraha Ramaditia Dwiyansaputra Ario Yudo Husodo <p>As the fact, that is Bima script did not familiar to bimanese<em>, </em>Bima script as a cultural heritage needs to be preserved. Pattern recognition has been used to recognize several of ancient script. Gray Level Co-occurence Matrix (GLCM) as features exctraction and K Nearest Neighbour (KNN) as a classifier show the good performance to recognize of an ancient script. so, in this research we use GLCM and KNN to recognize Bima script. we use 2640 images of handwritting bima Script that is collected from 10 volunter. Each volunter write 22 of Bima script twelve times each script. The experimental result show that the performance of our model is good enough, with 60.86% of accuracy that is obtained by manhattan distances.</p> 2021-06-25T22:20:19+08:00 Copyright (c) 2021 Fitri Bimantoro, Arik Aranta, Gibran Satya Nugraha, Ramaditia Dwiyansaputra, Ario Yudo Husodo Analisis Pengenalan Pola Daun Berdasarkan Fitur Canny Edge Detection dan Fitur GLCM Menggunakan Metode Klasifikasi k-Nearest Neighbor (kNN) 2021-07-05T16:07:39+08:00 Azizah Arif Paturrahman I Gede Pasek Suta Wijaya <p>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.</p> <p><br>Key words -- Leaf, canny edge detection, gray-level co-occurrence matrix (GLCM), the k-nearest neighbor (kNN)</p> 2021-06-25T22:26:59+08:00 Copyright (c) 2021 Azizah Arif Paturrahman, I Gede Pasek Suta Wijaya A Smart Monitoring Berbasis Internet of Things (IoT) Suhu dan Kelembaban pada Kandang Ayam Broiler 2021-07-05T16:12:00+08:00 Yogi Isro Mukti Fitria Rahmadayanti Diti Tri Utami Diti <p><strong>This study aims to design and build Internet of Things-based smart monitoring of temperature and humidity in broiler chicken coops in Talang Kemiling Pagar Alam that can facilitate and help breeders. This research is monitoring temperature and humidity which still uses the farmer's body itself as a temperature and humidity sensing tool, so that farmers do not know what the actual temperature and humidity are in the chicken coop. Therefore it is necessary to make a tool that can monitor temperature and humidity conditions in broiler chicken coops by utilizing the existing internet network using temperature and humidity sensors, namely DHT11, 1 channel relay for lamp control, and NODEMCU ESP8266 as a microcontroller that processes and transmits data from sensors. to the telegram server via the internet network, the telegram application on an android smartphone is used as an interface for monitoring temperature and humidity in broiler chicken coops remotely. This research uses the Rapid Application Development (RAD) system development method. The stages taken are Requirement Planning, Design Workshop, Build The System and Implementation. To obtain data in this study, researchers conducted data collection techniques including observation, interviews and literature study. The results expected from this study are the tools produced can help and facilitate breeders in raising broiler chickens.</strong></p> 2021-06-25T22:35:31+08:00 Copyright (c) 2021 Yogi Isro Mukti, Fitria Rahmadayanti, Diti Tri Utami Diti Support Vector Regression (SVR) Dalam Memprediksi Harga Minyak Kelapa Sawit di Indonesia dan Nilai Tukar Mata Uang EUR/USD 2021-07-01T06:50:26+08:00 Siti Saadah Fakhira Zahra Z Hasna Haifa Z <p class="Abstract">Support Vector Machine merupakan algoritma pembelajaran mesin yang banyak digunakan untuk melakukan prediksi. Salah satunya dengan menggunakan vector kernel radial basis. Dengan karakteristik regresi pada kernel RBF maka metode ini berhasil melakukan prediksi untuk permasalahan seasoning. Mengacu kepada hal tersebut, maka pada penelitian ini akan digunakan pendekatan RBF untuk prediksi forex exchange rate atau minyak kelapa sawit. Karakteristik dua data ini jauh memiliki kesamaan, yakni cenderung ke arah trend seasonal. Mengingat pentingnya dilakukan prediksi untuk kedua studi kasus tersebut, maka kedua permasalahan ini dikaji pada penelitian ini untuk diuji menggunakan algoritma SVR. Hasil yang diperoleh menunjukkan bahwa presentase akurasi untuk exchange rate yaitu 99.97%. Sementara, akurasi pada saat memprediksi minyak kelapa sawit yaitu pada kisaran 98%.</p> 2021-06-25T22:39:51+08:00 Copyright (c) 2021 Siti Saadah, Fakhira Zahra Z, Hasna Haifa Z