ILKOM Jurnal Ilmiah
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
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<![CDATA[ANALISA ALGORITMA HAVERSINE FORMULA UNTUK PENCARIAN LOKASI TERDEKAT RUMAH SAKIT DAN PUSKESMAS PROVINSI GORONTALO ]]>
<![CDATA[Farid Farid]]>;
<![CDATA[Yulanda Yunus]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 9, No 3 (2017) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v9i3.178.353-355
<![CDATA[Pemerintah Provinsi Gorontalo saat ini dihadapkan pada suatu masalah yang berhubungan dengan layanan informasi data. Data layanan informasi yang berkaitan dengan data sarana puskesmas dan rumah sakit belum terinci, sehingga pemerintah kesulitan dalam pengambilan keputusan dalam bentuk peta digital sehingga kebanyakan masyarakat Gorontalo apabila mengalami masalah kesehatan seperti sakit, kecelakaan, meninggal dan lain-lain, akan sering mengalami kesulitan dalam mencari lokasi terdekat layanan kesehatan. Kegunaaan dari Algoritma Haversine Formula adalah digunakan untuk menghitung jarak antara dua titik di bumi berdasarkan panjang garis lurus antar dua titik tanpa mengabaikan kelengkungan yang dimiliki bumi. Berdasarkan hasil analisa Algoritma Haversine Formula dapat menghitung jarak antara lokasi setiap rumah sakit dan puskesmas yang ada di Provinsi Gorontalo dan berdasarkan jarak tersebut maka masyarakat dapat mengetahui jarak lokasi terdekat antara rumah sakit ke rumah sakit lainnya, begitu juga dengan puskesmas ke puskesmas lainnya. ]]>
<![CDATA[Glucose level detection system in glucose solution using TCS3200 sensor with If-Else method ]]>
<![CDATA[Kemal Thoriq Al-Azis]]>;
<![CDATA[Alfian Ma'arif]]>;
<![CDATA[Sunardi Sunardi]]>;
<![CDATA[Fatma Nuraisyah]]>;
<![CDATA[Apik Rusdiarna Indrapraja]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 13, No 2 (2021) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v13i2.733.110-116
<![CDATA[Early and routine examination of glucose levels plays an important role in preventing and controlling diabetes mellitus and maintaining the quality of life. Checking blood sugar levels by hurting the body (invasive) can lead to infections caused by needles. As an alternative, the examination is carried out in a non-invasive way using excretory fluid in the form of urine, which is reacted with Benedict's solution that create a color change. Experts in the laboratory only carry out an examination using non-invasive methods because in determining glucose levels, it requires accuracy and eye health factors. Therefore, a glucose level detection system was created using a sample of glucose solution to determine the system's parameters using the if-else method. The glucose level detection system is conducted by mixing the glucose solution with Benedict's solution to produce a color change. Then the reaction results are read by the TCS3200 sensor and processed by Arduino to be classified, according to predetermined parameters. The decision results based on the classification of the glucose level parameters that have been determined are displayed on a 16x2 LCD. The results achieved in this study on 10 samples of glucose solution that were tested and processed by the if-else method were successfully read and classified based on predetermined parameters.]]>
<![CDATA[KOLABORASI FISH-NET DAN TECHNOLOGY UNTUK OPTIMALISASI ALAT TANGKAP IKAN ]]>
<![CDATA[Irwan Irwan]]>;
<![CDATA[Sul Fikar]]>;
<![CDATA[Winarto Surachmad]]>;
<![CDATA[Lilis Nur Hayati]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 10, No 2 (2018) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v10i2.318.207-214
<![CDATA[AbstractKelurahan Untia is one of the areas occupied by the fishermen and also the majority of the population work as fishermen catch fishermen. The dominant fishing gear used by fishermen in the village of Untia is surface gill net. But the use of surface gill net capture device is considered not effective and efficient because it has constraints on the process of checking the net, the old fishing process, the net tends to disappear, and the catch is considered less. So from that problem, we encourage us to create a technological innovation called FiNe-Tech (Fish Net Technology). FiNe-Tech is designed to be able to monitor fish trapped in surface gill net captures, simplify the process of catching fish in the sea, tracking the position of the jarring, speeding up the fishing process, and increasing the net catch through a smartphone application at close range and distance from the position nets even though we are at home though.]]>
<![CDATA[Classification of cendrawasih birds using convolutional neural network (CNN) keras recognition ]]>
<![CDATA[Warnia Nengsih]]>;
<![CDATA[Ardiyanto Ardiyanto]]>;
<![CDATA[Ayu Putri Lestari]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 13, No 3 (2021) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v13i3.865.259-265
<![CDATA[Classification is part of predictive modeling and supervised learning. This method is used to determine the data class based on the previous value. In solving certain cases, there are various classification methods with varying degrees of accuracy. Convolutional Neural Network (CNN) is part of the Multilayer Perceptron (MLP) for processing two-dimensional data. CNN is also part of the Deep Neural Network and is applied to image objects. From several sources, it is stated that the classification process using images is not properly implemented in this MLP. Of course, this will result in the accuracy of the method in handling certain cases. In this study, the object classification process uses hard recognition to determine the accuracy value of the method using the object of the bird of paradise. From the results of this study, a training model was conducted using 10 ephocs with an accuracy value of 0.0850 while a loss value of 2.5658. So these results indicate that MLP can successfully complete the classification process using images.]]>
<![CDATA[PEMANFAATAN MIDDLEWARE ROBOT OPERATING SYSTEM (ROS) DALAM MENJAWAB TANTANGAN REVOLUSI INDUSTRI 4.0 ]]>
<![CDATA[Abdul Jalil]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 11, No 1 (2019) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v11i1.412.45-52
<![CDATA[The industrial revolution 4.0 is the development of industrial based on the internet, control, robotics, and networking. This study describes the usage of a Robot Operating System (ROS) middleware to answer the challenges of the industrial revolution 4.0 for control the industrial electronic devices and household electronic equipment using Raspberry Pi. ROS has nodes, topics and messages that can be used to manage the Raspberry Pi GPIO pins to be active high (1) or active low (0). The result of this study is the system be able to control the on and off electronic devices through the GPIO pins of Raspberry Pi and relay module based on message commands from the ROS node. The electronic devices had controlled in this study are a fan, light, air conditioner, and room heater that have 110/220 AC voltage.]]>
<![CDATA[PROTOTIPE ROBOT PELAYAN RESTORAN MENGGUNAKAN SENSOR GARIS DENGAN ALGORITMA OPTIMASI LINTASAN ]]>
<![CDATA[Mirfan Mirfan]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 9, No 1 (2017) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v9i1.110.57-61
<![CDATA[Penelitian ini bertujuan merancang prototipe robot pelayan restoran menggunakan sensor garis dan sensor pir dengan algoritma optimasi lintasan yang implementasinya disematkan ke dalam mikrokontroller. Metode yang digunakan adalah metode eksprementaal yaitu dengan melakukan perancangan, pembuatan dan pengujian model sistem. Hasil eksperimen menunjukkan bahwa robot pelayan restoran ini dapat bekerja sesuai kemampuan atau daya baterai yang digunakan. Hal ini tidak terdapat pada tenaga kerja manusia yang bekerja secara part time. Cara kerja robot tidak dapat diintervensi oleh perintah lain dari pelanggan seperti panggilan suara atau sentuhan, diketahui akurasi atau ketepatan pada robot lebih baik karena sifatnya yang otomatis. Sebab sifat perintah pada robot sudah diprogram terlebih dahulu, sehingga kesalahan pada penempatan dapat dihindari sebagimana yang biasa terjadi pada tenaga kerja manusia.adalah emosi akibat kelelahan atau kesehatan dan beban pikiran yang menyertai ketika bekerja. Sedangkan pada robot, faktor itu tidak terjadi karena sifatnya yang mekanistik.]]>
<![CDATA[Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet ]]>
<![CDATA[Julius Rinaldi Simanungkalit]]>;
<![CDATA[Haviluddin Haviluddin]]>;
<![CDATA[Herman Santoso Pakpahan]]>;
<![CDATA[Novianti Puspitasari]]>;
<![CDATA[Masna Wati]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 1 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i1.521.32-38
<![CDATA[Rubber plantation sector is one of the leading commodities in East Kalimantan Province contributing greatly to non-oil and gas exports. Currently, the price of rubber in the world is increasingly competitive. The aim of this research is to predict the rubber prices as a reference for the government and companies in making policies and preparing work plans. Data of 60 months during the period of 2014-2018 taken from Plantation office of East Kalimantan Province has been analyzed using Backpropagation Neural Network (BPNN) algorithm in predicting rubber prices. Based on the testing results, parameters of the BPNN algorithm with ratio of 4: 1, architectural models 5-10-10-10-1, trainlm learning function, learning rate of 0.5, error tolerance of 0.01, and epoch of 1000 have gained good accuracy with a mean square error (MSE) of 0.00015464. The results showed that the BPNN algorithm can be used as an alternative method in forecasting.]]>
<![CDATA[SISTEM KONTROL PENERANGAN MENGGUNAKAN ARDUINO UNO PADA UNIVERSITAS ICHSAN GORONTALO ]]>
<![CDATA[Bahrin Bin Dahlan]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 9, No 3 (2017) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v9i3.158.282-289
<![CDATA[Dalam kehidupan sehari-hari, sadar atau tanpa kita sadari kita terus bertemu dengan suatu perangkat atau peralatan yang kerjanya terkendali secara otomatis baik terkendali sebagian maupun seluruhnya, sistem kendali adalah suatu alat atau kumpulan alat untuk mengendalikan, memerintah, dan mengatur keadaan dari suatu sistem, singkatnya, sistem yang digunakan untuk membuat suatu perangkat menjadi terkendali sesuai dengan keinginan manusia ini biasanya disebut sebagai sistem kendali, untuk mengatasi kesalahan manusia dalam mengatur penerangan yang ada di Universitas Ichsan Gorontalo seperti lupa mematikan lampu sehingga kurang efisien dalam penggunaan daya listrik yang dapat menyebabkan bertambahnya beban biaya univesitas ini, selain itu pula dengan menggunakan sakelar, sistem yang lama menjadi kurang aman dan memakan waktu untuk mematikan sebuah lampu mengingat gedung kampus bertingkat. Penelitian ini diharapkan mampu menjadi solusi dari masalah yang dikemukakan diatas sehingga dapat mengendalikan penerangan secara efisien.Bahwa Sistem Kendali yang dirancang dapat digunakan. Hal ini dibuktikan dalam metode pengujian test case dengan pendekatan pengujian white box dan pengujian Blackbox pada rancangan sistem, sehingga sistem tidak dapat menerima input yang tidak tepat. Dari hasil pengujian test case diperoleh CC = V(G) dimana CC = 7 dan V(G) = 7, hal ini menunjukkan bahwa penerapan pengujian sistem tersebut diatas dapat menghasilkan sistem dan proses looping (perulangan) pada flowchart yang membuat sistem menjadi lebih efektif.]]>
<![CDATA[Implementation of knowledge management system in cattle farming ]]>
<![CDATA[Edi Kusnadi]]>;
<![CDATA[Yessy Yanitasari]]>;
<![CDATA[Supriyadi Supriyadi]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 13, No 1 (2021) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v13i1.785.36-44
<![CDATA[Cattle are the livestock of the Bovinae family and subfamily Bovidae. They have been raised for beef and dairy products. Nowadays, beef production does not meet national needs. This is influenced by several factors. One of them is the farming pattern. However, knowledge about cattle farming is still scattered and unstructured. Knowledge Management System (KMS) offers a knowledge system that can be used to create, collect, store, maintain and disseminate knowledge. In this study, KMS for cattle farming has been implemented using the Knowledge Management System Life Cycle (KMSLC) method. The research results are web-based applications regarding cattle farming management that have been tested by experts and users, with the average test results declared good. The features possessed by this KMS application are knowledge capture, knowledge sharing, and knowledge application systems so that they can share knowledge between one user and another. In addition, this application is equipped with a discussion forum that can serve as a place to interact with fellow users or with experts.]]>
<![CDATA[Prediksi Jumlah Mahasiswa Registrasi Per Semester Menggunakan Linier Regresi Pada Universitas Ichsan Gorontalo ]]>
<![CDATA[Amiruddin Bengnga]]>;
<![CDATA[Rezqiwati Ishak]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 10, No 2 (2018) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v10i2.274.136-143
<![CDATA[Improving the quality of education, service quality and accreditation value increase is the hope of all universities, especially at Ichsan University of Gorontalo. One of the factors to achieve this is the increasing number of students who make the payment registration every semester. Based on the report in PDPT Dikti reporting year 2017/2018 has the number of students ± 9,000 people, from the analysis of the last 4 years the number of unregistered students tends to increase and students registerasi tends to decrease, if this is not considered, it will have an impact on the achievement of the above expectations. To overcome these problems, need to be done prediction technique using Linear Regression and MAPE method. The purpose of this research is to build an application to predict the number of registration students. Based on the results of research from 2 selected study program of Informatics Engineering, the result of error rate is 4.24% or the accuracy level is 95.76%, and for the Law Study program, the error rate is 7.69% or the accuracy level is 92.31%, thus the application already built is feasible to use.]]>
