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ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : -
Articles 580 Documents
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Pendeteksian Kerusakan Printer menggunakan Metode Forward Chaining Amriana Amriana; Albrecht Yordanus Erwin Dodu; Pebri Ramadhan Mas
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.523.47-57

Abstract

As technology advances, almost everyone has a printer in their home as a printing aid. but not a few printers that have small or big problems. with this technological advancement, almost all people have personal smarthpones. This research aims to build a moble based system where users can solve printer problems by using applications on their smartphones based on knowledge obtained from experts created by the Forward Chaining method. This expert system can detect nine different types of printers owned by Canon manufacturers with total of 26 Problem and 38 symptomps and has an 80 % of accuracy.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN DUTA MAHASISWA GENERASI BERENCANA BKKBN DENGAN METODE WEIGHTED PRODUCT (WP) (Studi Kasus Pada Kantor BKKBN Provinsi Gorontalo) Nurhayati Mursalin; Rezqiwati Ishak
ILKOM Jurnal Ilmiah Vol 9, No 3 (2017)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v9i3.161.301-308

Abstract

Pemilihan duta mahasiswa Generasi Berencana (GenRe) bertujuan tegar remaja yang berperilaku sehat, terhindar dari resiko tiga kesehatan reproduksi remaja, menunda usia pernikahan, dan memiliki perencanaan kehidupan berkeluarga untuk mewujudkan keluarga kecil bahagia dan sejahtera. Pemilihan Duta MahasiswaGenRe dimulai dari tahun 2010 hingga saat ini, dimana jumlah peserta setiap tahunnya ± 30 pasang dan hanya 1 pasang yang terpilih.Masalah yang muncul masaih ada unsur penilaian secara subyetif dan ada beberapa peserta yang memiliki nilai yang sama sehingga menyulitkan pihak pengambil keputusan untuk menentukan mana yang terbaik, untuk itu diperlukan sebuah sistem pendukung keputusan.Metodeyang digunakan adalah Weighted Product (WP).Berdasarkan hasil penelitian yang dilakukan, sistem ini dapat membantu pihak pengambil keputusan dalam menentukan alternatif terbaik menjadi duta mahasiswa GenRe untuk kategori Laki-Laki dengan nilai tertinggi 0,35119 dan kategori Perempuan dengan nilai tertinggi 0,35013 dan dengan adanya sistem iniproses pemilihannya menjadi lebih obyektif dan mudah.
Clustering the potential bandwidth upgrade of FTTH broadband subscribers Sasa Ani Arnomo; Yulia Yulia
ILKOM Jurnal Ilmiah Vol 13, No 1 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i1.805.51-57

Abstract

A company needs to consider determining the customers’ potential before deciding to upgrade their bandwidth. It is important because, previously, determination was conducted randomly. Therefore, potential determination is necessary by grouping customers who have similar characteristics based on their data and attributes. This study employs data mining techniques using clustering method with K-means algorithm on broadband users’ group of 263 FTTH. The determination was determined based on end centroid point in the grouping. The results were divided into 5 clusters consisting of 34 highly potential users (12.92%), 29 potential users (11.02%), 56 fairly potential users (21.3%), 54 less potential users (20.53%), and the remaining 90 not potential users (34.22%). The comparison of the validity of the Davies-Bouldin Index for the 5 (five) clusters is 0.538 for K-Means and 0.819 for K-Medois. This indicates that K-Means results better score. This method is useful for efficient bandwidth sharing.
Sistem Pakar Penyakit Liver Menggunakan K- Nearest Neighbors Algoritm Berbasis Website Citra Yustitya Gobel
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i2.296.152-159

Abstract

The liver is largest organ for humans, functioning as formation and secretion of bile, and as detoxification of toxins. Liver disease difficult to detect at early stage, but treatment of Liver disease in early stages will greatly help patient's recovery. With increasing number Liver Patients other than caused by unhealthy lifestyle, also caused by delay factor handling early symptoms. So this research aims to design Expert System Liver Disease based Website, that it can facilitate access information society need information about liver disease. Website based system design using K-NN Method because it is considered flexible enough and has tolerance to data that is’not appropriate and based on natural language. Expert system as a tool in determining whether the patient was suffering from Liver disease or not with the concept of K-NN. The results discussion that system design with a simple model so that can be used by all community.
Pengelompokan Minat Baca Mahasiswa menggunakan Metode K-Means Widya Safira Azis; Dedy Atmajaya
ILKOM Jurnal Ilmiah Vol 8, No 2 (2016)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v8i2.51.89-94

Abstract

Pengelompokkan minat baca mahasiswa berdasarkan kriteria buku yang dibaca, buku yang dipinjam, dan juga mempertimbangkan jumlah stok buku yang tersedia dapat membantu dalam proses penambahan koleksi buku yang telah ada pada perpustakaan Utsman Bin Affan UMI. Salah satu cara untuk mengelola data tersebut yaitu menggunakan data mining dengan memanfaatkan metode K-Means. Data buku dikelompokkan menjadi 3 cluster yaitu prioritas, dipertimbangkan, dan tidak diprioritaskan dalam perencanaan penambahan koleksi buku. Hasil dari penelitian ini adalah cluster dengan nilai terbesar pada centroid akhir merupakan cluster yang direkomendasikan dalam perencanaan penambahan koleksi buku.
Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination Rahmaddeni Rahmaddeni; M. Khairul Anam; Yuda Irawan; Susanti Susanti; Muhammad Jamaris
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The corona virus has become a global pandemic and has spread almost all over the world, including Indonesia. There are many negative impacts caused by the spread of COVID-19 in Indonesia, so the government takes vaccination measures in order to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive and some were not. The data used in this study came from Twitter which was taken using the drone emprit portal, using the keyword, namely "vaccination". The classification will be carried out using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public whether the Covid-19 vaccination actions carried out tend to be positive, neutral or negative opinions. The results of the sentiment evaluation that have been carried out can be seen that SVM has the highest accuracy of 83% with 90:10 data splitting, then XGBSVM produces 79% accuracy with 90:10 data splitting.
Classification of Coffee Bean Defects Using Gray-Level Co-Occurrence Matrix and K-Nearest Neighbor Mila Jumarlis; Mirfan Mirfan; Abdul Rachman Manga
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Defects in coffee beans can significantly affect the quality of coffee production so that defects in coffee beans can cause a decreasing the level of coffee production. The purpose of this study is to implement the GLCM (gray-level co-occurrence matrix) and the K-NN (k-nearest neighbor) method on a web-based program and provided a website to detect coffee bean defects. This study uses the GLCM algorithm to extract the features of the coffee images and uses the K-NN algorithm to classify the defect level of coffee beans. The system development was built using Unified Modeling Language. The development of this website was utilized the programming structure of PHP, HTML, CSS, Javascript, Mozilla Firefox as a browser for the website and MySql for the database management systems. The results show that the system can provide the output in the form of a classification level of the defect level of the coffee bean images. Then, the accuracy of the coffee bean defect assessment was achieved by 90%. Finally, this study concluded that the proposed system could help the coffee farmers determine the defect level of the coffee beans using images input.
Implementation of Fuzzy Logic in Fish Dryer Design Nur Yanti; Taufik Nur; Randis Randis
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The fish drying process aims to preserve fish, so as to reduce losses due to the spoilage process. There is sunlight, the drying process does not experience obstacles, however if it is raining, it will take a longer time, and give a smell effect that disturbs the surrounding environment for a relatively long time. Fish dryer designed to work automatically, aims to speed up drying time using fuzzy logic, thus minimizing rot and air pollution due to the smell of the fish drying process. The design of the tool used experimental methods through literature study as a source of study, planning and manufacturing of fish drying equipment consists of hardware using the Arduino Mega 2560 microcontroller, temperature sensor of DHT 22, load cell sensor, humidity sensor, fan, heating element and LCD and software using the Fuzzy Mamdani method. The results obtained are the weight of the fish that has undergone a drying process using an automatic drying device, namely 500 grams, indicating that the drying process is 50% of the initial weight of 1000 grams, with a drying time of 4.48 hours, while drying time by drying or manually takes 45 hours. Shows the control system using fuzzy logic on fish drying equipment,  speed up the drying time about 10 hours faster than the drying time by drying in the sun.  So that it can increase the amount of dry fish production, reduce the smell in the environment around the drying, because the fish are in the dryer closed. 
Implementation of Deep Learning for Handwriting Imagery of Sundanese Script Using Convolutional Neural Network Algorithm (CNN) Arif Purnama; Saeful Bahri; Gunawan Gunawan; Taufik Hidayatulloh; Satia Suhada
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Aksara Sunda becomes one of the cultures of sundanese land that needs to be preserved. Currently, not all people know Aksara Sunda because of the shift in cultural values and there is a presumption that Aksara Sunda is difficult to learn because it has a unique and complicated shape. The use of deep learning has been widely used, especially in the field of computer vision to classify images, one of the commonly used algorithms is the Convolutional Neural Network (CNN). The application of The Convolutional Neural Network (CNN) algorithm on sundanese handwriting imagery can make it easier for people to learn Sundanese script, this study aims to find out how accurate the neural network convolutional algorithm is in classifying Aksara Sunda imagery. Data collection techniques are done by distributing questionnaires to respondents. System testing using accuracy tests, testing on CNN models using data testing get 97.5% accuracy and model testing using applications get 98% accuracy. So based on the results of the trial, the implementation of deep learning methods using neural network convolution algorithms was able to classify the handwriting image of Aksara Sunda well.
Neural Network Method Based on Particle Swarm Optimization for Predicting Satisfaction of Recipients of Internet Data Support from the Ministry of Education and Culture Annahl Riadi; Irvan Muzakkir; Marniyati H. Botutihe
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The free quota assistance program for students and lecturers is an assistance program carried out by the ministry of education and culture, this program has been implemented since the impact of the covid-19 pandemic in all regions of Indonesia, this assistance is expected to help students and lecturers in carrying out online learning caused by the pandemic covid-19, the purpose of this study is to measure the level of visitor satisfaction through predictions of satisfaction so that it can help the government in advancing the world of education., data processing is carried out using the rapid miner application and using the neural network method with particle swarm optimization, from the results of data processing the results obtained are Values the accuracy for the neural network algorithm model is 42.44% and the accuracy value for the PSO-based neural network algorithm model is 91.86%.

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