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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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Deteksi Diabetik Retinopati menggunakan Regresi Logistik Tyasnurita, Raras; Pamungkas, Adhi Yoga Muris
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.578.130-135

Abstract

Retinopathy diabetic is a disease caused by diabetes mellitus complications that can cause damage to the retina of the eye. It has a direct impact on the disruption of the vision of the patient. Detecting this disease is very important to prevent total blindness on diabetes mellitus patients. One method to do the detection is by using machine learning. This research uses feature extraction data from an image that contains signs of retinopathy diabetic or not. In this research, we focus on retinopathy diabetic classification. We applied logistic regression algorithm for classification. There is four training condition in a machine learning model: using the default parameter, feature standardization, feature selection, and hyper-parameter tuning. The model used a regularization control (C) value of 11.288, iterations 200, and a regularization penalty (l1). The experimental results show that this proposed model with full features produced 80,17% accuracy in data validation.
Development of the Forensic Storage Framework using the Composite Logic method Rachman, Helmi; Sugiantoro, Bambang; Prayudi, Yudi
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

Abstract

Along with the development of information technology users, it is possible that crimes that take advantage of information technology continue to develop both directly and indirectly. Criminals often use computer devices to commit crimes. This is a major concern, so the need for handling digital evidence is very important. Therefore, a storage forensic framework is needed for handling digital evidence. This framework is designed by applying the Composite Logic method. The Composie Logic method is applied to determine the role model of each variable or initial pattern of the stages that you want to collaborate with. Composite Logic produces a role model who has a role to produce patterns so that they achieve the same goal. The logic composite method collaborates with existing frameworks for handling hdd, ssd, vmware and cloud which are then combined into a foreign storage framework. Based on the results of the tests carried out, this research has produced a new framework called the storage forensic framework. The advantage of this storage forensic framework compared to several other frameworks is that it is far required that it can be used to generate digital evidence in four storage, namely, SSD, HDD, VmWare, and cloud. This framework produces four main stages, namely preparation, collection, analysis and reports.
Sistem Cerdas dalam Mengidentifikasi Kematangan Buah Naga Berdasarkan Fitur Tekstur dengan Metode K-Nearest Neighbor Haba, Abd Rahmat Karim; Husdi, Husdi
ILKOM Jurnal Ilmiah Vol 12, No 3 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i3.665.225-232

Abstract

Selama ini petani kebun buah naga dalam melakukan pemilihan buah naga yang telah matang pada musim panen terkadang masih memiliki kendala seperti melalukan penyortiran untuk mengidentifikasi mana yang sudah matang atau belum matang, hal ini dikarenakan pada buah naga terdapat kulit atau teksturnya yang tebal. Salah satu inovasi informasi dan kominikasi dalam bidang pertanian dengan menggunakan cara menerapkan sistem pengidentifikasian menggunakan metode K-Nearest Neighboar pada system cerdas. Tujuan dari penelitian ini adalah untuk melakukan identifikasi kematangan buah naga dengan system cerdas dan untuk memperoleh system cerdas yang efektif dan efisien sehingga dapat di implementasikan. Penelitian ini menggunakan fitur ekstraksi GLCM sebagai metode untuk mendapatkan nilai tekstur pada gambar (citra) serta dalam melakukan pendeteksian menggunakan metode K-Nearest Neighbor.  Dari hasil identifikasi dapat diukur dari perhitungan sudut 00, 450, 900 dan 1300 serta jarak ketetanggan K=3, serta dapat di implementasikan.
Klasifikasi Topik Tugas Akhir Mahasiswa menggunakan Algoritma Particle Swarm Optimization dan K-Nearest Neighbor Sumarni, Sumarni; Rustam, Suhardi
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.604.168-175

Abstract

Problems the Topic of the final project is a form of scientific writing that contains the results of observations from a study of the problems that occur with the use of methods related to the particular field of science. Every student in every program of study must draw up a final project. However, before embarking on writing the final project, each student must have the topic area as a destination, the step of selection the topic of final project is an initial step before working on the final task. One way to get the final task is to see the value of general courses as well as courses, concentration majors, the value of which dominate the is is decent to scope the research topic. this research is conducted on the application of the method of K-Nearest Neighbor (KNN) for categorization of the value of the courses of concentration for the coverage of the research topic, topic the entire value in the dataset will be classified by KNN and in the optimization with the Particle swarm Optimization algorithm (PSO). The experimental categorization of the final project is built with the training data Mahasiswa Universitas Ichsan Gorontalo that has been classified previously and test data derived from the entire value of the courses is not yet known categories. The results of the experiments, the value of the resulting accuracy of algorithms KNN, namely the value of the best accuracy with K=3, K Folds = 10 has an accuracy that is 72.46% and the Algorithm of KNN-PSO best accuracy with K=3, K Folds = 10 has an accuracy that is 89.86%, shows the accuracy is better by using the optimization algorithm
Perbandingan Efisiensi Algoritma Sorting dalam Penggunaan Bandwidth Anggreani, Desi; Wibawa, Aji Prasetya; Purnawansyah, Purnawansyah; Herman, Herman
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.538.96-103

Abstract

The most used algorithm is the sorting algorithm. There have been many popping sorting algorithms that can be used, in this study researchers took three sorting algorithms namely Insertion Sort, Selection Sort, and Merge Sort. As for this study will analyze the comparison of execution time and memory usage by considering the number of enter data of each algorithm used. The data used in this study is ukhuwah NET network bandwidth usage data connected in the Faculty of Computer Science in the form of double data types. After implementing and analyzing in terms of execution time merge sort algorithm has a faster execution time in sorting data with an average execution time value of 108.593777 ms on the 3000 data count. While in the same amount of data for the most execution time is the Selection Sort algorithm with a large execution time of 144.498144 ms, in terms of memory usage with the amount of data3000 Merge Sort Algorithm has the highest memory usage compared to the other two algorithms which is 21,444 MB while the other two algorithms have a succession of memory usage of 20,837 MB and 20,325MB.
Comparison of K-Means and K-Medoids Algorithms for Clustering the Spread of the COVID-19 Outbreak in Indonesia Utomo, Wargijono
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

Abstract

The spread of Corono Virus 19 is very fast through effective human-to-human transmission through close contact and respiratory droplets such as coughing or sneezing. Various studies have been conducted to deal with COVID 19, but until now it has not been found how to stop the spread of this virus. Based on data obtained from the covid19.go.id page accessed on January 1, 2021 which was updated by the Ministry of Health, the overall number of confirmed cases was 1,078,314 active cases reaching 175,095 or 16.2% of confirmed cases, recovered 873,221 or 81.0% of cases confirmed, died 29,998 or 2.8% of the confirmed cases. In this study, comparing the two algorithms in the dataset which aims to analyze grouping patterns and determine the best method of data processing. The data used comes from the Ministry of Health, there are 4 attributes including confirmed cases, treatment, recovery and death, in this study only 2 attributes are used, namely confirmed cases and death.  From the results of data analysis and processing through a comparison between the K-Means method and the K-Medoids for grouping the spread of the corona 19 virus in Indonesia, with the Davies Boulden index value from the K2 to K9 values, it turns out that the K-Means method gets the smallest value at the K-value. 5 is 0.064, while K-Medoids at the k-2 value is 0.411. Thus, from the two methods used, it can be found that the best method for clustering the spread of the corona 19 virus outbreak in Indonesia is the K-Means method.
Local Binary Pattern untuk Pengenalan Jenis Daun Tanaman Obat menggunakan K-Nearest Neighbor Lamasigi, Zulfrianto Y; Hasan, Maryam; Lasena, Yulianti
ILKOM Jurnal Ilmiah Vol 12, No 3 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i3.667.208-218

Abstract

Tanaman obat tradisional merupakan jenis tanaman yang mengandung zat aktif yang berfungsi mengobati ataupun mencegah dari berbagai macam penyakit. Oleh karena itu dilakukan penelitian untuk menguji metode Local Binary Pattern untuk ektraksi ciri dari setiap tanaman obat tradisional dan K-Nearest Neighbor pada proses klasifikasi setelah dilakukan ektraksi dari metode Local Binary Pattern. Dari pengujian menggunakan metode Local Binary Pattern dan K-Nearest Neighbor mampu menghasilkan akurasi yang cukup baik yaitu sebesar 96.67%, nilai akurasi tersebut didapat dari perhitungan manual convusion matrix dengan nilai k=9. Sementara itu hasil akurasi terendah ada pada nilai k=1 yaitu 0%. Hasil ektraksi dan klasifikasi dari metode Local Binary Pattern dan K-Nearest Neighbor menggunakan 120 dataset yang dibagi menjadi 90 data training dengan 6 jenis daun tanaman obat yang terdiri dari 15 daun bayam duri, 15 daun binahong, 15 daun jarak, 15 daun afrika, dan 15 daun sirih dengan percobaan 30 data testing.
The weighted product method and portfolio assessment in ranking student achievement Andi Tenri Sumpala; Muhammad Nurtanzis Sutoyo; Huzain Azis; Fadhila Tangguh Admojo
ILKOM Jurnal Ilmiah Vol 13, No 2 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i2.827.148-154

Abstract

The learning process has a correlation with learning achievement which can be shown through the marks given by a teacher to students from several fields of study. The ranking of student learning achievements performed by the school refers to the grades of the subject is important for the SNMPTN (National Selection for State Higher Education). To determine student achievements, the method used in the current study is the weighted product. If the results of student ranking using the WP method have the same value, then a portfolio assessment is used. Of the 127 student achievement ratings, there were seven people who had the same Vector value. Then, the seven people who have the same vector value were graded using portfolio assessment. The results showed that the implementation of the WP method and portfolio assessment could determine the ranking of student achievement.
INVESTIGASI LIVE FORENSIK DARI SISI PENGGUNA UNTUK MENGANALISA SERANGAN MAN IN THE MIDDLE ATTACK BERBASIS EVIL TWIN Muhammad Sabri Ahmad; Imam Riadi; Yudi Prayudi
ILKOM Jurnal Ilmiah Vol 9, No 1 (2017)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v9i1.103.1-8

Abstract

MITM based Evil twin menjadi suatu ancaman yang berbahaya bagi para pengguna jaringan Wifi. Pelaku penyerangan ini memanfaatkan AP (Access Point) palsu dengan konfigurasi gateway yang berbeda dengan legitimate AP, sehingga jenis serangan ini menjadi cukup sulit untuk dideteksi, disisi lain proses pengungkapan kasus serangan MITM based Evil Twin hanya sebatas mendeteksi aktivitas serangan dan belum ada pembahasan lebih lanjut terkait digital forensik. Penelitian ini dilakukan dengan menerapkan pendekatan metode Live forensik dan pendekatan dari sisi user, untuk mendeteksi aktivitas ilegal yang terjadi di dalam jaringan Wifi, Proses investigasi MITM Based Evil dibagi menjadi empat tahapan, dimulai dari proses collection, examination, analysis dan reporting dan analisa Forensik, selain itu penelitian ini difokuskan pada dua proses penelitian yaitu proses analisa Wifi scanning dan analisa network trafik untuk proses penemuan barang bukti digital berupa informasi traffik data dari serangan mitm based evil twin.
ANALISIS PERFORMA METODE K-NEAREST NEIGHBOR UNTUK IDENTIFIKASI JENIS KACA Mus Mulyadi Baharuddin; Huzain Azis; Tasrif Hasanuddin
ILKOM Jurnal Ilmiah Vol 11, No 3 (2019)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v11i3.489.269-274

Abstract

Nowadays, the industry makes various types of goods that have glass-based materials, float car window panes, non-float building windows, lamps, jars, and tableware. These glasses have the same production material, the difference between one and the other is the composition of the production material. K-Nearest Neighbor (KNN) algorithm which is one of the classification methods in data mining and also a supervised learning algorithm in machine learning is a method for classifying objects based on learning data that is the closest distance to the object.. This study discusses the performance measurement (accuracy, precision, recall and f-measure) of the KNN method with a variety of values on 1000 glass type production data objects obtained from the central UCI Machine Learning Repository dataset. The conclusion of this research is the results of the value of K = 3 to K = 9, the best performance values obtained at K = 3, where the level of accuracy reaches 64%, 63% precision, 71% recall, and F-Measure of 67%.

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