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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.
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Articles 20 Documents
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Search results for , issue "Vol 15, No 1 (2023)" : 20 Documents clear
Fuzzy C-Means with Borda Algorithm in Cluster Determination System for Food Prone Areas in Aceh Utara Mutammimul Ula; Munirul Ula; Desvina Yulisda; Susanti Susanti
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1481.21-31

Abstract

In this research, the clustering of food prone areas in Aceh Utama is based on the Index Ketahanan Pangan (IKP) indicators compiled by Badan Ketahanan Pangan (BKP) using Fuzzy C-Means (FCM) and Borda algorithms. The fuzzy C-Means algorithm was used to classify food-prone areas with three clusters: very prone, moderately prone, and prone. The Borda algorithm was used to choose the most prone area from very prone clusters, which are considered urgently to be followed up by decision-makers. Based on the research results, it was found that in the aspect of food availability, four sub-districts are moderately prone, 10 are prone, and 13 are very prone. Regarding food affordability, it found that 12 sub-districts are moderately prone, seven are prone, and eight are very prone. Regarding food utilization, one sub-district is moderately prone, three are prone, and 23 are very prone. The results of voting using the Borda algorithm in very prone clusters are obtained Sawang District from the aspect of food availability, Syamtalira Aron District from the aspect of food affordability, and Lapang District from the aspect of food utilization. The clustering system is built based on the web using the PHP programming language.
[Retraction] Penerapan Forward Chaining Pada Sistem Pakar Pengendalian Internal Bank Pemberian Kredit Pemilikan Rumah Apriade Voutama; Adhi Rizal
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.839.201-214

Abstract

Sistem permohonan kredit pemilikan rumah yang biasa dikelola oleh bank saat ini sangat dibutuhkan banyak masyarakat terutama bagi nasabah yang sudah bekerja dan ingin memiliki rumah. Salah satu upaya yaitu menciptakan Sistem Pakar yang bisa memberikan kemudahan pihak internal dalam pengendalian pemberian kredit rumah kepada nasabah agar tidak terjadi kredit macet dan nasabah tidak terlalu lama menunggu untuk persetujuan permohonan kredit rumah. Sistem Pakar adalah salah satu bagian dari Kecerdasan Buatan yang mampu pengadopsi pola pikir manusia dengan cara pemecahan masalah selayakanya seorang pakar. Metode yang diterapkan yaitu Forward Chaining untuk pengendalian internal KPR. Metode Forward Chaining digunakan dengan membuat rule melalui kumpulan fakta dan data sebagai persyaratan KPR kemudian disusun menjadi pohon keputusan yaitu kesimpulan berdasarkan aturan. Proses tersebut menghasilkan beberapa keputusan layak atau tidaknya nasabah mengajukan kredit rumah, apabila diterima nasabah akan disesuaikan berdasarkan level solusi sesuai syarat-syarat nasabah. Hasil tersebut diimplementasikan menjadi Sistem Pakar sehingga pihak Bank akan mudah mengendalikan pemberian kredit kepada nasabah serta dapat pengantisipasi kredit macet.
Identification of the Freshness Level of Tuna based on Discrete Cosine Transform on Feature Extraction of Gray Level Co-Occurrence Matrix using K-Nearest Neighbor Zulfrianto Yusrin Lamasigi; Serwin Serwin; Yusrianto Malago
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1426.153-164

Abstract

Gorontalo Province is one of the provinces that have fishery potential and has a large sea area that can be managed to support the economy and development of the province. Gorontalo is also one of the tuna-producing provinces in Indonesia, where tuna is also one of the mainstay fisheries commodities.  This study aimed to combine transformation and texture feature extraction methods to improve the identification of the freshness level of tuna. This research used Discrete Cosine Transform as transformation detection and Gray Level Co-Occurrence Matrix as texture feature extraction. To find out the value of the proximity of the training data and image testing of tuna fish, the K-Nearest Neighbor classification method was employed. Then, the Confusion Matrix was used to calculate the accuracy level of the K-Nearest Neighbor classification.   This research was carried out with 4 stages of testing, namely at angles of 0°, 45°, 90°, and 135°, and using the values of k=1, 3, 5, and 7. The test results of using training data of 428 images and testing data of 161 images in four classes used with angles of 0°, 45°, 90°, 135°, and the value of k=1, 3, 5, 7. The highest accuracy results was obtained at an angle of 0° with a value of k = 1 of 94.40%, while the lowest accuracy value was at an angle of 90° and 135° with a value of k=7 of 59%. This showed that the Discrete Cosine Transform transformation method was very effective to improve the performance of texture feature extraction of Gray Level Co-Occurrence Matrix in extracting tuna image features. It was proven from the results of the accuracy of the K-Nearest Neighbor classification obtained.
Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope Supriyadi La Wungo; Mardewi Mardewi; Firman Aziz; Pertiwi Ishak; Hechmi SHILI
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1270.107-117

Abstract

Human Activity Recognition is technology introduced to recognize human activities. Several technologies that have been applied are Accelerometer sensors, Gyroscope sensors, Cameras, and GPS. The selection of the Support Vector Machine algorithm is due to its capabilities to minimize errors in training data sets and the Curse of dimensionality which can estimate parameters as well as its ability to find the best hyperplane that separates two classes. The SVM algorithm was originally developed for the classification of two classes. Problem raised if there are more than two classes. In addition, the performance will not optimal for the large-scale data. Therefore, modification the current design is needed. An ensemble technique can be used to combine the Support Vector Machine algorithm with the bagging algorithm. This study proposes the application of an ensemble SVM algorithm to classify human activities based on accelerometers and gyroscope sensors on smartphones.  The total data is 13725 records with 4575 representatives of each class. From the results of the overall data partition carried out in the calcification process using the ensemble SVM algorithm, the best performance was generated when comparing datasets with 80% training data and 20% test data from a total of 13725 records because it succeeded in increasing accuracy, precision, and sensitivity.
Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit Farrikh Alzami; Fikri Diva Sambasri; Mira Nabila; Rama Aria Megantara; Ahmad Akrom; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Puri Sulistiyawati
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1524.32-44

Abstract

E-commerce is selling and buying goods through an online or online system. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One thing that needs to be considered in the business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers to maintain good relationships with customers and increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature and display it in the form of a dashboard using the Streamlit framework. Several stages of research must be carried out. Firstly, taking data from the open public data site (Kaggle), then merging the data to select some data that needs to be used, understanding data by displaying it in graphic form, and conducting data selection to select features/attributes. The step follows the proposed method, performs data preprocessing, creates a model to get the cluster, and finally displays it as a dashboard using Streamlit. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
Analysis of the Dynamic Source Routing Protocol on the Performance of File Transfer Protocol and Video Conference Services in the Mobile AdHoc Network Simulation Triawan Adi Cahyanto; Rizky Dwi Antoko; Taufiq Timur Warisaji; Santosa Santosa; Rodianto Rodianto
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1526.165-174

Abstract

Current technological advancements make it easier for users to do their work effectively and efficiently, including the use of wireless networks to exchange data via File Transfer Protocol (FTP) and video conferencing services (VCS). A Mobile AdHoc Network (MANET) is a wireless network technology that applies a dynamic set of nodes. Data transmission on the MANET does not require the use of devices such as base stations. Because each node on the MANET can act as a router in determining the direction of the data sent, the number of nodes in the MANET will influence the quality of the data sent. Using the OPNET Modeler simulator, this paper shows how to assess the quality of FTP and VCS based on delay, jitter, and packet loss parameters. The simulation scenario employs five, fifteen, and thirty nodes with low, medium, and high traffic loads, using the Dynamic Source Routing (DSR) protocol. According to the measurement results, the FTP service with the bad category is the packet loss parameter in high traffic loads, which has the highest packet loss value of 56.6 percent with 15 nodes. In contrast, good results for VCS are only produced on the delay parameter. The jitter increases with the number of nodes, and it is 5 in this case. In all scenarios, the packet loss parameter yields poor results, with the highest packet loss value approaching 100%.
The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic Andi Bode; Zulfrianto Y Lamasigi; Ivo Colanus Rally Drajana
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1381.118-123

Abstract

Academic services are actions taken by state and private universities to provide convenience for student’s academic activities. During the current covid-19 pandemic, every university remains active in academic activities. This study aimed to apply the K-Nearest Neighbor algorithm in predicting the level of student satisfaction with online lectures at University Ichsan Gorontalo. Our main aim was to obtain quantitative information to measure student satisfaction with online lectures during the pandemic, which should be taken into account when making decisions. K-Nearest Neighbor is a non-parametric Algorithm that can be used for classification and regression, but K-Nearest Neighbor are better if feature selection is applied in selecting features that are not relevant to the model. Feature Selection used in this research is Forward Selection and Backward Elimination. Seeing the results of experiments that have been carried out with the application of the K-nearest Neighbor algorithm and the selection feature, the results of the forecasting can be used for consideration or policy in decision making. The highest level of accuracy in the K-Nearest Neighbor algorithm model used Forward Selection with an accuracy rate of 98.00%. Thus, the experimental results showed that feature selection, namely forward selection, was a better model in the relevant selection variables compared to backward elimination.
CNN Ensemble Learning Method for Transfer learning: A Review Yudha Islami Sulistya; Elsi Titasari Br Bangun; Dyah Aruming Tyas
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1541.45-63

Abstract

This  study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1-score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3, and ResNet-152.
Application of the Fuzzy C-Means Method in Grouping Heart Abnormalities Based on Electrocardiogram Medical Records Sumiati Sumiati; Suherman Suherman; Raden Muhamad Firzatullah; Agung Triayudi; Agung Rahmad Fadjar
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1272.82-100

Abstract

Heart disease is the main cause of death which can be diagnosed using an electrocardiogram. This study aims to classify heart defects using the Fuzzy C Means technique. The advantage of using Fuzzy C Means is that it is unsupervised and can reach a convergent cluster center under certain conditions. It is a clustering model that has the value of the objective function, number of iterations and completed time. In an unsupervised learning, the focus is more on exploring data such as looking for patterns in the data. Clustering itself aims to identify patterns of similar data to be grouped. It can be a solution to overcome the process of determining the risk of heart disease. The results showed that there were 10 data grouped into cluster 1 and 10 data into cluster 2. The first group (Cluster 1) consisted of patients with serial numbers 3,5,8,9,11,12,16,17,19,20, while the second group (Cluster 2) consisted of patients with serial numbers 1,2,4,6,7,10,13,14,15 and 18. Accuracy testing results in a success rate of 60%.
Sentiment Analysis and Classification of Forest Fires in Indonesia Indra Irawanto; Cynthia Widodo; Atin Hasanah; Prema Adhitya Dharma Kusumah; Kusirini Kusrini; Kusnawi Kusnawi
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1337.175-185

Abstract

Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%.

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