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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 580 Documents
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Hierarchical clustering for crime rate mapping in Indonesia Rendra Gustriansyah; Juhaini Alie; Nazori Suhandi
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1135.275-283

Abstract

The Sustainable Development Goals (SDGs) are a blueprint for improving the human life quality. Goal 16 (G16) is related to security, and it is in line with the Universal Declaration of Human Rights and the Preamble to the 1945 Constitution. To support the implementation of the G16 achievement, the Indonesian National Police (Polri) has made serious efforts to provide a sense of safety for the community and to minimize crime rates. One of the efforts that could be made is to map areas based on the level of crimes so that the Polri can determine the appropriate strategy/priority of action for mitigation. Therefore, this study aimed to cluster provinces in Indonesia based on the four G16 indicators of the SDGs related to security, namely the number of homicide cases, the victim proportion, the proportion of people who feel safe walking alone in the area where they live, and the proportion of victims of violence that  reported to the police in the past year using five hierarchical clustering methods, namely: Single-Linkage, Average-Linkage, Complete-Linkage, Ward, and Division Analysis. Then, methods were validated and compared using six cluster validations to obtain the most compact method. The results showed that Ward's method outperformed the others and produced three clusters. Clusters 1, 2, and 3 contained 18, 5, and 11 provinces respectively.
Factors influencing smartphone owners' acceptance of Biometric Authentication methods La Ode Abdul Wahid; Ahmad R Pratama
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1114.91-98

Abstract

Smartphones are the world's most widely used personal computing devices. PINs and passcodes have long been the most popular authentication methods in smartphones and even in the pre-smartphone era. Due to the inconvenient nature of PINs and passcodes, a new biometric authentication method for smartphones was developed and has been gaining traction in terms of adoption, beginning with flagship devices and progressing to some mid-range devices. This article aims to investigate the factors influencing smartphone owners' acceptance of biometric authentication methods by developing a new model based on the Technology Acceptance Model (TAM). It also validates the data with survey data from 233 Indonesian smartphone owners via an online survey and analyzed it using Structural Equation Modeling (SEM). The results from the SEM analysis show that all nine hypotheses in the proposed model are supported. In other words, all six factors in the proposed model (i.e., attitude toward the use, perceived usefulness, perceived the ease of use, perceived enjoyment, perceived security, and social influence) have significant effects on the behavioral intention of adopting biometric authentication methods among smartphone owners. More specifically, the findings indicate that most Indonesian smartphone users have a favorable attitude toward biometric authentication, which is why they are willing to adopt it. Furthermore, it is discovered that the perceived usefulness of a biometric authentication method on smartphones outweighs its perceived ease of use. It reveals that the user's belief in the intrinsic value of biometric authentication methods in the form of perceived security outweighs both the internal user motivation of perceived enjoyment and the external user motivation of social influence in terms of their acceptance of biometric authentication methods.
Design of digital kWh-Meter to top-up the electric pulse by automatically using Relay Module Based on SMS and Arduino Uno Syarifah Fitrah Ramadhani; Pujianti Wahyuningsih; Abdul Jalil; Syarifah Suryana
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1221.229-236

Abstract

This study aims to design a digital kWh-meter prototype to top-up the electricity pulse by automatically using relay modules based on Short Message Service (SMS) and Arduino Uno. The utilization of 12 relay modules to substitute the keypad input function in the digital kWh meter is our basic idea in this study. The method we used to replace the keypad input function with the relay module is based on the integration between the circuit path in the keypad board and the relay module as an electric switch that can activate when the relay gets a trigger from the Arduino Uno. In this study, when the user wants to charge the electric pulse, the user will send the voucher number to the GSM SIM900A module via SMS, then it will be processed to the Arduino Uno. Then Arduino Uno will trigger the relay to be activated so that it can automatically fill the voucher number to the digital kWh-meter. This study result is the success of relay modules can substitute the function of keypad input to fill the voucher pulse number to the digital kWh-meter through SMS with the successful voucher number filling up to 98%. The usefulness of the relay module to change the keypad input function on the digital kWh meter is our original idea for this study.
Analysis of Stroke Classification Using Random Forest Method Muhammad Firdaus Banjar; Irawati Irawati; Fitriyani Umar; Lilis Nur Hayati
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1252.186-193

Abstract

Stroke is a disease in which the sufferer experiences or experiences a rupture of a blood vessel in the brain so that the brain does not get a blood supply that provides oxygen. Patients who suffer from stroke will experience cognitive disorders ranging from decreased consciousness, visuospatial disorders, non-verbal learning disorders, communication disorders, and reduced levels of patient attention. Data from the World Stroke Organization shows that there are 13.7 million new stroke cases every year, and about 5.5 million deaths occur due to stroke. This research aims to analyze the attributes of any variables that affect the classification of strike disease and to test the performance of stroke classification in the form of accuracy, precision, recall, and f-measure. The method used is a random forest using a tree, namely 50, 100, 200, and 500. The classification of stroke is divided into stroke and no stroke. The data used is 5110, divided into 70% training data and 30% testing data. The results showed that the performance of a random forest using 100 trees was better than using 50, 200, and 500 trees, with an accuracy value of 86.82%, a precision of 15.76%, a recall of 38.15%, and an f1-score 22.30% after doing SMOTE.
Semantic segmentation of pendet dance images using multires U-Net architecture Hendri Ramdan; Moh. Arief Soeleman; Purwanto Purwanto; Bahtiar Imran; Ricardus Anggi Pramunendar
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1316.329-338

Abstract

As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.
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.

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