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INDONESIA
KOMPUTIKA - Jurnal Sistem Komputer
Published by Universitas Komputer Indonesia
ISSN : 22529039     EISSN : 26553198     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Jurnal Ilmiah KOMPUTIKA adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis di bidang kelimuan bidang Sistem Komputer.
Arjuna Subject : -
Articles 218 Documents
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Investigasi Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole Akrom, Muhamad; Sumarjono, Cornellius Adryan Putra; Trisnapradika, Gustina Alfa
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.11048

Abstract

This research aims to investigate the corrosion inhibition performance of Benzimidazole compounds using a machine learning (ML) approach. The main challenge in developing ML is to obtain a model with high accuracy so that the prediction results are relevant and accurate to the actual properties of a material. In this research, we evaluate various linear and non-linear algorithms to obtain the best model. Based on the coefficient of determination (R2) and root mean square error (RMSE) metrics, it was found that the AdaBoost Regressor (ADA) model was the model with the best predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds. This approach offers a new perspective on the ability of ML models to predict effective corrosion inhibitors.
Multi-Aspect Sentiment Analysis Pada Review Film Menggunakan Metode Bidirectional Encoder Representations From Transformers (BERT) Karimah, Nur; Baita, Anna
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.11098

Abstract

This research was conducted to apply the Bidirectional Encoder Representation from Transformer (BERT) method to multi-aspect sentiment analysis of film reviews. The review data was obtained using the scraping method. The dataset used consists of 1899 data to 3245 data having a positive sentiment, 4825 data with a neutral sentiment, and 1424 data with a negative sentiment. The proposed approach includes the aspects such as acting, plot, cast, animation, and music. The aspect with the most positive sentiment is music with a total of 631 data, the neutral sentiment is found in the animation aspect with a total of 1146, and the negative sentiment is found in the plot aspect with a total of 362. The dataset used went through cleaning data, including case folding and removing HTML tags, punctuation, numbers, and special characters. This research uses the BERTBASE-UNCASE model with four experiments using hyperparameters max_epoch 10, batch size 16, and learning rates of 1e-4, 5e-5, 3e-5, and 2e-5. The research results show that, from all experiments, the best accuracy value is achieved in the third experiment using a learning rate of 3e-5, which is 82,32%. Meanwhile, the best precision, recall, and f1-score values for the “animation” aspect are 86%, 85%, and 85%.
Design and Implementation of Bluetooth Low Energy Based Access Control System Sasongko, Arif; Prastya, Sidartha; Brillianshah, Elkhan Julian; Taruna, Muhamad; Hakim, Abdul
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.11227

Abstract

This paper discusses the design of a Bluetooth Low Energy (BLE) based access control system intended to make access control more practical to implement on areas with high personnel turnover rate by making access rules easy to set and making access keys relatively safe to distribute compared to existing key-based access control systems. The use of BLE technology allows the system to estimate key position within the system by utilizing the curve fitting method for distance estimation and the trilateral method for positioning. The proposed system consists of a server, an admin panel, electronic locks, access keys in the form of a wearable, and access keys in the form of an Android application. The system is found to be capable of implementing access control functionalities and capable of implementing indoor positioning based on sections.
Implementasi Analytical Hierarchy Proses Pada Sistem Pendukung Keputusan Pemilihan Beras Berkualitas Rismayani, Rismayani; SY, Hasyrif; Herlinda, Herlinda; MZ, St. Naziha Nasif; Tulwahdah, Farida
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.11423

Abstract

This study aims to process a Decision support system in selecting quality rice that fits the criteria and decision alternatives that apply the Analytical Hierarchy Process (AHP) and also builds a web-based system to process the data. The Logistics Affairs Agency (BULOG) is a state-owned company engaged in food logistics. These agencies include logistics/warehousing, surveys and eradication of pests, supply of plastic sacks, transportation business, trade in food commodities and retail industry. The method used is the Analytical Hierarchy Process (AHP) which produces a hierarchical order or ranking of alternatives, this system is expected to assist in the selection of quality rice. The result of the research is to build a web-based system by implementing AHP in determining quality rice and based on accuracy testing, 100% results are obtained. Based on the functional testing of the system using a black box valid results were brought, then in logic testing using a white box found data that was free from errors.
Prototipe Sederhana Sistem Deteksi Kriminal Berbasis Internet Of Things Menggunakan Teknologi YOLOv5 Nurfal Aziz, Afris; Khoiriyah, Hani’atul; Abdillah, Fauzan; Wiryawan, I Gede
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.12217

Abstract

Crime is any action or thing carried out by an individual, group or community that violates the law or is a criminal act, which disturbs social balance or stability in society. One of the tools used to monitor security in various places such as homes, offices and other public places is Closed-Circuit Television (CCTV). However, even though many CCTVs have been installed, many crimes still occur due to limitations in monitoring and supervision by security officers. Therefore, developing a crime detection system on CCTV using deep learning methods is considered important to increase security and reduce crime rates. The aim of a criminal detection system is to increase security and prevent criminal acts in a certain area or place. The technology used is YOLOv5 and is supported by Internet of Things-based hardware. The system succeeded in detecting violence objects 92% of the time and robbery 91% of the time in initial testing without background. In the second background test, the system succeeded in detecting violence objects 93% of the time and robbery 53% of the time. The system succeeded in detecting violence objects 91% of the time and robbery 83% of the time in real-time testing.
Klasifikasi Gagal Jantung menggunakan Metode SVM (Support Vector Machine) Farida, Laili Nur; Bahri, Saiful
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.11330

Abstract

Gagal jantung merupakan penyakit mematikan nomor satu di dunia. Menurut data WHO (World Health Organization) dan WHF (World Heart Federation), pada tahun 2025 diperkirakan penyakit jantung akan menjadi penyebab utama kematian di negara-negara Asia. Tahun ini, setidaknya 78% angka kematian global disebabkan oleh penyakit jantung yang terjadi pada orang miskin dan kelas menengah. Data RisKesDas (Riset Kesehatan Dasar) KemenKes (Kementerian Kesehatan) RI tahun 2018, prevalensi gagal jantung di Indonesia berdasarkan diagnosis dokter diperkirakan mencapai 5%, di mana lebih sering terjadi pada pria yaitu sebanyak 66% dibandingkan wanita yang hanya 34%. Tujuan dari penelitian ini adalah melakukan klasifikasi penyakit gagal jantung menggunakan metode Support Vector Machine. Proses uji coba menghasilkan akurasi tertinggi pada kernel linear, RBF dan polynomial masing-masing sebesar 85.96%, 85.84%, dan 84.50%. Kernel yang menghasilkan akurasi paling tinggi, yaitu kernel linear dengan cost 0.1. Proses pengujian menggunakan parameter tersebut menghasilkan akurasi, presisi, recall, dan F1-score berturut-turut sebesar 89.13%, 86.21%, 96.15%, dan 90.91%. Berdasarkan hasil penelitian, diperoleh kesimpulan bahwa metode Support Vector Machine cukup baik dalam melakukan klasifikasi pada penyakit gagal jantung.
Mengukur Faktor Demografi Psikologis: Memprediksi Depresi, Kecemasan, dan Stres dengan menggunakan Machine Learning Juwariyah, Siti; Hulvi, Alfajri; Riduan, Nor; Kusrini, Kusrini
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.11793

Abstract

Mental health is an important aspect of human life. Depression, anxiety and stress are some of the most common mental health disorders. These disorders can negatively impact daily life, including productivity, social relationships, and an individual's quality of life, requiring accurate prediction for early intervention. One of the psychological measurement tools used to assess a person's level of depression, anxiety, and stress is the DASS-42 (Depression Anxiety Stress Scales - Long Form). In addition to the DASS-42 results, demographic factors such as age, gender, education level, and social status are important to analyze to strengthen the analysis. Machine learning (ML) is a powerful tool for analyzing complex data such as predicting psychological demographic factors associated with these mental health conditions. This study explores the potential of ML using a comprehensive dataset, using K-Nearest Neighbor and Support Vector Machine algorithms to assess prediction performance. The findings highlighted the effectiveness of ML models in predicting depression, anxiety and stress with high accuracy. The best algorithm in this study for the classification of depression, anxiety and stress is SVM with 99% accuracy but the use of Exploratory Data Analysis (EDA) technic to process additional variables affects the accuracy of the model so it can be concluded that demographic variables have an influence on the classification of depression, anxiety and stress.
Comparative Analysis of Decision Tree and Logistic Regression Models in Employee Recruitment and Selection for Enterprise Success Khairina, Dyna Marisa; Wibowo, Adi; Warsito, Budi
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.11917

Abstract

Enterprise success is determined by competent Human Resources (HR). The recruitment and selection process of employee candidates plays an important role in producing competent human resources as an effective initial selection increases the chances of finding the right candidate for a particular role. This research predicts the likelihood of a candidate being further selected in the interview phase based on behavioral and functional recruitment and selection which are important aspects of a candidate's potential fit and contribution to the enterprise. The research uses a comparison of decision tree analysis models and logistic regression to make predictions with several measurement metrics to see the accuracy and confusion matrix of each model used. Based on evaluation and validation, the decision tree analysis model is superior in prediction even though the results tend to be the same as the logistic regression model. The accuracy value of the classification using the decision tree model was 86.67% with correct prediction results of 78 data from 90 testing data and the accuracy value of the logistic regression model was 85.55% with correct prediction results of 77 data from 90 testing data. The results of the comparison of the two models show that the performance of the decision tree classifier model tends to be better.
Penerapan Metode Fuzzy Time Series Cheng Pada Peramalan Inflasi di Indonesia Putri, Ikfira Agustina; El Maidah, Nova; Ariful Furqon, Muhammad
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12108

Abstract

Inflation is the increase in prices and goods in a certain period whose growth is always sought to remain low and stable in order to realize public welfare. High inflation fluctuations have a major influence on a country's economy, so forecasting is needed that can be used as a reference for the Government and Central Bank to prevent high inflation while maintaining price stability in the future. In addition, inflation forecasting can help economic actors in decision making. Forecasting can be done with various methods, one of which is Cheng's Fuzzy Time Series. The inflation data used in this study was obtained from the Bank Indonesia website from January 2003 to September 2023 with a monthly data period of 249 data. The prediction results for a 9-month period are 5.54% for the highest inflation and 2.92% for the lowest inflation. Based on the testing that has been done, the MAPE error value is 9.54% with a very good MAPE value category.
Segmentasi Kepala Janin pada Citra Ultrasound Menggunakan Arsitektur Jaringan U-Net Hermawati, Fajar Astuti; Jaya, Viko Adi
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12158

Abstract

Digital image processing has been utilized in various fields, including the medical field. One example is used to detect the location of vital organs in the human body. This research aims to produce a cut shape of the fetal head area on an ultrasound image (USG) by applying a deep-learning segmentation method. This research stage was carried out with ultrasound image acquisition, followed by an image preprocessing process to improve image quality so that the results of the segmentation process were better, followed by applying the segmentation method. This research focuses on using the U-Net method to segment the fetal head in ultrasound images. Using 995 ultrasound images of the fetal head in the training process, the best accuracy was obtained at 90.55%. The performance of the segmentation results for 335 ultrasound images of the fetal head at the testing stage using the Jaccard coefficient measurement was obtained on average at 87%. The results of this segmentation can be used for further purposes, such as fetal biometric measurements or 3D visualization. Keywords – Image Segmentation; USG Image; Fetal Head; U-Net Architecture; Image Preprocessing

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