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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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Sistem Pemeringkatan Daerah (Kelurahan) Dalam Pemberlakuan Pembatasan Sosial Berskala Besar (PSBB) Menggunakan Metode Analytical Hierarchy Process (AHP) Alexander J.P. Sibarani; Fajar Wahyudi
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): 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.v11i2.5248

Abstract

Pembatasan Sosial Berskala Besar merupakan salah satu peraturan yang dibuat oleh kementerian kesehatan (kemenkes) dalam rangka percepatan penanganan dan pengendalian virus covid-19. Kota Tangerang Selatan merupakan salah satu kota yang terkena dampak penyebaran virus covid-19 dan perlu untuk menerapkan mekanisme pembatasan sosial terhadap warganya. Penelitian ini memanfaatkan nilai jumlah orang dalam pemantauan (ODP), pasien dalam pengawasan (PDP), Positif Covid-19, jumlah pasien yang dinyatakan sembuh dari Covid-19, dan jumlah kematian untuk menghasilkan nilai pada sebuah kelurahan. Kemudian nilai dari seluruh kelurahan dihasilkan dan diperbandingkan untuk mengetahui kelurahan mana yang berada pada posisi tertinggi yang harus melakukan pembatasan sosial pada warganya. Metode yang digunakan pada penelitian ini adalah Analytical Hierarchy Process (AHP) yang mampu memberi bobot kepentingan pada tiap kriteria untuk menghasilkan pemeringkatan. Hasil akhirnya adalah sebuah aplikasi yang mampu memberikan peringkat seluruh kelurahan pada satu kota untuk dapat membantu mengambil keputusan dalam menentukan kelurahan apa yang harus melakukan pembatan sosial.
Analisis Perbandingan Kinerja Metode Rekursif dan Metode Iteratif dalam Algoritma Linear Search Erba Lutfina; Nur Inayati; Galuh Wilujeng Saraswati
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): 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.v11i2.5493

Abstract

The linear search algorithm is one of the most popular data search algorithms. In the process of searching data for a list using a linear search algorithm, it can be applied in an iterative and recursive way. The general view of linear search algorithms is that the iterative methods perform the same as recursive methods. However, some studies contradict this statement which may not apply in all cases. From this analysis, this study focuses on the comparison of recursive and iterative methods on linear search algorithms to find out which algorithm is the most suitable, efficient, and effective. The research was conducted using 3 case studies with data of 1 million, 10 million, and 100 million respectively. The research focuses on the results of memory usage and access time in the data search process using Big-O notation and Python programming language. The results show that the iterative linear search algorithm is more effective and efficient than recursive. Although both methods have the same Big-O complexity, the results of program execution show different results. With the results of an iterative linear search algorithm, the results of the execution time and memory usage are superior, namely, the access time and memory usage are less than the recursive method.
Analisis Sentimen Media Sosial Twiiter terhadap RUU Omnibus Law dengan Metode Naive Bayes dan Particle Swarm Optimization Syukri Adisakti Dainamang; Nur Hayatin; Didih Rizki Chandranegara
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): 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.v11i2.6037

Abstract

Social media is the most popular platform by the Indonesian people, starting from Facebook, Instagram and Twitter. Twitter is one of the most widely used social media, both for interacting with other people or looking for information or news that is trending topics, quickly various news or information spreads on Twitter such as issues that are currently trending, namely the Omnibus Law. , various responses given by twitter users regarding this policy that has been approved by the government. In this study, to classify the sentiments of the Indonesian people regarding the issue of Omnibus Law using the method Naïve Bayes and Particle Swarm Optimization (PSO) and divided into two test scenarios, the use of theAlgorithm Particle Swarm Optimization on Naive Bayes aims to optimize the accuracy results. The results obtained when using Naive Bayes based on Particle Swarm Optimization (PSO) are better than Naive Bayes. The best accuracy results are in scenario three with split 90% - 10% data using Naïve Bayes to get 85% results and using Naïve Bayes based on Particle Swarm Optimization the accuracy results change to higher 4% get 91% results, the amount in doing the split data is very influential on the results of the classification carried out. The response from the public is in the form of negative sentiment towards the Omnibus Law Bill.
Implementasi Algoritma Text Mining dan Cosine Similarity untuk Desain Sistem Aspirasi Publik Berbasis Mobile Rismayani Rismayani; Hasyrif SY; Tommy Darwansyah; Irsan Mansyur
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): 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.v11i2.6501

Abstract

Keberadaan DPRD di daerah sering disebut sebagai fungsi perwakilan karena bertugas menyuarakan aspirasi rakyat dan bertindak atas nama rakyat (pemerintah perwakilan) di bidang legislatif. Selama ini sebagian besar masyarakat masih sulit menyampaikan aspirasinya kepada Dewan Perwakilan Rakyat Daerah (DPRD) Kota Makassar dan mendapatkan masukan atas aspirasi yang disampaikan masyarakat kepada Dewan Perwakilan Rakyat Daerah (DPRD). Masalah penelitian adalah bagaimana mengolah data aspirasi masyarakat untuk mengkategorikan mereka berdasarkan komisi berbasis mobile dan data aspirasi masyarakat mendapatkan masukan dari Dewan Perwakilan Rakyat Daerah (DPRD) atas aspirasi yang mereka kirimkan. Tujuan dari penelitian ini adalah untuk membangun sebuah aplikasi yang dapat menampung aspirasi masyarakat dan kemudian dapat mengklasifikasikannya dan meneruskannya ke komisi berbasis mobile yang sesuai, untuk menyediakan sistem yang dapat digunakan DPRD untuk memberikan masukan. Metode penelitian adalah algoritma Text Mining dan Cosine Similarity. Hasil penelitian adalah Aplikasi menggunakan kombinasi metode text mining dan Cosine similarity mengukur kesamaan fungsional masing-masing komisi dengan aspirasi yang diinput oleh masyarakat sehingga aspirasi dapat tepat sasaran. Syarat penetapan multikomisi adalah harus ≥ 75% dari nilai maksimum.
Sistem Pengenalan Suara Dengan Metode Mel Frequency Cepstral Coefficients Dan Gaussian Mixture Model Ababil Azies Sasilo; Rizal Adi Saputra; Ika Purwanti Ningrum
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): 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.v11i2.6655

Abstract

ABSTRAK – Teknologi biometrik sedang menjadi tren teknologi dalam berbagai bidang kehidupan. Teknologi biometrik memanfaatkan bagian tubuh manusia sebagai alat ukur sistem yang memiliki keunikan disetiap individu. Suara merupakan bagian tubuh manusia yang memiliki keunikan dan cocok dijadikan sebagai alat ukur dalam sistem yang mengadopsi teknologi biometrik. Sistem pengenalan suara adalah salah satu penerapan teknologi biometrik yang fokus kepada suara manusia. Sistem pengenalan suara memerlukan metode ekstraksi fitur dan metode klasifikasi, salah satu metode ekstraksi fitur adalah MFCC. MFCC dimulai dari tahap pre-emphasis, frame blocking, windowing, fast fourier transform, mel frequency wrapping dan cepstrum. Sedangkan metode klasifikasi menggunakan GMM dengan menghitung likehood kesamaan antar suara. Berdasarkan hasil pengujian, metode MFCC-GMM pada kondisi ideal memiliki tingkat akurasi sebesar 82.22% sedangkan pada kondisi tidak ideal mendapatkan akurasi sebesar 66.67%. Kata Kunci – Suara, Pengenalan, MFCC, GMM, Sistem
Sistem Informasi Pengukuran Kadar Hemoglobin Non-Invasif Berbasis Android Menggunakan Algoritma Extreme Gradient Boosting Syarifuddin, Sri Dewi Sartika; Khurniawan, Amri; Munadi, Rendy; Sussi, Sussi
Komputika : Jurnal Sistem Komputer Vol 12 No 1 (2023): 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.v12i1.5049

Abstract

Measurement of hemoglobin levels nationally was carried out invasively using the sahli method of 27.9%. Measurement of invasive hemoglobin levels takes a long time due to the chemical analysis of patient blood samples in the blood laboratory. In general, blood sampling is done using a syringe, which can cause pain and increase the risk of spreading other diseases through needle-stick wounds. Measurement of hemoglobin levels can be done using multiwavelength oximetry technique. Therefore, in this study, a non-invasive real-time measurement system for hemoglobin levels based on the internet of things was created using the multiwavelength oximetry technique with the Extreme Gradient Boosting algorithm which is integrated with Real-time Database and an android-based information system capable of mapping users using QR Code. The test results using the RMSE parameter obtained a value of 0.801085 which indicates a high category level and an accuracy of 94.91%. The information system can display real-time measurement of hemoglobin levels with a delay of 317 ms and a throughput of 3138 bps. The results of testing the oxygen saturation functionality are 0.654% with the difference in the measurement value of the highest oxygen saturation level being 1.33% and the smallest being 0.08%.
Analisis dan Implementasi Sistem Monitoring Banjir Berdasarkan IoT Menggunakan Logika Fuzzy Sugeno Akbar, Alvijar; Clinton, Martin; Ashari, Ilham Firman
Komputika : Jurnal Sistem Komputer Vol 12 No 1 (2023): 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.v12i1.7089

Abstract

Flood disasters can have a detrimental impact such as damage to infrastructure, materials, and loss of life. One of the efforts that can be made to carry out early detection of flood disasters is to use a flood prediction system, where this system can monitor water levels, water flow rates, and predict real-time water increases. Information is sent to every citizen using the telegram chatbot. This system is built using several sensors and integrated with Telegram. The sensors used are ultrasonic and water flow sensors. The ultrasonic sensor is used to read the water level in the range of 0-50 cm and the water flow sensor is used to calculate the flow of water entering the test container with an interval of 0-10 liters / minute. Data is sent to telegram in realtime using the firebase database through NodeMCU ESP8266 and the WiFi module. The results of reading water level and water discharge data are processed using Sugeno fuzzy logic. The results obtained in this study indicate that the average error reading from the ultrasonic sensor is 2.43% or 97.58%. The water flow sensor shows an average error of 0.206 liters/minute or the percentage of tool accuracy is 87.06 %.
Alat Peringatan Volume Septic Tank dan Netralisasi Kadar Sewer Gas Berbasis Mikrokontroler dan Teknologi Panel Surya Khakim, Lukmanul; Sulasmoro, Arfan Haqiqi; Afriliana, Ida
Komputika : Jurnal Sistem Komputer Vol. 12 No. 1 (2023): 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.v12i1.7538

Abstract

The septic tank is a temporary waste disposal site, designed to be closed, there are two holes to drain the reservoir and another hole for evaporation. Septic tanks produce hazardous gases, for example sewer gas, have a pungent odor and are flammable, if the concentration is high, causing poisoning if inhaled, if in a closed room and there is a source of fire, it can trigger an explosion. Therefore, an automation tool is needed as a warning system for the volume of the septic tank and to neutralize the sewer gas level, which has been equipped with solar panels as battery chargers embedded in it. This tool consists of HC-SR04 to measure the remaining volume capacity, MQ2 detects and measures sewer gas levels, a fan to neutralize sewer gas, a buzzer for warning. From the results of this study, the HC-SR04 detects the remaining volume from 28% to 2%, meaning that the volume of the septic tank increases, so the buzzer is active as a notification. Furthermore, MQ2 detects gas concentrations from 2ppm to 64ppm, meaning that the gas concentration increases beyond the safe limit, which is 30ppm, causing the fan to work to neutralize sewer gas levels.
Pengembangan Aplikasi Penggajian Karyawan Dengan Menggunakan Metode Agile Berbasis Mobile Android Alda, Muhamad
Komputika : Jurnal Sistem Komputer Vol 12 No 1 (2023): 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.v12i1.8030

Abstract

The development of information technology is increasing, greatly affecting the human mindset in the process of meeting information needs and helping to complete work. Information technology is a tool or media that can help human life. PT. Sop Sumsum Langsa is a company engaged in the culinary field. In processing employee payroll data, PT. Sop Sumsum Langsa still uses a computer with a Microsoft Excel application. In this way, there are still obstacles and problems that occur. The author researches to help provide solutions in solving these problems by building an Android-based mobile application that can be used in the processing of employee salaries. The application development method used by the author is the agile method consisting of stages of system analysis, design, application development, testing, application deployment, revision and evaluation, and system maintenance. In testing the application, the author uses the black box testing method. The results of the research conducted by the author are an android-based mobile application that can help PT. Sop Sumsum Langsa in processing employee salary data effectively and efficiently.
A Comparative Analysis of Transfer Learning Architecture Performance on Convolutional Neural Network Models with Diverse Datasets Putra, Muhammad Daffa Arviano; Winanto, Tawang Sahro; Hendrowati, Retno; Primajaya, Aji; Adhinata, Faisal Dharma
Komputika : Jurnal Sistem Komputer Vol 12 No 1 (2023): 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.v12i1.8626

Abstract

Deep learning is a branch of machine learning with many highly successful applications. One application of deep learning is image classification using the Convolutional Neural Network (CNN) algorithm. Large image data is required to classify images with CNN to obtain satisfactory training results. However, this can be overcome with transfer learning architectural models, even with small image data. With transfer learning, the success rate of a model is likely to be higher. Since there are many transfer learning architecture models, it is necessary to compare each model's performance results to find the best-performing architecture. In this study, we conducted three experiments on different datasets to train models with various transfer learning architectures. We then performed a comprehensive comparative analysis for each experiment. The result is that the DenseNet-121 architecture is the best transfer learning architecture model for various datasets.

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