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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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Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision Indra, Dolly; Herman, Herman; Budi, Firman Shantya
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.v12i1.9082

Abstract

The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehicle counter system has been integrated between Raspberry Pi 4 and a mobile application on a smartphone where the smartphone functions to display information such as day, date, month, year and together with the number of cars and motorcycles. We tested this automatic vehicle counting system on steam services (car and motorcycle washing) for 3 days where 10 vehicles were collected every day. The test results show that the system is capable of detecting cars and motorcyles with an average accuracy rate of 46.6%. Keywords – Vehicle Detection, SSD-MobileNet V2, Computer Vision, Raspberry Pi, Smartphone
Implementasi dan Analisis Metode MOORA dan SMART pada pemilihan Platform Jual Beli Online menggunakan Decision Support System Ramadhan, Rizal Furqan
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.v12i1.9300

Abstract

Online buying and selling transactions have become a necessity and routine for generation Z in the era of technology 4.0. This phenomenon is an effect caused by the existence of the internet. The internet innovates for software developers to create communication applications without meeting face-to-face. Due to the rapid development of the internet, many online buying and selling platforms have emerged and are used by online shopping activists, especially generation Z. Of course, the various trading platforms have differences in appearance and usability. So it is necessary to research selecting the ideal platform using the Decision Support System as the data processing system. While the methods used are the MOORA and SMART methods, the two approaches will analyze the differences in the calculation results and the advantages of each technique. The results of the MOORA method are inversely proportional to the SMART method, but the difference is only that the difference in the final scores is not that great. Hence, the two approaches are ideal for a Decision Support System. Keywords – decision Support System, internet, jual beli, moora, smart.
Task Management System Acceptance Technical Procedure Instalasi Antenna Provider pada Tower Berbasis Web ( Studi Kasus : PT. Intisel Prodaktifakom Central Java ) Pamungkas, Prasetyo Adi; Christanto, Febrian Wahyu
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.v12i1.9378

Abstract

Perubahan gaya hidup masyarakat saat ini sangat membutuhkan akses informasi yang mudah dan cepat, untuk memenuhi kebutuhan yang bergantung dengan jaringan telekomunikasi. Sejalan dengan hal ini menuntut pengadaan sarana infrastruktur konstruksi pendukungnya yaitu instalasi antena sebagai media transmisi telekomunikasi lewat udara. PT. Intisel Prodaktifakom Jawa Tengah adalah salah satu perusahaan yang menangani dan bergerak dalam bidang jasa kontraktor telekomunikasi. Agar mampu menjadi perusahaan kontraktor yang sukses di bidang telekomunikasi, manajemen diperlukan untuk peningkatan efisiensi dan efektivitas pengelolaan proyek, karena banyaknya pekerjaan yang harus diselesaikan sering muncul berbagai masalah, minimnya teknisi yang idle sehingga terjadi delay pekerjaan, update pekerjaan yang tidak real-time, pemberian tugas kepada teknisi masih secara manual menggunakan Whatsapp Group, dan kesulitan dalam proses pengawasan. Untuk mencapai tujuan sebuah proyek yang memenuhi kriteria biaya, mutu dan waktu, sistem task management berbasis web menggunakan metode prototype dimaksudkan sebagai alat untuk peningkatan mutu dan pelayanan perusahaan. Berdasarkan dari pengujiaan yang telah dilakukan sistem Task Managemt ini mendapatkan hasil yang cukup baik, pada pengujian black box sistem berjalan dengan baik sesuai dengan perencanaan, dari pengujian reability didapatkan hasil rata-rata 100% per test yang dijalankan, kemudian untuk pengujian kepuasan pengguna menghasilkan rata-rata presentase yaitu 76% responden memilih sangat baik, 26% responden memilih baik. Diharapkan PT Intisel Prodaktifakom Jawa Tengah dapat lebih banyak memenangkan tander projek dan juga kepercayaan lebih kepada PT Intisel Prodaktifakom dari vendor yang menjadi langganan untuk PT Intisel Prodaktifakom Jawa Tengah.
Klasifikasi Penyakit Kanker Prostat Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbor Muzakir, Adi; Desiani, Anita; Amran, Ali
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.v12i1.9629

Abstract

Kanker prostat merupakan kanker yang berkembang di prostat dalam sistem reproduksi pria, hal ini terjadi ketika sel prostat mengalami keterikatan pada reseptor androgen melalui proses molecular docking. Insidensi kanker prostat meningkat seiring pertambahan usia, di mana risiko yang dimiliki pria untuk menderita kanker prostat dalam seumur hidupnya mendekati angka 10%. Deteksi dini terhadap kasus kanker prostat pada banyak pengidap atau pria yang rentan risiko kanker prostat penting dilakukan untuk memulai pengobatan dan perencanaan kebutuhan medis yang tepat. Salah satu cara yang dapat dilakukan dalam deteksi penyakit kanker prostat adalah dengan melakukan klasifi-kasi menggunakan pendekatan data mining dengan algoritma Naïve Bayes dan K-Nearest Neighbor (K-NN). Penelitian ini bertujuan untuk mendapatkan hasil klasifikasi terbaik untuk mendeteksi penyakit kanker prostat dengan membandingkan kedua algoritma tersebut. Hasil akurasi klasifikasi kanker prostat dengan menggunakan algoritma Naïve Bayes adalah 80% dan K-NN sebesar 90%. Sementara untuk rata-rata keseluruhan nilai presisi algoritma Na-ïve Bayes dan K-NN masing-masing berada di angka 71,5% dan 93%. Nilai recall untuk algoritma Naïve bayes didapatkan sebesar 88% dan algoritma K-NN yaitu 87,5%. Berdasarkan nilai akurasi, presisi, dan recall kedua algo-ritma tersebut, algoritma K-NN memiliki nilai yang lebih tinggi dibandingkan dengan algoritma Naïve Bayes, sehing-ga dapat dikatakan bahwa algoritma K-NN bekerja dengan baik dalam melakukan klasifikasi penyakit kanker prostat. Meskipun algoritma Naïve Bayes memiliki nilai yang lebih rendah dibandingkan dengan algoritma K-NN, tetapi nilai rata-rata untuk performa presisi, recall, dan akurasinya masih berada di atas 70%. Dapat dikatakan bahwa algoritma Naïve Bayes cukup baik dalam mengklasifikasi penyakit kanker prostat.
Robot Panen Hidroponik Berbasis Human Following Basit, Abdul; Budihartono, Eko
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.v12i1.9028

Abstract

– Pertanian modern banyak diminati oleh petani modern terutama di perkotaan karena banyak pengalihfungsian lahan. Hidroponik menjadi pertanian yang pailing popular karena tidak membutuhkan lahan yang besar, minim perawatan karna tidak membutuhkan pemupukan yang rutin akantetapi menghasilkan panen yang maksimal. Meningkatnya hasil panen perlu adanya proses panen lebih efisien, dengan teknologi robotic memungkinkan kita untuk membuat keranjang panen yang interaktif, dengan membuat robot human following. Robot dibangun menggunakan microcontroller arudino uno, ada 4 sensor untuk mendukung proses robot berjalan dengan baik, sensor ulrasonik digunakan untuk mendeteksi hambatan agar robot berjalan kedepan dengan jarak distance > 40 dan distance < 20. Sensor, Sensor ir digunanakan untuk proses mendeteksi hambatan agar robot bisa berbelok ke kanan dan kekiri, data dari sensor dikirm ke microcontroller untuk di proses oleh motor driver agar roda robot bisa bergerak sesuai dengan data sensor. Untk mempermudah mengetauhui berapa berat hasil panen digunakan sensor loadcell dan modul HX711 sensor merubah dari resistensi menjadi berat, data sensor akan dikirimkan ke microcontroller untuk diproses ke LCD 16x2 untuk menampilkan hasil proses timbang.
Analisis Sentimen Penilaian Masyarakat Terhadap Pejabat Publik Menggunakan Algoritma Naïve Bayes Classifier Chrisinta, Debora; Simarmata, Justin Eduardo
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.v12i1.9638

Abstract

Indonesian people convey opinions to public officials by involving community organizations in demonstrations. However, due to digital era, many people also choose to respond the performance of public officials by conveying it through social media, one of which is Twitter. Sociaty opinion recorded on Twitter can be used for structured analysis using sentiment analysis. Sentiment analysis aims to shape data into specific classes. The class classification in sentiment analysis is in the form of positive classes and negative classes. This study applies the Naïve Bayes algorithm in classifying the sentiment of Twitter data, sociaty assessments of public officials. The data used came from text data of 8000 Tweets which was then preprocessed to produce 7993 data for sentiment analysis. Evaluation of algorithm performance using confusion matrix to obtain accuracy and error rate values. The results of sentiment analysis show that the assessment of people with the highest frequency is in the negative class. The algorithm performance shows an accuracy value of 64.55% with an error rate of 35.45%.
Deteksi Aktivitas Mata, Mulut Dan Kemiringan Kepala Sebagai Fitur Untuk Deteksi Kantuk Pada Pengendara Mobil Sugeng, Sugeng; Nizar, Taufiq Nuzwir
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.v12i1.9688

Abstract

Drowsiness in four-wheeled drivers is one of the factors that cause traffic accidents. Drowsiness can be caused due to the tiredness of the journey that is passed by the driver. Utilization of artificial intelligence can be used to detect a driver's drowsiness, one of which is by observing eye activity or condition, mouth movement, and head position while driving. By knowing all these conditions, a machine can be made that can give a warning if the driver experiences possible drowsiness. This study utilizes a camera as data input to recognize the condition of the driver through activity, eyes, mouth, and head tilt position. The system will start by detecting the rider's face, then calculating each activity of eye blinking, the number or number of mouths open due to yawning, as well as head activity through poses and tilt of the head position. Face detection is used to determine the position of the face and then detect the position of the driver's eyes, mouth, and head. Utilizing artificial intelligence with the Blazeface method which is an algorithm used to map facial positions. As well as using the EAR (Eye Aspect Ratio) method to be able to determine whether the eyes and mouth are open or closed. The results of this study obtained a face detection accuracy of 98% and the system can only detect faces at an angle of 0-15 degrees. Keywords – Face Recognition, Mediapipe, Face Extraction, Machine Learning, Blazeface.
Implementasi Deep Feed-Forward Neural Network pada Perancangan Chatbot Berbasis Web di UPPIK RSUD M. YUNUS Faurina, Ruvita; Gazali, M. Jumli; Herani, Icha Dwi Aprilia
Komputika : Jurnal Sistem Komputer Vol. 12 No. 2 (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.v12i2.8914

Abstract

ABSTRACT – The UPPIK (Customer Information and Counseling Complaint Unit) at the M. Yunus Hospital has an important role in serving visitors who come to the hospital. However, visitors often complain about the UPPIK service due to limited working hours, so there is not always staff available to provide the information needed by visitors. In addition, the ongoing Covid-19 pandemic requires people to maintain distance and reduce interaction with others. To solve this problem, an automatic chatbot has been developed to provide service as if the visitor is speaking directly to the staff without any time constraints. This research uses a Deep Feed-Forward Neural Network algorithm. The dataset used is a collection of question-answer data collected through direct observation at the UPPIK, consisting of 1464 pairs of data. The best accuracy was obtained by spliting the dataset into 80% training data (1,185 data), 10% testing data (147 data), and 10% validation data (132 data) with 300 epochs, which resulted in an accuracy of 91.98%. Evaluation of these results showed a precision value of 0.99, a recall value of 0.98, and an f1-score of 0.99. Keywords - UPPIK RSUD M. Yunus Bengkulu; Artificial Intelligence; Chatbot; Deep Feed-Forward Neural Network; Deep Learning
Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter Fremmuzar, Pangestu; Baita, Anna
Komputika : Jurnal Sistem Komputer Vol. 12 No. 2 (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.v12i2.9460

Abstract

Telkomsel is an internet service provider in Indonesia which was launched in 1995. As an internet service provider with the most users, Telkomsel has become the center of attention of internet users in Indonesia. This invites user opinions and perspectives on Telkomsel, which is commonly referred to as sentiment. One of the media commonly used to express an opinion and point of view is Twitter. Twitter is a social media platform that is often a place for sharing and spreading the news, and discussing ideas, and opinions of Twitter users. In this study, the algorithm used is the Support Vector Machine. In the Support Vector Machine, there is a kernel trick that will be used to determine kernel performance and analyze sentiment. The sentiments analyzed amounted to 537 tweets collected by scraping. The collected tweets will go through the preprocessing stage, namely cleaning, case folding, tokenizing, normalization, stemming, stopword removal, and detokenizing. A sentiment is classified into 2 labels, namely positive and negative. Based on the test results, the sigmoid kernel has the best performance with an accuracy value of 0.950, a precision of 0.945, a recall of 0.860, an f1-score of 0.896, and sentiment tend toward negative.
Analisis Metode Kalman Filter, Particle Filter dan Correlation Filter Untuk Pelacakan Objek Sholehurrohman, Ridho; Habibi, Mochammad Reza; Ilman, Igit Sabda; Taufiq, Rahman; Muhaqiqin, Muhaqiqin
Komputika : Jurnal Sistem Komputer Vol. 12 No. 2 (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.v12i2.9567

Abstract

Object tracking is a challenging in computer vision. Object tracking is divided into two, which can be one object or several objects, depending on the object being observed. The process of tracking an object in the form of one object is to estimate the target in the next sequence based on information from the first frame given. In object tracking in the form of single object tracking, there are five steps that are often used in discriminatory methods, including motion models, feature extraction, observation models, model updates and integration methods. Although various algorithms of object tracking are proposed, there are still failures in the object tracking process caused by occlusion, non-rigid target deformation, and other factors. This study proposes the implementation of the Kalman filter, particle filter, and correlation filter methods for object tracking in video data. The results of the implementation of the three methods can track objects in traffic video data and the script circuit video. In object tracking calculations and method analysis, the kalman filter gets 96.89% where the kalman method is better in terms of accuracy compared to other methods. Meanwhile, in the average performance of computation time, the correlation method gets 26.69 FPS, where the correlation method is superior compared to other competitor methods. Keywords – Kalman Filter; Particle Filter; Correlation Filter; Object Tracking; Object Tracking in Video

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